We're creating a State of BI report and are interviewing data/analytics/BI leaders from large enterprises to explore BI's current state, long-term strategies, and AI's impact.
Oracle and IBM say a data catalog is an organized inventory of data assets in an organization. Now, what does this mean? In simple terms, a data catalog enables the discovery and understanding of an organization's data. A data catalog accelerates higher quality analysis from your analytical teams by minimizing the time spent searching for data and dissecting a data source.
Imagine you are a new data analyst at a car dealership, and your boss asks you to find the average maintenance cost per car sold last year. Sounds easy.....right???
If this car dealership is like most companies, it might have a different database with inventory information, customer information, maintenance history, and car data (a massive data lake)! The first step in your analytics process is probably locating the right data. If you're lucky, you might know right where this data resides, but most data analysts aren't that fortunate. According to CrowdFlower, 21% of a data scientist's job is just finding the right data. Data scientists may need to incessantly reach out to various departments for the data they need and wait weeks for it, only to find that the data is unclear and confusing! What now?!
After you locate the data, the next challenge will be understanding it. The car maintenance data might be filled with acronyms you do not understand. The inventory data may use a different time zone which you then have to convert. The customer information is manually entered and has hundreds of small errors. This is why almost half of a data scientist's job is just understanding the data.
A data catalog like Datalogz makes discovering data as simple as a google search and understanding it a breeze. If data analysts can quickly and accurately understand data, this means better data insights and quicker results.
A data catalog gives data science teams the tools to change their workflows, break the 80/20 rule of only spending 20% of their time on the actual analysis, and recover much of the 80% that they’re currently spending on discovery and understanding. Managing data in the age of big data and self-service analytics is not easy, but data catalogs can help mitigate the challenges of understanding and discovery.
