All About Data Optimization!

What is data Optimization? Data Optimization is playing a major and important role in Pinterest and Instagram marketing. But how?

In this digital era, which is powered by the Internet of Things (IoT), Social Media, AI, Machine Learning, along with increasing computing power like Quantum Computing, data is everything. That’s the reason, almost all businesses are on the big data adoption curve and the data captured and retained by their IT systems is one of the most valuable assets for them.

Undoubtedly, in this highly competitive environment, the effective use of data can be the key to making the business a market leader. But, on the other hand, if businesses can’t optimize their data, they are going to spend their precious time and resources digging through it, rather than utilizing it.

Data Optimization & its significance

The data, on which enterprises depends, is increasing rapidly day by day. It may have many sources and various structured as well as unstructured formats. On the other hand, in most of the cases, it is inaccurate, inconsistent, and redundant. Such anomalies make data unnecessarily difficult to handle and most importantly, the enterprises can't even access pertinent information in a timely and comprehensive fashion.

That’s why there is a need to optimize data.

Data optimization means collecting all the information at your disposal and managing it in a way that maximizes the speed and comprehensiveness with which critical information can be extracted, analyzed and used.

The data must live up to its potential, that's why in this growing environment, the data optimization strategy is like a flexible solution that can scale and adapt to any drastic changes in management operations. Moreover, if your system cannot naturally expand to handle more information, you won't get the most out of it.

With that in mind, let’s take a look at some significant benefits that data optimization can provide to any business:

1.  Fast & flexible Decision-Making

In today’s highly competitive business environment, the survival of any enterprise depends upon how fast & with what flexibility they take a decision in case of both threat and opportunity. Such decision making requires actual numbers and timely access to critical information. Isn’t it?

But, amalgamating data from various sources and formats can be time-consuming as well as error-prone task.

Here, data optimization comes into the picture. It restructures the data sets and filters out inaccuracies and noise. The result is an increase in getting critical information on time and flexibility in decision making.

2.  Enhanced company reputation

What do you expect from poor data quality? Can it make your business the market leader?

Poor data quality often leads to confusion, delay and the potential strife into a transaction with customers, business partners. Data quality brought by data optimization process minimizes a company’s exposure to such problems and eventually enhances the overall reputation.

3. Improved business process

There is no doubt that every company feels the effect of waste. On average, every year, inefficiencies cost many companies from 20-25 percent of their revenue. Think about what an enterprise can achieve with 25% more funds to use on customer retention or product development.  

Data optimization helps business leaders to understand and improve their business processes so that they can reduce the wastage of time and money.

4.  Meets consumer expectation

In this information age, consumers expect to get fast, accurate, and comprehensive information from the business they are dealing with. For companies, data optimization plays an important role to understand consumers and the market. That’s why it is often the key to providing real-time services in order to meet consumers demand & expectation.

5. Increased performance and ROI for IT infrastructure

Have you ever wondered how server, network, storage and other system software components of your IT operations are doing?

The infrastructure tools used for data optimization can provide insight into their performance. Such information greatly facilitates tasks such as planning, troubleshooting, and forecasting, which in result, more efficient use of hardware and software resources. 

Data optimization is incredibly useful. Right? So, how an organization can do data optimization? Come, let’s see.

PS:https://mydatareef.com

Ways to optimize data

Here are the top three ways an organization can do data optimization.

1. Move data to the cloud

There can be several arguments in favor and against the decision of moving the organization’s data to the cloud. But, for all good reasons, moving your data to the cloud is best suited for its optimization.

a)  Rather than having data all over the place you’ll get a common location from where you can call all your data anywhere, at any point, from nearly any device, all while acting as a backup, whenever you want.

b)  Cloud-based data management platforms provide security because it guarantees that only authorized person can access your data.

2. Leverage the latest technologies for turning data into decision

In order to optimize its data to the fullest, organizations must keep up with the latest technologies like Machine Learning. Through machine learning and other methods of data predictions, organizations can turn a massive amount of data into trends, which can be used for analysis and decision-making.

3. Standardized the data

One of the reasons for data inconsistencies is not to have 'one standard way' to write it. For example, two people may put data with different abbreviations at the same time, which might not recognize as a duplicate by your system.

Apart from the above benefits provided by data optimization to the companies, the marketing profession has been influenced by data optimization more than almost any other field. Even on the visual social networks like Pinterest and Instagram, marketers are significantly benefited by using data optimization to optimize their strategies. With the help of Pinterest case study, let’s find out how?

The Pinterest Case study: How Marketers leverage Data Optimization

Pinterest, the visual social network made its debut as a photo-sharing website in 2010. But today it boasts an engaged base of more than 150 million active users with 2 billion monthly searches. It also offers significant potential as a referrer and is responsible for roughly 8% of the referral traffic across the web.

Pinterest, with more than 175 billion items pinned onto 3 billion pinboards, has huge content to share with users. To that end, Pinterest has come to rely on Pinterest’s smart feed algorithm, based on elements like Pin Quality and Source Quality, to help decide what content to users wish to see.

So, Pinterest has BIG DATA and knows how to share it!   

On the other hand, smart marketers know the importance of the optimization of this big data to reach customers on these social networks.

For example, if we talk about the pin quality, marketers think of their Pinterest boards as their visual portfolios and try to make their pins as appealing as possible. But they also understand that the odds of certain pins going viral is higher than others. By keeping this in mind, shrewd marketers monitor the performance of different posts to see patterns and start creating similar ones to maximize their chances of success.

To avoid this overwhelming process, marketers are also using machine learning techniques that help them to make more engaging images. The stencil is such an image generation tool that, with the help of machine learning, can continually track the performance of different marketers and recommend new images.

Marketers also understand that the quality of the source is determined by how often people pin and repin content from a website or blog. That’s the reason they know, to get higher rankings they must pin their best content. For this, smart marketer sees and analyze monthly search volume of different keywords by using Pinterest’s own keyword analysis tool.

Just like Pinterest, there are lots of other companies that have integrated data optimization and gained a competitive edge. Let’s have a look at the top 5 of them.

1. Amazon

The online retail giant is having a massive amount of data in the form of customer names, addresses, payments, and search histories. They optimized this data to improve customer relations and cites various other data points to figure out what its customers want. That’s the reason Amazon hit a net worth of I trillion dollars late in 2018.

2. General Electric (GE)

GE is optimizing the huge amount of data coming from sensors on machinery like turbines and jet engines in order to optimize the ways to improve the working process and reliability. Based on company’s estimation, data optimization could boost productivity in the US by 1.5%, which, over a 20-year period could save enough cash to raise average national incomes by as much as 30%.

3. Starbucks

This coffeehouse behemoth uses data optimization to determine the potential success of each new location by considering the traffic, area demographics and customer behavior. No wonder, that’s why they can open three branches on the same street without suffering their business.

4. Capital One

One of the most common uses of big data is marketing. Capital One, ranked 10th on the list of largest banks in the US by assets, is at the top of the game in utilizing data optimization because it enables them to determine the optimal times to present various offers to clients.

5. Next Big Sound

Spotify streams, iTunes sales, SoundCloud plays, Facebook likes, Wikipedia page views, YouTube hits and, Twitter mentions are like data generating machines and producing a huge amount of data daily. Next Big Sound has figured out how to optimize this data to predict the next big thing in music. That’s the reason Billboard now publishes two charts based exclusively on NBS’s data. What a success! Isn’t it?

That’s all for now, readers! Hope this blog post proves to be insightful for you! You can check out our Data Science courses here to upgrade your skills in Data Science, AI & Machine Learning!