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Significance Of Data Quality Management
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.

Todayís 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, but majority of them donít 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 customerís 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, todayís 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., in collaboration with, help your sales teams find the right companies, contacts and industry information to gear them for compelling sales calls.

As one of the global leaders in on-demand business solutions provider carrying over 2500 data sources, allows you instant access to the latest high-quality data for your accounts, contacts and leads. Using this real-time comprehensive database of millions of global companies, facilitates enhancing data quality, drive more business value and speed-up prospect opportunities. OneSource Account Intelligence program, integrated with Salesforce technology, offers to help you:

Generate more revenue via fast and effective mass-lead generation.
Turn leads into lucrative opportunities.
Make account and territory planning much easier and efficient.
Reduce gradient time for new sales staff.
Study market changes, competitive trends and industry news.
Recognize and understand your prospects in terms of contact details, company size, structure news and possible complications.
Earn credibility and trust through industry knowledge and expertise.

In addition, the document management capabilities empower you to manage the content that drives business operations. Salesforce provides a common bank for storing all documents enabling effective and consistent communication at anytime and from anywhere. The available organizational tools and search capabilities allow you to access these documents whenever required.

Do you need a compatible DQM system tailored to your business needs, which lets you organize, collect pertinent business-related information, and make it instantly available?



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This page was updated on Nov 2009 and is Copyright © 2003 by Global Com Consulting Inc.