The Business Case for Data Management (MDM, Data Quality, Data Governance)!

In an earlier post, I had outlined impact of poor data management. In this post I would like to discuss the data quality and need for governance/accountability systems and technological solutions like MDM (Master Data Management).
Characteristics of good data
In order to analyze the data stored with applications and used by end to end business processes, let us review certain characteristics to check if our data is of good quality and where/what do we need to work on:
Completeness: Is all the requisite information available? Are data values missing, or in an unusable state?
Conformity: Are there expectations that data values conform to specified formats? If so, do all the values conform to those formats? Are these formats specified in the same way across all your applications (data silos)?
Consistency: Do you have conflicting information about the same object? Are values consistent across applications and business processes (data silos)? Do interdependent attributes always appropriately reflect their expected consistency?
Accuracy: Do data objects accurately represent the “real-world” values they are expected to model? Are there variations in spelling or reference information (id related to customer, supplier and employees)?
Duplication: Are there multiple instances of the same data objects within your data set?
Integrity: What data is missing important relationship linkages? Does the data adhere to a predefined set of rules?
Timeliness: Can the right people access the right data at the right time?

The fundamental nature of data is that it changes continuously making it difficult for organizations to put the data to the best possible use and achieve benefits. Furthermore, much of the data within organizations resides on different systems (for example, ERP, CRM, Order management, Customer service, PDM, PLM etc). And it is often difficult to keep all these systems in sync.

Poor data quality isn’t always apparent in processing your day-to-day business transactions. The purchasing department, for example, may not see the difference between entering “3M” or “3M Corporation” or “3MCorp.” in a database. All of these seem to get the job done. But if you dig deep, you will find that this could potentially save $$$ in time and resources utilized in creating duplicates of data.

In most organizations, most resources are fully utilized and certain “nice to have” functions are dropped as a result. One such item that is commonly ignored is data quality!

The benefits of accurate data are clear. They include decreased product development and sales costs, better customer service and increased employee productivity. However, building the business case in order to launch a data quality management initiative has traditionally been a challenge!

As organizations face stiff demand and need to churn out products and services faster, there is an increased demand placed on the availability of good data to support faster and better decision making! Without a doubt, data has become the raw material of the information economy, and data governance is a strategic imperative.

In addition, increased requirements on timely and accurate data placed by regulatory compliances like (SOX, FDA 21 CFR Part 11, RoHS, WEE, Reach, HiPAA, Osha) have stretched the capabilities of process and business systems owners.

These demands have resulted in the development of data governance and technological solutions like MDM. Master Data Management (MDM) is the technology, tools, and processes required to create and maintain consistent and accurate lists of master data.

Some organizations hope to improve data quality by moving data from legacy systems (or consolidating data silos) to (ERP) and (CRM) packages. Other organizations use data profiling or data cleansing tools to unearth dirty data, and then cleanse it with an extract/transform/load (ETL) tool for data warehouse (DW) applications.

A word of caution: unless data quality and governance is approached from a top down manner with alignment from all levels, we will not be able to achieve accurate, complete and timely data! A program to address data quality is not to fix a business system or application, neither is it an implementation of technology solution like MDM, BI, DW. This program is to fix behavior, flow of information across the enterprise and improve operational effectiveness, efficiency, control and success. Merely focusing on technology will result in the same problems but in a different application.

"Disclaimer: The views and opinions expressed here are my own only and in no way represent the views, positions or opinions - expressed or implied - of my employer (present and past) "
"Please post your comments - Swati Ranganathan"


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