Poor quality data costs businesses around $700 billion a year, or 30 percent of the average company’s revenue. Part of that cost is the inordinate amount of time sales reps spend researching incomplete data, according to studies done from Salesforce and Data.com that number is 21%. With a constant influx in leads it’s important to stay on top of things. Research from Dun and Bradstreet and Data.com have shown that, on average, every 30 minutes:
With big data and the big sales potential associated with it, there is no room for error, which sometimes is not the case. A recent Salesforce study found that, the average customer’s contact database is composed of 90% incomplete contacts, with 20% of records being useless due to several factors, such as 74% of the records needing updates and more than 25% of those being duplicates.
Just as using data to improve customer experience and creating 1:1 journeys for customers is a lengthy process, collecting data is also a process rather than a one-time event. For this reason, all team members should understand the importance of updating information at every given chance. Rather than seeing data cleansing as an event, team members should accept data validation as a way of doing business.
A recent test of 24 companies conducted by GS1 US showed that 50% of the data analyzed was inaccurate. One problem with taking a piecemeal approach to data quality is the possibility of introducing errors that affect the system at large.
Historical data should be an integral piece of the data collection process. John Staiger, in a QSM blog, explains the importance of historical data. He advocates collecting data at the end of a project, even though the project is done and management tends to want to dedicate resources to new projects rather than continuing to spend on something that is “complete.”
Data validation is a nightmare across multiple databases. If sales uses different software from customer service, while order fulfillment is handled by yet another system, the opportunity for misinterpretation of data and every other kind of error is too high.
When so much is dependant on current information, companies cannot afford to try to manage customer data with spreadsheets or with contacts left on local machines. An integrated CRM that is exclusively used by everyone is a wise investment in a company’s success.
Data quality isn't only an IT problem, it's a business problem. Everyone in the organization must work together to balance the appropriate level of data quality with the time and costs required to achieve the appropriate level of accuracy.
"The number one mistake that people make is to lead with data quality initiatives, rather than leading with a business initiative. You see a lot of data quality problems that are internal to IT where the business doesn't engage because it hasn't been involved in a way that it can engage," says Steve Jones, global VP of Capgemini's big data practice. “Data quality isn't a business outcome. It's about the business objective that data quality enables." Because the quality of the data affects the entire organization, data quality metrics should be a company-wide responsibility. Everyone from customer service to operations and from accounting to IT should work to ensure data accuracy.
Some companies get away with making data quality an IT responsibility because of the continued focus on schemas, but for companies that care about data quality metrics, everyone must take responsibility for data accuracy and ensuring data is of high quality. All team members need to see the benefit for them. "Big data has gotten people to realize the phase zero of data quality is being able to navigate among those data sets, not get everyone to conform to schema," Jones said.
When accurate data supports the business initiatives, data validation will come as a natural byproduct.
Track the return on investments made. Ensure that the data is thoroughly cleansed and of the highest quality possible. Effective data quality brings returns all over a company in terms of better customer experience, improved perception of the brand, time reclaimed from sales rep research, supporting more effective CRM, shorter processing time, reduced hardware costs, shorter sales cycles, more accurate analytics, reduced tele-marketing costs, increased return on existing technology investments, and higher cross-sell and upsell volumes. Each of these metrics reveal benefits of improved data quality.
Furthermore, as companies track the benefits of high quality data and the costs of bad data using quality metrics that apply to the business initiatives, team members can be motivated to make data accuracy a priority, thus further improving data accuracy.
From executive and senior managers to front-line employees, stress the importance of accurate data on the whole company. The leadership team must buy in and emphasize the company’s long term goals, department objectives, and key initiatives. When everyone is ensuring that data is of high quality in the natural course of their work, the company can expect the data to be good. It doesn’t take many people to introduce errors, duplicates, and bad information that can lead to misinterpretation of data.
Scott Ambler, of AgileData.org shares a shocking statistic: 66 percent of development/IT teams choose to bypass the data practices. By looking at data issues as a company-wide business issue rather than just an IT problem and by involving the entire company, the accuracy of data can be championed.
Companies might employ gamification strategies to help keep everyone on board. They may assign point values to different data cleansing tasks or hold contests that would continuously engage team members in the efforts based on data quality metrics that show where the organization as a whole may be getting complacent.
Cloud-based software allows for real-time updating and sharing data. Having reports in real-time means everyone is operating under the same assumptions and the same information.
"Bad science can come into play here. If you're going to do anything serious with data, you need to be concerned with data quality," said James Heires, data scientist for software estimation and management tool company Quantitative Software Management. A good CRM that integrates well with a variety of apps that make it easy for employees to collaborate. Different functions within a company use apps that support their work, and if those apps integrate with the CRM, then data can be updated quickly and can provide real-time data.
When the entire company is onboard and everyone sees data quality as a way of doing business, and something that they have responsibility for in order to support business objectives, and when the CRM has real-time, high-quality data, the costs of inaccurate and incomplete data plummet. Every division in the company will have the tools necessary to make data-driven decisions, to compete, to sell better, and to succeed.
It can be argued that every decision is made based on some amount of data. Don’t let your company make decisions based on small amounts of inaccurate data. While the costs of decisions based on bad data are felt in inventory, operational, production, labor, IT, and customer service, the effect of bad data on sales is particularly stinging. As Salesforce research shows, a little more than one-fifth of sales reps’ time is spent researching bad data.
Steps to ensuring high-quality data include having a master source such as a cloud-based CRM solution which offers the company a platform where all data can be stored in real-time. Additionally, data initiatives should be viewed as company-wide projects rather than relegated solely to the IT department. When everyone in the company is aware of these metrics, the quality of the data increases, and benefits everyone.