Data Projects ROI: Take a Top-Down Analytics Approach

Data Projects ROI

In recent years, the success of data and analytics projects has been mixed across enterprises.   While some have delivered significant value, many have spent millions in data lakes and analytics solutions without achieving the anticipated benefits resulting in disappointment in the ROI of these initiatives.  

Adopting a top-down approach to data and analytics can be transformational in accelerating business value.  Successful outcomes in data and analytics projects need to align with key business objectives in order to achieve the intended business value. In the diagram below, the problem-solving approach is 4-3-2-1 versus 1-2-3-4.  

A clear strategic focus on outcomes informs requirements for data models, data lakes and data management from the onset. This approach is key since building layer 2 often takes years and can cost millions.  Lack of focus in building this layer delays business value if it isn’t aligned with the core business objectives as outlined by the leadership team.

Data & analytics layers

4 key benefits of adopting a top-down approach

  • Focuses scarce resources on high impact data & analytics solutions that provide value
  • Accelerates solution delivery and value creation.
  • Makes data and analytics an integral part of the business and takes a key step towards building a data-driven organization.
  • Eliminates waste from data projects yielding limited value. Improves utilization of data and analytics resources and assets.

How to get there: define your enterprise data domains

Subdividing the information needed to lead your enterprise into data domains representing specific areas of expertise sets the framework for organizing your data. Domains should be defined in user-friendly terms versus technical terms (transactions, events, derived data, etc.). 

The diagram below provides an example of data domains for a bank.  Some domains are common across industries, for example, financials, HR, IT, suppliers while other domains are industry-specific, for example, risk and compliance in banking. 

Data domains (banking example)

Assign leaders and teams to domains

A business leader with strong analytics skills should steward the overall initiative, help teams set clear priorities and ensure alignment between business functions and data teams to deliver accurate data and high-value use cases. 

Domains are organized in groups, for example, growth, risk & compliance. Teams may be assigned to lead a specific domain or a group of domains.  Moreover, complex domains may be divided into sub-domains, for example, in a bank, the risk domain may be subdivided into credit risk, market risk, liquidity risk, and operational risk, each involving a distinct set of expertise.

Focus on a beachhead strategy

Each domain team proposes their ideal use cases for where they would like to monitor and extract business insights based on their key performance indicators. The goal is to develop a library of use cases for each domain.  Use cases may utilize different analytics methods including descriptive, robotics and machine learning.  

Following a robust debate, the team should focus on a beachhead strategy– a few, high impact use cases which:

  • Rapidly demonstrate business value
  • Build components of the data pipeline (layers 2-3 of the pyramid referenced above)

Iterate and implement in rapid sprints delivering working solutions

Agility is critical in data and analytics.  Teams should aim to deliver working solutions in rapid sprints.   Sample solutions by business outcome are described below:

Sample analytics solutions

The key is to focus on areas that are strategic priorities for the business.  From an execution standpoint, teams can “Think big, start small and scale fast”.   Building a proof of concept solution is important as it helps teams evaluate implementation cost/timeline as well as business impact and risk. From there, you can scale and expand your solutions and data stories throughout the enterprise once the value has been derived from the central KPI-focused initiatives.

In conclusion

Data and analytics have become a source of competitive advantage for firms using their data to identify new opportunities and systematically address issues in enterprise performance.  The gap between leaders and laggards is widening.  Adopting a top-down approach to data and analytics will help your enterprise accelerate business value and build a data-driven organization. 


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