High-quality data also takes a sound quality assurance process that builds confidence in data-driven decisions and marketing
Businesses that use consumer data for marketing decisions see an 8% increase in profits.
Businesses see a 10% decline in overall marketing costs when they use consumer data to make their marketing decisions.
High-quality data isn’t just accurate and reliable; decision-makers must be confident they have data they can rely on.
A sound quality assurance process is the secret to building data confidence.
Big data has made big changes in how businesses market to consumers. Organizations that use consumer data to make marketing decisions see an 8% increase in profits and a 10% decline in overall cost.
To achieve this growth, companies depend on high-quality data. However, quality data doesn’t just mean it’s accurate and reliable. Decision-makers must be confident that they can rely on the data, as well. Data vetting must be performed systematically to make sure it fits specific use cases and builds human confidence in the reliability of that data.
Data confidence is built on a sound data quality assurance process using these best practices:
1. Determine requirements
The first step to building a reliable data-vetting process is to understand your business needs and your data’s potential weaknesses.
- Compare organizational policies, processes, and rules with any instructions given to
data providers, as well as data collection forms and code in applications where data will be used.
- Make sure everyone who handles data is trained, technically proficient, and certified.
- Check with data providers and processors to determine the feasibility of any decisions.
It’s also important to ensure your company has, and follows, a change-management process, and that it adopts and complies with all professional standards regarding data collection, analysis, and reporting.
2. Take a look at your process
You may feel you have a well-defined process to ensure data quality, but how long has it been since it was reviewed? The first step here is to review your process with peers, any agencies you use, and any staff that touches data.
Your process instructions should include language that makes target dates reasonable and clear along with random checks during the production phase. In addition, most companies can improve their data vetting process by taking a few common steps:
- Drastically reduce the use of paper forms in favor of the automated/validated means of data entry.
- At the earliest point, automate entry verification. This would mean verification, for example, during keystrokes rather than after-the-fact from a printed audit.
- Maintenance is important, so perform it before any production takes place, even if it’s before or after regular business hours.
3. Document and communicate the process
As you go through these steps, you might feel like your process is pretty tight. But does everyone know the process? Is it documented and readily available to all staff? Is everyone following the process?
A data dictionary is a great help, and format specifications should be clearly outlined so both current and new staff have clear instructions to relay to any data vendor. All data providers and new staff should be trained and certified, as well.
Another important document to formalize is the instruction set for data providers and processors. This should match any internal specifications, and, if these vendors ever have any questions, someone should be immediately available to answer them.
In order to implement and maintain an efficient process, ensure that everything specified in the requirements is readily available, including hardware and software.
4. Verify the process is properly implemented
Now that you’ve created the best possible process to ensure optimal data quality, you need to make sure it is being followed and that implementation is effective. You can do this by using checklists and ensure accountability by requiring signoff for each key step. Some other actions to take include:
- Running and verifying sample data.
- Performing on-site reviews at various points in the process
- Making sure any problems are reported, documented, and corrected. These issues should be communicated to the source of the problem to keep them from happening in the future.
5. Compare and verify
Data integrity requires verification, so run audit reports. Have them reviewed by experts who know how to do a reasonableness check a common-sense check to make sure the data falls within an accepted range or consists of accepted values. Before this check can be made, a decision must be made about what is, in fact, reasonable and makes sense for your business.
Compare your data to past runs, your standards, or similar groups, and check for integrity across all data exchanges, crosswalks, and translations. Then, confirm calculations and conditional rules.
6. Perform appropriate analysis and create reports
Ensuring the integrity of your data is important, but so are consumer privacy rights. Make sure your data conforms to all federal and state privacy protections, and always stay aware of the CAN-SPAM Act, which sets the rules for commercial emails. The best data is collected via customer interactions using transaction records, surveys, and contests.
When you review data with stakeholders, present any analysis conclusions within a context after ensuring your analysis techniques meet proper use requirements. It’s also important to disclose any conditions that affect data interpretation.
The last step is to provide detailed data for verification via technical reports or files.
