Qlik Acquires Podium Data

Qlik, a leader in data analytics, today announced the acquisition of Podium Data, an enterprise-grade data management company whose solution simplifies and accelerates customers’ ability to manage, prepare and deliver analytics-ready data to every business user across a diverse data landscape. This acquisition expands Qlik’s mission, moving beyond analytics to being a provider of solutions that democratize data for every user to create a more data literate world.

Enterprise data strategies have evolved to rely heavily on the creation of data lakes, however, customers are realizing that these and other data sources aren’t designed to easily and quickly deliver data to the business user. In many instances, data lakes have only increased customer data complexity and management headaches. According to Gartner Inc., “Through 2018, 90% of deployed data lakes will be rendered useless, as they’re overwhelmed with information assets captured for uncertain use cases.” (Gartner. Derive Value From Data Lakes Using Analytics Design Patterns. 26 September 2017)

“We work closely with customers to build an analytics strategy that transforms many parts of their business with Qlik, and yet there is still huge untapped value in much of their data,” said Mike Capone, Qlik CEO. “You can’t be a leader in Business Intelligence and ignore the complexities of data management. Acquiring Podium Data furthers our goal of being the partner to handle a customer’s most difficult data challenges, driving both their analytics and data strategy.”

Qlik believes an organization’s analytics strategy is only optimized when paired with a strong data strategy, and yet most analytics vendors are falling short on delivering in this area. Customers lack an understanding of even what data they already have, and are left struggling to maximize their data’s value. Qlik’s multi-cloud capabilities and upcoming Associative Big Data Index are designed to help customers tap into all their data, explore massive data volumes in any direction and discover new insights. With Podium Data, Qlik will provide customers with an expanding enterprise data management solution to transform their raw data into a governed, analytics-aware information resource. Together Podium Data and Qlik will help break down bottlenecks and silos inherent in disparate enterprise data environments and expand the value of data throughout the enterprise.

Podium Data helps customers transform the passive data lake into a self-service data resource that efficiently manages data processes, reduces data prep time, and delivers data faster into the hands of business users. Enterprise customers such as Astellas, TD Bank, Charter Communications, and Cigna have relied on Podium Data to pivot to an agile data management strategy, delivering consumable information for business users through automated sourcing, cataloging, profiling, preparation and publishing of data at scale, whether it be in the cloud or on-premise.

“The promise of big data to deliver value to the full enterprise hinges on the ability to organize data and make it analytics ready,” said Paul Barth, CEO of Podium Data. “We’re excited to be joining Qlik to marry our data management capabilities with the analytics leader to bring data to life for every enterprise user.”

Podium Data will be the foundation for a Qlik data hub offering, encompassing a comprehensive set of capabilities to better manage, understand and act on data. Qlik envisions a data hub to be more than just data storage, preparation, and gathering of metadata. Businesses demand a dynamic ecosystem for enterprise data producers and consumers, including smart data catalog for all data assets, regardless of source or location. Qlik envisions a complete data hub offering that transforms data from raw to ready and includes key capabilities such as:

•             Intelligent Data Profiling and Onboarding: Ability to profile and register data from any source or location throughout the organization, providing a comprehensive understanding of every data element, with applied pattern matching, rules-based metadata enrichment, and auto obfuscation rules to protect sensitive data.

•             Automated Data Quality: Inspecting, improving and documenting the quality of incoming data through validation, formatting, and encryption.

•             Data Preparation and Publishing: Enriching and transforming data without additional programming, with the ability to publish data to downstream systems and be consumed by a broad base of users, including data scientists, analysts and business intelligence users.

•             Smart Data Catalog: A searchable data catalog organized with tags, business definitions and data lineage that makes it fast and easy for business users to find, understand and “shop” for data.