Customer intelligence is the lifeblood of organizations.
The hype around big data is deafening, and for valid reason. When it’s used to augment customer information, big data provides organizations with deep customer intelligence.
But big data has an Achilles heel: dirty data. Duplicate data, inaccuracies, and duplicate information.
Poor data quality costs the US economy approximately $3 trillion annually. As the old adage goes, “garbage in, garbage out.”
So today I’m giving you a few best practices for data hygiene.
#1: Perform an audit
The first step involved in a data cleansing process is to perform a sniff test so to speak.
Before taking any action, it’s critical to assess the quality of your data and establish a realistic baseline of your company’s data hygiene.
Evaluate all systems that your company relies on for customer information.
If customers are asked to give personal information too early in the buying process, they’ll be more apt to falsify information in order to protect their anonymity. Additionally, if forms contain too many inputs, customers may be tempted to provide false information. In both cases, the likelihood of dirty data increases.
#2: Automate data cleansing
Manual data cleansing is laborious and uneconomical. It’s well worth the time and effort to invest in systems that automatically enrich, append, clean, and/or de-dupe data.
It’s not surprising that human error represents the single most common cause of dirty data. A spelling error can result in a lost lead and thousands of potential dollars of unrealized revenue.
Cleansing systems are able to sift through masses of data and use algorithms to detect anomalies and identify outliers resulting from human error.
#3: Update data as frequently as possible, ideally in real-time
Data is, in many ways, akin to a piece of produce. It is subject to decay and rot. Customers change addresses, they get promoted, and they change companies.
Up to 60% of individuals change job functions within their organizations every year. Data decay occurs at rapid rates—sometimes more quickly than that overripe banana sitting on your kitchen counter.
Overall, an ounce of prevention is better than a pound of cure. The 1-10-100 Rule states that it takes $1 to verify a CRM record when it is initially inputted, $10 to clean it later, and $100 to do nothing.
So be proactive and ensure that the data that flows into your systems of records are accurate, reliable, and comprehensive with these best practices for data hygiene.
Leslie Youngblood is the Creative Director for Excelerate America, the fun, smart service for small businesses. Do you keep your data clean? Let Leslie know how by emailing her at email@example.com.