Following these six steps for vetting data should ensure its quality and value, so you’re able to make good marketing decisions and accurately target the right audience to grow your business.
Start your process with quality data from BDEX
The tools at BDEX help you get the quality data you need, right when you need it. Reach who you want to reach when, where, and how they want to hear from you.
To improve your ROI, you have to get the most out of your data. Otherwise, you’re wasting time, money, and resources by marketing to the wrong audiences at the wrong time with the wrong messages. BDEX can give you the right data you need to better connect with the person behind the signal.
Make real human connections with BDEX. Contact the team today to get started transforming the quality and accuracy of your data.
Today’s digital marketers understand the importance of customer data and take advantage of instant information to make stronger connections that drive business growth at every opportunity.
Because of this greater emphasis on data, it’s more important than ever to put data quality at the forefront of the industry. Numerous studies have shown just how much advertising ROI is impacted when data quality is poor.
Let’s dive in a bit deeper and look at these key considerations:
- What is return on ad spend?
- Data quality and ROI, by the numbers
- Why bad data is cost-inefficient
- The importance of a good data source
What is return on ad spend?
First, let’s take a brief look at what return on ad spend (ROAS) means. This term is another way to measure ROI that focuses on a specific aspect of the business (advertising). ROAS is an important metric that measures the revenue earned from advertising versus its cost.
ROAS shows how effective your advertising efforts are and helps you understand where dollars are being wasted. You want ROAS to remain high, which shows that the effort and money you’re putting into your campaigns are paying off with significant returns for the business.
To calculate ROAS, divide your conversion value by the cost of advertising. (The conversion value is the revenue earned from each conversion.) The result is a guide to the ROI of that spend.
If you track these numbers, you’ll begin to see that data quality goes hand-in-hand with improving ROI.
Data quality and ROI, by the numbers
It’s easy to say that good data means better ROI. But what do the numbers actually tell us?
- Poor data quality costs organizations an average of $15 million annually.
- It’s costlier to resolve a bad data issue than prevent it. One example from Dun and Bradstreet shows that a company with 500,000 records and 30% bad data would have to spend $15 million to correct issues but just $150,000 to prevent the inaccuracies in the first place.
- Data makes up 20% to 25% of all enterprise value.
- An increase of 10% in data accessibility would result in an increase of $65 million in revenue for most Fortune 1000 companies.
So, what is it about bad data that makes it so costly?
Why bad data costs you money
Poor data quality quickly takes its toll on business ROI and ROAS. For marketers, one bad data signal can infiltrate databases and systems, kicking off a domino effect of bad information. When targeting customers based on bad data, those targets become missed opportunities for connection and conversion. These unfortunate effects result in missed sales and lost revenue.
What’s more, bad data can cause lots of inefficiencies in marketing operations. When business decisions are being made by data analysis, that data must be high quality. Otherwise, decisions will not be based on the latest or most accurate information. This leads to inefficient processes that have to be identified, traced, and resolved later, wasting a lot of time and money on top of the mistakes already made.
The importance of a good data source
Even though data quality is clearly connected to ROAS, many marketers still struggle to find good data. According to Gartner’s Marketing Data and Analytics Survey 2020, poor data quality is still one of the top three reasons why some marketers are not entirely comfortable relying on marketing analytics for decision-making.
The first step to overcoming this obstacle is simply recognizing how much bad data can impact ROI. Next, organizations should establish a data quality management team that oversees key changes like eliminating data silos and integrating the latest data quality tools and technologies.
Another way to focus on data quality is to get right to the source. Bad data can quickly result from too many data sources or a disingenuous source. These dishonest sources are common now that the world runs on big data.
Using a data source you can trust, like BDEX’s Data Exchange Platform (DXP), is a significant part of a successful data quality strategy. Our data as a service (DaaS) platform and identity graph lead the market in quality, comprehensive data. Our BDEX ID Check filters out bad data from the platform so you know you’re always getting the latest and more accurate information.
To improve your data quality strategy, get in touch with the BDEX team and get started with our data solutions.