The Importance of Data Quality for Business
Data quality (DQ) is one of the essential elements for building a successful business in the 21st century. The quality of data is crucial for understanding what’s happening in a company. Bad data can result in bad decisions...
For instance, NFL teams pride themselves on their research before selecting players in the draft. Yet, nine teams passed over future Super Bowl champion Patrick Mahomes before he was selected as quarterback by the Kansas City Chiefs in 2017. In the corporate world, take the Enron scandal from the early 2000s. Enron's executives and the company's auditing firm provided fabricated data to the board of directors and shareholders to cover financial fraud that eventually led to the jailing of top executives and the downfall of the auditing firm.
What Is Data Quality?
DQ measures the condition of data based on accuracy, completeness, consistency, reliability and relevance. Measuring DQ levels can help companies find errors that need to be fixed. Companies can then determine if the data are suitable to fulfill their intended purpose.
High quality data can be easily processed and analyzed. Organizations will make better decisions. High-quality data are also essential to data analytics.
Data quality can be difficult to determine. For example, patient data in health care must be complete, accurate and available when required. The elements of DQ capture the characteristics that are specific to your business or organizational needs.
Related Article: Is Data Fabric the Future of Data Management?
The Benefits of Using Good Data
When organizations practice good data management, it provides numerous benefits. Some of those benefits include:
When a business uses high quality data to make business decisions, it is beneficial to the entire company. It helps create effective advertising campaigns, provides information about which products need increased production and which products you need to drop. Internally, it allows for better decisions about promotion and job selection. Quality data tells you which player your team should select in a professional football or baseball draft. Your employees will have more confidence in their decisions and you reduce your risk of a bad decision.
Targeting Your Audience
Data quality allows a business to better target its preferred audience, either internal or external. Otherwise, companies use a scattershot approach to their marketing, resulting in higher costs and fewer sales. Internal data can be used to target selected groups of employees for messaging. High quality data gives you a better idea about your audience, what they like and how you should approach them. This allows you to develop content and products they find more appealing.
The more you know your audience, the more you can design content its members will find engaging. For instance, HR leaders can tailor learning content to groups and individual employees. If you run a food website, data should show you what type of recipes interest your visitors the most. Your audience members could prefer to bake bread. Knowing that, you can tailor your content to mirror these preferences. Feature more bread recipes that your audience find appealing, encouraging them to subscribe to your service.
The more you know about your audience, the more you can create captivating campaigns that capture their interest in your services or products. If specific information on your company intranet is seeing high traffic, that tells you employees may need more information about that topic. Externally, let's say you run a high end clothing website. Data tells you that professional women between 25 and 49 regularly visit your website. You can create campaigns and products that speak to their clothing needs.
The better your DQ, the better you’ll be able to give your internal and external customers what they want. Good DQ gives you a clear picture of their preferences, interests and needs. This allows you to develop products or services that appeal to them or anticipate what they might want. People dislike being bombarded with information or ads that have little relevance to their lives. You stand a better chance when you can show them content and ads that speak directly to their lifestyle.
Beating the Competition
By relying on good DQ, you can use that data to attract new customers or employees before competitors whose data is not as robust. The result is a competitive advantage. Knowing them better lets you anticipate their needs and preferences. On the other hand, you could also lose employees and customers to competitors who have better data management techniques.
Related Article: Disorganized Data Can Hold You Back — Here's How to Fix It
Characteristics of Data Quality
Businesses need to differentiate good data from bad data. It can mean the difference between success and failure for campaigns, products, services and maybe the entire business. When looking for high-quality data, you could look for some of the following characteristics.
Does your data give you the information you need about your employees and customers today? You want as much up-to-date information as possible. If you’re using data from last year or even last month, you’re going to be in trouble. Many companies now use data systems that provide them with real-time information.
High quality data is free from redundancies and records missing information. Regardless of how many sources you used to collect data, you need to ensure they’re accurate and combine them into a single record. Manually, this can be a challenge. Fortunately, software now exists that allows you to seamlessly integrate your data so that you only have a single record for each distinct individual.
