Whether you sell cars or cookies, access to quality data is the backbone of business.
Good data helps companies measure the effectiveness of marketing campaigns, it helps sales reps uncover and decipher customer trends, and it helps executives identify cost-cutting opportunities in the supply chain - all while increasing workforce efficiency and productivity. It might not sound sexy at first, but access to quality data can be the difference between reaching a quota or falling short. Unfortunately, bad, incomplete or missing data can burden and even cripple a company.
But don't fear. Paralysis as a result of poor data can be avoided if a strong business case is made for improving data quality.
Building the Case for Quality Data
I am frequently contacted by people who say, "I have a meeting with the CIO, and he/she is going to ask me to give an ROI for purchasing a data quality product. What do I say?" First, don't panic. In fact, be excited that executive management decided to make quality data a potential strategic advantage for the business. This is rarely the way the case for data quality is made, because it is uncommon to find data quality metrics in place or clearly defined.
Top Down or Bottom Up?
While the top-down approach is ultimately more efficient, chances are the need to improve data quality will bubble up organically from inside the company. The bottom-up approach is far more common and provides a great opportunity for employees to advocate for increased data quality and, as a result, have a positive impact on the business.
Here's how to make the case for quality data in five steps.
Data quality improvement can be applied to a wide array of departments and business units, but for the sake of this article, let's focus on marketing and sales as the primary examples.
1. Understand how the business measures performance and put data quality ROI in terms the business can understand.
It might sound obvious, but to best understand how a business measures performance, ask the people directly responsible for making business decisions, which in this case would be marketing and sales executives. I expect you will learn that marketing wants to increase direct brand awareness and demand generation through improved mail campaigns, and sales wants to increase conversion rates while increasing the overall pool of potential clients. Once you understand how these groups measure performance, you will also understand the areas in which improvements should be made.
With this information in hand, put data quality ROI in terms that decision-makers can understand. For example, state the business claim that access to quality mailing address data can improve conversion rates by 3 percent. By improving the quality of the address data so that an additional 10 percent is delivered, it's possible to boost conversion rates several points without increasing campaign costs.
By making it easy for businesspeople to see the link between improved quality and increased revenue, it will be much easier to secure buyoff for data quality projects.
2. Take ownership of the data and analyze and identify anomalies.
An issue that often trips up those on a quest to increase data quality is determining who owns the overall issue of data quality: is it the IT department or the business? It's critical to collaborate to clean up the data.
Bad data and the paralysis created by poor data can impact IT and the business equally, so, ultimately, it's not an either/or issue. Get everyone on the same page and ensure that IT understands how the business uses and measures data and business leaders understand the impact that quality data can have on bottom line results.
From there, analyze the data and identify anomalies. This is where a data profiling tool can be indispensible. By profiling data, it's easy to reveal its content, quality and structure with minimal effort. Data profiling will also help you identify actual problems with the data as it relates to business needs. If the marketing group wants to increase the impact of a mail campaign, data profiling can help identify anomalies, like empty or missing address or phone records, incorrect reference data entries, duplicate entries for the same contact and problems in data uniformity.
A little data legwork can go a long way and dramatically impact the course of a campaign.
3. Categorize the anomalies.
Once initial analysis is complete, it will be necessary to put anomalies into categories. Categorizing data quality issues into six data groups can help build the data quality ROI case as well as ensure that the results are easily digestible for both IT and business leaders. These six categories are:
- Completeness. Is all the requisite information available? Are all the address fields populated?
- Conformity. Are there expectations that data values meet specified formats? If so, do all the values conform to those formats?
- Consistency. Do interdependent attributes always appropriately reflect their expected consistency? If the country code is U.S., then the currency code should be USD.
- Accuracy. Do data objects correctly represent the real-world values they are expected to model?
- Duplication. Are there multiple, unnecessary representations of the same data objects within the data set?
- Integrity. What data is missing important relationship linkages? Are all our commissioned partners in our sales system in our partner database?
From an ROI perspective, once the data is categorized, it becomes much easier to take an appropriate set of actions to fix bad data. For example, if a marketing mailing list is found to have inaccurate and incomplete contact data, it's easy to make the case that it would be less expensive to correct inaccurate data rather than spending time to research the correct contacts or buying a new contact list.
Categorizing anomalies will help business leaders quickly understand problem areas and make informed decisions that increase efficiency and maximize data quality ROI.
4. Identify the business impact of data anomalies.
After profiling the data and identifying anomalies, it becomes critical to work with decision-makers to understand the effect the anomalies will have on the business.
Consider the following example. A profile of customer sales data shows that 8 percent of the records have inoperable or missing phone numbers. How does that impact the ability to up-sell or cross-sell your other line-of-business products to existing customers?
Chances are, if business leaders knew at the start of the month that their sales force's operational ceiling would at best be 92 percent efficiency, there would be quite a bit of explaining to do to senior leadership.
5. State the case for data quality.
When stating the case for data quality, it pays to draw a link between how a lack of quality data can adversely affect the operational efficiency of an organization as well as the accuracy of business decisions. Now that the data has been compiled, analyzed, categorized and flagged for known anomalies, the case can effectively be made to increase data quality practices.
Don't Risk It
Every day a business suffers from poor, incomplete or unorganized data, the more it risks falling victim to poor data paralysis. While I've focused on the impact that poor data can have on marketing and sales teams in particular, poor data stretches far beyond this group. In fact, poor data can result in duplicate vendor payments, improper calculations for chargebacks and rebates and, worst of all, misguided business decisions based on faulty information.
Don't be a victim; make the case for quality data and free your company from poor data paralysis.
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