Qlik’s announcement that it is acquiring Attunity may have come as a surprise for many people, but it really shouldn’t have. Those who understand the macro trends in data analytics industry will recognize that Qlik’s recent acquisitions (it acquired data management company Podium Data in 2018) represent a product strategy that aligns with the enterprise analytic needs found at many companies across different industries. The need for an overall enterprise data analytics strategy that scales self-service data analytics across the enterprise is a top priority in companies for realizing business value alongside traditional BI/DW and newer AI/ML initiatives, and Qlik is building out its product offerings to enable the full analytics lifecycle.
The point here is that many vendors offer point solutions or a piece of the puzzle that is necessary for enabling enterprise self-service data analytics, but we’re increasingly seeing vendors on a strategic path to toward providing a complete self-service platform for meeting customers’ needs. Solutions that are designed to deliver the business analytics capability of self-service data analytics include many components, such as intuitive data prep and analytics development (with augmented intelligence assistance), data management needs (including data catalog, data lake governance), and an agile no-code data ingestion capability. Companies are interested in buying fewer products that are more complete for self-service to avoid brittle product integrations and multiple vendor upgrade management. With the data management and replication technologies acquired through Attunity, Qlik is bringing in the necessary components to provide an end-to-end solution that can stream real-time operational data, govern cloud-based data lakes, and empower users to build data and analytics with that data. A year ago, you would have to buy their products separately from Attunity, Podium Data, and Qlik to accomplish this.
Our advisory services establish, assess, and recalibrate the data and analytic strategies for companies, and in doing so we have observed firsthand that delivering self-service data analytics has typically been a high priority – and significant challenge – for delivering business value and pain-relief. This includes agile data ingestion of operational systems data along with establishing a well-governed data lake as the enterprise data foundation. A challenge has been the time it takes to evaluate and test the integration of the technologies that enable enterprise self-service data analytics beyond the user’s tool. Vendors that can meet the requirements while providing the components needed to support a full enterprise strategy have an advantage.
Today, analytics strategies must be considered broadly to include data management and data unification that support the modern needs of user-driven data analytics and complement established BI/DW approaches. Traditional BI development has data analysis, management, and governance baked into the delivery process. Now, self-service data analytics relies upon technology to deliver data governance with data discovery and management built in, rather than as part of the process.