Planning and Getting Information Quality

But data is only useful if it’s high-quality. Bad data is at best inconsequential. In the worst case scenario, it can lead companies to make costly mistakes. IBM estimates that bad data costs the U.S. economy $3.1 trillion per year. Those costs come from the time employees must spend correcting bad data and errors that cause mistakes with customers.

Several factors contribute to the quality of data, including:

1. Accuracy

Among marketers who purchase demographic data, 84 percent say that accuracy is very important to their purchasing decisions. Accuracy refers to how well the data describes the real-world conditions it aims to describe. Inaccurate data creates clear problems, as it can cause you to come to incorrect conclusions. The actions you take based on those conclusions might not have the effects you expect because they’re based on inaccurate data. For example, data might lead a marketer to believe that their customers are mostly females in their 20s. If that data is inaccurate and their customers are actually primarily men in their 40s, then they will end up targeting the wrong group with their advertisements.

2. Completeness

Data Completeness

If data is complete, there are no gaps in it. Everything that was supposed to be collected was successfully collected. If a customer skipped several questions on a survey, for example, the data they submitted would not be complete. If your data is incomplete, you might have trouble gathering accurate insights from it. If someone skips some of the questions on a survey, it may make the rest of the information they provide less useful. For instance, if a respondent doesn’t include their age, it will be harder to target content to people based on their age.

3. Relevancy

The data you collect should also be useful for the campaigns and initiatives you plan to use it for. Even if the information you collect has all the other characteristics of quality data, if it’s not relevant to your goals, it’s not useful to you. It’s important to set goals for your data collection so that you know what kind of data to collect.

4. Validity

Data Validity

Validity refers to how the data is collected rather than the data itself. Data is valid if it is in the right format, of the correct type and falls within the right range. If data does not meet these criteria, you might run into trouble organizing and analyzing it. Some software can help you convert data to the correct format. For example, if you are collecting data about the time of day users visit your site, you must decide on the format you will use. You might choose to use 24-hour time and use two digits for minutes and two for hours. Examples of this data format would include 14:34, 17:05 and 08:42. Data that doesn’t follow this format would be invalid.

5. Timeliness

Timeliness refers to how recently the event the data represents occurred. Generally, data should be recorded as soon after the real world event as possible. Data typically becomes less useful and less accurate as time goes on. Data that reflects events that happened more recently are more likely to reflect the current reality. Using outdated data can lead to inaccurate results and taking actions that don’t reflect the current reality.

6. Consistency

When comparing a data item or its counterpart across multiple data sets or databases, it should be the same. This lack of difference between multiple versions of a single data item is referred to as consistency. A data item should be consistent both in its content and its format. If your data isn’t consistent, different groups may be operating under different assumptions about what is true. This can mean that the different departments within your company will not be well coordinated and may even unknowingly be working against one another.

An Example of Good Data Quality

Let’s say, for example, that you’re a marketer, and you’re crafting a campaign to promote a brand of organic dog food. You want to determine the best times of day to run online ads to promote the brand’s web store. To figure this out, you can collect data from the brand’s website about when people typically purchase dog food on it. Here’s how you can ensure your data is high-quality:

• Accuracy: Since you’re collecting the data directly right from your client’s website, you can be confident in its accuracy.
• Completeness: To make sure your data is complete, collect the same information about every customer. For example, you may want to know what items they purchased, their order total, how they paid and the times they started and completed the transactions.
• Relevancy: Only the data related to dog food purchases will be relevant to your campaign.
• Validity: Ensure that you collect all of your time data in the same format.
• Timeliness: Import your data as soon as you can and only use data from within a pre-determined timeframe.
• Consistency: If you store your data in multiple places, make sure it is consistent across all of them.

If your data meets all of these criteria, you can be confident that your data is high-quality.

Data Quality

As data management techniques and technologies improve, data continues to become increasingly important for businesses. Growing numbers of companies are using data to make decisions about marketing, product development, finance and more. As more firms reap the benefits of data, using it is increasingly becoming a matter of keeping up with the competition. Companies that don’t take advantage of data and related technologies risk falling behind.

For data to be beneficial though, it needs to be of high quality. The better your data’s quality, the more you can get out of it. If your information is low-quality, it can even be harmful. If you base a decision on bad data, you’re likely to make the wrong choice.

New technologies are also increasing the importance of data and its quality. Technologies such as artificial intelligence and automation have enormous potential, but success with these technologies depends heavily on data quality. Machine learning, for example, requires large volumes of accurate data. The more good data a machine learning algorithm has, the faster it can produce results, and the better those results will be. In a recent survey of senior executives by New Vantage Partners, more than three-fourths of respondents said that the increase in data volumes and sources is driving increased investments in AI and cognitive learning.

Data is becoming increasingly integral to business’ operations. Rather than treating data as separate from their other functions, some of today’s most successful companies integrate it into everything they do. This increased integration means that data quality can impact many aspects of a business from marketing to sales to content creation.

