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Data teams can provide insight into important information. They can help with making key decisions, as well. However, data teams are also expensive. The need to hire the right people and equip them with the necessary tools to collect, organize and analyze data can be costly.
If you can’t afford to have a data team on staff, there are still plenty of ways to make sure you’re collecting reliable data for your enterprise — which isn’t just a good idea. It’s become a requirement.
The growing need for quality data
We live in a data-driven age. Data is everywhere you look. As a business, it’s important to collect key data, process it, analyze it and then use it to inform future decision-making.
Data and analytics used to be an activity that set companies apart and helped them dominate their industries. Now it’s become so commonplace that brands must utilize data or they won’t survive.
Exacerbating this issue is the question of how we collect and use data in the first place.
Far too often, the collection of data leads to dark data (vast collections of unused, unstructured data that sit in isolated data lakes). This leads to wasted resources and untapped potential. It can also become a liability if you have sensitive data, such as customer information.
Even worse, if data is poor or it’s analyzed incorrectly, it can become a problem. Bad data can lead to subpar decision-making, which undermines the entire reason for collecting the data in the first place.
This creates a conundrum when it comes to figuring out how to use data correctly without taking on the expense and rigmarole of establishing a team to oversee your data (a cost that many companies cannot justify). How can leaders tap into the power of data without investing copious resources into keeping that data under a data team’s watchful eye?
If your company can’t benefit from a full-scale data team, you can still find ways to improve the reliability of both your existing and your future data. Here are three suggestions for ways to improve data reliability without a professional team to back you up.
1. Lean on AI and machine learning
If you want your data to help you, you need to invest in data observability. This solution takes care of monitoring your data to detect and predict, as well as prevent and ultimately resolve data issues within your company’s infrastructure.
Good data observability happens in real time — and it can happen without a data team’s eye, too. There are data observability platforms available that utilize AI and machine learning. These go further than regular APM (application performance monitoring tools). They can monitor data on a rudimentary level and oversee nitty-gritty elements, like controlling data pipelines.
Dependable data starts with a clean internal structure. Using AI and machine learning tools can help you maintain that structure.
2. Target data collection
Dark data is one of the primary dangers of any data collection activity. Dark data refers to the accumulation of data that isn’t used, something that Forbes refers to as the “unseen data conundrum.”
It’s estimated that by 2025, 80% of data will remain unstructured and 90% will never be analyzed. In other words, the amount of data you have has the potential to drown you in its uselessness.
One of the best ways to generate reliable data is to cultivate it carefully. Ensure that you’re getting your data from targeted sources with a specific end goal in mind instead of collecting data everywhere possible for its own sake.
3. Improve the quality of data
Along with considering where you’re getting your data, also review the quality of the incoming data itself. Simplilearn outlines six key elements that define quality data:
- Accuracy: Does your data represent the real world?
- Completeness: Is your data complete enough to draw dependable conclusions?
- Consistency: How consistent is your data within your infrastructure?
- Time: How current is your data?
- Uniqueness: Do you have multiple copies of the same data or is everything recorded in your system once?
- Validity: Do you collect data in a manner that conforms to your company’s established formats?
If you find that your data doesn’t line up with these conditions, look for ways to improve its quality.
For instance, if consistency is an issue, consider setting up requirements to normalize your data collection. Make sure everyone knows how to categorize, label and organize data to keep it consistent from one department to the next.
These are just three ways to get started with improving your data. There are others out there. The important thing is that one way or another, you make an effort to maintain your data’s reliability, even if you don’t have the professionals on staff to do so for you.
Remember, data is either a liability or an asset depending on how you use it (or don’t use it). Apply these tips to ensure that your data is helping rather than hindering your company’s activities.
Rashan Dixon is a senior business consultant for Microsoft, an entrepreneur and a writer for various publications.