In the series introduction, I used a brain metaphor to compare and contrast operational and analytical data management as two half-brains designed to work together as a single, integrated whole in one complete data management brain.
In Part 1, I discussed operational data management, which focuses on the upstream systems where data is created by the business and technical processes that support daily activities.
In Part 2, I will discuss analytical data management, which focuses on the downstream systems where data is used to make the business decisions that drive tactical and strategic initiatives.
The Data-Information Bridge
In Part 1, the connection between the operational and analytical hemispheres of the data management brain was described as a data exchange.
Data cleansing, transformation and enrichment occurs during this data exchange, the result of which is often passed downstream without being used to update the upstream source systems where the operational data originated.
The Fourth Law of Data Quality explained that data quality standards include both objective data quality and subjective information quality.
Data’s quality is determined by evaluating its fitness for the purpose of business use. However, in the vast majority of cases, data has multiple business uses, and data of sufficient quality for one use may not be for other, and perhaps unintended, uses.
I define information as customized data and information quality as fitness for the purpose of a specific business use, which meets the subjective needs of a particular business unit and/or a particular tactical or strategic initiative.
Therefore, I like to refer to the data exchange between the operational and analytical hemispheres of the data management brain as the Data-Information Bridge.
By passing over this bridge, the operational data becomes the analytical information used to make the business decisions that drive tactical and strategic initiatives.
Some of the most important activities of enterprise data management actually occur on the Data-Information Bridge, where preventing critical disconnects between operational and analytical data is essential for success.
I will discuss these aspects in more detail in Part 3 of this series.
For now, let’s focus on the analytical hemisphere of the data management brain.
Analytical Information Management
Since after passing over the Data-Information Bridge data becomes information, it’s more accurate to call the analytical hemisphere of the data management brain: Analytical Information Management.
In Is your data complete and accurate, but useless to your business?, I explained that enterprise data management is performed for the sake of implementing data-driven solutions for business problems, enabling better business decisions and delivering optimal business performance.
While optimal business performance is the goal of operational data management, making better business decisions is the goal of analytical information management.
Converting operational data into analytical information was only the beginning.
In order to facilitate decision support, analytical information management must perform an additional phase of data conversion.
Ultimately, since the real value of data is the business insight it can provide, through decision-centric analysis, information is converted into the business insight used to drive the tactical and strategic initiatives of the enterprise.
When well orchestrated, this repeatable process consistently results in well-executed performances of The Data-Decision Symphony.
In other words, each business decision requires the complete thought-process of the data management brain, which can be summarized with the following formula:
Operational Data + Analytical Information = Business Insight
What Say You?
This series will conclude next week by discussing the need for a holistic approach that synchronizes operational data management and analytical information management.
As always, your feedback will be greatly appreciated throughout the series.
Your comments will be reviewed by the moderator and panelists, and perhaps posed as questions to help guide the discussion during the DataFlux IDEAS Expert Panel.
Therefore, please contribute to the discussion by posting your comment below.