Your data is only as good as it is complete. Lacking information about employees and customers, such as their official business titles, locations, phone numbers and mailing addresses, results in bad quality data. For instance, if you’re missing location information in your data, it’s harder to design a campaign that may focus on a geographical area.
Regardless of the source of your data, they need to be consistent. You need to ensure that all data funnels into the same format. This reduces the chances of having duplicate data sets or data containing mistakes.
Related Article: Management Information Systems: The Engine of Business Operations
How Do You Improve Your Quality of Data?
It isn’t always easy to ensure DQ. Different departments may be using different software to collect data. Some departments may still be entering data manually, which inevitably leads to human mistakes. You need to make sure that data from your sales department and your marketing department integrate seamlessly, for example.
Addressing Employee Needs and Wants with a Digital Workplace
The workplace is getting more and more digital – both in how we work and where we work
Maintaining a Human-Centered Approach During Digital Transformation
When it comes to digital transformation - people drive change, not technology
The Evolution of Employee Recognition
Leveraging the power of appreciation to improve the employee experience
How to Build a More Innovative and Resilient Workplace Culture
What would happen if every member of your team came to work focused on finding solutions and creating better results?
Data quality management refers to the design of policies and use of technologies that create DQ standards. It allows your company to make informed decisions quickly. DQ management helps reduce expenses and maintain compliance with regulations of data governance.
Here are some steps that you can take to improve your chances of creating high quality data.
Create a Data Collection Plan
Like so many tasks, having a plan makes a big difference. The same is true of data collection. Create a strategy that focuses on the data you need, why you need that data, and how to collect and manage each data source. The plan should include everyone involved in collecting the data. Detail how they will communicate with each other, especially if they work in different departments. Be specific as possible. Then circulate this plan to everyone involved in the collection of data.
You want everybody to be singing from the same prayer book.
If you create data standards, you will know what data you want to keep and what data you want to toss away. Not all data you collect will be helpful. Make this part of your data plan. Everyone in every department should know which data to keep. Standards help ensure data collection consistency and save valuable time making corrections to the data.
There may still be mistakes in your data. When you establish how to collect data and the standards that you’ll be using, create rules for correcting data. Everyone involved in the process should know who is responsible for correcting data. Let them know how to get data to that person or team.
Ultimately, the goal of correcting data is to improve overall data quality before making important business decisions. When you're working with small data sets, it may be possible to correct data manually. Large datasets require data correction tools or platforms.
An Ongoing Process
Don't think of your data plan as something you create, and your problems are solved. You’ll need to be continually tweaking and upgrading your data plan. Get feedback from your various departments and individuals connected with data collection. Seek suggestions about ways you can improve the plan. As your plan grows and evolves, so will the quality of your data.
Communicate the Benefits of Data Quality
As a data quality leader, you need to regularly let your employees or coworkers know about the benefits of data quality. If improved data results in a 10% increase in employee retention, it's worth celebrating. Employees can receive service quicker and better from HR representatives or managers when they work with quality data.
The Cost of Bad Data
Bad data results in experiences that are embarrassing for the company. Think of airlines that make booking mistakes or businesses that send the wrong product or the right product to an incorrect address.
Bad data costs your company money. Gartner research reported in 2018 that the financial cost of bad data to an average company is $9.7 million a year.
Bad data slows down your employees. Forrester reported in 2018 that 33% of business analysts spent as much as 40% of their time validating their data.
Bad data undermines confidence in your business. Most people are willing to deal with an infrequent mistake. However, when bad data repeatedly results in that same mistake, your employees and customers will turn to someone else for what they need.
It’s becoming increasingly difficult for businesses in the 21st century to be successful without data quality. Your company’s HR, IT and sales and marketing departments especially need access to high quality data. With this data in hand, they can design better campaigns, keep employees and customers happier, sell more products or services and increase the company’s profitability.