Data Quality Management

With the advent of the digital economy, businesses now have more data at their disposal than ever before. Insightful business decisions can be made with the use of this data. Companies of all sizes and in all sectors are becoming increasingly data-focused.

Artificial intelligence, machine learning, and the Internet of Things are redefining what it means to be successful in businesses of all sizes. To what extent an organization can stay ahead of the competition depends on how well it adapts to and capitalizes on these technologies.

Data provides the basis for critical judgments and strategic plans when it is dissected and analyzed. Consequently, the quality of your data will establish the reliability of this basis and the success of your future actions.

Organizations are in a stronger position to harvest the most benefits from quality data if they recognize its potential and worth early, manage it properly, and base decisions on facts. Companies need to invest in data management solutions that increase transparency, dependability, security, and scalability to give employees access to the information they need to make sound decisions.

What is Data Quality Management?

The term “data quality management” (DQM) describes a set of procedures that companies follow to ensure that their data is of sufficient quality for decision-makers. The data quality process flow is preserved throughout the lifecycle thanks to solid data quality and integration framework.

Users may, for instance, designate multiple data quality checks to be performed at various points in the process as part of an organizational data quality management strategy, with the ultimate goal of eradicating inconsistencies and errors and guaranteeing accurate data for analytics and business intelligence tasks.

Data quality management has become less of a headache thanks to advanced Data Quality Tools such as exMon. Those that can be easily integrated give value to an organization’s quality management system, which benefits the quality of the products and the satisfaction of the company’s clientele.

How Crucial Is It To Manage The Quality Of Your Data?

Big data undeniably has had and will have a lasting impact on the business world. Consider the vast amounts of data constantly being streamed from IoT devices or an excessive amount of shipment tracking information that inundates company servers and must be sorted through before any meaningful conclusions can be drawn. The data quality management challenges posed by big data are only growing. You can sum these up into multiple main points.


Standard practice today is to reuse data sets for entirely new purposes. This has the unintended consequence of making it so that the same facts can be interpreted in many different ways depending on the context. You’ll need high-quality data to make sense of these massive troves of structured and unstructured data.


It can be challenging to incorporate controls for validation when working with the commonly used externally created data sets in big data. Fixing the mistakes will result in data that doesn’t match the original, yet maintaining consistency often requires sacrificing quality. As a result, it is crucial to have data quality management capabilities that can help strike a balance between monitoring and large data sets.


While data rejuvenation allows for the use of previously archived data, it also raises the stakes regarding validation and control. It must be properly integrated into more recent data sets to gain valuable insights from historical data.


Errors are less likely to occur when proper data management procedures are in place. These procedures and policies help instill confidence in the data used to make decisions across the organization. Businesses can better meet the needs of their target market and adapt to shifting market conditions with accurate, timely data.


With authentication and encryption software, data management can safeguard your business and its employees from data theft, fraud, and loss. If a company’s primary data source becomes inaccessible, strong data security will allow for its safe storage and subsequent retrieval. Security becomes even more crucial if your data includes highly sensitive information that must be adequately maintained to comply with consumer protection rules.


Organizations can efficiently scale their data and usage instances with the help of data management, which employs standardized procedures to ensure that their data and metadata are always accurate. If procedures are simple to replicate, your business won’t waste time and money on things like having workers conduct the same research multiple times or rerunning expensive queries.

4 Aspects of Data Quality Management


Inefficiency, mistakes, extra expenses, and possible fines all stem from a lack of attention to data quality and management. The current status of your company’s legal entity information, client and counterparty records, security master, and other vital data assets must be known with absolute precision. With the proper data quality assessment, you can find trouble spots and improve your organization’s compliance with industry regulations without investing in heavy analytics software or hiring additional staff.

The first step is to evaluate the current state of data quality, divide the data into manageable chunks for analysis, and establish a baseline and governance for data quality. Efficient prioritization requires careful assessment, which should be repeated at regular intervals.


This strategy focuses on updating and improving the data under scrutiny due to risk and regulatory reporting requirements but is often out of date, inaccurate, or inconsistent. Integrate additional, crucial data attributes into your records and update them regularly. There could be anywhere from 5,000 to 2,000,000 customers, as well as national and international authorities, depending on your company, all of whom rely on the integrity of your data.

Data setup and verification, data cleansing initiatives, cognitive training, and corpus construction are all crucial steps. In the short term, remediation will impress the leadership, but in the long run, it will waste time and money.


The expansion of data, management, and quality assurance through big cutting-edge data, analytics, and AI techniques. Locate departments or enterprise-wide initiatives with data-centric objectives. Find out what needs to be done concerning data quality and data governance. Create a plan for securities, clients and counterparties, and organizational structures, and pinpoint the supplementary information that can enhance utility on top of a solid legal entity foundation.

