How to Get More From Your Data in 2019
These three trends will lead the way to exploiting the value in your data.
It’s no surprise that data has become an important asset for most organizations. The ascension has been propelled by many trends. In the 80s it was the database, in the 90s it was the data warehouse, in the 2000s it was cloud and social data, and in the 2010s it was big data. Over the next decade, natural language processing (NLP), information catalogs, and multicloud architectures will drive the innovations that enable organizations to enjoy even greater benefits from data.
Data professionals can ride this wave to a better career by paying attention to these data trends!
SQL has been a bedrock of querying data from analytical engines for the past 30 years. Just as structured data has given way to unstructured data in the past decade, the ability to query data sets from data warehouses and cubes is now in flux. Analysts are simply typing, “What are the most profitable products in the current year?” instead of constructing a SQL query that sums up the sales for the current year by joining the products and point-of-sales data tables, and then selecting products that meet a certain profit threshold. Thanks to natural language processing (NLP), data analysts no longer have to be SQL jockeys!
NLP parses the textual commands and tokenizes the sentence into keywords that can be used to construct a query. Then, a real-time engine such as data virtualization, can take that construct and create a SQL query under the covers. It does this by going across multiple data sources, extracting the relevant data from those sources, and combining the data into a result set that answers the question. Now, the data analyst can view the profitable products as a list or a chart within a visualization tool. The benefit of NLP to querying data using English sentences will be the explosion of self-service among the analysts and business users alike.
Thus far, data governance regimes have been siloed across technology projects such as data quality, master data management, or a data warehouse. However, the need for enterprise-wide data governance that transcends all technologies already exists. Now, thanks to information catalogs, business users and IT have a means to discover, organize, and describe enterprise data assets in a single place.
