Every day, 2.5 quintillion bytes of data are created. More than 90% of the data in existence today was created in the past two years. In order for this data to be useful, it has to be analyzed. Multi-layer data correlation can be used to detect gaps in the quality of data and data processes. Consider these four ways that using this type of correlation can improve the quality of your data analysis and the results you achieve.
1. Characterization of the Data
Before data can be used, it has to be characterized. There are unstructured, semi-structured and structured types of data. Structured data is observational. It’s collected from experiments or direct observation. The data is clearly defined. For example, temperature readings at a particular latitude and longitude are types of structured data. If a piece of structured data is missing, it’s easy to locate the gap.
2. Creation of a Data Quality Dashboard
It’s not as easy to figure out whether semi-structured or unstructured data is of a high quality. Analytical tools that use multi-layer data correlation allow you to generate and review data quality visualization reports. With these reports, you can see if there are missing pieces of data. When there are a lot of gaps in data collection, you may want to use extreme caution in drawing any conclusions about the analyses you perform.
3. Define the Scope of Your Data Program
When you have several layers of data to work with, it’s easier to define the scope of what you have and what you can do with it. For example, large financial institutions usually have a broad scope of their data program. They use their data to find gaps related to potential risks. This information is important for cyber security, financial planning, shareholder information, compliance and strategic planning.
4. Develop a Hub for Customer Intelligence
A successful business has to know who its customers are and how their behavior is changing over time. An ideal way to determine that is through data collection and a customer relationship management system. A multi-layer data correlation allows you to cross-check the information in different databases and from different data sources. If you have one resource that appears to be an outlier, you can delve into why that is the case. A data hub also gets people out of their silos. When people communicate across sectors within an agency, it’s easier to keep track of key pieces of information. You’ll also know early on if there’s a problem with a process or the quality of the data. Early detection of a data quality concern can prevent a lot of future problems.