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 discussed analytical information management, which focuses on the downstream systems where data is used to make the business decisions that drive tactical and strategic initiatives.
In Part 3, the series concludes by discussing the need for a holistic approach that synchronizes operational data management and analytical information management.
The Data Management Brain
To download this diagram, click on the following link: Holistic Data Management
The Data-Information Bridge
In Part 2, I stated that some of the most important data-related activities occur on the Data-Information Bridge, which is what connects the operational and analytical hemispheres of the data management brain.
As operational data travels over the Data-Information Bridge, it becomes analytical information, and therefore data-information synchronization is vital for success.
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, perhaps unintended, uses.
Many times, it is the unknown analytical uses of the operational data that is the context for what, in hindsight, appear to be obvious data quality issues.
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.
My recent blog post about data accuracy sparked an excellent debate between Graham Rhind and Peter Benson, who is the Project Leader of ISO 8000, which is the international standards for data quality. The debate included their perspectives on the key differences and interdependencies that exist between data and information, as well as between data quality and information quality.
In his recent blog post, Henrik Liliendahl Sørensen explained the top 5 reasons for performing data cleansing downstream from the operational source systems where the data was created and is still managed for the purpose of its initial business use.
One of the greatest data management challenges is the synchronization between these upstream and downstream systems, between operational data and analytical information, between what happens on either side of the Data-Information Bridge.
Without diligent attention to this aspect of data management, critical, and possibly irreparable, disconnects occur between operational data and analytical information.
The worst case scenario is when the Data-Information Bridge is severed, when operational data and analytical information are managed independently, which will eventually lead to disastrous business results such as customer service nightmares, regulatory compliance failures or financial reporting scandals, to name but a few.
Holistic Data Management
While optimal business performance is the goal of operational data management, making better business decisions is the goal of analytical information management.
Holistic data management is not limited to either the sources or the destinations of data (although both are, of course, very important), but instead is primarily focused on the many (and often unpredictable) journeys that data will take as it travels throughout the enterprise, acknowledging both the objective and subjective aspects of data quality, as data is used within a rapidly evolving business environment.
A holistic approach, which synchronizes the operational and analytical hemispheres of the data management brain, enables a powerful enterprise to manage their data as a corporate asset, implementing data-driven solutions for their business problems.
As William Shakespeare once wrote:
“There is nothing either good or bad, but thinking makes it so.”
The complete thought-process of the holistic data management brain is summarized by the following formula:
Operational Data + Analytical Information = Business Insight
Therefore, perhaps the Bard of Data Management would write:
“There is nothing either good or bad about data, but the thoughts of the data management brain makes it so.”
Holistic data management is about thinking good thoughts with your organization’s complete data management brain, managing your data as a corporate asset, and enabling both better business decisions and optimal business performance.
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