Certificate in Advanced MS Excel
Coupon code: ADVANCEXL | Offer price: 3840/-
Home > Blogs > Top Analytics tools Every Data Scientist must Learn
Becoming a successful data scientist is not just about knowing statistical concepts, data analysis techniques, algorithms and data model very well. Data scientists also need to master the right set of analytics tools in order to carry out their tasks efficiently. These tools fall in various categories based on the data scientist’s role in data analytics, such as:
i. Data capture and tracking tools
ii. Data processing, cleansing, and data transformation tools
iii. Data visualization tools
iv. Data modeling and analytics tools
In this article, we are going to discuss a few most important tools every data scientist must learn in order to sharpen their analytic skills and deliver impressive results with optimum efficiency.
Tableau is one of the markets leading data visualization tools. It allows you to transform data into a vast range of compelling visuals, charts, and dashboards. This, in turn, allows you to discover the insights your data consists of. It supports integrations with numerous data sources and various mechanisms of importing data points, along with an ability to massage data to suit your chosen visualization.
Available as a desktop application, an on-premise server application, a public cloud hosted as well as fully hosted by Tableau, it fulfills the need of almost every I.T. setup, ranging from an individual data scientist, and start-up to a very large enterprise.
i. Widest range of visualizations supported.
ii. A large variety of support data formats and data import mechanisms.
iii. Wide range of deployment and hosting options available.
iv. Great customer support.
ii. No free version available.
iii. Limited ability to version control the visualizations.
iv. No support for scheduling reports and notifications.
There is a one-month free trial available. The pricing starts from $70 per user/month and includes several pricing plans.
Image source: https://heap.io
i. A minimal amount of coding required.
ii. Automatic tracking of most events, unlike most other analytics tools.
iii. More control with the Heap user instead of the developer on how to define visualizations and reports.
iv. Support for a range of application types.
v. Great documentation (API and product documentation, both).
i. A relatively newer player in the market, maturity is yet to be understood
ii. No API to export data.
iii. User is the topmost entity in its data model. This disallows creating funnels of any other entities than users.
iv. Basic HTML skills required for tagging.
Heap’s pricing starts with a free trial having limitation of a single user and a limited number of data points, which is great news for websites/apps with lower traffic, maintained by a single user. For other plans, it is worth visiting https://heap.io/pricing.
Alteryx is a great tool specializing in self-service data analytics. It provides a great user interface to collect and process data from multiple sources, followed by performing analytics on the refined data sets. In addition to offering a great ETL (Extract, Transform, Load) capability to perform the data transformations and transfers, it also is equipped with the complex analytics capabilities, including predictive, spatial, and statistical. A highly popular feature of Alteryx is to obtain the data from multiple data sets, visually clean and blend it and then, share it with the data visualization applications, such as Tableau or PowerBI.
i. Highly scalable.
ii. Supports many data sources, such as spreadsheets, cloud or on-premise data stores, AWS, and Salesforce.
iii. Excellent analytics capabilities.
iv. Great user experience.
v. Ability to export datasets directly to popular data visualization applications.
ii. Not great at data visualizations.
iii. Occasional glitches and errors in reading and updating data.
Despite a limited free trial, Alteryx is an expensive product and starts with $5,195
/user per year. Other pricing plans can be seen at https://www.alteryx.com/products/platform-details/pricing.
Image source: https://mixpanel.com
Similar to Heap Analytics, MixPanel is also a web and mobile analytics tool and excels at a few important aspects of analytics. In addition to event tracking and capturing of user-related data points, it allows A/B testing, predictive analytics, and can identify behaviours that correlate to high retention. In addition, it includes features related to messaging with efficient targeting and also, provides analysis on target recipients of the messages. It also offers a machine learning interface to help discover the insights in a data set.
i. Excellent customer support
ii. Custom event-driven data model
iii. A/B testing capability
iv. Funnels and events
i. Doesn’t include attribution to support advertising analysis.
ii. Tag management is not available.
iii. Gets expensive with an increase in data points.
It includes a free trial which works very well for start-ups or individual webmasters. On the paid plans, its pricing starts $999 per year and can be easily calculated using their pricing tool available at, https://mixpanel.com/pricing/. It also offers a monthly plan in addition to a yearly plan.
Image source: https://marketingplatform.google.com/about/analytics/
Google Analytics is one of the most popular tools that allow you to track and capture user behaviours from the web and mobile apps. With the widest followership, Google Analytics is almost a benchmark all other solutions are often compared against. In addition to capabilities like tracking, attribution, tag management and visualizations, it integrates very well into the large ecosystem of tools powered by Google. Also, it offers a free tier which suffices for most of the needs a typical website or mobile app has.
i. Integrates well with the remaining components of the Google ecosystem.
iii. Funnels and custom events.
iv. Tag management using Google Tag Manager.
v. Free for the majority of use cases.
vi. Very intuitive user interface.
i. No ability to track PII (Personally Identifiable Information) and that can be a major showstopper for a business.
ii. Limited ability to download datasets. Only the data sets visible on a screen can be downloaded, not the entire dataset matching a condition.
iii. No A/B testing or custom analytics capabilities.
Standard plan of Google Analytics is completely free to use. However, for advanced and high data-centric usages, Google also offers a commercial product, Google Analytics 360, which supports a much larger number of data points, premium support, custom metrics, etc. While its pricing is not publicly available on Google’s website, it can be found here.
Image source: https://www.amplitude.com/
Falling in the family of Google Analytics and Heap Analytics, Amplitude is also a challenger with impressive tracking capabilities. In addition to the analytics related features, Amplitude has a distinctive edge with its product packaging, compliance, performance, and scalability it offers. It offers one of the most feature-rich free tiers that includes unlimited data retentions, ability to track up to 10 million data points and unlimited user seats per free license.
i. Great performance of data retrieval.
ii. Support for SQL queries.
iii. Compliant with GDPR, SOC Type 2, ISO 27001, EU Privacy Shield.
iv. Highly scalable and thus, suited for high traffic applications too.
v. Powerful free plan with unlimited data retention.
i. A relatively complex user experience that involves a learning curve for beginners in web analytics.
ii. Report customization requires a short learning curve.
iii. Ability to track an omnichannel experience of a user requires some training.
Although Amplitude offers a great free plan, which is sufficient for many use cases, their pricing for the Growth and Enterprise plans is available upon contacting their sales team.
In addition to the above tools, a data scientist should be well versed with other technical skills, such as programming using R and Python, SAS, and Microsoft Excel. A combination of skills such as statistics, modelling techniques, algorithms, data science-related programming and expertise in using the analytics tools can ensure a bright and shiny career as a data scientist.
Manipal Prolearn R Certification Training and Business Analytics Certification Training can give you a head start towards your journey of becoming a successful data scientist. In addition, Manipal Prolearn offers an excellent array of training and courses to choose from. These programs are designed to give you an in-depth understanding and 360-degree analysis of data science.
Home > Blogs > Fashion Brands to Nuclear Research, 10 companies making innovative use of Data Sciences – A Case Study
Do you find something common between Steam Engines, Age of Science and Digital Technology? These three are known as the first three industrial revolutions that transformed our modern society and fundamentally the world around us.
We are experiencing this for the fourth time as well. But this Fourth Industrial Revolution is powered by Edge Computing, the Internet of Things (IoT), Social Media, AI, Machine Learning, along with increasing computing power like Quantum Computing. Data is the driver and fuel of this new Industrial Revolution. Isn’t it? “Information is the oil of the 21st century, and analytics is the combustion engine”, these words from Peter Sondergaard, former Senior VP, Gartner, and Chairman of the Board, 2021.AI also proves this.
Whenever business leaders need to take a better decision or to make a better marketing strategy, Data Science always helps them out. But, in this fourth industrial revolution, by using data science in more innovative ways, businesses are looking forward to becoming the market leaders.
Here are 10 companies which are making the most innovative use of Data Science:
1. ZARA: What do you think, apart from the designers, who design clothes at ZARA? It is the team of data scientists, analyzing and processing the data that is being captured from POS terminals, PDA devices, customer surveys, RFID tags on clothing, Instagram, social media, and probably many other sources also. At the Inditex Group which Zara belongs to, along with manufacturing, this valuable customer information is applied in all the departments including customer service center, design team, sales team, production team, etc.
Zara designers at work in Arteixo, Spain. Source: Inditex
This innovative use of data science enables this fast-fashion retailer,
a) to know their customer inside out, and when they get it wrong, they can adapt faster than their competitors.
b) to move a new product from sketches to the rack in a blindingly short time of two to three weeks.
