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Understanding the Importance of Data Management
A Decision Maker’s Guide

by Lyndsay WiseMonday, June 2, 2008

Although executive interaction with business intelligence and performance management solutions generally involves accessing and interacting with dashboards and reports, it is still essential to understand how backend data comes together to provide the necessary ingredients to enable better decision making. In a world where business units are becoming more self sufficient and knowledgeable about managing their overall processes through the use of technology, it becomes more important to identify the value of data and its interaction. 

The ability to break down concepts and understand how things work enables business units to build and manage applications on their own. Alternatively, this knowledge allows executives to understand how disparate business units function and what IT needs in order to do their jobs and develop effective end user applications.  With the increasing ability to deploy solutions without the aid of IT, organizations may be asking, why is knowing about data and its infrastructure important? 

Why data matters

The reality is that even though executives may not require this knowledge for their day-to-day tasks, understanding how data interrelate only increases their ability to link information, performance, and strategy more effectively.  By identifying how business processes and operations link to data, organizations can turn that data into information that can be used for decision making purposes.  For instance, many organizations use different sources of information for planning, trends analysis, and managing performance.  Consequently, the value of the information is only as good as its point of entry into the system. This means that when decision makers across the organization are analyzing different numbers in order to make decisions that will affect the company, and the information does not add up the blame for why these numbers are invalid lies at the point of entry of the information into the operational systems.  Data entry errors and processing inefficiencies are but a few causes of error-prone data that end up being used to drive an organization’s decisions.

Although seemingly inconsequential, small discrepancies can make a significant difference to a company’s bottom line.  However, if data quality control initiatives exist particularly with respect to the data that is used to drive decision making, the picture changes as a broader and more correct view of the information becomes available. 

Within business intelligence applications the idea of bringing in the right data at the right time becomes critical to reporting and analysis applications.  Without accurate information, the data being analyzed and reported on becomes meaningless. A general overview of how data integration works will provide an outline for decision makers who want a better understanding of how information is gathered to help with the decision making process.

How it works

The following diagrams provide a good overview of how data integration works. 

(source: http://www.info-alchemy.com/)

As shown above, data integration involves transferring data from one location to another.  For business intelligence applications (i.e. scorecards and dashboards) this generally involves moving data from operational systems such as ERP or CRM to a data warehouse for use in reporting and analytical applications. 

The first step is the identification of what data is required and where it resides. For instance, customer sales may exist in several systems across the organization. It becomes important to identify where the best access point is to capture the information. Once the data is identified it is copied (i.e. extracted) and can then be cleansed and transformed into a usable format before it is loaded into the data warehouse. On a separate note, data integration can also be used for purposes outside of business intelligence including consolidating organizational information due to acquisitions, master data management initiatives, and integrating data from legacy systems into ERP and CRM systems.

As an extension of simple integration activities such as ETL (extract transform and load) the ability to perform CDC (change data capture) and EII (enterprise information integration) activities also exist.  Change data capture (CDC) simply involves capturing only the updated data fields.  Instead of re-capturing source data each time updates are performed, only new entries are updated, thus saving time and space.
Enterprise Information Integration (EII) on the other hand, provides a single interface to data to see a single view of information within the organization.

The Informatica diagram below shows the process involved in data integration activities.

(Source: http://www.informatica.com/products_services/Pages/data_integration_lifecycle.aspx)

By looking at data integration as a process, it is possible to see how data is moved and transformed from being strictly operational into information that enables decision-making. Once data is brought into a data warehouse or captured for reporting or in a dashboard, executives can gain a fuller and broader view of what is happening within the organization. 

The bottom line for business

Understanding how information is tied together and how each piece of the data puzzle interrelates to form the big picture enables better decision making, higher process efficiencies and can lower overall costs.  As organizations continue to struggle to maintain competitive advantage, information becomes the key component in enabling executives and decision makers to make informed decisions based on a 360-degree view of the organization and its various operational processes.

Without an adequate understanding of the importance of an organization’s data and its structures, it is difficult to develop analytical tools that will enable effective decision-making and provide an overall view of what is happening, both within the organization and outside of it.

(Copyright 2008 - Dashboard Insight - All Rights Reserved.)

About the Author

Lyndsay Wise is an industry analyst for business intelligence. For over seven years, she has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Lyndsay is a monthly columnist for DMReview and conducts research of leading technologies, products and vendors in business intelligence, marketing performance management, master data management, and unstructured data. She can be reached at lwise@wiseanalytics.com. And please visit Lyndsay's blog at myblog.wiseanalytics.com.

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