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 .
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 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 , 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.
As one of the global leaders in on-demand business solutions provider carrying over 2500 data sources, Salesforce.com 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?
Get in touch with for a Free Trial on fast and easy to use applications / tools.
What is Data Quality Management? Data quality management is the process of tracking and analyzing the data in customer and business accounts, ensuring it's accurate and up-to-date. This includes periodic updates and cleaning, pruning data for old and outdated information, analyzing data fields, and ensuring all personnel have reliable data they can count on for lead management, integration, and much more. Data quality management typically follows the structure outlined below:
Planning a Successful Strategy The primary step in implementing true quality data management is through implementing and planning a successful strategy for migrating and managing the data. This depends directly on the integrity of existing data and how it is consolidated and organized.
Unfortunately, many company's data systems are messy, with information and files spread across a number of different data field, often complete with duplicate or incomplete records. Thus a carefully planned, unifying data management strategy is a must; if your company is organized, all processes will run smoother and quicker.
Implementing Data Migration for CRM Consolidating data into one source is one of the most important steps a company can take in managing the quality if the data that is fed into its system. Many companies struggle with multiple data sources, and this can waste valuable time and resources.
If an in-house sales team has different data than the team in the field, things can get sticky and spiral out of control. Thus, the correct CRM application can help a company migrate its data effectively, quickly, and efficiently, allowing for more success and a higher quality of data.
Cleansing and De-duplicating Data Cleansed and de-duplicated data is data that has been purged of annoyances like duplicate records or content and user problems due to integrity issues. This aspect of a CRM strategy's quality data management is meant to guide a company through the process of cleaning data before or after it has migrated from one source to another. This results in a more efficient, less cluttered system that is optimal for users and ultimately increases productivity.
How does Data Quality Management affect CRM? Data quality management is integral to the success of a strong CRM strategy because all the system's users, from the sales force personnel to the executives and marketing teams, have to access the same quality of data. A central source of qualified data ensures everyone is on the same page and knows what’s going on. This leads to a more effective sales team, better customer service, efficient lead management, and an overall more solid, targeted CRM program.
What happens if Data Quality Management isn't figured into your CRM strategy? If data quality management isn't figured into your CRM strategy, it means that your company may be working with faulty, inaccurate information; this can lead to all sorts of unmitigated disasters, from cold, lost leads to unsatisfied customers and a confused sales force. Data quality management is essential to the way a company or organization does business; thus, a CRM strategy without data quality management is too weak to be truly effective.
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