The data that flows around our organizations and across our industry is the result of complex business processes that can represent legal obligations, client commitments or critical parameters related to research. Content that is extracted from these original sources must be precise and understood in context - but have often been transformed, modified and renamed to drive the software that propels our applications.
The result is the use of common words that mean different things and the expression of common concepts using a variety of words. The problem is exacerbated when seeking to align systems and processes because the terms used in one application do not always capture critical nuances that are needed for onward processing.
We refer to this problem as “data incongruence” and it is a serious liability. It was created by technology fragmentation and proprietary approaches to data integration. It adds cost, complexity and structural rigidity to operations. It diverts resources away from business goals and leads to frustration by stakeholders. It extends the time-to-value for new initiatives and often leads to more regulatory oversight challenges.
This is a problem that can be solved. The payoff is significant, and the value is sustainable. There is a real cost associated with the transformation, but the cost is relatively short-term (and the transition can be managed incrementally). And while it is not a ‘rip and replace’ technology cost - it does require a mindset shift and does include a series of data integration challenges.
About This Speaker
Michael Atkin has over 30 years of experience as a strategic analyst to financial institutions, regulators and market authorities on the principles, practices and operational realities of data management.
Mike has served as a senior advisor to several regulatory bodies in both the US and Europe. He was the founder of the Enterprise Data Management Council and the Enterprise Knowledge Graph Foundation. Mike is currently the managing director of Content Strategies, a consulting firm in data management. ---
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