Business drivers for investing in data quality
Businesses today create and evolve their business strategies to increase or create new revenue streams. They do this using data to provide unique services that customers value. Businesses also exchange data with business partners and vendors and internally make data available in order to create operational efficiencies and analytical insights that allow for a unique competitive advantage. Therefore timely, correct and validated data is of high importance for a business thus becoming an asset.
Nurturing data as an asset
Businesses over time create, evolve and replace their technical capabilities and business processes in order to stay relevant and to deliver the best possible business outcome. Business models aren’t static in today’s disruptive technical landscape. Businesses are striving to be “data driven” as a way to increase business performance and do it with agility knowing that they will need to account for unplanned business opportunities that may arise . Data architecture is key factor, It can enable or impact the way in which a business operates. Data strategies equip business with an articulated roadmap and framework to nurture data into the asset it needs to be.
What and where it’s required
Technically speaking, technical architects of various domains look for and consider best practice data architecture patterns and supporting programs that are purposefully designed to create, store and share data as information. Primary examples include: data governance programs, data management disciplines and enterprise data architectures (MDM, RDM, DM, PIM, EDW, DH) that serve as highly valued data infrastructures.
Deploying data quality solutions and techniques isn’t new. What is new is the variety of data that we encounter today, the speed at which we are producing it, where it emanates from and the businesses need to access it, process it and take action on it. Because of this, ensuring that data is “fit for purpose” and available for consumption requires technologists to consider new data quality deployments with relatively new technical solutions. While traditional data warehouse and enterprise data patterns for data quality are still needed and well invested in, newer techniques need to further emerge to determine the validity of data as close to the source of creation as possible.
