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Home > Blogs > How Much Control do Data Scientists Have over a Product Change?
From getting up in the morning to reading news, exercising, making breakfast choices, taking a cab or driving to work, working, socializing, driving back and getting some entertainment in the evening – everything we do today involves technology personalized to you. Wondering how those apps on your phone, tablets, or computers know about you and tailor their offerings to your best usage. The answer lies in the data!
The data they collect, process, and utilize to personalize your experience powers these technology products. This utilization of data is not limited to the consumer apps, it’s far widespread and finds its usage in all walks of life, industries, and society at large.
The people who derive value from scattered data and transform it into immensely useful form are ‘data scientists’. Their role takes the center stage in envisioning, building, and shaping of a product. World-leading products, such as Facebook, Google, AirBnB, Twitter and etc. have teams with a solid data science skillset. It’s really the synergy between the cross-disciplined teams that give rise to truly habit-forming products.
In this article, we’ll discuss the role data scientists play in transforming an idea into reality.
Role of Data Scientists in a Product’s Lifecycle
Data scientists play a very important role in various aspects related to product development, which includes:
1.Ensuring product viability
2.Identifying the right target markets and product’s sweet spot
3.Defining and refining user journeys
4.Product building and implementation
5.Product progress tracking on metrics
6.Success tracking of a product
7.Course correction and providing feedback
8.Marketing and sales
9.Future roadmap definition
A typical relationship between data science and product development looks like:
Myths Related to Data Scientists’ Roles in Product Development
There are several myths associated with the role of data scientists in the process of product development, such as:
Data scientists are an external entity to a product team
Data scientists are responsible to perform important functions at every single stage of the product lifecycle. Their deliverables help multiple stakeholders, including product leadership, technical teams, and customers. This is why considering them as external to the core system is one of the worst myths.
Data sciences are purely about predicting outcomes
Data science includes many skills beyond just predictive analytics. They, along with the data architects, are responsible for defining ways to collect data points, process and cleanse the data, store and secure the data, and in the end, perform analytics based on the business needs.
Data Science is an elegant way of producing reports
Reporting is just one of the several deliverables expected from data scientists. In addition to reporting, data scientists provide actionable insights, build data science related features, perform prescriptive, descriptive and predictive analytics and contribute to numerous decisions that involve data.
Accuracy of Analytics Outcomes is only dependent on the quantity of data
A common myth is, you need to provide large volumes of data to get correct results. This is absolutely untrue because of multiple reasons, such as:
1.Incorrect models will not deliver good results regardless of the data volumes
2.Quality of data directly affects the accuracy of outcomes
3.Too many independent variables introduce complexity that may impact results
Success Stories of Companies Using Data Science to Shape Their Products
1) DBS uses Data Sciences to Expand its Reach while Reducing Trade Anomalies
DBS, with its flagship product DigiBank, uses data sciences for multiple purposes, including marketing, business insights, and for transaction monitoring, credit monitoring, risk scoring, and fraud detection. For marketing and business insights, it utilizes data sciences to achieve identification of the right target segments, designing and running campaigns, tracking effectiveness and understanding the next best actions for their targeted segments.
Also, they have developed a trade alerts program that gave the bank a robust platform for detecting trade anomalies. This has boosted their ability to understand their clients better, make accurate judgments about the nature of their transactions, and even detect fraud anomalies based on the transaction trends.
With the use of data science effectively, DBS is now better equipped to comply with AML (Anti Money Laundering) regulations.
2) Google Maps Uses Data Sciences to Guide you Accurately
Do you realize how Google Maps offers you extremely accurate driving directions based on your constantly changing location on the go? The accuracy of their prediction is dependent upon the countless data points they capture and analyze user data sciences as their core weapon.
Google takes into account data obtained from its users’ movements as well as from its partnerships with the local city authorities to elicit construction updates, road closures, and accident data. For this specific purpose, they acquired a start-up, known as Waze, in 2013 and integrated it into their maps offering.
3) Shell Improves Productivity by Detecting Machine Failures
An Oil & Gas giant, Shell successfully uses data sciences to gain a multi-faceted impact on the quality of its processes, business decisions, maintenance costs, and environmental impact. Shell has transformed its asset management systems to use SAS that helps detect the machine anomalies at various sites, regardless of accessibility by humans. This directly extends the lifespans of their machines, which is a significant saving for the organization.
At Shell, data science is also empowering their business leaders to make decisions based on real data, eliminating the guesswork from decision making. This greatly reduces the operational, financial and reputational risk for them. In addition, usage of data sciences helps them understand and take proactive actions in the scenarios related to human safety and environmental impact of their exploration.
4) Your iPhones Recognizes You By Using Data Sciences
Apple introduced Face ID to enable its users to unlock their iPhones using their facial patterns. A unique combination of powerful hardware and data science algorithms it employs allows you to use Face ID to recognize every user uniquely and accurately. Face ID is able to automatically accommodate the changes in your appearances, such as growing facial hair, wearing cosmetic makeup and even, with accessories, such as hats, glasses, contact lenses, scarves, etc.
The TrueDepth camera used by Face ID records facial data and captures over 30,000 dots to create a depth map of a user’s face. It then employs its neural network engine to transform the captured data into a mathematical representation, which is then used each time authentication is attempted by a user.
Tips for Data Scientists to Contribute Better and Effectively to the Existing Products
Emphasis On ‘Supremacy Of Data As Source Of Truth’
As a data scientist, you have a special skill, that is, to rely solely on data as opposed to opinions or notions. You should establish yourself as a champion promoting the value of data as the most reliable source of truth. This involves creating a culture where claims are supported by evidence & data.
A great starting point can be identifying the metrics that can help product during its envisioning, market research, development process, go-to-market plans, and post-deployment activities. Identification, followed by collecting the relevant data and then, analyzing it to present findings that translate into actionable insights, can be your strategy to effect a wider change.
Sharpen Your Data Skills Beyond Pure Analytics
Data science is much more than pure analytics. It includes other aspects, such as data collection, data processing, data storage, and securing the data as well. In order to contribute more effectively to product development, it is advisable to become the owner of such aspects. Of course, this requires gaining these skills before you can play the role of a trusted advisor to the product team. Taking an online data science course can prove an extremely advantageous decision.
Build Reusable Data Sciences Tools To Help Business
Everyone, including the leadership, business teams, clients, and developers can become a true beneficiary of your data science expertise. In order to accomplish this, you can build a data science toolbox that delivers them information analyzed and synthesized per their needs. This requires understanding the demands of the stakeholders per role and then, collecting and analyzing data such that it really helps them make impactful business or technical decisions. In addition, it is vital to obtain regular feedback to ensure that your solution consistently stays on top of the situation.
Blend Into The Product Development Workstream
One big mistake often made by data scientists is that they tend to work in isolation as long as they have the data to analyze. This reduces their ability to contribute to product development. It is important for the data scientists to act as a core member of the team, following the overall development process, utilizing the skills available in the team, ensuring that data artefacts are tracked, version controlled and deployed in the same way the overall team is following.
And that’s a wrap! Hope this blog post proves to be insightful for you! 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.