Strong data management doesn’t just begin on the back end, when the data actually hits a database. It begins long before that, early in the data lifecycle, and across many areas of the organization.
One of the crucial elements in becoming a data-centric organization is in culturally changing the awareness of thinking about data from a variety of prisms. Strategies come down from top management; specific goals and objectives then get developed. The next questions should be: what data do we need to support those goals, objectives, programs etc? Does that data already exist in the organization or do we have a gap? For the gaps, how do we close them and how do we ensure tightness and alignment with existing data management strategies?
There are a number of control points that come out of this scenario:
- Strategic planning – What data do we need to measure success?
- Goals and objectives – What KPIs and metrics are important? What type of reporting and dashboards are required? Do we have all the data that we need for reporting and metrics measurements? Do we trust in the quality and integrity of the data that we need for reporting? If not, what gaps do we need to close to build trust?
- Budgeting and financing – What controls have we implemented to support the optimization of our data investments across the entire enterprise? Are we aligning various programs across the organization such that we reduce data silos and redundancy, and optimize information sharing and infrastructure development where possible? Does someone have stated authority and responsibility for overseeing this planning and budgeting?
- Business case development – What data do we have in-house (presumes a knowledge of all enterprise data assets) to support new programs or applications? Can we leverage these in-house data sets for this purpose (compliance/regulatory check-point)? How do we close the data gaps we have (can we capture data via existing sources? Do we need to purchase data from 3rd parties? Are there open data sets that are leverageable?)
- Requirements gathering – Where are the authoritative sources of the different data sets we need? Are we leveraging organizational reference and master data?
- Build vs. buy decisioning – If we build something in-house, how can we maximize previous infrastructure investments in data, hardware, middleware, and exchange mechanisms so as to minimize duplication or silo building? Buying a solution means building in checkpoints for ensuring ease of integration and data extraction.
- Contracts and Procurement – What language do we have in our contracts to enforce compliance or alignment with internal data management and data security policies? Do we always get a data dictionary? Do we ask vendors to provide us mapping to our conceptual and logical data models? Do we ensure data quality levels (for certain types of acquisitions)? Who actually owns the data? If we’re outsourcing our data, what are our access rights for transactional, analytical, regulatory, and recovery purposes?
Organizations that think this way are truly data-centric organizations. Not only do they understand data as an asset, but also both try to protect it from dilution and look for the multiplier effect on their data investment by improving the leveragability of data across the organization and its ecosystem.