The relevance of data cannot be over emphasized in today’s world, where change is the only constant. Decisions that managers and executives tend to make emanate from the availability of data analysis. While the turnaround time to collect the data, analyze, interpret and act has shrunk significantly, those who are able to do this in not only shortest possible time but also effectively and efficiently enjoy the first-mover advantages.
Strong data strategies must account for the following:
- Prerequisites of data—Integrity of data is must, because actions of organizations are based on representative data collected and analyzed. Insight of data with the key elements of reliability, consistency and timeliness make these data a fit foundation for long-term sustainability and appropriate actions.
- The concept of master and transactional data—Any attribute of data is broadly classified into master or transactional data. This basic classification drives further strategies of data, on which pivotal decisions of data centralization and data sharing are heavily dependent.
- Integration of business intelligence and market intelligence—A representative yardstick of corporate objectives are based on business intelligence. A correlation of these metrics to industry data through market intelligence is vital to be in sync with industry outlook. This integration reflects not only how realistic the corporate objectives are, but it also asks if corporate objectives align with industry outlook, and more importantly to what extent they are practical and achievable.
- Data use—How do different business use data to understanding buyer behavior and preferences?
Timely and correct data analysis is a universal requirement. Consider the medical profession, in which a prescription of a medicine by a doctor depends on the report of a patient. The sooner the diagnosis, the sooner the remedy can be administered. But in addition to time, the accuracy of reports is vital. Similarly in sports, data related to the top players' strengths are used to determine the game plan.
From many transformation projects that I have been part of, one thing is conclusive: One size does not fit all. Those who are able to strike a balance between internal data quality, integrity and timeliness on one hand and optimizing data centralization, data sharing and automation on the other could make themselves adaptable to the changing needs of organizations more effectively.
Read Rajul Kambli's recent Journal article:
"Value Creation Through Effective Data Strategy," ISACA Journal, volume 5, 2019.