This is the second half of a 2-part series. You can find the first post here.
The Case for Proactivity
There are two ways to approach data quality management. You can be proactive and spend time, energy and money to insure your data resources are healthy. This calls for an up-front investment. But because the ROI for data quality improvement expenditures is nebulous at best, many companies adopt a reactive approach, waiting for some disaster before fixing data problems.
The Impact on Sales and Marketing
The nebulosity of any ROI on data quality expenditures will require someone to become the company champion for the project. Remember who you are fighting for, however. The last thing you want is for upper management to decide you only want some new tech toys to play with and have no clear plan for turning that expense into profit for the organization. Focus on the impact of good data quality on sales and marketing:
- It is critical for effective sales and marketing alignment that both teams look at the same data. Too often sales knows more about the customer than marketing. That can be fixed collaboratively between sales, marketing and data management working as a data quality improvement team.
- Data systems should simplify and expand data collection so that the process is not only simplified, but more effective for both the data collectors and customers providing the data. An investment in new technology and improvements in data collection instruments and procedures now can yield huge dividends later in laser-targeted marketing campaigns, less ineffective advertising and improved sales.
- Organized data quality improvement costs significant staff time and effort, but yield significant cost-savings in preventing the adoption of abortive technologies. Too many companies rush to spend money on new computer systems and database software solutions without involving sales and marketing in the decision. Excluding the people that most depend on the data collected from the development process can be costly.
The owner of a fishing trawler spends a lot of money buying a good boat, keeping the nets and gear in order, installing radios, GPS navigation systems and refrigerated storage. They buy accurate charts, study navigation in case the electronic systems break down and learn about the fish they pursue. The whole point of all that expense is to ensure the trawler’s crew arrive at some place where there are lots of fish. Poor research and shoddy equipment yields fewer and smaller fish.
Think of a data quality improvement program as your company’s fishing boat and gear. The job of good data is to transport your sales and marketing people to the best place to catch customers. That has to be made clear to the whole team before you ever start your data quality improvement efforts.