Big data hype has been deafening over the last decade, as businesses realize the potential of understanding their customers, enhancing operations, and improving profitability. Major technology brands such as Amazon, Google, and Facebook show us the power of data to bring personalized experiences, across multiple channels, to a digitally savvy consumer base.

However, data comes with a critical Achilles Heel, known as dirty data. Dirty data includes incomplete records, inaccuracies, duplicates, missing details, and other anomalies that represent a considerable risk. Experts say bad data costs as much as $14 trillion per year. In fact, only one-third of marketers say they can rely on CRM data being accurate enough to make decisions.

Imagine if Amazon did not look after their data hygiene. The dirty data would lead to wrong customer recommendations, incorrect personalization, and poor customer experience. The bottom line of dirty data is a decrease in sales and profit.

Businesses need to take care of their data hygiene to help mitigate the potential risks associated with it.

Complete a data hygiene audit

According to IBM, nearly 30% of business leaders do not know how much of their data is accurate. Before making any changes, you need to assess what the benchmark is and give yourself a starting point.

A data hygiene audit will take into account all the systems that the company relies on for information. For each of those systems, an analysis should look at every bit of data to understand if it is relevant, complete, and useful.

Following the initial audit phase, evaluate any front end sources that information comes from. For example, if you are relying on customers to enter information, it will impact your data hygiene, if fields don’t have sufficient verification in place.

A data hygiene audit logs the current state of affairs, data sources, and potential resolutions to existing issues.

Data standardization

A typical root cause of poor data hygiene is a lack of standardization. For example, customer’s birth dates are in different formats depending on the system they originate from. Another example might be where states are listed as either NY or New York. A marketing campaign to New York customers could miss a segment of business do to a lack of data standardization.

Robust data hygiene practices will take care of the format and consistency of data, establishing constraints and validation to minimize potential problems.

Keeping data up-to-date

In the B2B world, up to 18% of telephone numbers change year on year, and 60% of people change their job or function. Third-party tools can update contact records to avoid incorrect data, chasing dead leads, and losing customers.

The Direct Marketing Association found that email addresses change at a rate of 31% each year. Without proper data hygiene practices, every email campaign can risk a failure to communicate with two-thirds of the customer base.

Data cleansing

Investing in systems that automatically take care of data cleansing can quickly improve your data hygiene. Human error is the most common cause of dirty data, with the smallest of typos having the potential to lose thousands of dollars.

A data cleansing tool will use sophisticated algorithms to find patterns and identify anomalies from human errors. Less than half of sales and marketing teams deploy a cleansing tool for data hygiene, leading to a risk of problems.

Data Silos

In a typical business, departments will work using their own platforms. While this is fine in principle as they all have different needs, the data they use should still be consistent and act as a central source of truth.

All teams that can edit data must work with the same rules and standards to ensure data hygiene. The 1-10-100 says that it costs $1 to verify a CRM record at the source, but $10 to clean it later, or $100 if you do nothing.

Proactive data hygiene in all departments will give your business a clean bill of health. If you need data hygiene services, StrategicDB can help, get a free Data Audit.