Dope, isn't it?
2. IBM: What is the biggest dream of a tennis player? Of course, it is to win a Grand Slam tournament. What if I say big data is playing the role of a tennis coach and helping players to win matches? Yes, IBM makes it possible by using data science in an innovative way on the tennis court. They developed a real-time statistics and visualization platform, SlamTracker, which combines real-time data delivered from the tennis court with an approximation of 41 million data points collected in the past 8 years at various grand slam tournaments.
Image source: https://datafloq.com/read/the-australian-open/518
IBM SlamTracker uses predictive analysis technology, enabling fans and media to follow all statistics in real-time. By using real-time data analytics software and NLP, it also catches the sentiments of present viewers as well as followers on various social media platforms, for determining the popularity of a player.
These amazing capabilities of Data Science have arrived in tennis and will stay for-the-ages, for sure.
3. SmartMat: Data Science and Yoga? It seems someone has written the sentence wrong or at least these two words don’t deserve to be fit in a single sentence. That’s what I thought until I heard about SmartMat.
If you like to do yoga for staying healthy, nothing can be more useful than getting real-time feedback from your yoga mat. SmartMat makes it possible by using Data analytics. It is embedded with pressurized sensors for providing feedback on yoga students' balance and alignment for almost 62 yoga poses. What’s so interesting about SmartMat is that it learns and improves over time: the more you practice on it, the better the resulting feedback would be.
4. Ginger.io: How would you feel if, data science cares about your mental health, helps to reduce symptoms of anxiety & depression?
For instance, Ginger.io is using data science to provide emotional support for its users. For viewing how a user is feeling, they analyze data from the user’s mobile phone activity, which further helps the medical professional to better understand the mental health condition of their patients.
What an innovative use of Data Science!
5. Cornerstone: In respect to businesses, we usually talk about the use of data science in making business strategies, in taking improved decisions. But have we ever thought about the use of Data Science in hiring and retaining employees--the biggest asset as well as biggest expense for an organization?
A company named Cornerstone has thought about this and now offering a unique solution in the form of Cornerstone Software tool to help organizations tackle this challenge. It helps assess and understand employees by analyzing and processing 0.5 billion data points, collected from employees working across 18 industries in 13 different countries. The data points are the measurements that provide information on everything from how long employee travel to work, to how often they speak to their boss.
It causes many positive changes in the organization using it. For example, by simply allowing more employees to take their breaks together, Bank of America reportedly decrease stress level by 19% and in result improved performance by 23%.
6. Tendril: We are living in a scenario where the primary concerns related to energy, are optimization and distribution. A big question, can we use data science to beat this challenge?
Tendril, Home Energy software management company opted for a hybrid approach that combined both collaborative and content filtering at their end. By structuring and synthesize millions of data points, they provide consumer-based solutions to the energy suppliers.
7. CERN (The European Organization for Nuclear Research): In this industrial revolution, Data Science is bringing new possibilities and as a result, Science & Research is currently being transformed by them as well. The most suitable example for this is CERN with its Large Hadron Collider, the world’s largest and most powerful particle accelerator. This CERN experiment, to unlock the secrets of our universe, generates a massive amount of data, approximately 30 petabytes. CERN is applying computing powers of Data Science to analyze this data at their 150 data centers across the world having around 73000 processors collectively. It’s not wrong to say; Data Science helps CERN understand the universe!
CERN Data Center
8. GE (General Electric): In 2012, with the announcement of investing $1 billion in their state-of-art analytics center in San Ramon, California, GE has come further and faster into the world of data science than most of its old-school tech competitors.
GE, in partnership with Accenture, developed Taleris Intelligent Operations technology, to improve fuel economy, maintenance cost, reduction in delays & cancellations and optimizing flight schedules. The driver of this successful technology is Data Science. A huge amount of data are recorded from each aircraft in real-time, analyzed and processed to recovering from disruption and returning to a regular schedule.
On the other hand, GE’s renewable energy sector is also benefited by Data Science. Their 22,000 wind turbines are rigged with sensors which stream constant data to the cloud. Operators can use this data to remotely fine-tune the pitch, speed and directions of blades to capture as much of the energy from the wind as possible.
9. Qlik: In the relentless momentum of technology, we often lose sight of the greatest purpose of our work that is certain to do something for society by solving real-life problems. But the software company named Qlik is an exception. It made a name for itself as a company that offers crucial support to nonprofit agencies working for the social wellbeing, using Data Science.
For example, Qlik is known for partnering various nonprofit organizations to halt the spread of the Ebola virus in Africa. Agencies use Qlik’s data visualization to see the treatments that were most effective for Ebola.
Image Source: http://time.com/3943047/liberia-ebola-virus-cases/
10. DrivenData: Last but not the least, DrivenData, a newer socially-focused competition platform, uses data science as well as the knowledge of data scientists to solve real-life problems. The solutions will be utilized by partnered non-profit organizations so that they can more effectively carry out their missions for solving difficult social problems.
Among various projects, Using Yelp reviews to flag restaurant health risks and Promoting digital financial services in Tanzania are the two that left a huge impact on society. You can visit here to check the competitions hosted by DrivenData.
That’s all for now, readers! Hope this blog post proves to be insightful for you! We would love to hear your thoughts too. Don’t hesitate to leave your comments in the section below. You can also check out our Data Science courses here.
Home > Blogs > Hooked to your Gaming Console? Blame, Data Science!
Be it the popularity of Mortal Kombat, Mario Kart, and now Fortnite, the global gaming industry has largely been successful in attracting and increasing its number of gamers. With over 2.3 billion active players all across the world, the global gaming industry stands at a worth of around $138 billion for the year 2018.
Thanks to its growing pool of online games and players, the online gaming industry is generating large volumes of data on player interaction, game levels, and average scores making it suitable for some insightful data-based analytics.
Be it in improving game design or development or in visual effects, Data Science is being effective in expanding the overall reach of the online gaming industry. This is very evident in the growing popularity of the online battle game, Fortnite that is being played by over 125 million players and has generated overall revenues in excess of $1 billion since its release. Hosting more than 3 million active players at any time, the average Fornite player is spending 6-10 hours each week playing this engaging game.
So, what are the main aspects of games like Fornite and PUBG that are hooking increasing number of online players? In his best-selling book, “Hooked: How to Build Habit-Forming Products” published in 2014, Nir Eyal describes how the 4 main steps of his “Hooked model” enables technology companies to develop and use hooks that attract users to a favourite product or service and engage with them on a continuous basis.
So, how are popular games like Fornite and PUBG leveraging from both Nir Eyal’s “Hooked model” and data science to engage with increasing number of players across the globe?
Let’s first look at how the hooked model is improving user engagement particularly in the gaming industry.
How the Gaming industry is adopting Hooked model:
Image Source: https://www.nirandfar.com/
In his book “Hooked”, Nir Eyal points out that just attracting users to a great product or service is no longer sufficient to building a great business. Companies need to build products that can change user habits and hook them to using the product on a daily or regular basis. Nir points out that once a user habit is formed, they are automatically triggered to use a product or service (for example, play an online game like Fortnite or look for entertainment videos on YouTube) without relying heavily on online ads or promotions.
So, what is the Hooked model and how is it being leveraged by the gaming industry?
As illustrated, the hooked model comprises of the following 4 steps, namely:
The hook model starts with the “Trigger,” that prompts users into the first action. An effective trigger is a combination of external trigger (example, a Facebook invitation, a marketing e-mail, or a friend notification on Fortnite) and an internal trigger (example, negative emotions such as boredom or the social need to connect with friends on Facebook).
Online games like Fortnite use a lot of external triggers such as sending interesting notifications to the player’s friends to join the game. It also combines the external triggers with appropriate internal triggers that appeal to the need for socially connect with friends. In addition to inviting friends, you can do online chats with them and even watch them play the game.
The Trigger step of the Hook model is followed by the “Action” step that prompts an online user to be motivated enough to perform the required action (example, accept a Facebook invitation or join the Fortnite game). According to Nir Eyal, the action must be made simple enough to increase its chance of being executed and completed (example, completing just a few steps to start playing the Fortnite game).
To ensure simplicity and ease of use, the Hook model recommends the following 6 elements (as recommended by BJ Fogg’s Behaviour model) that must be kept to a minimum to complete any action:
a) Time required to complete an action
b) Money or cost of completing an action
c) Physical effort required to complete an action
d) Brain cycles or the mental effort required for an action
e) Social acceptance of the user behaviour
f) Non-routine or the impact of the action on existing routines
Have you ever wondered why slot machines in casino shops are so enticing to gamblers? It’s because of the unpredictability and surprise element that appeals to our human desire. The Hook model enshrines the power of the “variable reward” step that brings the same element of unpredictability into the way we interact with products (example, scrolling down our Facebook feed).
