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Implementing A Data Management Strategy
A First Look At Information Management

by Lyndsay Wise, President, WiseAnalyticsWednesday, October 27, 2010

The concept of implementing a data management strategy can seem overwhelming to many organizations.  Whether master data management (MDM), data governance, data quality or ensuring compliance, the reality is that business is driven by having valid, correct data at the right time and delivered to the right people.  Companies understand that ensuring successful business practices and increasing efficiencies requires initiatives that take into account the ongoing management of their information assets.

Even with this understanding, the actual development and implementation of a data management strategy will differ greatly based on the requirements of the company.  For instance, is the organization looking to create an MDM hub to manage their customers and associated relationships?  Is there a glaring data quality issue based on the data being captured and analyzed within a BI framework?  Or does the company need to meet compliance requirements and put an internal auditing system in place?

All of these considerations require an increase in data management; however the type of project and the level of comfort will differ based on the current IT environment and the business interaction with information.  This article looks at the slow maturity curve and general adoption of data management initiatives, in addition to how many organizations are transitioning towards these approaches to managing their data.  This article also looks at what is required as a set of first steps when considering a data management initiative.

Traditional data management starting point

The ability to successfully manage information and drive decision-making through increased data visibility is not easy.  In many cases, early adoption of MDM came from companies that were well versed in business intelligence and data warehousing.  Due to the nature and requirements of managing a data warehouse, these organizations were well poised to take the next step.  Data warehousing requires the capturing, cleansing, managing and delivering of data to support reporting and analytics.  Aside from supporting business decision-making, the adoption of a data warehouse usually brings data issues to the forefront.  For instance, if duplicates exist or if there are many data-entry errors, the reports that come out of the data warehouse will leave no room for debate.  Because of this, it becomes possible to identify a starting point for customer or product information management.

Beyond this comes the concept of creating a program of continual data quality or data governance to ensure a specific level of data quality over time as well as the ability to tie good data management to better processes within an company’s overall IT infrastructure.  Organizations that consider these initiatives are committed to increasing data visibility and cutting costs associated with bad data.  These include cleaning up customer or product lists, making sure that business processes are meeting internal efficiencies, and ensuring collaboration across the organization.

New approaches to managing information assets

Due to similarities that exist between data warehousing and other data management initiatives, using BI as a starting point in a data management initiative seems like a natural extension.  However, there are also companies that develop MDM or data governance initiatives independent of their BI environments.  In these cases, organizations will start with one entity such as customer or product and create a common definition of what that means across the organization.  For example, the concept of what a customer is will differ based on employee role.  Some people consider customers as members within the organization, while others look at customers as consumers of products or services.  The ability to manage entities on an overall level saves operational time and money as companies can eliminate multiple mailings, identify the overall value of the customer, and increase the effectiveness of marketing campaigns. 

Businesses are starting to understand the value and important role operational data plays in building strategy, goals and monitoring performance.  Consequently, the way an organization manages their information can help broaden internal efficiencies and overall customer experience.  In turn, this transforms the way companies interact with data and use it to drive decision-making leading to data management.

As organizations move towards this approach, software providers are meeting these needs head on.  Instead of specific MDM vendors offering hubs for customer or product management, data warehousing and data integration vendors offer integrated data management solutions as part of their offerings.  Companies are looking beyond traditional MDM products and integrating data management solutions to help them manage their data integration processes or look at how compliance initiatives are being met.

Choosing a starting point

With a general background of how these programs work and how many organizations approach the topic of internal data management, it becomes possible to choose a starting point within the company.  Traditionally, this would be product or customer but with the maturity of the market comes the flexibility to choose options that better reflect internal needs.  For instance, looking at compliance requirements or starting a data governance body might be a better place to commence depending on what business pains are being faced within the organization.

Unlike newer business intelligence offerings (e.g., some dashboards and Software as a Service offerings), any data management initiative involves a lot of time and effort to achieve results.  Business units and IT are both required to develop and to maintain a successful initiative.  For companies looking for a strong data management starting point, an initial understanding of data relationships and the validity of current information flow is a good place to start.  In many cases, manual data entry, copying and pasting data into Excel, general manipulating of data, and combining multiple data sets into a common spreadsheet create unreliable information.

To rectify this, organizations require formalized processes and automated data flows to ensure that information is valid and reliable over time.  Data governance and data quality initiatives provide a good starting point for companies looking to effectively manage data and increase overall data efficiency.  Although no one fix will work for every business, the reality is that due to increased competition and exponential data growth, organizations can no longer overlook the increasing importance of including a formal data management program as part of the overall strategic management of the business.

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 the channel expert for BI for the Mid-Market at B-eye-Network 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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