The analytical DELTA and the Data Lake

The analytical DELTA is an acronym for the five success factors that you need to have a successful analytics program or project in your organization, so that you can compete in the market by data powered decision-making. The Data Lake or the Enterprise Data Hub are the common terms used in the industry to represent a massive central data storage (typically Hadoop based), where the data of the entire enterprise is stored and made available for analytical goals.

The analytical DELTA has been described in the book titled Analytics at Work by Thomas H. Davenport, Jeanne G. Harris publisher Harvard Business Press.

DELTA covers the following five success factors:

• D for accessible, high quality data
• E for an enterprise orientation
• L for analytical leadership
• T for strategic targets
• A for analysts

All these success factors gave to be present if an analytics project or program should succeed in an enterprise. A missing or a weak element will cause delay and wasted effort.

The further analysis of five elements reveals that two elements, D&E, are technology centric. High Quality Data and Enterprise Orientation are the two main deliverables of a Data Lake. Data Lake is the centralized data store where high quality data about the whole enterprise is available for analysis. The promise of cheap and reliable storage on Hadoop/HDFS ensures that you can store all enterprise data in one central storage cluster without worrying about storage costs.

The element L is related to the visionary leadership that makes analytics programs succeed in the enterprise. You need senior managers who manage by facts and commit to the success of analytics program.

The element T tells us that the importance of business case and good matrices using which you can measure whether an analytics project is a success or failure. ROI or cost benefit analysis of an analytics also falls under this element.

The last element, A, covers the ability of organization to attract the right talent (analysts) who build and maintain the models. In addition to analysts, in my view, engineering talent also plays a crucial role in creating a strong A.

Using the DELTA model, you can check if you have all the success factors in place in equal strength before embarking upon an analytics project or program. When these elements work together then you encounter fewer roadblocks to success.

When you hear a vendor’s pitch for the Data Lake or the Enterprise Data Hub to get the value out of big data for your enterprise, you need to remember that weak L, T and A needs to strengthened first before you start your data lake journey.