Posts Tagged Data Governance
Organizations are increasingly coming to the realization that data is a core strategic asset, the new source of competitive advantage. Success in today’s economy is predicated on how organizations leverage data as much as any other corporate function. The challenge organizations face, then, is how to leverage this asset to its maximum potential in ways that are as efficient as possible and also minimize risk.
This is no small feat. It involves data itself (identifying and managing sources of data), technology (tools and systems to ingest, process, store, analyze and share data), governance (ensuring data is used ethically and in compliance with relevant policies/regulations) and people (aligning various stakeholders and business objectives.)
I’ve already laid out my predictions for Big Data in 2014, but I also wanted to let the Wikibon community know how my colleagues and I plan to cover Big Data in the year ahead. We’ve organized our research agenda into three major buckets.
Technology. Clearly the technologies and products that collectively make up Big Data – including Hadoop, NoSQL data stores, analytic databases, data visualization tools and more – are maturing at a rapid pace (much faster, for example, than relational databases did in the 1980s.) Big Data is also applicable across industries, meaning these technologies are inevitably and increasingly intersecting with adjacent technology movements, namely the cloud, mobile computing and social media. As we have for the last several years, Wikibon will devote significant coverage to these developments with an eye on putting technology innovations in context for enterprise Big Data practitioners (both technology practitioners and line-of-business practitioners.)
When talking about Big Data, the conversation tends to focus on Data Science and analytics. That is, the stories about Big Data that hit the front pages of the mainstream press and the hallway conversations taking place at events like Strata are mostly about all the cool new ways to use data to greater effect.
But Big Data Analytics doesn’t take place in a vacuum. It takes place in the enterprise. And any time you mix data and the enterprise, you can’t afford to ignore data management best practices. It may not be as sexy as predictive analytics, but failure to apply fundamental data management best practices to Big Data projects can lead not just to failed projects, but to potential legal consequences as well.