The Fortnite game leverages this element by building uncertainty or unpredictability around every game session. For example, Fortnite players are not certain of who their opponents will be in each session or the virtual terrain that they are playing on. Variable rewards appeal to the user’s internal triggers thus satisfying their need for virtual entertainment.
The final step to completing the Hook model is when the user makes an investment back into the product (or service) that could be in the form of time engagement, data, or even capital investment. The investment element increases the probability of repeated user engagement with the product in the future. Example, data-based investments can be leveraged by the business to understand customer preference and to improve personalization.
Image Source: https://www.nirandfar.com/
The Fortnite game improves its bottom line revenue by allowing its players to personalize their game characters by purchasing customized outfits and even their dance moves.
How Data Science leverages the Hook model
Based on the type of investment made by users in the Hook model, data analytics can derive valuable gaming insights based on how players interact with their games. Data-based insights are driving gaming innovation and design thus making games like Fortnite more appealing to new players.
How is gaming data driving game enhancement?
Along with innovative visualization and animation skills, gaming programmers are using data science technologies like artificial intelligence and real-world image recognition to create a more natural gaming environment and to replicate natural human movements within the game. Gaming companies may have a large base of online players but have few active players, which means that most players are no longer attracted to the game. Data science techniques like personalization can be used to increases player engagement and the number of active players. For instance, Fortnite implements personalization by allowing users to create their own customized playing characters.
Apart from the use of personalization in game development, personalization in the marketing of products including online games enables product companies to effectively target their desired customers. On the other hand, personalization is also desirable for online users to escape from an overload of digital advertisements or promotions. Based on the data collected from user interactions, companies can create personalized and meaningful marketing messages to be delivered to the right customer base. Personalized marketing can in turn, increase user activity and foster their habits as they respond more favourably to marketing messages.
The use of the Hook Model is effective in building good products (including online games) that can change user habits and improve user engagement on a long-term basis. The use of data science to create a personalized customer experience is generating higher customer lifetime value (or CLV) for companies including gaming enterprises.
The adoption of data science techniques like AI and image recognition is transforming various industry domains including the online gaming industry and is proving to be integral to game design, development, and marketing.
On that note, we complete our thoughts about the role of data science and the successful Hook model in the online gaming industry. Do let us know if you agree with the role that data science is playing in this fast-growing industry sector? Do leave behind your opinion in the Comments section below. Do also check out some of our online courses in the field of data analysis and visualization.
Home > Blogs > 9 Ways AI is Going to Disrupt Marketing by 2020
Artificial Intelligence has almost become the buzzword to kickstart all talks about technology and the future. Based on the same situation, it’s not wrong to assume that AI would have a noticeable impact on marketing, at least by 2020. Don’t worry, Artificial Intelligence is not going to take over the Marketing Manager and Digital Marketing career positions that you have, but it would definitely disrupt the entire domain of marketing.
So, if you are interested in marketing, it’s imperative that you know about these upcoming changes. And, perhaps, you may want to focus on something a bit more specific, you know. For instance, if you are in the world of digital advertising, you’d want to embrace the rise of AI-powered advertising networks. Or, perhaps, you’d want to have expertise in dealing with AI-powered chatbots.
In this article, however, we will be exploring some 9 ways AI is going to disrupt marketing by 2020. You may want to keep your fingers crossed if you are a digital marketer who doesn’t want to be left out.
#1 AI-powered Modern Digital Advertising
Modern Digital Advertising is currently the best way to reach the targeted audience without spending a fortune on the campaigns. With the impact of AI, these ads will become more effective in persuasion, leading to conversion-based marketing. As of now, digital advertising based on data collected from users is not up to the mark. In the basic example, you would have seen product recommendations that you do not clearly like. That is going to change when AI steps in.
AI-powered data analytics bots would have the ability to bring product suggestions that are quite effective. In addition to this, advertisers will have more control over how a campaign runs on the networks. Because AI has the ability to make decisions based on provided data, the whole marketing campaign would require a minimal set of (rudimentary) intervention from the marketer. However, a proper understanding of this technology is essential if you want to succeed.
#2 Better Data Analytics
Data Analytics is another area where we would see the disruption by AI. As you may have guessed, Data Analytics is not just related to marketing, but marketing cannot exist if we don’t have data to analyze. AI, combined with Machine Learning, can be the first to step in here. Just like we said in the case of digital advertising, AI can help the marketer in dealing with rudimentary tasks as well as tasks that require a lot more smartness.
For instance, by analyzing the click and conversion data from an email marketing campaign, an AI can tell you the best time to send your next newsletter. Similarly, for Social Media Marketing, you can get to know about customer preferences as well as the specific details to focus on. In short, your digital marketing campaigns could convert more when you have the right data analytics insight from AI. The best part is that AI can display all these insights in easy-to-comprehend forms.
#3 Personalized User Experience
User Experience Development is indeed one of the most sought-out practices in order to make a product experience seamless for users. Also, UX design plays a crucial role in digital marketing due to various reasons. Here too, we can see the potential impact of AI. To make the basic ideas clear here, any User Experience — be it an app or website — must have a personal approach. Thanks to the intelligent algorithms that AI can create on its own, such discretion is possible and you can offer a better experience for each user, based on data.
Google’s research constantly marks the importance of personalized user experience solutions which can boost conversion rates like never before. When enabled, the AI will pick the best-suited design and elements from the inventory and present them in front of the customer. This is also a great way to build a personal connection between you and the user, ultimately leading to better results. And, all these could not be possible without the power of AI.
#4 AI-powered Customer Relations
Customer Relations is indeed the strongest pillar when it comes to marketing. This helps in maintaining quality and gathering information, but employing people for this job is quite expensive and not-so-effective. AI would potentially disrupt this area as well, and the biggest example would be AI-powered chatbots. These bots are super-useful when it comes to offering assistance to customers, and at the same time collecting important information.
Of course, using AI instead of humans comes with a lot of advantages. For instance, the customer always has access to a digital assistant that can answer their queries. It also means AI can offer tailor-made replies depending on what the customer has asked. Probably most importantly, a single advanced AI chatbot can answer thousands of queries from hundreds of customers at the same, collecting all their data. So, from all perspectives, it’s a better option for most people.
#5 The Voice Domination
Voice Search and Digital Assistants are two of AI sub-areas to have a bigger impact on marketing. We are already living in a world that uses voice to search more than they search via typing. This is powered by an AI that is incredibly good at learning and evolving and the same would disrupt how we search for stuff. As it happens, the changes in the Search Engine Marketing sector are inevitable. Optimizing your campaigns for voice is the first step.
The idea would be to become readily accessible when someone asks Google Digital Assistant or Siri for something important. This optimization should happen in the various sectors of your Digital Marketing, including but not limited to PPC, SEM as well as the traditional SEO campaigns that you are conducting. Just so you know, AI is also making Search Engines more powerful and intelligent, that they know which content is really worthy and which one is a gimmick. Here are some more insights on voice Strategy by Google.
#6 Easier, Excellent Designs
There was a time when you had just two choices for a great marketing image: hire a professional or make yourself an expert in Photoshop, Illustrator, and whatnot. The web was overkill since you had to master HTML, CSS and some other languages to create your own ways. Thanks to the power of AI again, this is changing. There are now a number of services that let you create wonderful webpages and optimized business profiles for better conversion.
Canva entirely changed how marketers and professionals used designing by making it simpler. But there’s more to AI-powered design tools. Find out more on how AI will disrupt designing here. These services are quite easy to use and you can drag and drop elements. What this means for marketing is that the overall efforts would become minimal. You will be able to spend less time and monetary efforts on the website design but rather focus more on the content and how your service is delivered. Of course, this is another advantage that is reserved to the digital services and not much for the businesses you come across offline.
#7 Intelligent Search Algorithms
As it turns out, Search Engines and SERPs have become the forefront of conversion-oriented marketing. Considering this point, it’s big news that Search Engines are gearing up with brand-new search algorithms, powered by AI. Google, the big gun, has already said that it’s using natural language processing and some other snippets to rank content on SERPs. However, the real deal goes beyond what is to the naked eye.
Here’s a recent study by Sparktoro which details some infuriating realities of search’s future. But if there is one thing to AI-powered algorithms, it’s the constant evolution. AI is learning every minute. For instance, if a customer does not stay in the first-result webpage, it would understand the lack of usability there. The next set of decision by the AI would be based on this understanding. Long story short, this is expected to change how quality content is delivered to the commons and digital marketing community has to brace for impact here.
