Business needs information. Indeed, business thrives on it. But only if it is widely accessible, governed with agility, and provided timeously. Delivering data with these qualities has long been central to the data warehouse, as I’ve recently described in “Thirty Years of Data Warehousing”. The longevity of the concept testifies both to the strength of its architectural underpinnings and the successes that many businesses have achieved through insights gained through this environment.

Barry DevlinNonetheless, complacency is not an option! The flood of data arriving from social media and the Internet of Things is causing widespread disruption to existing business models and offering a wealth of new opportunities to those willing to take them. Responding to these challenges demands levels of agility that traditional data warehousing struggles to achieve. New privacy regulations, such as GDPR, require levels of data management and governance previously unseen. Meanwhile, artificial intelligence and other modern analytic techniques are radically changing how business people use information in decision making.

When it comes to information, we are entering a new age of discovery where business can and, indeed, must be enabled to extract more value, more easily and consistently, than ever before. To do this, IT requires an integrated, automated delivery environment that spans from all imaginable data sources to any data discovery or analytic tool the business needs.

TimeXtender’s Discover Hub® is a prime example of such a modern information delivery and management environment, building on the conceptual foundation of the data warehouse, and adding automation and semantic integration.

Business requirements driving the Discovery Hub® include:

  • Being the first stop shop for all business users who need any business information
  •  Offering a one stop shop for all core business information to ensure the business sees maximum consistency of such key information
  • Supporting the structure-neutral repository for all widely used business information, allowing business people to “play” with it in the most suitable form
  • Providing freedom of information for users to go elsewhere for the data they need, while guiding them to the best sources in the Hub

Further details of the concepts and function of the Discovery Hub can be found in my white paper “Data Discovery Automation—Learning from the Warehouse Experience.”

As businesses shift increasingly toward a reliance on information in every aspect of their operations, there are many more circumstances when implementing a Discovery Hub is the right approach. For IT, recognising that specific circumstance and reacting accordingly can make the difference between success and failure.

One of the most common—and successful—scenarios for implementing a Discovery Hub® occurs when business people are in the first flush of their love affair with a data discovery tool such as Qlik or PowerBI. Soon, they begin to notice some strange or inexplicable inconsistencies in their results or insights. They review their analyses and calculations, but all seems fine. These symptoms usually indicate that the sourcing and preparation of the data they’re using is the problem.

For IT, this is the time to “nip the problem in the bud.” At this moment, the business people are happy. They are getting good value and insights from data, perhaps for the first time ever. They can grasp the concept of dirty data and will be supportive of getting it fixed. But remedial action must be prompt and non-disruptive of the results they have achieved. A Discovery Hub implementation focused on the areas of data inconsistency can be fast and largely painless. It will fix the symptom but, more importantly, it can provide the foundation for further success: First, with the businesspeople, who see immediate progress and learn to trust IT. And second, as the basis for extending the governance of data to wider areas in the business and reducing or preventing the occurrence of similar data quality problems in the future.

Another scenario for successful introduction of a Discovery Hub® is when an existing data warehouse begins to run out of steam. This situation often occurs when business evolution requires extensive new or changed data to monitor the business or gain insights. Existing data sourcing infrastructure, such as ETL tools, aren’t agile enough to allow IT to react quickly to these changes. IT then gets a bad name as the bottleneck to business progress, and business people may react by sourcing the data they need independently. Data quality deteriorates and decision making suffers.

This is a good moment for a move by IT from the failing infrastructure to a Discovery Hub®. With a focus on automation, agility, and user involvement, this approach offers a fast response to the problems of the existing environment, as well as assuring the business that IT is “on the ball” and up to the challenge of addressing the information needs of ongoing business transformation.

Join me at TDWI Munich on Tuesday 26th June when I’ll be exploring these scenarios and use cases based on them.