Data Management and Data Quality Issues

Risk # 2 - Data Management and Data Quality Issues

This is the 4th article in the series titled 5 IT Risks That Could Cripple Your Business: How to Mitigate Them.  Please refer to last week’s article for risk #3

Data is one of the most important assets in any organization today.  I spent many years in Oil and Gas and there is a saying “Data is the New Oil”.  It just highlights the importance of data to an organization.  But like Oil, it is often unrefined when initially collected and must be refined to extract maximum value.  It must also be managed properly.  I have seen firsthand the devastating impact that data management and data quality issues can have on a business.  For example, a company may send out coupons to the wrong customer address if contact information is not accurate leading to lost sales opportunities.  Another example is an Oil and gas company may need production numbers on a weekly basis to determine the effectiveness of production but find that the data is delivered monthly. 

IT departments spend a lot of time focused on data, especially data quality.  Companies desire data but it has to be accurate, timely, reliable, and complete.  This is so critical because if employees lose confidence in the data, they will create their own versions leading companies to end up with multiple versions of the truth, if you know what I mean.  If you want to know how effective a marketing campaign is, check the data.  Want to know how well a product is selling in a market, check the data.  Want to know the churn rate of your employees, check the data.  Data reveals many obvious as well as hidden stories.  In fact, whether it is customer contact information in a CRM, company secret sauce, or something else, it goes a long way in increasing the value of a company.  So if it is this important, the company would probably want to protect and manage it to their best.       

Have you heard of AI – Artificial Intelligence ?  Of course you have.  AI is everywhere these days.  Seems like every industry are coming up with new use cases for it.   However, do you know what fuels AI, it is data!  If companies are going to use data to make data driven decisions or fuel AI initiatives, they will need accurate and timely data.  

Data Management and Data Quality Issues

Why Data Management and Data Quality issues is a Huge Risk:

I am not going to kid you, it is not easy for most companies to ensure consistent, quality data.  If it was, then data management would be a breeze for everyone.  Data management can be one of the greatest challenges to an organization.  During my career in technology, it was not uncommon for organizations to think their data was not great.  In fact, I cannot remember many who thought their data was really good as a whole.  Many would say it is ok, or some systems are good while others are not so good.  Some have vast amounts of data and management is not only a challenge but improper collection and management is a huge risk.  Before we get into the risks, let’s be clear that data is collected by a number of means  including software systems that companies use to run their business, like inventory control systems, or HR  management systems as well as statistics or Key Performance Indicator (KPIs) collected from business processes that could reveal a lot when examined.  So what are the risks ? 

  • Ability to make data driven decisions – Accurate data is crucial for informed decision-making. Poor data quality can lead to incorrect analysis, which might misguide executive decision-making, potentially resulting in missed opportunities.  In worst cases, it could result in financial losses. 
  • Loss of Confidence in Data – It is crucial that companies  have confidence in their data.  We often use the term one version of the truth. This means that if an employee in the accounting department pulls sales numbers and another person in the customer service department pulls the same sales numbers, then the numbers should match.  The data should be consistent across all departments.  What happens if this is not the case ? Please continue to read. 
  • Data silos – Data silos are when data typically owned and managed by a group in a company is held separately from other parts of the company.  It is as if this data is not part of the larger organization.  The larger the organization, the more common this happens.  This may work well for a department but does not easily integrate with other departments in the company.  One of the reasons this happens is lack of confidence in the company’s data assets as a whole so each group tends to hold their data tightly as their version of the truth.  They treat it like their personal data and not a part of the larger whole.  This really hinders getting a full view of the organization because it requires integrated data from all groups.
  • Competitive Disadvantage – In a data-driven market, the ability to leverage accurate and timely data is a competitive advantage. Poor data management and quality can hinder an organization’s ability to stay competitive, innovate, and respond swiftly to market changes.
  • Compliance violations – Many industries have strict regulations around data management and data quality.  If a company fails to comply with these regulations, they could face heavy fines and penalties.  For example, a healthcare company that fails to protect patient data could face serious HIPAA violations.
  • Data Privacy – Everyone understands the value of data and seems to want your data these days.  A lot of grocery stores give out store cards to customers with the promise of giving discounts to those consumers whom use the cards.  What they fail to reveal is that the card contains a history of everything a consumer has purchased.  That makes it really easy to know which products to market to them.  But even more troubling, a customer’s purchase history is personal.  Do you want everyone to know every type of personal product you have purchased or each prescription that you may have filled.  It should be protected against improper access or theft.
  • Employee productivity – Poor data management can lead to operational inefficiencies throughout the organization. For example, if data is not properly organized or accessible, employees may waste time searching for the information they need to do their jobs. This can lead to decreased productivity and increased costs.  In addition,  poor data quality can also lead to increased errors, such as shipping products to the wrong address or sending invoices to the wrong customer.  Errors like this damage customer relationships and lead to lost revenue.
  • Reputation Damage – One last thing to think about is damage to a company’s reputation.  Data breaches are so common these days.  Happens to large and small companies.  However, when it happens, the unwelcome recognition can be intense.  Most of us have probably received an email or letter in the mail from a vendor stating that there was a data breach and some personal information may have been compromised.  It is difficult for the consumer when companies ask for data that they cannot protect or do not take the proper precaution to protect. 
Data Management and Data Quality Issues

How can we mitigate the risks caused by Data Management and Data Quality issues? 

The risks for data management issues are great.  But there are many things companies can do to reduce them or reduce the impact.  As a long time IT consultant, data management, governance, and data quality are a never-ending challenge.  However, companies can take a number of actions to improve quality, data management and reduce the impact of issues.    

  • Implement a data governance program – Data governance is key to managing company’s data assets.  A data governance program will help companies to define and enforce policies and procedures for managing their data to ensure data is accurate, available, and secure.  A robust program ensures that data remains a valuable asset and also meets legal and regulatory requirements.  But remember that it takes patience.  Data typically becomes bad over a number of years, so to correct it will not happen overnight.  Being diligent can begin to show ROI really soon.
  • Assign data stewards – One of the most effective ways to quickly improve data quality is by assigning data stewards that are responsible for data quality and compliance in their departments.  Establishing clear roles and responsibilities really works well.  Having stewards take ownership is often more effective than any tool that can be purchased because of the human element.
  • Data Quality – It is important to regularly monitor data quality to identify and address any issues.  There are a number of data quality tools available that can help companies identify and correct errors in their data.  It is key that data is accurate, consistent and complete.  This will require regular monitoring and auditing.
  • Train employees –  Train employees on data management best practices.  Ensure employees understand the importance of data governance and how to properly manage data. They should also be aware of the latest security threats and how to protect the company’s data.
  • Better Security – Implement security measures to protect data from unauthorized access, theft, and loss. This includes using strong passwords, encryption, and firewalls.  



In closing, organizations today clearly understand the value of good data assets.  Companies should invest in proper data governance processes and data management solutions to help protect their data and reduce risks.  Organizations will have to remain proactive.  Remember, data can go from good to bad pretty quickly, whether literally or figuratively, often due to lack of confidence from employees who use the data.

Please look for the final article on Risk #1 next week.

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Data Management and Data Quality Issues