note: don't miss the link below to download a chapter of the BI Guidebook - From Data Integration to Analytics on Reed Elsivier's SciTech Connect blog.
Data is everywhere in the enterprise, from large legacy systems to departmental databases and spreadsheets. No one controls all of it, it’s often duplicated erratically across systems, and the quality spans a wide range. You might say it spreads like kudzu, but despite the tendency for chaos, the bulk of data is the lifeblood of an enterprise. If it doesn’t smell like a rose – that is, if it’s not clean, current, comprehensive, and consistent – the enterprise is in trouble. And this is why the architecture of data is so important.
You may be more accustomed to hearing the term architecture applied to homes and buildings, but it’s an important term for data, too. And while data geeks may be the only ones delving deeply into this topic, data architecture does indirectly affect everyone in the enterprise.
The data architecture defines the data along with the schemas, integration, transformations, storage, and workflow required to enable the analytical requirements of the information architecture. A solid data architecture is a blueprint that helps align your company’s data with its business strategies. The data architecture guides how the data is collected, integrated, enhanced, stored, and delivered to business people who use it to do their jobs. It helps make data available, accurate, and complete so it can be used for business decision-making.