Data is at the heart of success in any field on the planet. Whether you’re a farmer or an astronaut, data can improve your day-to-day processes and directly affect the outcome of your efforts. But not just any data will do; success requires clean, credible, up-to-date information.
So how do you know if your data is good data or dirty data? Unfortunately, dirty data is not hard to find. It can also be incredibly enticing, as it is often easy to acquire and tends to build quickly on itself. But making assessments based on bad data is a dangerous game. In the B2B marketing space, bad data can lead to wasted money and audience alienation, and even to the downfall of an organization.
Good, Clean Data
In general, data is assessed by some key principles to help you achieve the clearest and most accurate picture of what’s happening across your organization. Five of the most important rules to abide by when it comes to data are as follows:
- Good data IN = Good data OUT
This means anyone and everyone who feeds information into every system needs to be playing by the same rules. Standard operating procedure should be set for all workflows. Tasks need to be logged in the same way. All required fields need to be completed. This, of course, means training the entire team, because a single bad data point can paint a completely false picture. That being said, you can’t always trust people, because people cannot be programmed. So a solid defense needs to be built to identify and tackle dirty data. Anomalies should be flagged programmatically at various checkpoints as data moves from point to point and to the eventual data warehouse.
- Defining the data
The truth is, dirty data is just as bad as good data that is misunderstood. Having good data does not necessarily ensure that you’ll have good data analysis. Understanding the nuances of the data is key. Does everyone consuming the data analyses know what each view represents? But more importantly, does the report developer, who is publishing dashboards for consumption, truly understand the relationships and significance of every piece of the puzzle? Raw data in the wrong hands can be a very dangerous thing.
- An agile data environment
Building an environment that can deal with changes in the data structure is crucial. Was a new product introduced? Does that mean you now have to go back to the drawing board and rebuild your whole network of data relationships? It shouldn’t. Be sure to account for both major and minor changes so that data analysis doesn’t come to a screeching halt when something new is introduced.
- Depth of data points
Data is most useful when you have the deepest level of detail you can possibly acquire. How can you learn from sales data unless you know the when, where, how, who, how much, and how long? More details = more insight, and the power to discover anomalies and meaningful patterns often comes from the minute details that can’t be analyzed by the naked brain. Good data analysis is your brain’s lens.
Timing is everything with data. A lot can change in a few days, or even a few hours. It’s important to gather the information as soon as possible after it occurs. If you’re keeping an eye on daily website traffic, updating the data set weekly will prevent you from discovering what’s happening at the moment, which means you can’t be reactive when your audience tells you that you need to switch up your marketing strategy.
A Clear Picture
Good data is all good and well. But what is it worth if you can put the puzzle pieces together? Without a clear picture of what’s happening, data is just data. As much as we all think we understand individual data points in an Excel spreadsheet, sometimes that’s just not enough. Having a team of experienced data analysts who live and breathe this stuff will pay off in the long run because they will make connections between data points that you never dreamed were possible. Go with the pros because the power to unveil the truth is the fastest path to understanding how you can fly past the competition.