Data quality management is essential in the modern business world. Data is a valuable commodity, and insightful data analytics are the foundation of practically all strategic business decisions. However, data analytics, business intelligence, and other HiTech tools are only as good as the data that is input. If you’re not collecting accurate data, you cannot expect to gain any of the insights or advantages typically associated with data analytics.
If your business wants to use data to gain insights and take strategic action, you need to put a data quality management strategy in place. Data management helps assure you that the information you are collecting from your web services and other platforms is accurate and valid.
Let’s define what data quality management is, why it is important for businesses to pay attention to it, and finally, look at some of the reasons why your data could be poor or inaccurate.
What is Data Quality Management?
Data quality management refers to a set of business practices that are geared towards maintaining accurate, quality data. Data management starts at the acquisition of data, incorporates the implementation of business intelligence tools, and finally, includes the methodology of data distribution.
Most organizations will create a specific managerial role to oversee data management. In today’s data-driven business environment, businesses recognize that data quality management is an important facet of consistent data analytics. Accurate data is crucial in order to glean valuable insights and actionable strategies.
How is Data Quality Defined?
If you don’t know how to define quality data, you won’t know if you are collecting it or not. Quality data can be broken down into the following facets:
- Accuracy
- Integrity
- Timeliness
- Completeness
- Consistency
In order to have quality data, each facet of your data must be addressed. Familiarizing yourself and your organization with these facets of data will also help your business make improvements to your low-quality data.
Accuracy
Does the data you have collected represent the thing that it should? The simplest way to verify the accuracy of your data is to compare it to the source documentation. These could be interactions, transactions, etc.
Integrity
Does your data comply with the structural procedures you have outlined? For example, your data should have uniform formatting for the date, month, year. Integrity ensures that your data is valid structurally. Ensuring integrity can help you avoid errors later.
Timeliness
Is your data available in time for you to use to make strategic decisions? Data analytics can provide businesses with valuable insights, but if the data is not available in a timely fashion, any insights gained could be too late to make a difference. Many business intelligence tools rely on real-time data to provide businesses with an advantage over their competitors.
Completeness
Are you looking at the complete picture? Without a complete data set, you can’t be expected to draw any insightful conclusions. Quality data starts with completeness. Anything short of the entire picture could lead you to the wrong conclusions.
Consistency
Is the same piece of data pulled from two different data sources consistent? Just because two pieces of data are the same doesn’t necessarily mean that they are accurate, but it is far more likely that they are. Consistency in big data sets is important to ensuring quality data. If you happen to find consistent data that is also inaccurate, you should have an easier time figuring out where your data was corrupted.
Why is Data Quality Management Important?
Modern businesses rely on quality data to make strategic decisions and drive growth. Data is used in almost every part of an organization, from the sales and marketing team to mobile app development. There are a number of reasons why your business should be concerned with data quality management, including:
- Identifying opportunities and improving outcomes
- Successfully migrating data
- Reducing operational costs
Identifying Opportunities and Improving Outcomes
One of the biggest reasons businesses need quality data is to identify new opportunities and improve the outcomes of their existing operations. Time and time again, business leaders have seen that decisions made with good data lead to better outcomes.
Let’s say you have low-quality data like overstated profits. This information will likely result in erroneous and misleading financial ratios. When an organization’s past performance is being analyzed using this data, key decisions into target markets and price changes will be affected. Low-quality data hampers your ability to make crucial business decisions and find new opportunities for sustainable growth.
Similarly, if data into your cash flows are bad and you make decisions based on this data, you could wind up overextending your business and find yourself unable to pay vendors or employees. Hopefully, your data wouldn’t be so bad as to make a major error like this, but without data quality management, it is possible.
Successfully Migrating Data
The number one reason that data migrations fail is due to low-quality data. You spend time planning for data security because the importance of good security has been highlighted over and over again.
When you’re migrating big data from disparate sources, you need to make sure that you’re only moving quality data. Data quality management can help you find and remediate data errors before migration takes place. This will help you complete migration faster with greater accuracy.
If you’re moving all of your data into a data warehouse, you’ll want to make sure that duplicate records, missing information, and data entry errors are spotted and fixed before migrating to a centralized storage system.
Reducing Operational Costs
Low-quality data leads to financial losses. Some organizations estimate that they lose millions of dollars a year due to poor data. There are a few reasons why bad data can hurt the bottom line. We’ve already touched on missed opportunities and poor outcomes. These lead to preventable financial losses.
Additionally, low-quality data often leads to more work for your employees. If bad data is found, someone needs to make the appropriate corrections and determine the source of the bad data and ensure that future data is good. Depending on what the data error was, it could lead to rework for other segments of your business in order to fix the problem.
An ongoing data quality management strategy will help reduce low-quality data and the overall amount of rework needed. This helps drive down operational costs while also helping to improve outcomes. Quality data helps your business make more and spend less money.
Why Your Data Might Be Inaccurate
If you’re struggling with low-quality data, it can be frustrating. You might be wondering what you are doing wrong. The most common reasons for low-quality data are:
- Manual data entry
- Multiple entry users
- Conversion or migration
Manual Data Entry
There is a common phrase, “to err is human.” Entrusting data entry to a human can lead to errors, especially if their entire job consists of entering data into various systems. There are a number of reasons why manual entry may lead to errors, including lack of experience and the monotony of repetitive tasks, but at the end of the day, humans make mistakes, even the best ones.
Manual data entry can also lead to duplicate data and missing information. If your business relies on the manual entry of data, data quality management is essential to ensure that all of your data is of high quality.
Multiple Entry Users
If one human entering data leads to errors, what do you think happens when multiple users are handling and entering data in the same place? Most organizations have multiple employees who will handle and modify data. This leads to entry errors.
Most commonly, you will find duplicate entries or inconsistent data entries. If multiple users are going to be handling, modifying, and entering data, data quality management is going to be crucial in ensuring that your data stays consistent and accurate.
Conversion or Migration
In most cases, data migration will require some level of data conversion as well. When you are migrating data or converting data, it is easy to lose data or duplicate data. If you’re embarking on a data project of any magnitude, you need to be conscious of the way you are managing your master data. A data quality management team and strategy can help guide your business through this process.
Final Thoughts
High-quality master data is essential to modern business operations. If you don’t already have data management systems in place, you should strongly consider hiring a team and creating a strategy for the management of your master data.
If you need help navigating the complexities of data management, turn to a development partner. They do much more than develop software and mobile apps. A development team can help you hone in on the right software solutions, craft a data quality management strategy, and help you implement new systems for data management.
Trust the industry experience and technical expertise a partner can bring to the table if you’re in need of some help.