#8 The AI-Blockchain Hybrid
The combination of Artificial Intelligence and Blockchain Technology is one of the most discussed things in the world of marketing. It’s no doubt that blockchain will find its way to even the smaller businesses in some time. To anchor that, big firms like Google and Facebook are reportedly working on some great things. When such a to-the-common change happens, that would mean a lot of things to businesses in every place.
While AI makes sure that the customer has access to better recommendations and tailor-made content, Blockchain would address the biggest issue ever — privacy. It means digital marketing would find a way to flourish without putting user privacy and security at stake. Now, this is one of the most impressive things about how the new tech can better our lives. This hybrid, however, will need some more time to establish itself completely.
#9 AI for Content Creation
Did you know that Artificial Intelligence is capable of generating content? Well, it can’t still create a great piece of fiction or a greater verse, but AI just rocks when it comes to reports and non-creative content. By 2020, it is clear that the AI will also be capable of providing the best set of services for the same. Don’t worry, content writers would still need the job, but mundane tasks can be avoided from a checklist nowadays.
Content Creation abilities by AI would also help in better data analytics. Instead of looking at raw data and numbers, you can now have detailed reports that are made by the AI tech. This is a cool feature when we consider the potential uses shown in the future. And, yes, there may be a day when you can actually ask an AI to create a mesmerizing poem, but for now, content writers can still hold onto their jobs.
As you can see, the impact of AI on Marketing is really pervasive. There is no way you can proceed with your Digital Marketing campaigns unless you understand the basic concepts of Artificial Intelligence. Also, this may be the right time to brush up your Marketing skills to fulfill the new demands.
So, that’s how we wrap up and we hope you got something good from here. If you’re looking forward to enhancing your Digital Marketing skills as we said, don’t miss an opportunity to check out our Digital Marketing courses.
Home > Blogs > 5 Ultimate Data Science Principles that can be used in Any Industry Project
In the previous article, we discussed success stories of companies that utilized data science to shape their products and brought revolutionary improvements to their business. Such stories are testimonies of how an effective data science strategy can drive business value and why hiring data scientists can help increase revenue and profit. Following such success stories, most organizations are now making attempts to be more data-driven.
However, data science must be used wisely to be able to deliver value. In this article, we have covered the top 5 industry-agnostic data science principles that can help drive your data science strategy towards success.
Top 5 Effective Data Science Principles:
1) Begin With the End in Mind
“Data scientists often run into the issue of trying to add artificial intelligence or machine learning capabilities without concrete objectives”.
In 2014 FIFA world-cup, the end-goal of the German team was to defeat the ‘Brazilian’. In 2012, Germany hired about 50 students at the German Sport University Cologne and poured over countless hours of footage of the Brazilian players. These students spent days noting their individual running patterns, their reactions to fouls, and every other quantifiable aspect. Never-before-noticed patterns started to emerge, and these patterns became a key part of how German team players prepared for the game.
Instead of keeping the goal to win the world-cup and spending time studying every team, they were clear that their main target was Brazil and hence could prepare better.
Many ‘new data scientists’ turn to data mining and use it as a fishing expedition to explore possibilities with the available data. Whereas some unsupervised data mining is important to ensure we do not miss any possible patterns, trend or correlation from the data set, it is important to be explicit about what we are trying to achieve in the first place. When we know the end-goal, it goes a long way in ensuring that we choose the correct data sets, recruit the right talent, and choose the right algorithms to process the data.
The lack of clarity about the problem in hand is one of the major challenges that most data scientists have to deal with. Therefore, whenever taking a data science task or project, one must ask:
1) What is the end goal and what are we trying to achieve?
2) Do we have a plan?
3) How to decide if the results achieved are the desired results?
Quantifying end-goals with measurable metrics will make it easier to build the right data visualizations and to find out where the project has reached in terms of the ‘success threshold’.
2) Ensure that Your Organization is ready for AI
With the increasing popularity of data science, more and more companies are now hiring data scientists. However, more often than not, these companies are not ready for data science implementation. Most of these companies lack the basic infrastructure needed to implement the data science algorithms and operations.
Companies need to be aware that machine learning comes at a very later stage in the entire data stack. Before doing anything with the data, it has to be first reliably collected, transformed, stored, secured, and then explored. When a data stack follows a correct and correlated upstream process, then only the data can be easily explored and subsequently used for analytics, AI, and Machine Learning.
This means that if your company is not ready to adopt machine learning yet, then it should focus on building the basic infrastructure first. Hiring an experienced data scientist without proper tracking and database system in place can prove to be disastrous, both for the company as well as the employee.
3) Data is Not Even, and that Makes it Special
Most people assume that good data is perfectly clean and evenly distributed. However, in reality, the real data is neither clean nor evenly distributed. And that is, in fact, the most awesome thing about the data. Many times, these asymmetries, anomalies and other ‘warts’ give us interesting facts about the domain we are studying.
For example, while performing segmentation, outliers are often dismissed, ignored or clipped from the data assuming it to be some noise or anomalies in data. But, what if a totally new type of behaviour is represented by those outliers?
The ability to discover the unexpected features of the data is what makes data science novel and interesting. If you want to make the most of the data-in-hand, spend some time with those long tail, the outliers, the QQ tail, etc. You might make a ‘Surprise Discovery’. In reality, this very characteristic of data enables us to make unusual and unexpected observations in the domain of our study. Apply some non-parametric statistics test to open a whole new world of data-driven discovery.
Following the chosen path and simply recommending the obvious is not going to make a difference. Explore the unexplored and you will fall in love with your data, because of its diversity.
4) Work on Projects that Add Value to the Business
It is very important to ask the right questions when choosing a machine learning or data science project to work on regardless of the industry. It is essential mainly because of two factors:
1. The extended timeline of machine learning projects which implies that the cost of undertaking a wrong project can be huge (and may exceed benefits)
2. Relying on data points which may not be timely available and thus, results are not guaranteed.
Calculating the opportunity size and its potential impact to the business will help you determine if it is worth undertaking the project. Always, without fail, embark on only the projects whose output directly impacts business levers. In other words, the results of the projects should always be directly actionable.
Generic formula to calculate the size of business opportunity:
“Number of customers affected * target size of effect = Estimated project impact”.
For example, a real estate company may ask its data science team to make a rough estimate of the people who are looking to buy flats in a certain area. Merely having the number of potential buyers does not impact any business levers as you do not know their preferences. You are not aware of their choices or budget. Just identifying the potential customer is not enough. Finding their specific needs is what will make all the difference.
5) Adopt an Iterative Approach
Winners of most machine learning competitions follow an iterative approach. This means you should start with a simple working model and then, iterate. The iterative approach in data science focuses on reaching the ‘first working model’ quickly rather than starting with a model with tons of variable and features. Once the first basic model is built, features are added as the focus shifts on continual improvement.
Iterative Nature of Data Science Model
In order to take advantage of the empirical nature of machine learning, we need to reduce the cost per attempt. This means, having a higher number of trials (say, N) and allocating 1/Nth time to each trial to minimize the probability of missing anything and thus, maximizing the profit. The baseline models need not be tested on the full data before implementation. For example, you can perform A/B testing on a given model for customers from single geography at one time and can be repeated for a few geographies before testing it for global customers.
Data scientists use model error analysis to find weak areas of the model and often take feedback of domain experts on areas that need improvement. Typically, an iterative approach is much shorter and ends when the model has improved enough to meet the business requirements.
The above-mentioned data science principles are simple to understand and easy to follow. Knowing the ‘end result’ will help you quantify your success. Ensuring that your company is ready to adopt AI and is following an AI hierarchy is essential for scalable machine learning.
Similarly, you can break boundaries and make surprising discoveries by delving deep into the uneven, untraded data. Also, asking the right questions will ensure that the projects deliver value and ‘worthy value’ at that.
And finally, following an iterative approach helps to reduce cost per iteration and minimizes the probability of having inconsequential results.
To understand these principles and devise some of your own, it is important to have a 360-degree knowledge of data science. And, the best approach is to pursue a certificate program/course that covers the A-Z of Data Science. For example, Manipal ProLearn’s Data Science course covers all these beneficial resources with its in-depth curriculum and practical learning methodology and helps you build a solid portfolio required for a career in Data Science.
Thanks for reading. I hope you found the article insightful and useful. Feel free to share your thoughts in the comments section! Also, if you seek to upskill your Data Science skills, feel free to check out our Data Science Courses here.