Third-party data integration, legacy system data integration, and aggregating and disseminating mastered data are all crucial steps. Without strong leadership and accurate evaluation, enrichment initiatives are doomed to fail.


Using automation, you can solve the problems of managing corporate actions in your reference data. Entity data could be affected by various events, so it’s essential to constantly monitor relevant sources for information about these things. Data is provided in a feed layout that can be used to fulfill risk, regulatory, and operational needs, allowing for a more preventative method of reviewing corporate actions.

Among the most crucial procedures are:

  • The computerization of corporate deeds.
  • The administration of new legal entities.
  • The extraction of information from contracts and agreements.
  • The more characteristics you add to your dataset through enrichment or as it expands in size, the more care you’ll need to take in maintaining it.

Characteristics of Data Quality


For the vast majority of businesses that invest in demographic data, precision is crucial. Data accuracy is measured by how accurately it depicts the conditions it sets out to describe in the real world. The problems caused by inaccurate data are apparent, as they can lead to the wrong conclusions.

Because of this, any subsequent actions you take based on these inferences may not produce the desired results. As an illustration, a marketer may infer from the demographics of their clientele that they cater primarily to women in their 20s. Advertisements would be wasted if the demographic information was inaccurate, for example, if the company’s customers are disproportionately men in their forties.


Information has no blank spots if it is complete. It appears that all required data collection has been obtained. For instance, the information provided by a customer who skips several survey questions will be incomplete. Incorrect conclusions can be drawn from incomplete data. It’s possible that the information gathered from a respondent who skips questions on a survey will be less reliable. For instance, if a respondent does not provide their age, it will be more challenging to tailor content to specific demographics.


Any campaigns or projects you want to use the data for should find the information you collect valuable. If the data you collect is not pertinent to your objectives, it is useless even if it meets all the other criteria for high-quality data. You must establish your data collection goals to know what information is valid.


Instead of focusing on the information, validity examines the methods used to gather it. For information to be considered valid, it must be of the appropriate format, type, and range. It may be challenging to organize and analyze data that does not conform to these standards. Data conversion software Validity to assist in making the necessary adjustments.


How recently an event was that the data reflects what is meant by “timeliness.” In most cases, information should be recorded after the corresponding real-world event as soon as possible. As time passes, data typically loses its relevance and reliability. Recent events are better reflected in data because they are closer to the present. Results and actions based on old information may not reflect current affairs.


A data item or its counterpart should be the same across all databases or data sets being compared. When there is no noticeable variation between various representations of the same data, we say that the data is consistent. Both the content and the structure of a data item must be consistent. Different teams may decide based on diverging assumptions if your information is inconsistent. This can lead to poor communication and cooperation between teams within your organization and may even cause some teams to work against one another.

What Are Data Quality Management Tools?

Data quality management tools help find, understand, and correct data problems. DQM tools improve business process efficiency and decision-making by improving data quality. Data profiling collects information about data sets and identifies potential outlier values; it also matches records, deletes duplicates, validates new data, establishes remediation policies, and identifies personally identifiable information.

Data quality initiatives may include the creation of handling rules for data, the discovery of relationships between data, and the execution of automated transformations on that data, all of which can be managed from a centralized control panel.

Choosing DQM Tools

Because of the importance of data in making decisions, businesses have made improving data quality a top priority. Manually completing the process, however, can lead to data quality errors and additional time spent due to increased data volumes and disparity. DQM instruments are helpful for this purpose. When deciding on the best DQM solution, businesses should take into account the following factors:

Capabilities for Profiling and Scrubbing Data

The ability to profile and analyze data is crucial for any data quality tool. A DQM tool facilitates the automated discovery of metadata and gives you transparent access to the original data, so you can easily spot any inconsistencies.

Additionally, a data management tool’s data cleansing features can help stop and fix errors before they’re even loaded onto a destination.

Data Quality Checks

When it comes to monitoring and reporting problems in the data processing process, sophisticated DQM software has objects and rules embedded into the information flow. They check the processed data for errors using predetermined business rules.

Lineage Data Management

Data quality management tools help in tracking down where data came from. Detailing the data’s beginning and travel, including the steps it took to be altered or written to its destination, this aids in controlling and analyzing the flow of information.

Access to a Wide Variety of Data Sources

With the rising diversity and amount of data sources, it has become necessary to examine and validate internal and external data sets. Thus, firms should opt for DQM technologies that can handle data of varying types, structures, and ages, including but not limited to structured and unstructured data, flat and hierarchical data, and legacy and modern data.

