In 1492, Christopher Columbus crossed the Atlantic Ocean to find an alternative route to Asia. However, Columbus relied on the deficient calculation by the geographer Alfraganus to chart his route. Columbus landed in the American continent due to such deficient calculations, calling this geography “India”. While this “bad data” seems to be lucky from today’s perspective, it actually gave rise to a major problem.
Your company might not always be that “lucky”.
So what is this “bad data”?
Your company is expanding, and the progress being made on your software also appears to be very good. Naturally, you have plentiful data. It is inevitable that there are faults in some of all this data. But you first need to define and understand what “bad data” is. In short, “bad data” is a whole set of false information, including inaccurate, missing, unconfirmed, inappropriate and duplicated data and bad data entries (misspellings, typos, spelling differences, formats, etc.). As for artificial intelligence software, the performance of your software and the credibility of your company may be affected adversely when this bad data is not managed properly.
At Salesforce and INSPARK, we see high-quality data as the heart of artificial intelligence. That’s why we are aware that bad data severely affects the performance and accuracy of artificial intelligence algorithms just as junk food is harmful to our hearts.
How data is controlled and managed?
When companies know what their customers do, when, why and how, it gets easier for them to see the “big picture”. And that’s the simple key to incorporating high-quality data into artificial intelligence. You have a whole bundle of data, but is this data current, confirmed and complete? To be knowledgeable about this, it is an important step to accept that this data pool may include bad data. Because defining this bad data as a driving force for improvement in problematic areas, rather than pretending that the system includes no bad data, might be half the battle.
As is well known, the artificial intelligence technology is data-centered. So, you may need to eliminate duplicates, inappropriate/unrelated values, faults and all other things that might adversely affect your decision making pattern to ensure the quality of your data. For example, you can correlate your data sources (in marketing, sales, service, commerce) with a single registry that is updated in real time. This will allow the sources that feed artificial intelligence with data to have developed the right content for its performance.
What to do?
Customer data is at the heart of progress in your company business. For an effective artificial intelligence program, your data does not need to be perfect, but should be clean and of good quality. And this means the non-presence of bad formats, duplicates or labeling due to good data. For this reason, our data specialists at Salesforce Tableau provide a guidance for you showing how to cleanse your data: Data Cleaning Guidance: Definition, Benefits, Components + How to Enhance the Quality of Your Data
The most important steps of the guidance is listed as follows:
*First remove duplicated and seemingly unrelated data. It is likely that you acquire the same data from different departments, based on the nature of the jobs performed. Additionally, the data from different departments may not always be data with related content. That’s why specialists recommend that you should start here first.
*Fix structural and concrete faults. Structural faults emerge when you measure or transfer data and notice odd naming, typos or improper capitalization. These inconsistencies may lead to mislabeled categories or classes. For example, you may think that both “No Result” and “Not Found” phrases are the same, but it is very important for the algorithm of artificial intelligence whether such phrases are analyzed in the same category.
*Filter out unwanted, unrelated, inappropriate and inaccurate data values. It may be necessary to start with data that seems hard to analyze at first glance. Doing this is recommended for the performance of artificial intelligence. Remember that high-quality data is directly associated with the productivity and performance of artificial intelligence.
*Handle missing data. It is very likely that some missing data will appear to have been lost since many algorithms will not accept missing values. There are three ways that need to be followed here: 1.delete missing data (while doing this, make sure that no correlated data is damaged) 2.complete missing data by updating it (pay attention to the integrity of the information) 3.intervene technically in the way data registries are formed by finding the reasons for missing data.
*Make sure that you can answer certain questions for verification.
— Is this data logical?
— Is this data consistent with the rules stipulated by the field?
— Does this data support your work, is it supplemental or misleading?
— Using this data, can you find the trends in data that would help you develop your next work?
— If no, is this the outcome of a data quality issue?
What does identification and mitigation of bias in data represent for companies?
Bias in data is the systematic production of false results in the artificial intelligence algorithm by using bad data. False results originating from bad or “dirty” data may cause a poor business strategy and decision-making process. For example, your work might bring shame to your company when you realize during a reporting meeting that your data has not been reviewed or analyzed. For this reason, it is essential to create a “high-quality data culture” in your company. First, you need to clearly define the tools that you can use to create this culture and what data quality means to you. Then, you can start the data cleansing process, after which detailed reports of fault processes can help your efforts to create a “high-quality data culture”.
It is now easier to control your data thanks to the products and alternative solutions by Salesforce and INSPARK.