Home > Blogs > 6 examples of a Good Cloud Service Support
Over the past decade, cloud computing services have become a strong driving force for businesses in the technology world. Cloud is no longer considered as a tool or simply a storage framework. In recent years, cloud computing has been playing a major role in every business’ strategy. Given the massive growth of cloud computing, most businesses in different domains have started to embrace this technology in different ways to achieve their business targets faster and efficiently. According to a recent survey report from LogicMonitor, about 83% of enterprise workloads will be running in the cloud by the year 2020.
In today’s business scenario, cloud computing and its services are becoming the foundation for most of the IT businesses and their infrastructure. Be it a small start-up company or the industry leader – the cloud technology provides a level playing field and the perfect platform for business to have a healthy competition.
While cloud computing is known to improve collaboration and productivity, businesses also realize savings specific to cost and scalability by implementing a cloud strategy. Keep reading as we discuss how leading businesses across different industries have adopted cloud computing services and the benefits they have achieved from this implementation.
If you recently had a chat conversation with any business on their website on the chat window pop-up, chances are high that your chat was powered by Intercom. Intercom is the leading customer messaging app platform. Intercom chat is used by various businesses in different industries as a tool to generate more sales. More than 30,000 businesses use Intercom to grow their business.
During the early days, the engineers at Intercom migrated a live database (with over two billion rows) to Amazon Aurora with almost no downtime and no data loss. In an effort to free up space and resources for further innovation, foster product development and deliver better customer value, the team at Intercom took advantage of Amazon Web Services (AWS) Lambda. By implementing a serverless solution based on Amazon Athena and Amazon Kinesis Data Firehose for a business logic upgrade in the billing system, Intercom reduced the costs by 90 percent and saved almost 800 hours of maintenance every year. This implementation also makes it easy for the engineers to easily adapt to any immediate changes by simply updating the SQL definitions. Intercom was confident enough to even move their storage account (with user data) to Amazon DynamoDB.
With the move to AWS, Intercom delivers the best real-time conversations with a low latency and a consistent speed that helps its customers to generate quality leads and grow their business.
Freshdesk, a global leader in customer support software with 28,000 customers, chose Amazon AWS as the core platform for their infrastructure to support their cloud-based SaaS solutions. Being a SaaS-based platform, Freshdesk wanted to move to the cloud to save on the investment cost in setting up a local data center and also to focus the efforts towards building a great product.
Before moving to AWS, Freshdesk had the “Pay-per-use” model. With the help of Amazon EC2 Reserved Instances, the biggest benefit for Freshdesk was the reduction in costs by almost 75 percent. Freshdesk opted to host their platform on Amazon’s cloud mainly for the accessibility, availability and security features. With AWS infrastructure, Freshdesk support agents can have all-time access to the customer data from anywhere. Thanks to the scalability feature in AWS, Freshdesk has been able to scale their business and almost double their customer base.
Freshdesk also makes use of Amazon Redshift for the next generation reporting capabilities. In addition, the entire deployment of the application, infrastructure and the code is completely automated by using AWS OpsWorks.
Airbnb is the world’s largest accommodation-sharing site where you get to stay in someone’s house instead of a hotel. Users can register with Airbnb as a host and list their house (or a portion in the house) for people to stay. Founded in 2008, Airbnb is available across 191+ countries in the world with over 6 million listings. As the business grew in numbers, in order to cater to the heavy demand and focus on scaling the business, Airbnb opted to switch to AWS cloud-based infrastructure. Airbnb uses over 1000 Amazon Elastic Compute Cloud (Amazon EC2) instances for their application, servers, production traffic and so on. Airbnb also makes use of the Amazon Simple Storage Service (Amazon S3) to store backups and high-quality user images of over 50TB.
Airbnb is an early adopter of the Amazon Relational Database Service (Amazon RDS) as it simplifies the administrative tasks associated with databases. They migrated their entire database (MySQL) to Amazon RDS with just a 15-minute downtime. Airbnb stores over 2 Billion rows of data in Amazon RDS.
Framestore, an Oscar-winning creative studio and visual effects production firm, and well recognized for delivering the visual effects for Hollywood movies like Avatar, Guardians of the Galaxy, makes use of Google Cloud services in their back end processing.
For every single visual effect, the team at Framestore carefully plan the scope, predict and plan the effort as the entire process consumes a lot of computing power. Certain times, when working on multiple projects at the same time, Framestore uses up almost 15,000 Intel cores of computing power. When the demand increased, the team found it difficult without the resources to finish their projects successfully.
Framestore took advantage of Google Cloud that gives them the option of extending the resources on demand when there is a high production requirement. Framestore opted for the Preemptible Virtual Machines which gave them the perfect combination of cost efficiency and capacity to meet the demand. When needed, additional instances of the machine can be spun up according to the requirement and spun down when not required.
Google Cloud (Google Cloud Storage and Google Cloud Networking) has brought a significant change in how Framestore plan for their visual effect. It gives them a lot of confidence to plan and deliver multiple projects and still stay on top of the different tasks involved during the process.
5. United States Government
Not only enterprises are making the shift to cloud services; even Governments have started to adopt cloud technologies for different reasons. Since the “Cloud First” strategy was implemented in 2010, the United States Government has taken a leap forward in making use of cloud computing services to save billions of dollars. Few of the noted government cloud projects are –
a) USA.gov – USA.gov is the United States government’s official Web portal run by the General Services Administration (GSA). According to the report by Frost and Sullivan, post the migration to the cloud, the website costs the government 72% less than the cost before the migration. There was also a significant improvement in the upgrade time to just one day with almost no downtime (99% availability). Moving to the cloud helped the GSA to save money, time and increased the scalability factor.
b) The CluE Project – Over the last few years, the National Science Foundation (NSF) has made partnerships with IBM and Google Academic Cloud Computing in an initiative to fund the Big Data research. This project is named the CluE program (Cluster Exploratory program). NSF funds almost 20% of all the federally-funded academic research projects.
Similarly, IBM helped to implement a government-funded cloud computing center in Wuxi (China). IBM used the IBM e-commerce patterns and the IBM® SmartCloud® Orchestrator components for this deployment. This cloud solution reduces the service deployment time by almost 85% and brings a 75% reduction in the system recovery time in case of unplanned downtime.
ASOS is the largest online fashion and beauty retailer in the UK market. With over 30000 products and serving more than 15 million customers, the success of ASOS attributes mainly to the adoption of cloud infrastructure. ASOS transformed from an on-premises eCommerce system to using microservices on a Microsoft Azure platform. Given the resiliency of the Azure platform across multiple data centers, ASOS was relieved from a single point of failure problems. They also make use of Azure Cosmos DB to handle the ordering, inventory, product recommendations, and other similar functions. In addition to Cosmos DB, they also take advantage of Azure SQL Database and Azure Traffic Manager to deliver the best user experience.
The latest advancements in cloud computing give businesses across different industries the perfect solution for their infrastructure problems and the golden opportunity to accomplish and deliver the best cloud services to businesses at variegated levels. If you want to learn to design, implement and manage a cloud computing system, feel free to check out our PG Certificate program in Cloud Computing.
Home > Blogs > Incredible Ways Data Science Is Transforming The Way We Shop Online
According to Barilliance stats, personalized product recommendation account for almost 31% of the revenues in the global E-commerce industry. The conversion rate for shoppers who do not go through recommendations stands at a measly 1.02%, while that percentage increases to a massive 288% after the first interaction. A separate study made by Salesforce found that online shoppers are 4.5 times more likely to add items to the shopping cart and complete a purchase after clicking on any product recommendation.
Personalized product recommendations on popular E-commerce websites like Amazon and Netflix is just one the many ways in which data science technologies like Machine learning (ML) is transforming customer experience from simply good to exceptional.
If you thought data analytics are only for newly-launched E-commerce businesses, you are mistaken. Langston’s Western Wear, founded in the year 1913, is using Google Analytics to improve marketing campaigns and to maintain its edge over the competition.
Besides enabling personalization, data science technologies can benefit E-commerce retailers and solutions in multiple ways, as detailed in this article.
More on Personalization
So, why is product personalization so critical for online retailers? A study conducted by Segment reveals that only around 22% of online shoppers are satisfied with the level of personalized shopping experience that they receive with E-commerce brands. A market study conducted by technology giant, Infosys concludes that 31% of the surveyed customers are wishing for a more personalized shopping experience. Despite the ongoing debate about protecting user privacy on the Internet, a Salesforce research found that 52% of online shoppers are willing to share their personal data in return for more personalized product recommendations.
