6 Bad Data Management Habits that Are Costing You Thousands

Data is the lifeblood of any business today. According to IDC, some 59 zettabytes of data was set to be created, captured, copied, and consumed globally in 2020, while by 2025, this will have risen to 149 zettabytes. That means more data will be generated in the next three years than in the previous 30.

However, more than half of digital data held by businesses (55%) is never used. This so-called 'dark data' typically falls into two categories - data firms are aware of but don't know how to exploit, and that which businesses don’t even know they have.

Even if companies are looking to use their data, if they don't do this properly, it can cost them significant amounts of money. Research from Dun and Bradstreet suggests nearly a quarter of firms have relied on inaccurate financial forecasts created as the result of bad data management practices, while almost 20% have lost a customer thanks to poor use of data. As a result, it's estimated that US businesses lose $3.1 trillion every year due to these issues.

Yet despite the potential costs, many firms continue with bad habits. In order to tackle this, you first need to know what you're doing wrong. Here are six common mistakes that could well be costing you money.

1. Conflating data management with data governance

Many people talk about data management and data governance as though they're interchangeable, when this isn’t the case. While data governance focuses on how data is handled - such as who owns it and who has access - data management is a more wide-ranging term that encompasses every part of your strategy. Confusing the two can mean you miss out on critical steps or don't have the right controls and policies in place.

2. Continuing to use data silos

Data silos are the bane of many analytics professionals' existence today. These outdated solutions for managing data, which see information isolated in their own individual departments, make it impossible to effectively share data across an organization, leading to an incomplete view of what's happening, reduced efficiency and poorer customer experiences. Breaking down these silos and making information available across the business boosts collaboration and leads to better decision-making across the board.

3. Hoarding too much data

For some firms, the sheer volume of data they collect may make it difficult to identify the most relevant material and derive useful insight. Having to wade through huge quantities of information makes analysis much slower, but also creates other problems. For example, there's the cost involved with archiving and storing all this data, which can quickly mount up.

Having too much data can also leave you exposed to legal repercussions. For instance, if the business is subject to GDPR rules, this makes it clear that you must minimize the amount of data you collect and ensure the information you do have has a valid, specific purpose.

4. Poor quality data

Poor quality data can also create problems. Common issues include duplicated data, incomplete records and entries that don't conform to the standard format. For example, dates and addresses can be especially prone to these issues as different users and customers may often enter the same information in different ways. This can make it harder to find good leads or provide existing customers with the right offers or information.

To prevent this, data cleansing tools that can evaluate information before it's loaded into key systems is a must. The benefits of this are clear to see, as research shows it costs $1 to prevent a duplicate entry, but left untouched, such data can lead to a $100 expense.

5. Incorrect data

Similarly, inaccurate data can also lead to major issues. While poor quality data can be cleaned up, incorrect or outdated details need to be highlighted so it can be updated, amended or removed. There can be many problems associated with this. For instance, data entry errors that result in a customer's address being incorrect can mean money is wasted on marketing materials that never reach their destination.

To avoid these issues, it's vital you have strong data quality processes in place across the business, especially for manual data creation processes that are more prone to human error.

6. Data that's lost or hard to access

An inability to access data can also greatly slow down firms. According to Veritas, IT decision-makers can spend up to two hours a day hunting down relevant data, which results in a 16% decrease in overall efficiency.

This often happens because data is stored away in multiple locations across the business  - such as in data silos - or hidden within larger sets of irrelevant information. This means valuable time and effort is wasted, which could otherwise be spent analyzing the data itself.