After reading the bestselling book of Yuval Noah Harari (Homo Deus: A Brief History of Tomorrow) I was glad to conclude that building systems that are artificially conscious, is still a long way to go.

However, in the upcoming years artificial intelligence (AI) will impact many different types of work, especially where intelligence can be automated. A lot of systems and software are available on the market to create AI applications, but most of them expect the data on which AI can be executed to be ready to be processed. Unfortunately this is often not the case. AI applications (as any application which create insight based on data) are the end of a chain starting with data created by systems and/or devices.

Between these two opposites a number of process steps and capabilities are required to produce reliable and valid insights. Besides this, the same set of data might be used differently, for instance to create a report as well as for simulation purposes. Implementing AI should therefore be an integrated part of an organization’s “data-centric” or “insights-driven” strategy.

Within The Open Platform 3.0 Big Data Project, of which I’m a member, we recognized that a reference architecture describing standards which help organizations setting up this “insights-driven” strategy, was needed. We all agreed that a standard should be based on a Business Data Lake as a provisioning environment for data to create insights.

This standard, called ‘Open Business Data Lake Conceptual Framework’ (O-BDL) was published in March 2017 and is a first step towards a Reference Architecture for an Open Business Data Lake. An O-BDL is presented a platform providing enterprise capabilities to:

•       Consolidate and preserve enterprise data sets along the data lifecycle

•       Assemble preserved enterprise data sets by applying suitable structural definitions and transformations

•       Consume these assembled data to explore data and/or to create (AI)-insights.

As part of the O-BDL a set of concepts (data, ingestion, processing and data management related) is described, including a number of business scenarios which can be enabled by O-BDL implementations, such as the off-load of Enterprise Data Warehouse (EDW), the creation of Big Data Apps, the Data-Driven Enterprise and an ecosystem of data-driven enterprises.

Next Wednesday July 19, I’ll present the O-BDL at the Open Group Conference in Ottawa. In my next blog I’ll describe the characteristics of an O-BDL.