Data corruption is real, and it happens far too often. Be smart; keep your company safe.
How do you make sure your data is accurate? If you're like many B2B companies, you invest significant time and effort collecting data to make better business decisions. This means you're probably knee-deep in big data, which Google defines as "extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions."
Of course, websites – a gigantic source of data – are constantly changing, and the potential for costly mistakes can skyrocket with those changes. The result is uncertainty about the future, a stagnant organization that always seems to be one step behind its competitors, and increasing frustration with data collection methods and inaccuracies.
To stay ahead of potential breakpoints, here are six ways B2B brands can avoid unreliable data.
1. Use tags to collect website data.
Tags on your website collect data, letting you measure traffic and optimize your online marketing. These tags can drive better reporting, advertising, personalization and more.
DataTrue is an example of a high-end tool that uses tag solutions to make it easy for marketers, developers, and agencies to add tags and collect even more data. It reduces errors and helps protect against loss of data. It independently audits how data is collected before and after every change and regularly monitors websites. It can even alert you and your team of any changes as they happen.
2. Be wary of confirmation bias.
Dataconomy reports that confirmation bias, our human tendency to rely on data that confirms our beliefs, is a common mistake. It's easy to believe data that confirms our hypothesis instead of disputing it. It's important to be as objective as possible, considering what the data tells us instead of our gut. Bouncing these findings off other people is a good way to see if you're the only one interpreting results in a certain manner.
3. Avoid selection bias.
A common error is to rely on individual results instead of the larger data set. These individual results may be outliers – i.e., have low statistical significance – and pull you away from looking at the bigger picture, said Hilary Mason, chief executive and founder of Fast Forward Labs, in an interview with The Wall Street Journal.
4. Watch out for 'black swan' events.
Another pitfall when analyzing large amounts of data is not thinking deeply about the things the data does not show you. Author Nassim Nicholas Taleb wrote about "black swans" – highly improbable, unpredictable events – in his book of the same name, which explores probability, uncertainty and risk.
Avoiding or profiting from black swan events is harder than it sounds. The best defense is to identify weak spots in your organization to try to minimize setbacks from unexpected events.
5. Don't collect lots of random data.
Sohini Bagchi says that gathering a large quantity of irrelevant data is another problem. If you're using the wrong data set to begin with, you're already going down the wrong path. Remember, big data doesn't mean all data or more data. It means collecting and analyzing large quantities of relevant information.
6. Beware of apps that can disrupt your data.
Lastly, be careful with devices that are running other apps in the background that can throw things off, as these apps can make accurate data collection practically impossible. Going further, anytime you add new features or functionality to your website, you risk disrupting data collection. Even small changes like publishing new content can have consequences.
Dealing with change
Dealing with anything online means constant change. Faster web development. Faster data collection. Increasing amounts of information. It's easy to get overwhelmed. But understanding how to collect, analyze, and implement organizational change quickly and effectively will give you and your team a clear competitive advantage.
With all of this in mind, what are your biggest concerns when it comes to navigating the maze of big data and avoiding costly mistakes for your B2B company? Internally, as a team, address them now. Later on, when your data's safe and sound, you'll be glad you did.