Significance of Data Quality Management

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Become A Manager Of Data Quality

Why data quality managing is so important

The basic ingredient involved in controlling and monitoring business information is Data Quality Management (DQM). This application ensures that important data stored within an enterprise is reliable, accurate and complete. Organizing data is the most critical and mandatory task as the available information is meant to be shared by different people to make strategic business decisions within a company. This makes an integrated DQM application a dire necessity for any organization.

Many organizations are stacked with volumes of data, which contain off-color information. It may be noted that having clumps of unhealthy information can cause more harm to the health of a company compared to having no information at all. Therefore, it becomes essential to deploy transactional data intelligence to acquire operational efficiency, better performance and enhance bottom-line results. Volumes of data and transactions that organizations generate daily have magnified the need for data quality management.

Today’s corporate business culture lays much focus on internal controls. Unfortunately, most data management solutions fail to provide the need-driven analytics necessary to validate the effectiveness of internal controls. They generally lay more emphasis on business operations and transactional processes. Inferior data often runs through such applications, potentially defeating the purpose for which they were initially designed. Data quality issues often surface while transforming data: -

In system conversions and integration projects that accompany mergers and acquisitions.
When building data banks to feed management reporting and business intelligence systems.

With the ability to rapidly maneuver huge volumes of data drawn from multiple operating systems, database structures and enterprise applications, powerful data analytics give companies acute visibility into their transactional information.

How should a company formulate a data quality management strategy?

The very first step taken on this front is to assess the current state of data within the enterprise. Post assessment, DQM policies should be evaluated along the given four parameters: -

1.Data classification: Determining the data to be maintained, degree of accuracy, compliance and completion, and timeframe to be followed. (Real-time, daily, monthly).
2.Organizational structure: Defining authority with the ultimate responsibility for maintaining data quality and laying greater emphasis on bottom-up (successful) efforts rather than top-down efforts.
3.User classification:Assigning responsibility for maintaining data quality on the user end, especially at entry and transition points.
4.Applicable Technologies:Data profiling, data standardization, data enrichment, data integration and data monitoring tools.

When determining the kind of resources and degree of involvement required in your DQM efforts, it is essential to receive active participation from all the relevant business owners and users who are responsible for any success or downfall. In order to convert every initiative into a positive outcome, it is advisable to form a work group of representatives from each business unit, conduct regular meetings to discuss and update DQM policies and procedures, evaluate prevailing technologies and tweak the existing system for gaps and success.

The IT industry has recognized the importance of Data Quality Management (DQM), but majority of them don’t deploy the binding technology or processes that can bring out the best possible from their data-quality efforts. Until now, IT sector has used DQM for fixing data in batch jobs or at a customer’s request. Many professionals avoid using DQM technology because they are not aware of its deep-rooted advantages. They exploit it more like demographic-data updating software.

On the contrary, today’s DQM applications are strategic issues that need to be dealt with careful thought and planning. They capacitate enriching and profiling data; help companies integrate authentic data from discrepant sources, and monitor contacts, leads and sales functions as an ongoing process.

How will you ensure that your sales team identifies the right prospect companies to increase your customer population?

Consulting an Account Intelligence vendor appears to be an ideal option for quality data management. Salesforce.com, in collaboration with OneSource.com, help your sales teams find the right companies, contacts and industry information to gear them for compelling sales calls.