3 Ways to Eliminate Data Biases at Your Company
Data may be the future, but it's anything but perfect in the present.
AI and machine learning are becoming synonymous with business success. Everywhere you look, companies are utilizing data to reach new heights.
In spite of its benefits, though, utilizing data analysis in business still comes with its fair share of issues. One of these is data bias.
Data bias occurs when an enterprise uses data that isn't representative of the end-user or any other focal point of a study. In other words, as cold and calculated as data may seem, there are many ways that information can become skewed as it works its way through a company's system.
Here are a few ways to ensure that data bias isn't impacting your company.
1. Assemble the right team
One of the biggest issues with data bias is the fact that you can't trust a piece of software to naturally discover its own biases. It's up to humans to figure that out.
That's why a foundational step in eliminating data bias is assembling the right team. Start by finding individuals whom you can trust to bring a sense of accountability to your data analysis. But don't stop there.
The right team should also be capable of interpreting data and discovering flaws. When mitigating potential data biases, Hank Prybylski, EY Global Vice Chair of Transformation, includes people in the planning process who are willing to find and address deficiencies between the product design and its execution. This ensures the accountability necessary to effectively address data biases.
There are many activities and filters that you can set up to prevent data bias. However, having the right people "at the table" to implement them is a cornerstone to sustained success.
2. Identify areas of potential bias
The next step in perfecting your data analysis is looking for areas of potential concern. Collecting, processing and analyzing data is a complex business. It involves a variety of activities, any of which can introduce bias into a system.
For instance, Ronald Schmelzer, founder of the artificial intelligence-focused analyst and advisory firm Cognilytica, highlights six different ways machine learning can introduce bias into a situation. These are:
- Sampling bias, in which a company collects data in a biased manner
- Exclusion bias, in which a company removes or underutilizes certain collected data in comparison to other areas
- Measurement bias, in which a company poorly organizes or otherwise manages collected data
- Observer bias, in which an experimenter can create inconsistencies through the act of recording data
- Prejudicial bias, in which human prejudice influences the collection and processing of data
- Confirmation bias, in which a desired outcome has an effect on the outcome of data analysis
- Bandwagoning, in which a company overemphasizes or gives undue attention to a particular trend in data
All of these can introduce bias into your data — both on purpose and by accident. It's important to consider how each one might be impacting your team's data in a negative manner.
3. Clean up your data
There are nefarious ways that data can become biased. However, in many cases, a data set becomes skewed accidentally. This is becoming easier and easier to find as companies grapple with a growing stack of data within their businesses.
This ongoing "collection" can lead to lakes of dark data that sit unused. Even when utilized, if data isn't taken seriously, it can leave companies floundering in statistics that they cannot apply with good effect.
Data observability tools offer an excellent way to "make head or tails" out of existing data. It can clean up collection methods and restore a sense of organization. This can go a long way in reducing the threat of bias by ensuring that a system is operating at peak efficiency and with maximum accuracy.
Data is the way of the future. And yet, it remains a flawed way of doing business.
Nevertheless, data's mathematical roots mean the practice of data analysis continues to have great potential precision. The important thing is that companies put in the extra effort to ensure that its AI and machine learning protocols aren't operating on auto-pilot.
Instead, competent teams must be trained and tasked with looking for any potential data inconsistencies within a system. Data observability tools can assist with this process, allowing companies to accurately assess and eliminate data bias whenever and wherever it may appear.
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