The acquisition of Podium Data by Qlik expands Qlik’s Portfolio Beyond Analytics, Enabling Analytics-Ready Data Strategy to Drive Data-Driven Transformation
Qlik has reported the acquisition of Podium Data. This acquisition extends Qlik’s main goal, moving past analytics to being a supplier of solutions that democratize data for each client to make a more data educated world.
Endeavor data techniques have developed to depend intensely on the making of data lakes, in any case, clients are understanding that these and other data sources aren’t designed to effectively and rapidly convey data to the business client. In numerous examples, data lakes have just expanded client data multifaceted nature and administration migraines. As per Gartner Inc., “Through 2018, 90% of conveyed data lakes will be rendered pointless, as they’re overpowered with data resources caught for indeterminate utilize cases.” (Gartner. Get Value From Data Lakes Using Analytics Design Patterns. 26 September 2017)
“We work intimately with clients to fabricate an analytics strategy that changes numerous parts of their business with Qlik, but then there is as yet enormous undiscovered incentive in a lot of their data,” said Mike Capone, Qlik CEO. “You can’t be a pioneer in Business Intelligence and overlook the complexities of data administration. Procuring Podium Data assists our objective of being the accomplice to deal with a client’s most troublesome data challenges, driving both their analytics and data strategy.”
Qlik trusts an association’s analytics strategy is just upgraded when matched with a solid data strategy, but then most analytics sellers are missing the mark on conveying here. Customers do not have a comprehension of even what data they already have, and are left attempting to expand their data’s esteem. Qlik’s multi-cloud capacities and forthcoming Associative Big Data Index are designed to enable customers to take advantage of every one of their data, investigate gigantic data volumes toward any path and find new experiences. With Podium Data, Qlik will furnish customers with a growing endeavor data administration answer for change their raw data into an administered, analytics-mindful data asset. Together Podium Data and Qlik will enable separate to bottlenecks and silos inherent in dissimilar endeavor data conditions and grow the estimation of data all through the undertaking.
Podium Data enables customers to change the uninvolved data lake into a self-benefit data asset that proficiently oversees data forms, decreases data planning time, and conveys data speedier under the control of business clients. Endeavor customers, for example, Astellas, TD Bank, Charter Communications, and Cigna have depended on Podium Data to rotate to a coordinated data administration strategy, conveying consumable data for business clients through automated sourcing, indexing, profiling, readiness and distributing of data at scale, regardless of whether it be in the cloud or on-preface.
“The guarantee of big data to convey an incentive to the full venture depends on the capacity to sort out data and make it analytics ready,” said Paul Barth, CEO of Podium Data. “We’re eager to join Qlik to wed our data administration capacities with the analytics pioneer to breath life into data for each endeavor client.”
Podium Data will be the establishment for a Qlik data hub offering, enveloping a thorough arrangement of abilities to better oversee, comprehend and follow up on data. Qlik imagines a data hub to be something other than data storage, arrangement, and social affair of metadata. Organizations request a dynamic biological system for big business data makers and customers, including smart data list for all data resources, paying little mind to source or area. Qlik imagines a total data hub offering that changes data from raw to ready and incorporates key abilities, for example,
- Intelligent Data Profiling and Onboarding: Capacity to profile and enlist data from any source or area all through the association, giving a far reaching comprehension of each datum element, with connected example coordinating, rules-based metadata enhancement, and auto obfuscation rules to ensure sensitive data.
- Automated Data Quality: Inspecting, improving and documenting the quality of incoming data through validation, formatting, and encryption.
- Data Preparation and Publishing: Enhancing and changing data without extra programming, with the capacity to distribute data to downstream systems and be devoured by an expansive base of clients, including data scientists, investigators and business insight clients.
- 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.