Product recommendations based on “what customers ultimately buy” or the “best-selling product” along with sending customer e-mails with personalized product recommendations are also improving conversions, particularly among first-time customers.
Predictive forecasting and intelligence
Enabled by Artificial Intelligence (AI), predictive forecasting is a technique that can disrupt E-commerce sales forecasting on the basis of Big data and seasonal indicators. For example, AI technology can use current weather forecast data to predict short-term demand and sales trends.
To make its predictions, predictive forecasting uses a variety of data sources including:
1. History of previous sales
2. Economic indicators
3. Customer searches
4. Demographic data
Along with predictive forecasting, AI-powered predictive intelligence technology is being used to predict and deliver what online customers need even before they look for a product. Among the many customer success stories for Salesforce, predictive intelligence enabled online furniture retailer, Room & Board to increase its return on investment by a whopping 2900% simply by predicting and recommending additional purchases to its customers. B2B analytics companies like Lattice Engines and Mintigo combine customer data with individual activities on social media and websites to accurately identify sales prospects for their customers.
Customer Behaviour and Shopping Patterns
Apart from the business benefits of personalization, Big data analytics can be beneficial in determining customer behavior and shopping patterns. For example, which are the retail brands that are most in demand among online shoppers? When do customers shop more for the type of products that you offer? When do online shoppers make high-value purchases?
Based on these insights, E-commerce retailers can predict the market demand for products (or services) and devise more appropriate marketing strategies to tap into this demand.
Online shopping patterns are also useful in determining the right inventory level for a line of products. Online retailers can optimize their stock levels by predicting if the products in demand are going to be overstocked or understocked. Based on the insights provided by Big data analytics, you can manage your E-commerce operations such as supply chain, inventory, marketing channels, and product pricing more efficiently.
Among the major changes in shopping patterns, online shoppers are no longer following a linear path from product awareness to the actual purchase. Today’s shoppers are searching and purchasing products (even in the same brand and categories) using varied methods, as highlighted in this Google article.
Customer-related KPIs and metrics
Among the important metrics (or KPIs) for E-commerce business, Customer Lifetime Value (or CLV) determines the overall value of revenue that each customer will bring during their association with the company.
Image Source: https://crealytics.com/wp-content/uploads/2017/09/c73371fb-df48-4105-869a-95b4b41e39fb_customer-lifetime-value-curve.jpg
CLV benefits E-commerce retailers in multiple ways, including:
1. Determine the right marketing strategies.
2. Determine the average cost of acquiring customers or Customer Acquisition Cost.
3. Set business objectives for future growth, expenses, revenue, and net profit.
4. Personalize customer purchases through up-selling and cross-selling.
5. Optimize business spending on marketing campaigns and online advertisements.
As an E-commerce retailer, you know the challenges of acquiring a new customer. At the same time, after customer acquisition, customer retention is an important objective for online retailers. This is because loyal and repeat purchase customers generate around 40% of the company’s revenue. Customer retention is also key to increasing your CLV.
A customer churn model is effective for retailers to identify the customer who is more likely to switch to their competitor’s products and to take measures to retain these customers. Based on metrics such as a number (and percentage) of lost customer and value (and percentage) of lost recurring business, the customer churn model can help E-commerce shops to:
1. Identify potential churn customers and devise retention campaigns.
2. Maintain and increase CLV.
3. Minimize customer churn.
Be it through identity thefts, phishing, or account thefts, online frauds grew at a rate of 30% in the year 2017 making it almost twice the percentage growth in retail sales. Apart from these types of thefts, shipping, and billing-related frauds are also on the rise.
Image Source: https://sift.com/image/sift-edu/fraud-basics/basics-header-2x.png
Besides providing good products and exceptional customer experience, online retailers must ensure customers of the safety aspect of online transactions performed on their website. Online fraud can cause loss of revenue and also create a negative perception about the business among online shoppers leading them to avoid making online purchases with the concerned retailer.
A combination of data science and machine learning can be used to detect suspicious behavior through the following indicators:
1. Different shipping and billing address
2. Large value orders
3. Use of multiple modes of payment for the same shipping address
4. International orders
Can data science help in improving customer service and their online experiences?
According to a study conducted by Deloitte, 72% of companies can effectively use Big data analytics to improve customer experience. This article reports that 72% of companies believe that Speech analytics can be an effective tool in improving customer experience and delivering business benefits.
While traditional forms of customer service comprised of product (or service) feedback from customers or reaching out to customers through phone or e-mail, the rise of data analytics has provided online retailers with valuable insights that is helping them provide better services.
Enabled by natural language processing (or NLP), Sentiment analysis is an effective tool that can derive valuable insights from a large number of online customer reviews and ratings about a given product or brand. Data analytics tools such as the Word Cloud and N-grams can be used to make sense of user reviews by looking for selected words or word associations that convey what users think about the product or brand.
Data analytics can help E-commerce retailers to identify and resolve issues in products or services, thus enhancing the overall customer experience.
For more insights on how data science helped various eCommerce sellers, read Google’s analysis.
The benefits of using data science technologies including AI, machine learning, and natural language processing are immense and are driving the phenomenal growth of the global E-commerce industry. This article outlines 6 of the crucial areas where data science is making an impact. Be it a small or a global E-commerce retailer, investing in data science technologies can enable you to understand customer needs, improve customer service, design better products or services, and prevent online fraud, among other benefits.
And that completes our thoughts about the use of data science in E-commerce! We hope this article has been informative for your business. Do you agree with the multiple benefits of data science for E-commerce players as outlined in this article? We would love to hear your feedback in the comments section provided below. In the meantime, you can also check out our certification courses in Data sciences and Big data analytics.
Home > Blogs > 5 trends that define the next shift in Data Intelligence
Let’s have a look at the graph from Google Trends (05-2014 to 05-2019). It reveals an interesting fact that five years back, the term “Data Science” was almost unknown. But, on the other hand, the vast changes prevailing in today’s technological landscape has changed the whole picture.
The role of Data science, Big Data, and Data Analytics, which collectively can be termed as Data Intelligence, has been elevated to many folds in almost every industry, whether big or small because extracting value from collected information has proven to be invaluable for business. This is the reason, the trends in data intelligence are also changing from a departmental approach to business-driven data approach. In this competitive era for business, to stay ahead in the competition, organizations need to implement the right data-driven Data Intelligence trend.
If we see Data Intelligence trends in the last couple of years, they are mainly revolving around some big names like Artificial Intelligence, Machine Learning, along with some newer technologies like Blockchain, Serverless Computing, and Digital Twins. Although, all these technologies are doing remarkably well, whenever it comes to the future of Data Intelligence, the prediction of the co-founder and CEO of Kaggle, Anthony Goldbloom, comes in mind that soon Data Centers are going to be replaced by business-specific data Sciecne terms. What does this mean? This means, for higher efficiency and productivity, the coming years may set the stage for advanced data intelligence techniques to take-over the routine business processes.
To take it a step further even, in this article, we are going to discuss 5 trends that define the next shift in Data Intelligence. Curious to know! So, let’s get started.
1. Quantum Computing: According to DOMO’s Data Never Sleeps 6.0 report, “Over 2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7 MB of data will be created every second for every person on earth.”
As the complexity and size of our data set balloon are growing day by day, we need really a fast way to process, organize and extract value from this data. The search stops at Quantum Computing.
By using ‘qubits’ for storing information, instead of 1 or 0 used in classical computing, QC will be able to complete complex calculations in mere seconds, cut processing time immensely. It gives companies the opportunity to make a timely decision to achieve more desired results. Isn’t it?
Despite being in its infancy, some big tech giants like IBM, Google, Microsoft, D-Wave best positioned for a quantum leap in quantum computing.
3) D-Wave Systems, the only company producing and selling commercial quantum computers, builds D-Wave 2X. Currently, D-Wave has the largest claimed number of qubits in its processor at 2048 qubits.
4) In the race of quantum computing, Microsoft is not far behind and launched Q# the quantum development kit.
For sure, Quantum Computing is defining the next shift in Data Intelligence and is enabling organizations to optimize large chunks of unstructured data for all types of use cases and portfolio analyses.
2. Fast growing IoT Networks: IoT (Internet of Things) are becoming quite common and influencing our lifestyle from the way we react to the way we behave. The home appliances we can control with our smartphone, the smart cars providing the best route or the smartwatch which is tracking our daily activities, thanks to IoT for all this. The growing craze for IoT is drawing more and more companies to invest in this technology.
IoT, a giant network with all the connected devices, where these connected devices gather and share data about how they are used and in which environment they are operated. This results in a vast amount of data which need to be managed and analyzed in a manner to get valuable insight into consumer behavior. In the near future, organizations will jump on the opportunity to provide better IoT solutions.
