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Transforming a Passive Data Catalog into an Active Data Hub

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2 www.zaloni.com Introduction According to Gartner, organiza ons that provide access to a curated catalog of internal and external data assets will derive twice as much business value from their analy cs investments by 2020 than those that do not. 1 That's a ringing endorsement of data catalogs, and a growing number of enterprises seem to agree. In fact, the global data catalog market is expected to grow from US$210.0 million in 2017 to US$620.0 million by 2022, at a Compound Annual Growth Rate (CAGR) of 24.2%. 2 Why such large and intensifying demand for data catalogs? The primary driver is that many organiza ons are working to modernize their data pla orms with data lakes, cloud-based data warehouses, advanced analy cs and various SaaS applica ons in order to grow profitable digital ini a ves. To support these digital ini a ves and other business impera ves, organiza ons need more reliable, faster access to their data. However, modernizing data pla orms can create problems, including data sprawl and the propaga on of ungoverned data and data quality issues. Although data catalogs can deliver some value by providing visibility into datasets across an organiza on and helping users be er understand relevancy, usability, and rela onships between data, is a tradi onal data catalog ul mately the best solu on? Organiza ons looking to take a "future-proof" approach to data pla orm moderniza on may want to consider their op ons before jumping on the data catalog bandwagon. Consider this: a tradi onal data catalog is "passive" and can only deliver limited business value. Enterprises that truly want to derive value from data into the future need to put in place an "ac ve data hub" that enables broader self-service access and data transforma on capabili es. Let's look at why and how. Defining the data catalog A data catalog is an inventory of an organiza on's data assets that provides context through descrip on and organiza on that enables data consumers, including business users, data scien sts and data analysts to understand and discover datasets needed for business ini a ves. In our work, we generally see two types of data catalogs. There are "pure-play," single-purpose solu ons that generally focus on inventorying data using machine learning, and also may enable data annota on and some governance for updates. The second type of data catalog is one that is embedded into data management, data governance and analy c applica ons. These catalogs have many of the same features as single-purpose data catalogs but are more geared towards improving the inventory of data for the overall applica on. Analy c applica ons may provide an improved or integrated catalog for models and model usage that is superior to that of single-purpose solu ons. 1 Magic Quadrant for Analy cs and Business Intelligence Pla orms, Gartner, Feb. 2018, h ps://www.gartner. com/doc/3861464/magic-quadrant-analy cs-business-intelligence 2 Global Data Catalog Market Forecasts to 2022 Expected to Grow at a CAGR of 24.2% Driven by Growing Adop on of Self-Service Analy cs, Research and Markets, APril 2019, h ps://www.prnewswire.com/news-re- leases/global-data-catalog-market-forecasts-to-2022-expected-to-grow-at-a-cagr-of-24-2-driven-by-growing- adop on-of-self-service-analy cs-300627275.html The global data catalog market is expected to grow from US$210.0 million in 2017 to US$620.0 million by 2022, at a Compound Annual Growth Rate (CAGR) of 24.2%. 2

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