In today’s fast-paced digital world, data is the key to success for many organizations. There’s a reason why the big data analytics market is expected to grow to an enormous $103 billion in value by 2023 – it helps businesses of all types better understand the market they’re serving, all so that they can leverage this insight to create better relationships with customers and to make sure they’re always moving in the right direction. This is something that wouldn’t have been possible even as recently as a decade ago. Yet at the same time, not all data is created equally. Estimates say that poor data quality ultimately costs the United States economy about $3.1 trillion on an annual basis.
Leveraging your data sources is an opportunity to become more efficient and profitable. But if you’re not careful, it might end up doing the opposite.
Therefore, if you want to make sure you’re operating as efficiently as you can and are only working with quality data, there are a few key things to keep in mind.
1. Understanding the Quality of Data from Your Incoming Data Sources is Key
One of the keys to achieving clear business decisions based on data involves understanding as much as you can about the quality of the data.
Businesses of all types are creating massive volumes of data on a daily basis but not all of it is going to be 100% accurate all the time. For the best results, you need to know the quality of data, regardless of its source. That’s where data observability comes in because it can observe your data as it comes into your company (at the source) and proactively alert you if there are any issues with the data. For example, you’ll know immediately if some of your data is missing a zip code.
This is very helpful if you are paying a 3rd party source for data. Because you can quickly alert them to the quality of the data and have them rectify the anomaly. This is before the incorrect data affects any downstream reports on algorithms.
This includes learning whether data was created internally. Or rather it was sent from another business or whether it was collected by a third-party application, and more.
Doing so also allows you to maintain better control over your data, especially in terms of consistency. It’s far easier to quickly identify any abnormalities. Or to single out incomplete records if you already know the validity of the source.
2. Avoid Duplicate Data at All Costs
Duplicate data can be an issue for a large list of reasons. If one isn’t aware that they’re dealing with a record that already exists elsewhere in the enterprise, leaders could use it to make the wrong conclusions about where their organization needs to go in the future. It can also bog down the process with inefficiencies. This makes it harder and more time-consuming to access those critical insights that people need to do their jobs.
To avoid this, data governance programs need to be developed that encourage people to share data with each other, but not in a way that ends up creating multiple versions of the same records. Also, employ centralized data management best practices to create a “single source of truth” for a company.
3. Learn Your Data Requirements
Another one of the best ways to ensure and sustain data quality involves coming to a better understanding of what you need to use that data for in the first place.
Are you trying to use this insight to expand into a new marketplace? Are you trying to use it to create more innovative products and services in the future? Do you just want to gain more analysis of the customers you’re targeting? Data can do all of this and more, but it isn’t going to happen automatically.
You need to first make sure that all key stakeholders are clear on what you’re trying to accomplish. This is so that you can then utilize data to help make that happen. This process will include data discovery. Along with including a careful analysis of the information you have and concise communication between the various departments in a business along the way.
4. Have the Right Team in Place
All of the above steps will be far easier if you have a dedicated data quality team in place. These are professionals who focus on things like quality assurance, for example. Whenever changes happen to your data – and they will often happen – the team can make sure that data quality levels are where they need to be. Plus, anything problematic can be either corrected or eliminated.
They’ll also work with people in other positions like business analysts. They’ll make sure that necessary tools are in place to ensure data quality. Custom dashboards are a great way to take the insight collected from data and present it in a visual way that is easy for anyone to understand. They’re also an opportunity to quickly identify any abnormalities or unusual activity. All so that they can get to the bottom of a small issue now before it becomes a much bigger one down the road.
5. Be Proactive, Not Reactive
Finally, ensuring and sustaining data quality requires all parties to come to terms with the fact that this is not something you “do once and forget about.”
Especially if you’re implementing data analytics tools for the first time, it’s easy to assume that once you get everything up and running and configured in the way that you need, the system will more or less operate itself.
In reality, the reverse is almost true. Just as the quality of the insights you’re getting is only as good as the quality of the data you’re feeding into the system, you must take a proactive approach to data management.
Meaning, test systems often to make sure that everything functions properly. Sources need continual examination on a regular basis. This makes sure they’re getting the right information to the right people at precisely the right time. As your business continues to evolve, requirements and rules will change. Your data management practices need to change right along with them.
Once you have such a workflow in place, you’ll have the rock-solid foundation you need that you can then continue to build from over time.