I’ve spent quite a bit of time, over the past month or so, talking to a variety of people on the topic of report analytics and realized that a common theme developed in most of my conversations.
To fully understand the theme, I believe it’s important to understand the major premise of report analytics. Report Analytics is a new name for a relatively mature technology and is all about extracting data from the information contained in business reports.
I use the different terms “information” and “data” purposely. Reports deliver data that is presented so as to be read by a human. To a certain extent, relevant information is provided solely by the fact that it is eye-readable. The position of the data on the page and its relationship to other data on the page is indicative of a hierarchical relationship. In other words - where the data appears can often-times indicate its relative importance, its sort order and its “belonging” to other data on the page. This positioning and referential integrity provides information from data.
Of course, the very fact that the structure of the report is necessarily static, it limits the use of the information in the report to the specific purpose that report was originally designed for. Even though the report may (and most likely does) contain data that can be used for other purposes and to answer other business questions, it is unusable in the originally designed format for anything other than its original purpose.
These statements, invoices and reports, stored in an Enterprise Report Management system serve the very important original purpose of providing a legal archive and record of transactions that can be used to answer legal and regulatory questions as well as the, perhaps more important - customer queries. But these stored documents can provide so much more.
So, back to the common theme… Although most of the people that I spoke with had knowledge of report mining, none really understood report analytics and the power of extracting data from the information contained in reports. Report Analytics is report mining taken to the next level – Extraction of data from the information contained in reports, invoices, statements, etc. and transforming, repurposing and combining the data with information from external sources results in knowledge that can be used to gain additional insight, analysis and intelligence.
Learning more about internal processes, manufacturing time-lines, quality control, customer purchasing patterns, customer satisfaction levels and a host of other valuable information is readily available in the reports, invoices, Explanation of Benefits, statements, etc. that are produced on a normal everyday basis throughout business and industry. Timely acquisition of this verified, substantiated data can help increase revenue and reduce costs.
In many conversations, I had the gratifying experience of creating and “Aha!” moment. Recognition that the reports stored in an Enterprise Report Management (ERM) system could be used for data acquisition and not just data and information distribution is, admittedly, somewhat of a paradigm shift for many. But, once realized, it’s a paradigm that opens the door to reduced costs, improved operational reporting and a practical, pragmatic way to leverage existing infrastructure.
To fully understand the theme, I believe it’s important to understand the major premise of report analytics. Report Analytics is a new name for a relatively mature technology and is all about extracting data from the information contained in business reports.
I use the different terms “information” and “data” purposely. Reports deliver data that is presented so as to be read by a human. To a certain extent, relevant information is provided solely by the fact that it is eye-readable. The position of the data on the page and its relationship to other data on the page is indicative of a hierarchical relationship. In other words - where the data appears can often-times indicate its relative importance, its sort order and its “belonging” to other data on the page. This positioning and referential integrity provides information from data.
Of course, the very fact that the structure of the report is necessarily static, it limits the use of the information in the report to the specific purpose that report was originally designed for. Even though the report may (and most likely does) contain data that can be used for other purposes and to answer other business questions, it is unusable in the originally designed format for anything other than its original purpose.
These statements, invoices and reports, stored in an Enterprise Report Management system serve the very important original purpose of providing a legal archive and record of transactions that can be used to answer legal and regulatory questions as well as the, perhaps more important - customer queries. But these stored documents can provide so much more.
So, back to the common theme… Although most of the people that I spoke with had knowledge of report mining, none really understood report analytics and the power of extracting data from the information contained in reports. Report Analytics is report mining taken to the next level – Extraction of data from the information contained in reports, invoices, statements, etc. and transforming, repurposing and combining the data with information from external sources results in knowledge that can be used to gain additional insight, analysis and intelligence.
Learning more about internal processes, manufacturing time-lines, quality control, customer purchasing patterns, customer satisfaction levels and a host of other valuable information is readily available in the reports, invoices, Explanation of Benefits, statements, etc. that are produced on a normal everyday basis throughout business and industry. Timely acquisition of this verified, substantiated data can help increase revenue and reduce costs.
In many conversations, I had the gratifying experience of creating and “Aha!” moment. Recognition that the reports stored in an Enterprise Report Management (ERM) system could be used for data acquisition and not just data and information distribution is, admittedly, somewhat of a paradigm shift for many. But, once realized, it’s a paradigm that opens the door to reduced costs, improved operational reporting and a practical, pragmatic way to leverage existing infrastructure.