Organizations are drowning in their Big Data and are unsure of how to even begin to find the value in it. While many have set up “data lakes” as an initial step, they still struggle with understanding what data they have. The fact is, these large warehouses and repositories that store endless streams of high-volume, diverse data that flow into organizations every day may provide a good way to collect Big Data, but they do nothing to help organizations drive real business benefits.
It is imperative for business and IT stakeholders to efficiently wade through the wide array of dirty and irrelevant information in these “data lakes” and pinpoint the truly insightful data that should be driving many of their mission-critical business initiatives. The key to uncovering this valuable information is establishing a data quality strategy that enables you to access, analyze and validate all of your data. According to Gartner, “data quality is actually more important in the world of big data.” This sounds great…but how can you get started?
The first step in creating value out of your data lake is obtaining a clear view of what is inside it. Until you have an accurate understanding of the types of information available to your organization, you cannot expect a return on investment from any Big Data projects or the technologies that support them. The ability to access and profile all of your information – including structured, unstructured, and semi-structured data – will enable your business leaders to align data with business initiatives. Not all information in your data lake may be valuable right now, but ongoing discovery and analysis processes will help you determine which data sets can support specific business initiatives and have the greatest impact on your business.
Additionally, the ability to integrate and match internal and external third party data sources will shape master records that offer new business insights, power more effective marketing campaigns, identify fraudulent or high-risk activity, and feed accurate predictive analytics models. Without a data quality solution at the forefront of your Big Data initiative, you risk making critical business decisions based on the wrong information.
Big Data is daunting, but it is critical to start your Big Data projects the right way. With proven data quality strategies, you can see below the surface of your data lakes to uncover hidden insights and harness the full value of your business information.