The logical data warehouse: The new secret weapon for leading businesses
Wendy Lucas could not have said it better: “Enterprise data warehouses remain as relevant as ever in today’s business environment.” But the ever-expanding world of data has certainly left its mark on the data warehouse. In the past, a single enterprise data warehouse (EDW) could contain all the data that might need analyzing, perhaps helped along by a nightly batch data feed or overnight reports. No one worried about high availability in the early days of warehousing.
But today data is flooding in at speeds never before seen—and with it, data types are proliferating. Accordingly, dependence on analytics has gone from being a luxury to being a competitive weapon.
Quick, nimble analytics is rapidly making the traditional data warehouse model outdated. EDWs struggled because they were simply unable to keep up with the demands of business users—who were clamoring for more analytics, more access to data and more ways to do self-service and data discovery—while also meeting the consistent need for highly governed and secure data for financial and compliance reporting and other such applications. Gartner, calling this IT’s ultimate dilemma, said that IT must thus be bimodal, responding to the needs of the business while maintaining core systems.
Seeing opportunity in big data
Ultimately, the data warehouse evolved into what we call the logical data warehouse. Why? Because businesses cannot wait for answers; they want ever more control over data access as well as ever shorter turnaround times on analytics.
Today’s businesses do not shy away from mounds of data. Rather, a highly competitive firm sees a secret weapon—an opportunity—in data and builds its culture around data-driven decision-making. Such businesses know more than the competition does, and they know it sooner—giving them an edge. They want self-service capabilities that make analytics truly available. Moreover, they want agility, which often requires immediate insight into changing business conditions. Thus these businesses set the standard for how data and analytics change our world.
Turning data into insight
But, you may wonder, how will you get insights from your data? How will you manage it all? The logical data warehouse is made up of different “fit for purpose” stores across systems of record, systems of engagement and systems of insight. Data can range from transaction data and financial information to social media data, personalization preferences and sensor information—and everything in between. Name the data type, and you probably have it.
However, you must deliver data insights across these multiple, diverse stores. Accordingly, systems of insight drive accurate insights by combining data from systems of record and systems of engagement. For example, suppose you want to explore data, assessing its value before you commit to adding it to your main data warehouse. The logical data warehouse offers data exploration and discovery using new data types—in some cases, in environments that are not fully structured.
Meeting the needs of business
A second challenge is the drive to self-service for the business. The EDW was not designed to be an agile self-service environment; rather, it is a single trusted and well-governed source for reporting. And so the logical data warehouse becomes the next generation of the EDW—a way to meet the expanded needs of business. The logical data warehouse allows for self-service exploration as well as agile deployment methods, including hybrid cloud.
The logical data warehouse also makes possible new data types and new types of analytics, giving organizations more analytic options than ever. After settling on key analytics, who wouldn’t want to operationalize the insights gained, making them immediately available for the organization to consume on a broad scale? The traditional data warehouse stores and provides access to such insights.
Bringing the query to the data
A chief concern for many customers about the evolution of data warehouses lies in handling different data containers when data is not well managed and neatly boxed. IBM has always preferred to “bring the query to the data”—and, indeed, this is the design point behind the Fluid Query capability, which helps provide ever deeper insights from increasing amounts of data. Why navigate massive data volumes when you could simply move the query to where the data is? Fluid Query provides unified insights from across Hadoop, Spark, PureData stores and relational stores such as DB2, dashDB, Oracle and other PureData warehouses.
IBM also brings a full portfolio to the evolving logical data warehouse, offering solutions to cover many data types and needs. Among these solutions is data governance, an essential but often overlooked capability that can help ensure that insights are based on high-quality, secure data.
To explore what IBM brings to the logical data warehouse, read Gartner’s Magic Quadrant for data warehouse and data management solutions for analytics to see how IBM stacks up. And don’t forget that all this is only the beginning of how data can change lives. Indeed, the logical data warehouse is an enabling technology driving rapid and exciting changes in how people use and relate to data.