Why data fatigue is a growth killer and what to do about it
2.5 quintillion bytes of data are created every day. While this abundance of data can provide important insights for companies, many brands struggle with how to make sense of all the data at their fingertips. In fact, some studies found that less than half of a brand’s structured data is actively used in making decisions and less than 1% of unstructured data is analyzed or used at all.[i]
Data fatigue happens when a brand accumulates data faster than they are able to make sense of it and derive actionable insights. Often, the data collected is simply too overwhelming, messy or flawed for teams to be able to easily analyze and use it in meaningful ways. When brands simply gather data without investing in the people, technology, and organizational policies needed to put it to work, data becomes a burden instead of a crucial tool to drive growth and improve efficiency.
Here are six ways brands can be proactive about avoiding data fatigue.
Be sure to identify, define and implement a structured data governance and management process. Data governance is the set of policies and rules that organizations implement for managing their data. The goal of data governance is to ensure usability, availability, consistency, and quality of the data. A robust data governance initiative sets the framework to reduce risk and waste and maximize data effectiveness. Be sure you can answer these questions:
According to Forbes, only 46% of sales professionals use tools to clean their data before it enters their database; this is bad data hygiene. Data hygiene refers to the processes of inspecting and cleansing data. Some organizations employ skilled data scientists to run these processes in-house. However, not every company has the resources or wants to task a costly data science team with routine database maintenance. Data scientists are highly-skilled and if data hygiene practices are outsourced to a solution provider, the brand’s in-house scientists are free to spend their time analyzing data to create actionable insights. For companies with large amounts of data, investing in data hygiene services to clean, append, enrich, and de-dupe data saves time and effort.
Best practices for data hygiene dictate that most organizations should cleanse their data every 3 months. If companies do not have the resources to do it that often, they should aim for a minimum of twice per year or get outside help.
Assigning a single data owner, ideally supported by a team of data professionals, is the best approach to data management. Without a single data owner, different departments and executives might develop clashing data strategies associated with different standards and processes.
Decentralized data ownership can also result in siloed data, meaning each department only has access to certain data, translating into a limited ability to develop comprehensive insights and act upon them.
Deployment of a ‘Single Source of Truth’ (SSOT) data structure is increasingly used in enterprise settings where inaccurate or duplicate data elements result in incorrect information being used for sales and marketing purposes. The goal of the SSOT is to provide a single view of an organization’s customers and prospects, by providing clean, easy-to-analyze data.
Tips for establishing an SSOT:
Organizations can set themselves up for success if they make quality control a priority at the point of data collection. In fact, a recent case study confirmed that on-screen validation resulted in a 22% increase in success rates and a 31% increase in satisfaction rates.
Here are some ways to validate records as they come in:
When acquiring data from outside sources, it’s important for brands to confirm that the data they receive has been verified. A responsible provider will provide data that:
Best practices for vetting 3rd party data providers:
After working with the same data solution provider for years, payment processing company, Sekure Merchant Solutions noticed that prospecting data performance was declining, despite the sales team hitting the phones hard. They suspected the culprit was data fatigue – too much unverified data meant their sales team was wasting time on calls, which contributed to dead-end leads.
Switching data providers helped Sekure Merchant Solutions beat data fatigue. Having access to more accurate, human-verified business data and historical data, allowed the brand to double their sales conversion rates, lower abandonment rates, and increase ROI. Partnering with Data Axle gave Sekure Merchant Solutions the power to take data and turn it into revenue.
Want to know more about data? Check out the different types of marketing data and how brands have used them to find success.