5 Strategic Steps to Avoid Paying the Price of Poor Data Quality

05/13/2019 by Ken Matz Modernization - Analytics

If you deal with reporting, preparing, cleansing or analyzing data in any form or fashion, then you have had exposure to poor data quality during your work. Depending on your role, the impact of the data quality will impact you differently. If you are a PR person, poor data quality impacts you when it is exposed to the public and you need to right a bad PR event that was the result of a bad data nightmare. If you are an IT person, you (or someone on your team) are directly responsible for the strategy and execution of the data quality. If you are the CEO, you may feel the impact on the reputation of the company and on the stock price when a data quality event occurs.

Regardless of your role, poor data quality impacts everyone in some way. Let’s review some of the key areas in which poor data quality impacts the organization.

Productivity Cost of Poor Data Quality

One of the key costs of poor data quality is lower productivity. Forrester reports that “Nearly one third of analysts spend more than 40 percent of their time vetting and validating their analytics data before it can be used for strategic decision-making.”

Poor data quality can affect:

  • Staffing
  • Workloads
  • Throughput
  • Output quality
  • Supply
  • Volume

The costs will vary depending on the inefficiencies at your organization. The costs related to manually implementing, monitoring, validating and correcting analytics solutions and data can impact marketing, production and development efforts. Analytics solutions and tag management systems can collect and help manage data, but they only go so far. They are not responsible for validation of the data.

A few examples of reduced productivity include:

  • At an energy services company, inconsistent supplier data results in early (and incorrect) payments. This will lead to an increased cost for entering the same data multiple times.
  • At a telecommunications company, applied revenue assurance to detect underbilling indicated revenue leakage of just over 3 percent of total revenue due to poor data quality.
  • At a call center, lack of trust in the data can negatively affect agent productivity. Agents start to question the validity of the underlying data when data inconsistencies are left unchecked. This means that agents often ask a customer to validate product, service and customer data during an interaction — increasing handle times, decreasing productivity.
  • At the department of defense “… the inability to match payroll records to the official employment record can cost millions in payroll overpayments to deserters, prisoners, and ‘ghost’ soldiers.

Financial Cost of Poor Data Quality

Financial cost includes lost revenue and additional overhead cost. According to Gartner research, “organizations believe poor data quality to be responsible for an average of $15 million per year in losses.”

The negative financial impacts related to data errors, inconsistent data, duplicate data can include increased operating costs, decreased revenues, missed opportunities, reduction or delays in cash flow, or increased penalties, fines, or other charges.

General business examples of the financial impact of poor data quality include:

  • Lost opportunity costs
  • Inability to consistently identify high net worth customers
  • Time and costs of cleansing data or processing corrections
  • Inaccurate and inconsistent performance measurements for employees
  • Inability to identify suppliers for spend analysis

Solutions for Poor Data Quality

What can you do about poor data quality at your company? Several solutions can help to improve productivity and reduce the financial impact of poor data quality in your organization include:

  • Create a team to set the proper objectives
  • Focus on the data you need now as the highest priority
  • Automate the process of data quality when data volumes grow too large
  • Make the process of data quality repeatable – it requires regular care and feeding
  • Beware of data that lives in separate databases

In order to prove to yourself and to anyone who you are conversing with related to data quality that you are serious about data quality, create a team who owns the data quality process. The size of the team is not as important as the membership from the parts of the organization that have the right impact and knowledge in the process. When the team is set, make sure that they create a set of goals and objectives for data quality. In order to gauge performance, you need a set of metrics to be able to measure the performance.

After you create the proper team to govern your data quality, ensure that the team focuses on the data you need first. Everyone knows the rules of “good data in, good data out” and “bad data in, bad data out.” To put this to work, make sure that your team knows the relevant business questions that are in progress across various data projects to make sure that they focus on the data that supports answering those questions. Once you do that, you can look at the potential data quality issues associated with each of the relevant downstream business questions and put the proper processes and data quality routines in place to ensure that poor data quality has a low probability of infecting that data. As you decide which data to focus on, remember that the size of the data isn’t the most critical factor — having the right data is.

When data volumes become unwieldy and difficult to manage the quality, automate the process. There are many data quality tools in the market that do a good job of removing the manual effort from the process. Open source options include Talend and DataCleaner. Commercial products include offerings from DataFlux, Informatica, Alteryx and Software AG. As you search for the right tool for you and your team, beware that the tools help with the organization and automation. However, the right processes and knowledge of your company’s data can’t be replaced by the tool.

Remember that the process is not a one-time activity. It requires regular care and feeding. While good data quality can save you a lot of time, energy and money downstream, it does take time, investment and practice to do well. As you improve the quality of your data and the processes around that quality, you will want to look for other opportunities to avoid data quality mishaps. The good news is that if you have followed some of the former solutions, you should have more time to invest in looking for the edge cases. As always, look for the opportunities with the biggest bang for the buck first. You don’t want to be answering questions from the steering committee about why you are looking for differences between “HR” and “Hr” if you haven’t solved bigger issues like knowing the difference between “Human Resources” and “Resources,” for example.

As you look for opportunities to tune your organization’s data quality, one of the top priorities should be looking at data that that lives in separate databases. When you merge or join data from separate databases, you have human factors to consider when planning your data quality rules. This situation can present itself due a merger with another company. However, it can also be present in everyday data management. The data silos and people silos need to be managed during this process.

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