Evolving Role of Data Scientist in the Age of Personalization

Industrial Revolution

Over a period, humans have evolved themselves by perfecting the industry with the help of technical evolution and by reinventing new resources which are more robust in nature thereby making life easier.

The industry has benefitted from all these technically qualitative advancements which were so ingrained in a certain time and have had such an overwhelming impact that we dubbed them “revolutions”. These revolutions are always important as they have always dominated and changed the world in a big way. The industrial revolution resulted in far-reaching changes in the history of humanity and the influence of these changes continues to sweep us even today in our daily life’s.

In short, the Industrial Revolution is the “game changer” of modern world history. More than anything else, it’s what makes the modern world, well, “modern.”

History of the Industrial Revolution can broadly be categorized into below-given phases

Digitalization

Today Industry 4.0 is all about digitalization. The world is running on data and intelligent agents. This entire digitalization in the new world is driven by 4 important design principles of the system. They are ---

• Interoperability — Machines, devices, sensors, and humans need to seamlessly connect through an ecosystem of connected devices.
• Information Transparency — Sensors collect data about the physical world and use smart analytics to index this unstructured data to help with decision making.
• Technical Assistance — Systems support people in the decision-making process and assist with tasks that are difficult or unsafe.
• Decentralized Decisions — Intelligent systems operate autonomously and can make simple decisions and resolve conflicts faster

All the above 4 design principles are related to Data Analytics & Artificial Intelligence (DAAI) which requires data-driven insights.

Elements such as the Digital Twins, Cognitive Computing, Industrial Internet of Things (IIoT) and Cyber-Physical Systems have aligned with DAAI as unseparated elements, providing the necessary ingredients for a paradigm shift.

Therefore, data science is becoming a guiding source for the industry to transform the information into knowledge using algorithms created in the pre-digital era and data Scientist plays a key role in adapting to these transformations.

Evolution of Data and Data Scientist along with Industrial Disruption

The data scientists are playing a fundamental role in the NEW to understand enterprise activity. Nevertheless, a new challenge appears where the data engineers might face a challenge in the industry: they must deal with unknown processes, procedures, operations & science. A new set of multidisciplinary skills must be added to the data scientist expertise to provide the required added value in the specialized analysis for each challenge.

A recent survey conducted by Data Scientists and Analytics society has given a clear indication that data scientists are going to play a key role in all the industries. Below table gives an industry-wise summary of data scientists utilization and this is only going to grow further in the organization as we move towards personalization ( Industry 5.0).

From the above table, we can clearly see that data scientists have already started their work on most of the industries and once they take over, the roles like data analysts, data-driven decision makers, and functional analysts will slowly be taken over by data scientists. If we further deep dive, it is imperative that every industry needs its own specific skills to understand the data and reap the benefits out of it. So, the industry should also have data scientists specific to the line of business. Below chart shows the projections on how various industries are slowly moving towards DAAI soon

So, to align and grow with the industry, a data scientist should make himself more comfortable with the line of business he is working with. Here are a few examples across each industry –

• Banking & finance sectors are extensively using data science for Customer segmentation, fraud detection, risk management and offering personal services - data scientist is helping them in achieving their goals. In the future, data scientist 5.0 might further need to work towards algorithmic trading, automated risk management techniques, deep personalization, and customization services
• In the retail sector, a data scientist is already helping customers by providing recommendation engines & location services. Data scientist 5.0 could further move towards market basket analytics, warranty analytics, price optimization for the customers. For the industry, they should think of implementing automated inventory management, customer lifetime value prediction
• In Travel & Transportation sector, a data scientist is helping the travelers by doing predictive analytics of the dynamic pricing and fair costing, but data scientist 5.0 could further grow and upgrade towards travel bots, route optimization mechanisms and customer segmentation analysis resulting in minimizing travel costs, time & distance.
• In Pharma & health care Industry, a data scientist can blend data between discovery and clinical development to smash silos that previously caused clinicians and researchers to miss important elements. The advance predictive analytics & data-driven drug trials enable data scientist 5.0 to curb the Skyrocketing Costs of Pharmaceutical R&D.

Evolution of Technology Disruption

If we look at the various disruptions which are happening at a technology level, we can clearly say that data is playing a pivotal role in all the technology disruptions. The data scientist should be more engaged with all the disruptions which are happening at a technology level along with the industry level.

