There is pressure more than ever on the data and analytics organizations to work across businesses and bring more efficient data infrastructure to life. Speed and accuracy in delivering new insights for monetization of data for net new revenue or operational excellence is paramount. Changing regulatory landscape, market dynamics and supply chain issues are some of the reasons why faster delivery of analytics across all "information demand" is a common topic.

Data as an Asset (DaaA) is the ultimate objective for any companies on a conscious journey toward enabling data culture. These companies use well defined business outcomes mapped to identified data strategy while supporting changes to existing processes in the data value chain. 

We will review a proposed decision map for all “Information Demand” for effective speed to market based on the “Data Initiative” type. Data Driven companies will have typical delivery of Utility type projects in Days/Weeks, Business Driver type initiatives delivered in Weeks/Months and Strategic Data Enabler Initiatives delivered in Months / Years. 

The proposed decision map assumes that foundational enterprise data management maturity practices exist and are operational at Defined, Measured or Optimized Levels. Maturity level of 1 (Performed) or 2 (Managed) does not allow the decision map to scale across the enterprise.  

Identification and management of all critical, sensitive, and monetized data elements is required to support all information needs. Data culture supporting data governance similar to product governance, where product is equivalent to data, is foundational to agile information delivery.  

Information demand in reference from Gartner can be categorized in three major buckets — Utility, Driver and Enabler.  Strategic management of this demand using the proposed six decisions allows trust building regarding information delivery across organizations. 

Data as Utility calls for data to be available for all purposes, to all stakeholders, at any moment as a Utility. The  “Self Service” information delivery model here has two decision points. Is the data available in the data lake/house or Is a tactical solution available? If the answer is yes to any of the questions, approved data users can leverage existing analytics platforms for decision making in less than days.

Data Driver Initiatives enlighten, inform and inspire businesses for new opportunities. These data driver projects are typical “Data Science” proof of concepts, prototyping or data discovery type of projects.

Information delivery models for “self-service discovery” have two decision points. Do we have known data requirements? And do we have a Sandbox? If the answer is yes to any of the decision points, these projects can typically be completed in weeks/months. Data Science operationalization after successful acceptance of the prototype would be a separate project in this case.

Data Enabler Initiatives serve specific business needs and solve specific business problems. These enabler data projects are typical “Data as Foundation” for a new business strategy, merger and acquisition, regulation needs type of initiatives. These enabler initiates are best candidates to prioritize using a Data ROI or business case approach. 

Information delivery models for these “foundational data” initiatives have two decision points. Do we have data ingestion or data modeling defined and developed? And do we have data in source systems? 

If the answer is yes to any of the decision points, these projects can typically be completed in months with Iterative BI, new subject area acquisitions, or operational/source system enhancements.

In cases when data initiatives take more than one year, the following (not inclusive) list of foundational principles of Data Management may need to be addressed: 

• Agile & Automated release management in every part of the data value chain.

• Data as a Product with data driven operating model and targeted business use cases.

• Data Operations-enabled data consolidated from siloed applications and processes.

• Specific business problems and outcome-focused data initiatives.

In summary, there are six key proposed decision nodes for three majority types of “information demand.”These decision nodes are followed to allow for a high level of confidence on “information delivery” and continued trust and a guiding path for data ecosystem that enables speed to market.