Data quality is also critical because of compliance-related issues. As regulations regarding data continue to evolve, it’s become increasingly important that companies manage their data properly. It’s harder to demonstrate compliance if your data is disorganized or poorly maintained. This is especially vital for financial data and sensitive personal data but can apply to other types of information as well.

Benefits of Good Data Quality

Ensure You’re Using Quality Data

Good data management is crucial for keeping up with the competition and taking advantage of opportunities. High-quality data can also provide various concrete benefits for businesses. Some of the potential benefits of good data quality include:

1. More Informed Decision-Making

Improved data quality leads to better decision-making across an organization. The more high-quality data you have, the more confidence you can have in your decisions. Good data decreases risk and can result in consistent improvements in results.

2. Better Audience Targeting

Data quality also leads to improved audience targeting. Without high-quality data, marketers are forced to try to apply to a broad audience, which is not efficient. Worse, they may have to guess at who their target audience should be. When you have high-quality data, you can more accurately determine who your target audience should be. You can do so by collecting data about your current audience and then finding potential new customers with similar attributes. You can use this knowledge to more accurately target advertising campaigns and develop products or content that appeal to the right people.

3. More Effective Content and Marketing Campaigns

In addition to improving targeting, data quality can also help to improve your content and marketing campaigns themselves. The more you know about your audience, the more reliably you can create content or ads that appeal to them. For instance, if you’re a publisher of a sports website, you can gather data that tells you which sports your website users are most interested in. If you discover that golf is one of your most popular categories, you can direct your content team to create more golf-related articles and videos. If you find that golf is especially popular among visitors to your site who are men between the ages of 45 and 64, you can show golf content to users in this age range when they visit your site. A similar technique can be applied to content used as part of a marketing campaign.

4. Improved Relationships With Customers

High-quality data can also help you improve your relationships with customers, which is crucial for success in any industry. Gathering data about your customers helps you get to know them better. You can use information about your customers’ preferences, interests and needs to provide them with content that appeals to them and even anticipates their needs. This can help you build strong relationships with them. Proper data management also helps prevent you from delivering duplicate content to customers, which can become annoying to your audience and damage your reputation.

5. Easier Implementation of Data

High-quality data is also much easier to use than poor-quality data. Having quality data at your fingertips increases your company’s efficiency as well. If your information is not complete or consistent, you have to spend significant amounts of time fixing that data to make it useable. This takes time away from other activities and means it takes longer for you to implement the insights your data uncovered. Quality data also helps to keep your company’s various departments on the same page so that they can work together more effectively.

6. Competitive Advantage

If you have better quality data than your competitors or use your data more effectively than they do, you gain a competitive advantage. Data is one of the most valuable resources that today’s companies have, as long as it’s high-quality. Better data quality means that you can discover opportunities before your competitors do. You can better anticipate prospects’ needs and, therefore, beat competitors to sales. A lack of good data means missed opportunities and falling behind the competition.

7. Increased Profitability

Ultimately, high-quality data can lead to increased profitability. It can help you to craft more effective marketing campaigns and increase sales numbers. It also decreases ad waste, making your marketing campaigns more cost-effective. Similarly, if you’re a publisher, data can show which types of content are the most popular on your site and which bring in the most revenue. Having this information enables you to focus more of your time and resources on these kinds of content.

How to Collect High-Quality Data

Collecting high-quality data can be challenging. Problems with data quality may occur when a company is attempting to integrate data systems across different departments or applications, implementing new software or manually entering data. They may also occur because a company does not have the proper tools or processes in place. There are things that companies can do, however, to help improve data quality. Taking the following steps can help ensure the collection of quality data.

• Implement a data collection plan: To ensure that the data you’re collecting is high-quality, you need to have a data collection plan in place. Determine the kind of data you need to meet your goals and the methods you’ll use to collect and manage it. You plan should also define the roles of all personnel involved in collecting the data and establish processes for how you’ll communicate between departments on matters related to data. Be specific in your plan to avoid confusion and ensure that you can measure your progress.
• Set data quality standards: Create data quality standards that you’ll use to determine which data to keep, which to get rid of and which to correct. Everyone involved in managing your data should agree on and understand these standards. This ensures consistency across your organization.
• Create a plan for data correction: You’ll need to create rules for correcting data. These rules should define who is responsible for correcting data and the methods they should use to fix it. This is also important for ensuring consistency in your data.
• Plan for data integration and distribution across departments: Additionally, you should create a plan for integrating and distributing your data across the various departments within your organization. Data quality issues often occur at this step, because copying data, manually editing it or sending it to different software platforms creates opportunities to alter it. Creating concrete plans for this step can help avoid these issues.
• Set goals for ongoing data collection: It’s important to remember that improving data quality isn’t a one-time task. Ensuring data quality requires ongoing effort. Documenting data quality issues can help with this ongoing effort by ensuring that mistakes aren’t repeated. The focus should be on continuous improvement of your data collection plan.