The following table provides a deep insight as to how we expect the role of the data scientist in the future

What’s ahead for Data Scientist? 

With GDPR and regulatory rules, it is even more important that the laws of countries must be adapted and thus, appear a new generation of data scientist and technical experts with a clear orientation for regulatory advising or legislators consultancy, initiating the path to a consensual data management.

Today, data scientists 4.0 already are aware of the algorithms to apply on top of the data ecosystem and this fact requires to be multidisciplinary and specialized at the same time. They are already working towards the growing demands of data science services applied across industries.

In future, when the industry moves to personalization (Industry 5.0), it is imperative to understand that the skills they have now may not meet the then organization goals and thus they should have additional skills on top of the skills they have today. They should have extensive industry-specific knowledge to understand the Line of Business rather than having only knowledge on algorithms implementing applied intelligence. They should understand that Industry 5.0 is more about Human-Machine collaboration and should be ready to accept the fact and ensure that human emotions work with cognitive computing in the new world.

Data Scientist 5.0

Key enablers to be a Data Scientist in the personalization era –

Data science will get democratized

It will become more democratized with greater automation, platforms with embedded solutions and easy to use tools. This will enable the non-specialists to do the significant model building, analysis and prediction using DIY kits. These citizen data-scientists will affect a substantial decentralization of the analytics function within the organization. The data scientists will devote their time and energy to create more advanced techniques that lead to better and faster solutions.

Data science will evolve to accommodate specializations

Data Science is all about statistics and maths. As technologies grew broader and deeper, data science will become more and more specialized, we expect that the superman data scientist will give life to several forms — specializations across techniques and/ or domains like Risk Data Scientists and Cyber Security Data Scientists just to name a few. We will also have the Data Visualizer, the Machine Learning expert, the AI star, and data scientists who are skilled at providing solutions to specific business problems.

Industry / Domain orientation of data scientists

The data scientist will move far closer to business than today. Once a good number of key algorithms get templatized and menu driven, the focus of the data scientist will shift to the business side of things. Data Scientists will enter an era of sharp, function or vertical focused applications — for example, Govt of AP is providing DWCRA (Development of Women and Children in Rural Areas) assistance to all the women to empower them and grow towards small scale industry entrepreneurship and amounts are disbursed over a period, based on the progress they have made. Can we develop an application to help Govt to identify the ability of these people to pay back after a period of duration? This will require deep knowledge of their financial, socio-economic conditions and understanding of all the external/internal environmental factors or variables that may impact a person’s ability to pay.

The next data scientist will have to be a Data Pundit.

Deeper domain knowledge and availability of a much wider source base for data will drive personalization. In the DWCRA example, data on financial and socio-economic conditions over a period of time will be critical to assess the financial health of the beneficiary according to external/internal environmental factors, given the number of benefits she receives from the Govt.

Is the Data Scientist ready for 5.0?

The future looks bright and exciting for data scientists of tomorrow. But the future starts today since most of the technologies mentioned above are available and data scientist should now look out for new disruptions to act as an interface between technology and Industry.

In this continuous evolving DAAI powered world, data scientists are definitely on the better side of the deal. Data scientists do a varied set of jobs to help businesses grow. Few activities like data governance framework, data cleansing, data visualization and diagnostic analysis with set frameworks can be automated. There are certain activities like identifying the right business problem, Tweaking ML techniques to fit the problem, creatively leveraging solutions from cross domains and creating a business strategy from analytics which surely requires data scientist’s intervention. With good certainty, it can be said that even though the data science stream will stay up and running for the long term, the roles and responsibilities of these jobs are up for big changes. Data Scientists who have challenges upgrading to these new roles and responsibilities will face strong setbacks in progressing in a career.

Considering the above, Data Scientists across the globe are not yet ready for Industry 5.0. In order to reach there, Data Scientists need to upgrade himself on domain/industry-specific skills and also evolve in specialized roles like - Risk Data Scientists and Cyber Security Data Scientists, etc. 

The data scientist of 2022 will have to be a DATA Pundit – not from a technology perspective but from the perspective of understating, identifying and accessing the right sources of data and bringing it all together to enrich the analysis.