Gain more value by prioritizing your data initiatives

For our esteemed clients, we are regularly conducting a data discovery workshop to identify information needs related to visibility, control, and information related to business intelligence. These are NOT the typical discovery sessions you may be familiar with, as our clients find this very valuable.

At a high level, we conduct these workshops to understand their key objectives, what kind of decisions they are trying to make, how they measure success in different organizational units, what are the data sources and applications from which they want to create reports and insights.

The outcome of this exercise is intense and comes in the shape of –

• A road map and architecture document
• Initiatives
• Projects for data integration, aggregation, and warehousing
• Creating a common taxonomy to use
• List of various dashboards and reports

Since these workshops involve different stakeholders from business and IT and various horizontal functions, everyone has a different priority set and viewpoint on what challenges they are trying to solve (or what makes them awake at night).

How to prioritize rationally and thoughtfully is part of our engagement, where we use a framework to prioritize which things to take in the first phase, and what we can plan for later phases.

Firstly, we determine what dimensions are affecting prioritization – these could be different based on in  what industry or domain the organization is running their business.

These can be functional areas, organizational roles which require this information, geographical regions, data sources that need to be integrated, amount of structured and unstructured sources.

Let’s look by an example of a healthcare organization – a hospital which is providing patient and clinical services. For those, dimensions would be –

• Functional Areas

• Finance
• Clinical
• Patients

• Organizational levels

• C Level
• Executives
• Doctors
• Clinicians

• Other dimensions

• Multicity operations
• Different hospital management and clinical management applications
• Datastores residing on-premise and on cloud

The second attribute is the purpose of that initiative which can be classified as –

• Visibility – of what is happening on a regular interval
• Control – related to compliance or action needed if something goes beyond the baseline
• Information – Understanding and further analysis

So, for each dimension (which could be a combination of dimensions), we put purpose and identify a final set of initiatives for prioritization.

To do the prioritization, we generally check for three things and discuss with stakeholder to rate each initiative based on:

• Strategic Impact

• Hard impact – Quantifiable
• Soft impact – Meaningful

• Complexity

• What are the variables involved?
• Which functions and locations involved?

• Value

• Cumulative $ value from the decision
• The opportunity cost of not taking/postponing the decision

There are other factors we need to consider related to data like – data availability and relevance, data definitions, data quality, data standards, integrity, uniformity.

There are scenarios where if an initiative has a quantifiable hard impact on business and providing cumulative value but complex to implement, this can become a part of phase 1 deliverable.  However, it might be a case that costs and time to implement in comparison to other initiatives are more impactful; then, stakeholders can decide which to take as the first phase and what initiative can be part of the backlog. The good thing with this approach is that all stakeholders are aware and informed about the decision, and they also know the roadmap of solution implementation.

We know from our own experience that this approach helps a lot. Initially, our customers were skeptical about this whole exercise but appreciated and found this valuable after completion.

Let me know if you are planning a data analytics or BI initiative organization-wide or in your SBU.