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

In an era of rapid changes brought by digitalization and the pandemic—norms are broken. The way people used to work, do business, and communicate have all changed. These changes have created more data which quickly facilitates additional real-time insights everywhere. Therefore, there is a need for making concurrent decisions to capture every business opportunity. However, we face the complexity of data volume, velocity, variety, and veracity. Linked insights and decisions impact each other.  

What matters is connecting everything instantly, thus empowering better decisions in a well-designed ecosystem. Meanwhile, we must jointly maximize business values while everyone can function with autonomy and optimize local efficiency. Faced with many options, tools, and variables in building machine intelligence, we need to focus on avoiding chaos and making optimal choices and singling out the most relevant factors.  

To help address these issues, the article introduces the top seven focuses to help data science leaders to develop a winning strategy. The goal is to gain clarity from chaos and build a data science ecosystem that consistently delivers business value and wins respect from others.  

The top seven focuses start with the Why in the center and are followed by What and How.  

Focus 1: The WhyBusiness Value and KPIs

The first focus is to address the Why factor: Why do data science? What is the purpose of an enterprise and what is the objective of doing data science? Creating business values is the core focus and everything else is connected to it. It is the main thread in a business fabric.  

A business cannot exist without creating any value. Just as a doctor’s value grows by healing patients, a business gains by helping customers solve problems.  

Data science is to develop solutions for business problems that include evolving existing solutions or innovating new solutions by identifying the right business use cases. They may include customer interactions or 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 business. 

Focus 2: The What – Products/Services

The second focus is the What. The products or services are built based on use cases and are solutions for customers, revenue generators or money machines.  A 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 propositions to meet their business goals.  

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

Focus 3: The How – Technology and Platform

  • Technology and Platform 

Once products and services are identified, the third focus is on How to implement them with what kind of technology and platform. Since there are so many options in the market, choose the best to meet your unique product or service-specific needs. Consider both efficiency and effectiveness, and the cost and return on investment. Using well-defined evaluation metrics—guide the process to choose a platform that is the best fit for your unique requirement. 

  • Data or Content  

Data is the oil of any business. That does not mean that more data is always better. In fact, if there is too much data, and a lot of it is just sitting without being well used—the data center becomes a cost-center instead of creating values. The focus should be on gaining a thorough understanding of the decision-making processes and identifying relevant data so that the entire process can be optimized. Data collection is driven by business use cases so irrelevant data should be avoided at all costs. Data should help business users to ask smart business questions also.  

  • ML/AI and Processes  

Like tools and platforms, there are many ML/AI options. Businesses can focus on solutions that best fit their unique needs with capabilities for automating, and orchestrating DevOps, processes, workflows, and MLOpt/AIOps for quick and robust deliverables.  

  • Fusion Teams with Right Talents and Roles 

Without the right talent and alignment, no matter how compelling a business vision is and how advanced your technology is, you cannot deliver optimal outcomes. This is because humans are key drivers of making everything happen. Data science leaders must know how to recruit and manage talent, i.e., to release their full potential to maximize the outcome. When an employee’s value is maximized, business outcomes are optimized too. It is the art of leadership and the power of optimizing both human intelligence and machine effort. 

  • 360 Degree Communication 

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


To empower data and analytics capabilities, you must build a winning strategy focusing on the seven areas mentioned above. They will allow you to build an enterprise ecosystem with an effective fabric that connects each piece seamlessly. To maximize business value, you should optimize business outcomes by quantifying them, developing the right products or services by using appropriate technology and platforms, and deploying intelligent solutions robustly. You should also put the right talent in appropriate places, and communicate in a continuous feedback loop from multiple perspectives so that the ecosystem can run healthily and accelerate business growth and operational excellence.  


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