3. Edge Computing: For almost every sector, the vast volume of data, produced by various sources, represents treasure troves of actionable information. But with the increase in data volume and velocity, inefficiency to transmit all this information to a cloud or data center also increases. What if we put applications and data closer to the users or “things” that need them—that’s where Edge Computing plays an important role. Edge data centers deliver several key advantages like higher bandwidth, lower latency, regulatory compliance around location and data privacy.
IoT and edge computing seem well made for each other. As per the estimates, by 2020 the total number of connected devices are going to be staggering 26 billion in number. As of now, the cloud is the only solution taking care of storage, analysis, and numerous other stuff. But the flood of information from these massive number of new devices can impede the whole cloud operation. Do we need to worry? NO, with the advent of modern science and technology, “Edge Computing” is the solution. In fact, IDC research predicts, in the next three years almost 6 billion devices will be connected to the edge computing solution and around 45% of the IoT created data will be stored, processed and analyzed at the edge or close to the network.
When the talk is about disruptive technologies, the name of big tech giants like Microsoft, Google, Amazon will invariably come. These companies are the frontrunners in this case for promoting the next generation breakthrough Edge and IoT technology.
1) Microsoft’s product Azure IoT has already bagged second place and they recently launched Windows 10 IoT core also.
2) In recent years, Amazon has also ramped up by investing aggressively in IoT. Amazon’s AWS IoT is the latest innovation from this tech giant.
4. Predictive Analysis: The use of analytics tools to process the data and determine the reason why certain events happen, remains the key strategy for businesses to have a competitive edge. But, what if companies can peep into the future and predict consumer’s next action before they even do it? Yes, predictive analysis, a sub-field of Data Analytics and Business Intelligence, makes it possible. PA, combining the power of Data Mining, Data Modeling, Data science, Artificial Intelligence, and Machine Learning, deals with an in-depth analysis of past events and forecasts in future events.
Predictive marketing that extends well beyond the marketing department, is the next shift in the marketing and advertising sphere. It requires the Integration between marketing executives and technology specialist. Organizations that develop and leverage PA capabilities will have an advantage in this competitive era.
5. Social Media:We’re all connected, all the time, it’s the biggest impact of social media on our lives. In this globalized village, it is Earth’s biggest focus group. Isn’t it? But, if you are wondering how social media defines the next shift in Data Intelligence, think about the enormous data, 2.77 billion active users (increasing day by day) are generating through various social media platforms like Facebook, Instagram, YouTube, Twitter, etc.
We post our emotions, thoughts, and opinions on social media platforms, giving unprecedented levels of insight that directly affect company strategy. Whenever there's a slight or massive change or upgrade in social media features, it results in: viral campaigns, new advertising ideas from brands, benefits to brands, benefit to the audience, etc. This is going to be the age of dynamism where data will never be static yet will be in huge demand for the audience insights it provides.
In the form of social media marketing, an incredibly fascinating battle for the heart of eCommerce lies ahead. How? Let’s have a look at the two examples below:
6) Virtual Spaces: The future of marketing belongs to “Virtual Spaces”, where virtual reality and social media converge. Facebook provided a glimpse of it in 2017. With the immersive AR experiences, companies invite people out of their filter bubble. Would you want to “test drive” your favorite car from the comfort of your home? Of course, Yes!
7) Smart Speakers: A profound development for marketers is products like Alexa, Google Home, Cortana, Siri. We can say them, Smart Speakers because when we ask them to find an answer, we don’t get the list of videos, research reports or articles, we get the ANSWER.
And that’s a wrap! Hope this blog post proves to be insightful for you! Share your thoughts in the comment section and we would be glad to read them. Looking up to building a career in Data Science? Explore our Data Science courses here to upskill in data science.
Home > Blogs > 5 Cloud trends that will revamp the SaaS space in 2019
Gone are the days where software is made available as a standalone package and installed from physical storage devices like CD and DVD-ROMs on a local computer or a network setup. The growth of technology (cloud computing) has given businesses the advantage of moving from on-premises to on-demand – from servers being hosted on data centers in remote locations to software, platform, and even infrastructure being made available as a service on demand. The biggest advantage of all, cloud technology allows businesses to access their applications and data from anywhere, anytime. Companies realize additional savings from maintenance costs by moving their data to the cloud.
Efficient Use of cloud by existing SaaS businesses:
Talk of cloud computing and the most commonly heard term is SaaS. Software as a Service (SaaS) is one of the three categories of cloud computing in addition to Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). With SaaS, the vendor (third party company) hosts the application on a server and delivers it on a mobile app or a web page via the internet. A recent tech poll reveals that the biggest increase in IT spending in the year 2019 will be towards cloud computing strategies. Therefore, more than 70% of the organizations are looking to move their applications to SaaS by 2020. With SaaS, businesses no longer need to worry about storing applications on their servers, server downtime, and scalability to handle peak load.
While most Small and Midsize Businesses (SMBs) adopt the cloud to take advantage of the SaaS model, large enterprise companies are also following suit to make their applications available on the internet. Below are a few of the industry giants who invested in the SaaS model and how they make use of the cloud.
Dropbox was one of the early cloud storage solutions for users to save their files and photos and sync them between multiple devices. With more than 500 million active users, during the initial years, Dropbox used a hybrid architecture with more than 10,000 physical servers to store user content along with Amazon Web Services (AWS). While user metadata was stored in their own data center, the actual files were available in Amazon Simple Storage Service (Amazon S3). The communication between data center and cloud storage was set up using Amazon EC2 instances.
Shopify is an eCommerce platform to start, manage and grow your business. Shopify started off with an all-cloud strategy since 2006 when they hosted their smaller services on the public cloud. Later, to meet the demand and have the best tools for their team, Shopify switched to Google Cloud and moved over 50% of the workload into Google Cloud. In 2016, Shopify considered using Kubernetes – the container orchestration system to automate deployment, scaling, and management of containerized applications.
The list goes on. Even Microsoft and Google have a SaaS-based offering of their applications such as the G Suite, Microsoft Office 365. Everything works the same way, except that your data resides in the cloud and you can access it anywhere, anytime!
Top Challenges with SaaS industry:
While the global SaaS market is on course to reach $164.29 Bn by 2022, Gartner, the world’s leading research and advisory company, predicts that almost 80% of the software providers will migrate to SaaS subscription-based model by 2020. Despite the growth, SaaS has its own limitations/challenges that hinder the expansion and growth. Here are the most common SaaS limitations and how you can overcome them –
1. Lack of integration support
One of the biggest challenges faced by SaaS business owners is integrating their cloud applications with on-premise systems. A typical cloud to on-premise integration (hybrid integration) requires data to be extracted, translated and made available across the systems. Setting up a hybrid integration is a tedious process and presents higher security risks.
The solution to overcome this problem is to switch from a SaaS-based approach to an Integrated Platform as a Service (iPaaS) model. With iPaaS, application integration is made very simple and less complex as it is meant for hybrid integration scenarios.
2. Vendor Lock-In
In the case of SaaS, your workloads are located on the data centers of a single cloud solution provider. Once your data is hosted, it’s clear that you are forced to adhere to the vendor’s business terms. If the business plans to migrate the workload into another cloud platform, they will incur a heavy cost mainly because of the lack of standardized APIs between different cloud providers.
To overcome this problem (especially for SMBs and new cloud adopters), it’s important to make sure the cloud service provider supports cloud portability that allows to share data between different providers.
3. Data Security
These days, data breaches happen quite common where millions of company records are stolen by hackers. Back in 2012, Dropbox faced one of the worst data breaches where over 68 million email addresses of registered Dropbox users were compromised. Therefore, it’s important for SaaS businesses to have the necessary data protection measures in place to secure user data.
To overcome this challenge, it’s important businesses choose the right cloud service provider such as AWS, Google Cloud, Microsoft Azure for their infrastructure, as they already have the necessary security measures in place. SaaS application providers should also implement two-factor authentication to allow secure access for users.
5 cloud trends and its significance for SaaS products in 2019 and beyond:
As enterprises continue to invest heavily in the cloud, the signs are very clear for the cloud and SaaS products to reach greater heights in the year 2019. Here are the top 5 cloud trends that we feel will be of the maximum significance for SaaS products and businesses in 2019 –
1. Multi-cloud and hybrid cloud adoption
With a multi-cloud architecture, businesses can use multiple public cloud services from different providers. This comes very handily to avoid vendor lock-in problems. Almost 84% of businesses have a multi-cloud strategy in place.
