Analytics, artificial intelligence and machine learning are powerful enablers of business transformation, including in the sales and marketing functions. However, just adopting these technologies will not ensure the desired outcomes. AI and analytics solutions use data captured in enterprise systems, and poor-quality data in those source systems will vitiate the output that the analytics engine produced.
Reasons For Poor Data Quality
Poor data quality is the result of gaps in the interplay between technology governance and people — although it's fair to say that the buck stops with people, whether they are from "business" or "IT."
- Technology: Over time, enterprises acquire and adopt a mix of built-to-order systems and third-party products. These may use disparate technologies and different standards of product design and engineering.
Many organizations do not have global standards for data and harmonization. As a result, different parts of the enterprise may build/acquire systems with diverse data standards. There is often no clear, repeatable way to measure and score data quality, nor is there a clearly enunciated strategy for improving data quality.
The frequency of master data synchronization is another common reason. The absence of a middleware capable of standardizing data simply compounds the situation. In response to evolving needs, when "business" asks for information, "IT" typically looks to pull data from the most convenient source. This results in further propagation of "dirty data."
- People: Users of various systems who are often nontechnical in the IT sense do not fully understand how their actions contribute to poor data quality and the implications of poor data quality on their own lives.
While organizations train people in using various features of the software, not enough attention is devoted to driving home the importance of maintaining consistency and accuracy. Too many exceptions and manual workarounds are allowed, giving rise to opportunities for poor data quality.
Impact Of Data Quality On Sales Effectiveness
The impact of poor data quality on sales effectiveness can occur at various levels. While some manifest as inefficiencies (or irritation), others have more significant consequences.
For example, filling the ZIP code or state may not be useful to the salesperson responsible for that territory. But what if the company wants to run a corporate sales campaign targeting customers in certain states or ZIP codes? At such times, the lack of data about the customer's location can be a handicap. The campaign may ignore certain customers, leading to a loss in potential sales.
Errors could occur when typing in the names of individual executives. "Jan" mistyped as "Jon" or "Janet" as "Jane T" could create awkward moments for a new member of the sales team who has just taken over the account. It will also, of course, erode the power of personalized communication.
The use of different names to refer to the same customer may also cause trouble if those fields are later used for certain analyses. A miss will surely embarrass the presenting team. It could trigger even more embarrassment if a quote configuration solution eventually uses the data and the customer is denied the benefit of special pricing based on the value of the business relationship because not all deals were accounted for.
Poor data quality in product master databases can wreak havoc with pricing updates or integration with e-commerce, leading to dissatisfied customers, incorrect pricing, wrongly applied discounts and more.
In an effort to institutionalize relationships, account managers are asked to capture various details about key customer executives. This is used to map them and devise suitable ways of strengthening the organization's relationship with the executives. Unless the information is updated at the right place and there's only one version of the truth, chaos will reign. This is critical, especially when there is a change in personnel and one account manager hands over to another.
What Can Sales Leaders Do?
Sales leaders must address the data quality challenges at both the technology/governance level and at the people level. Here are 10 things they can do:
• Make sales and account management teams aware of the importance of quality data as a strategic asset. Explain that accurate data entry is not just a matter of compliance, but is a way to make their jobs easier. Also, explain its linkages with the jobs that others do.
• When new members join, ensure that their introduction includes the organization's emphasis on data quality and what is expected of them in that regard.
• Evangelize the fact that although data resides in a system, it's not the responsibility of "IT" users. Members of the sales/account management team are the primary owners.
• Conduct random audits to ensure that gaps are identified and plugged as early as possible. Cover all regions, lines of business and so on.
• Make compliance with data quality norms a part of performance evaluations. To show how seriously the organization takes data quality, you could go as far as docking people for poor data quality compliance even if they "meet their numbers."
• Create a team to periodically monitor sales/marketing data to ensure quality. This will highlight gaps quickly and help fix the issue. To ensure that everyone understands the importance, rotate the team members responsible for data quality.
• Make data quality an agenda item on weekly/fortnightly sales calls and all sales reviews. Ensure that the designated team members report on the good, the bad and the ugly so that the larger team realizes this is not a fad.
• Do not rush into automation — first, ensure that existing data is clean and that process/people gaps that contribute to "bad data" are plugged.
• Make representatives of the data quality team a mandatory part of all IT initiatives related to sales/marketing so that the data quality perspective is on the radar during the selection/development of new software and integration or migration.
• Push for a global, organizationwide data quality policy. If there's one being developed, ensure that it covers sales/marketing data as well.
Organizations spend a lot of money to derive insights and analytics from data. By following these steps, you can increase the likelihood that your data is high quality and minimize the chances of bad data leading to bad decision making.
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December 09, 2019 at 07:30PM
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Data Quality Is Critical For Sales Excellence - Forbes
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