Top Seven Focuses to Develop a Winning Data Science Strategy

How do data science leaders build an ecosystem and consistently create business values?

In an era with rapid changes brought by digitalization and pandemic, norms are broken, the ways people used to work, do business, and communicate, etc. are all changed. The changes create more data quickly and more insights are drawn at real time everywhere, it addresses a need for making real time decisions to capture every business opportunities. However facing the complexity from data volume, velocity, variety and veracity, linked insights and decisions impact each other, how to connect everything instantly to empower better decisions in a well designed an ecosystem so each part is mutually benefited and jointly maximize business values meanwhile every can function with its own autonomy to optimize local efficiency? Facing many options of tools and variables in building machine intelligence, how to avoid chaos to make optimal choices and single out the most important relevant factors? To help answer above questions, the article introduces the top seven focuses to help data science leaders to develop a winning strategy and get clarity from chaos and build a data science ecosystem to consistently deliver business values and win respects from others.

The top seven focuses start with the why in the center and followed by what and how.

Focus 1: Business Value and KPIs (Why)

The first focus is to address why factor, why to do data science? What is the purpose that an enterprise exists and the objective of doing data science? Creating business values is the core focus and everything else is connected to it and for it, it’s the main thread in a business fabric.

A business cannot exist without creating any values by solving customers' problems just as a doctor’s value is showed by healing a patient’s illness.

Data science is to develop solutions for business problems that include evolving existing solutions or innovating new solutions by identifying right business use cases. They may include customer interactions and process flows involved to solve a specific problem and the corresponding outcomes. Outcomes should be measured by KPIs to size use cases. KPIs vary by businesses.

Focus 2: Products/Services (What)

The second focus is to focus on what, the products or services are built based on the use cases and they are solutions for customers or revenue generators or money machines. ?One product or service can be associated with one or more use cases and priced by their outcome and market demand.

As products or services are usually owned by stakeholders, data science leaders should start with them to identify unique value proposition to meet their business goals.

The next five focuses are about how to build products or services and consistently create business values.

Focus 3: Technology and Platform (How)

Once products or service are identified, the third focus is about how to implement them with what kind of technology and platform. As there are so many options in markets, choose the best to meet your unique product or service specific needs and consider both efficiency and effectiveness, the cost and return on investment. Using a well defined evaluation metric guides the process to choose a platform that best best fit your unique requirement.

Focus 4: Data or Content (How)

Data is the oil of a business, that does not mean the more data you have, the better. The challenge is that there are too much data and many data sitting within a company is not well used. The data center becomes a cost center instead of creating values. The focus should have a thorough understanding about the decision making process and identifying the right relevant data so that the least amount of data is used to maximize business value. Data collection is driven by business use cases so irrelevant data is avoided and cost is reduced. Data should help business users to ask smart business questions also.

Focus 5: ?ML/AI and Processes (How)

Similar to tools and platforms, there are many ML/AI options. With business use cases driven, focus on solutions that best fit your unique needs with capabilities for automating, orchestrating DevOpts, processes, workflows, and MLOpts/AIOpts for quick and robust deliverables.

Focus 6: Fusion Teams with Right Talents and Roles (How)

Without right talents and alignment, no matter how compelling a business vision is and how advanced technology is used, you cannot deliver optimal outcomes as humans are key drivers to make everything happen. Data science leaders have to know how to recruit and manage talents; release their full potential to maximize the outcome. When employee’s values are maximized, business outcome are optimized. It’s the art of leadership and the power of optimizing both human intelligence and machine efforts.

Focus 7: 360 Degree Communication (How)

The last focus but not least is communication. Communication happens in a continuous feedback loop about insights, changes, problems and results. Effective and in-time communications can help avoiding potential problems and support each team to focus on right efforts and maximize business values.

Conclusion

To empower data and analytics capabilities, you have to build a winning strategy with the top seven focuses to build an enterprise ecosystem to form an effective fabric that connects each piece seamlessly. To maximize business value, you have to optimize business outcome by quantifying outcomes and developing right products or services by using appropriate technology and platform, deploying intelligent solutions robustly; you also have to put right talents in place and communicate in a continuing feedback loop from multiple perspectives so the ecosystem can run healthily and accelerate business growth and operational excellence.

Please leave comments or notes to share your thoughts or insight.

Reference

[1] Top 3 Challenges for Data Analytics Leaders

[2] Data Science Challenges

[3] The Gartner 2022 Leadership Vision for Data & Analytics Leaders