April 22, 2020

6 Ways to Beat Data Fatigue

Eric Kider - Senior Vice President, General Manager, Credit.net
6 Ways to Beat Data Fatigue

Why data fatigue is a growth killer and what to do about it

2.5 quintillion bytes of data are created every day.[1] 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.

  1. Prioritize data governance

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:

  • Who owns the data?
  • What internal system has the ‘master’ customer file?
  • Who has access to and/or can update or modify specific fields?
  • Is there a data audit process (checks and balances) in place to ensure good data isn’t overwritten by bad or modified without review?
  1. Employ data hygiene best practices

According to Forbes, only 46% of sales professionals use tools to clean their data before it enters their database[2]; 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.

  1. Centralize data stewardship

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.

  1. Establish a ‘Single Source of Truth’

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:

  • Have a unique identifier for each entity in your database to ensure there aren’t duplicate records
  • Have a specific process in place to identify and resolve conflicting and out-of-date information. For example, set up alerts for duplicate records or records with conflicting information and create rules to verify and merge them
  • Implement identity resolution tools to help you create a single view of your customers and prospects – whether they are at home, at work, on their cell phone or computer, online, or offline
  1. Improve data quality through validation best practices

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:

  • Use real-time validation tools, such as CAPTCHAs and validation processes such as double opt-in to ensure point-of-entry information is accurate.
  • Institute an approved format for all fields that feed into your database. This will ensure that answers are standardized in the same format and easily read by analysis technologies; i.e. when entering phone numbers, for example, you will receive “111-222-3333” instead of a number of variations such as 111.222.3333 or 1112223333.
  1. Ensure accuracy of third-party data through data verification

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:

  • Has a variety of different sources – consumer surveys, online and offline activity, public records & profiles, and more – in order to provide more comprehensive data with a smaller margin of error.
  • Is verified – a reputable vendor will not provide a list of email addresses based on a best guess of a target company’s syntax (e.g. lastname@acme.com); they will verify that the emails are active and safe to mail.
  • Is human-verified, when/if possible – human verification (typically via a call center) is the tallest order of verification that a vendor can offer and it accounts for data of superior quality and accuracy.

Best practices for vetting 3rd party data providers:

  • Confirm the company’s data sources, verification process and verification frequency
  • Do a spot check – does the vendor’s data match up with the data you already have on your customers? Does it paint a similar picture or is it way off base?
  • Ask for a referral – any reliable provider will have a client who is willing to vouch for the accuracy of their data.

Brand example:

Sekure Merchant Solutions overcomes data fatigue and increases ROI

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 Infogroup 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.

[1] https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#73802fb060ba

[2] https://www.forbes.com/sites/falonfatemi/2019/01/30/best-practices-for-data-hygiene/#269bdc9a2395

[i] https://hbr.org/2017/05/whats-your-data-strategy?referral=03759&cm_vc=rr_item_page.bottom