Hybrid cloud is a combination of private and public cloud to deliver a seamless experience. This offers businesses the option to take advantage of a cost-effective public cloud and use the private cloud to store their sensitive data. The biggest advantage of a hybrid cloud is that it is cost-effective and increases scalability. More organizations are making the move to hybrid cloud to gain a competitive advantage in their domain.
2. Cloud security (GDPR)
With the introduction of EU General Data Protection Regulation (GDPR) in 2018, businesses in the European Union will have to be extra cautious about where customer data is stored and how effectively the data is deleted when a user unsubscribes from the service. Companies are held responsible to ensure the safety and security of user information and heavy penalties will apply if there is any breach of user data.
3. The dominance of Kubernetes for container orchestration
While the use of Docker will continue to grow in 2019, Kubernetes is seeing an increased rate of adoption by almost 48 percent (according to the 2019 State of the Cloud Survey), higher than AWS containers, Azure Container Service, and Google Container Engine.
4. SaaS to PaaS for retaining customers
While SaaS matures and is expected to grow significantly in 2019, businesses will look to invest more into PaaS as it is expected to grow up to 56% in 2019 (from its 32% in 2016). With the PaaS infrastructure, businesses can quickly deploy their application code and add new applications to their existing suite.
5. Cloud services will continue to grow exponentially
Similar to PaaS, other cloud services like SaaS and IaaS will also see a higher number in 2019. According to Bain & Company, SaaS is expected to grow at 18% Compound Annual Growth Rate (CAGR) by the end of 2020. Similarly, the global IaaS market is expected to hit $72.4 Bn in the next couple of years. Gartner also predicts that by 2021, the total public cloud revenue will be $278 Bn.
One thing that is pretty clear is that cloud computing is evolving and SaaS businesses are seeing the advantages of cloud over traditional implementations mainly in terms of cost, security, and scalability. 2019 promises to be a great year for cloud and SaaS businesses. For businesses, this is the best time to move your workload into the cloud, if you haven’t yet embraced this technology. So, if you are looking to gain expertise and make a full-fledged career in the cloud computing field and master the cloud services, check out our Post Graduate Certificate Program in Cloud Computing.
Home > Blogs > Cloud Computing and the Indian Government
Cloud computing is one of the greatest revolutionary changes in the history of technology. Since the dawn of the computing era, every organization had an on-premises data center. They have their own computer systems, storage, and networking facilities. They would run any application on top of these systems, this classic representation of enterprise computing. Cloud computing is basically an on-demand computing resource which is being provided to the users over a virtual online network. The scope of cloud computing is endless in terms of development and technological advancement.
The cloud computing technology can aid the Indian government to implement educational, social, and economic reforms in the country. Moreover, cloud computing can set down as a base for a whole new revolutionary industry including telemedicine, online classrooms, online employment, and a potential commerce based industry that would generate employment for the emerging youth of the nation.
The role of cloud computation in bridging existing government limitation
One of the limitations correlated with the traditional IT architectural approach is the physical server model. These physical servers have the inability to process all the request due to the growing demands on the go and cause slow response time or either the additional server that are purchased to meet the demands remains idle for long due to normal usage. This over/under utilization of servers results in the wastage of valuable resources. On the other hand, the cloud provides optimum utilization by the pay as you go approach and thus proves to be cost efficient by reducing the capital expenditure. The advantage of cloud computing architecture is that it provides rapid elasticity and scalability. Its on-demand functionality allows to scale its capacity automatically whenever required and hence proves to be flexible enough to accommodate the sudden spikes in the usage demand.
The Potential transformation laid down by cloud computing on the Indian Government
Cloud computing holds the key to reform the very basic structure of operations in the Indian government. Major countries across the globe such as Australia, US, Singapore, UK, and EU utilize the benefits of cloud computing with the aim to increase agility, eliminate redundancy, share information and optimize communication technology at economic costs. The GI cloud initiative by the Indian government was one such step that aimed at bridging the gap between the government and the citizens and by delivering prompt e-services to its citizens. The Potential transformation that the Indian government can yield by implementing cloud services is Transparent and rapid information delivery system; optimum infrastructure utilization; higher employment opportunities and Global economic integration. Cloud computing services: Software as a Service (SaaS); Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) have been successfully utilized by NIC under the MeghRaj cloud initiative portal. The Government of India deployed this cloud computing platform which is used by center and state government departments and agencies. It is an initiative of the Ministry of Electronics and Information Technology(MeitY). During MeghRaj a common platform called eGov App store was also introduced to host and run the application. It is easily customizable and configurable without much time and effort and available for reuse. For instance, the RTI (Right to Information) also faced challenges in performances prior to the cloud due to huge number of users requests and inability to process all of them, this caused application downtime. Adopting the cloud services provided efficient performance due to scalability and proper utilization of resources
The evolution of cloud computing has opened numerous paths toward development and transformation. The introduction of cloud computing was a rather rapid development, leaving people with scarce time to equip themselves with the skill set required to understand and operate the dynamics of cloud computing. Therefore, the Indian government should implement nationwide training programs to prepare potential trainees for the development of cloud computing.
The rationale behind the deployment of Cloud technology
In today's digital era the cloud computing technologies are the game changer in any of the businesses across industries and it is hence creating a new benchmark in relentlessly providing cost-effectiveness and business innovation by delivering hosted services through the internet. There is no doubt on how this technology is gaining popularity and growing day by day as most of the private sector in India has already adapted and started building on the power of cloud computing. Hence, it also provides an opportunity for the Indian government to embrace the change with this technological innovation and explore the limitless possibilities that cloud offers.
Conversely, cloud computing can prove to be a challenging rendezvous if it is not implemented with robust security mechanisms. The technology is not free from perils and associates itself with some key security risks and limitations. Data security possess as a key concern in the execution of cloud computing. The privacy and security control of the user's personal data is exposed to vulnerabilities through third-party servers. Consequently, it is crucial to encrypt every third party data server passage in order to value the privacy of cloud users. An additional threat to cloud computing is a cyber attack. The concept of cyber attack goes hand in hand with online data storage and processing. Cyber threats such as bot malware, virtual machines, and brute force attacks are a common endeavor in online data processing. Hence, it is essential to reap the benefits of cloud computing with high-end security measures and complex concealed programming that is updated on a regular basis.
Potential Mishaps that can take place if cloud computing is not leveraged
The cutting edge technology plays a vital role in the digital transformation and growth of our nation and it also supports the digital India mission. In a country of a population of over a billion people, there is a growing need to manage the enormous amount of data and also make them voluntarily available to the citizens through digital cloud services. But the implementation at this level would require the infrastructure to be scalable enough along with a very strong and robust technology stack that is itself capable of managing and quickly deliver the right resources during this massive inflow and outflow of data effortlessly. Hence the cloud has a very essential role in facilitating this change and fulfilling the storage capacity, application performance and compute requirements. They will act as building blocks in the development of the country.
Cloud servers are almost entirely dependent on functional data services. The business/government organization utilizing cloud server can suffer an irreparable data loss if the cloud servers crash even for a short period of time. Therefore, the government needs to develop a layered mechanism that protects the cloud data server from crash and undertake preventative measure for potential mishaps.
The evolution of Cloud computing has taken the world by storm. The idea of cloud computing provides scalability, flexibility, data centralization, agility, high performances, security and cost/time efficacy. Conversely, it lacks a proper governance system that guides the policies and procedures implemented in the utilization of cloud computing assets. At present, the cloud-based system does not allow complete operation infrastructural access that poses as a key limitation for IT to provide effective governance and compliance management. Therefore the need to implement key modifications in the IT governance strategy is crucial in order to deliver the best possible cloud computing system with minimal challenges and uncertainties.
In a developing nation like India, the government is considered as active if it is agile and can keep up with the growing demands and expectation of its citizens with the help of its services and computing capabilities. The information technology can help in this prospect by delivering the services that boost the government initiatives and at the same time keep the infrastructure unsophisticated and straightforward. The cloud has exhibited the potential to be cost-effective and flexible when it comes to digitizing the governance systems. The world is open to possibilities and it is an immense opportunity for India to lead the front of the technological innovation and encourages the government to embrace this change by adopting the cloud computing revolution for economic growth and to enrich the lives of its people. Subsequently, the fragility of cloud servers poses a potential threat to the invariability of cloud computing. As a result, it is immensely crucial that the cloud servers are strategically programmed through layered securities and dynamic coding mechanisms. Moreover, the Indian government should invest its resources in order to devise an optimal strategy to utilize the best of cloud computing with minimal security and control risks.