SAS: 5 Models for Data Stewardship
Posted: 2 May 2017 | Source: SAS
Former Intel CEO Andy Grove once coined the phrase, “Technology happens.” As true as Grove’s pat aphorism has become, it’s not always good news. Twenty years ago, no one ever got fired for buying IBM. In the heyday of customer relationship management (CRM), companies bought first and asked questions later. Nowadays, executives are being enlightened by the promise of big data technologies and the role data plays in the fact-based enterprise. Leaders in business and IT alike are waking up to the reality that – despite the hype around platforms and processing speeds – their companies have failed to established sustained processes and skills around data.
A technology’s success or failure is not proportional to the existence of an executive sponsor, solid requirements, or even a deliberately crafted business case. Instead it depends on the existence of rigorous processes and dedicated skills to implement and maintain it. When it comes to the aforementioned solutions, data stewardship is seen as the glue that binds heterogeneous information – ensuring common, meaningful data across applications and systems. It seems obvious that data stewardship is important to the business. However, is it really a critical success factor?