Harness the Benefits of Using A State-Of-The-Art Data Quality Management Software

Bragason Consulting is an exMon Partner that you can rely on. You may improve the quality of your financial data with the assistance of exMon Data Quality. Take charge of all aspects of your financial planning and budgeting. Discover any problems or submissions that have been overlooked before it is too late.

ExMon is put to use by Bragason Consulting’s retail clients to keep an eye on their cash registers and other back-office systems. Maintain vigilance over the supply chain procedures, inventory levels, and huge discounts. ExMon can assist in managing these operations and ensure that the data remains consistent between the source and the destination.

Benefits of data quality management

Informed Decision-Making

We’ve seen that every business choice, whether made by an individual or a whole company, affects profits somehow. It does more harm than good if these choices are based on sloppy data management.

Organizations can exert more control over the results of their decisions if their data meets the quality standards of good data. This, in turn, boosts trust in data, efficiency, error prevention, and risk mitigation. Good data is accurate, consistent, complete, up-to-date, and sourced from trustworthy, secure, and trusted locations. Your analytics and business intelligence solution will provide valuable, reliable insights if your data has these characteristics.

Success in Developing Stronger Relationships with Clients

Relationships with consumers are essential to the success of any business, and high-quality data can help you strengthen those connections. A deeper understanding of your clientele can be gained through systematic data collection. Customers’ likes, dislikes, hobbies, and pain points may all be used to craft content that will more deeply resonate with them and help you better meet their expectations.

Having a solid foundation to build upon can help you create lasting bonds with them. Duplicate content can anger your audience and ruin your reputation, two things you want to avoid at all costs, which is why proper data management is so important.

Facilitates and sustains conformity

When working with our clients, one area we pay close attention to is emphasizing the value of cultivating a culture of data. The fact that it requires other components to operate well is related to its ability to make choices. Companies can automatically satisfy compliance rules and maintain the highest levels of accuracy and governance because data integrity primarily depends on the company’s culture and adaptability across the board.

Competitive advantage

The essential resource for any company or industry today is data. Any business that can effectively use high-quality data to its full potential will have a significant leg up on the competition.

Gain an edge over the competition by using the data you collect or by collecting more high-quality data than they do. Assuming its quality, data is one of the essential assets for modern businesses. If your data is of higher quality, you can spot business prospects ahead of the competition. You may outflank the competition by selling to customers before they know what they want. When solid data is lacking, opportunities are lost, and progress is slowed.

Data can be used more easily

In addition to being more useful, high-quality data is significantly simpler to implement. Having reliable information readily available also boosts productivity inside an organization. You will waste a lot of time correcting incomplete or inconsistent data before you can use it.

This means you can’t focus on other tasks simultaneously, slowing down the rate at which you can implement your data insights. Good information also aids in keeping your company’s multiple divisions on the same page, which leads to better communication and cooperation.

Create content that resonates with your audience and helps you target them more precisely

Making educated guesses about your intended market’s demographics and interests will provide incredibly low conversion rates when developing and implementing a marketing strategy.

But, if you know more about your target demographic, you may look for overlapping characteristics, such as occupation and interests, and target your message more precisely. Knowing your audience allows you to tailor your content and products to each subset for more effective marketing.

Increased Profitability

In the long run, profitability can be improved by collecting and analyzing more accurate data. It can aid in the creation of more efficient advertising strategies, which in turn can boost sales. It also helps cut down on unnecessary advertisements, saving you money in the long run. Analytics can reveal which articles and videos are most read and profitable if you’re a publisher. Now that you know this, you can devote more time and energy to producing this material.

Bottom Line

Today’s most significant movements, decisions, and actions are all driven by data. You can better understand the market and your customers’ wants and needs with reliable information at your fingertips.

To maintain a competitive advantage, it is crucial to quickly recognize the value of high-quality data and act on it. Transform your firm into a trend-setter by using quality data. 

Get In Touch

Leverage the power of a data quality management tool and consultation with Bragason Consulting 

Combining our Data Quality Consulting Services and the exMon Data Quality Tool can profoundly affect your business. Bragason Consulting works with organizations to develop and implement a plan to increase the reliability and accuracy of their data storage systems. You can use exMon to pinpoint data problems in your systems and assign them to the appropriate workers. exMon includes monitoring and analytics right out of the box to keep tabs on progress and data quality over time.

Business data that could result in a loss of revenue or security issues can be spotted by exMon. You can regain faith in your IT infrastructure by discovering anomalies or disparities in the data between different systems. exMon provides a straightforward yet effective method for managing master data in addition to data quality management. It includes a workflow management application that allows you to integrate data quality checks into the process flows.