Artificial intelligence and data quality — the what, why and how.
How to address the artificial intelligence and data quality issue
If poor quality data is bad, then how do you combat this? How do you ensure your business utilizes artificial intelligence and data quality in tandem. Evidence has shown that companies that take a holistic approach to data analytics and data pipeline get better outcomes from their analytics than companies who don’t. To break down what this means, we’ll be looking at some recent findings from an IDC survey with business decision-makers and also some industry best practices.
How much data do you capture?
Of course, capturing data is an essential part of data science. However, the survey found some key differences in how different companies perceive the volume of data they collect. Most respondents reported that they can capture 70-90% of relevant data. The keyword here is ‘relevant’. It’s thought that most respondents were referring to data they have generated themselves from their internal systems. However, companies who admitted to struggling with finding relevant data were also the companies considered to be in the top 20% of leaders. Why? Because these companies were taking a more holistic approach to data capture. They weren’t just focused on their data, but also the relevant data that could be gathered from their external partners or other parties.
What is high-quality data?
Additionally, almost 50% of respondents reported that they have issues with data quality. The key to addressing this issue is to continually tweak your solutions until they are fit for purpose. For data to be considered high quality, 5 criteria need to be met:
- Accuracy — The data needs to be accurate to be useful.
- Completeness — There can’t be missing values because this will lead to inaccurate results.
- Relevancy — The data must be relevant to the purpose it’s being used for.
- Up-to-date — Old data can tell you a lot about the past, but the more up-to-date data you have, the better. The world is changing rapidly, and this will be reflected in your data. If you use old data to make new decisions, you’ll get out-of-date solutions to modern problems.
- Consistency — The format of the data must be consistent to avoid erroneous results.
To succeed in all of these areas, you need to start taking control of your data by utilizing good data profiling tools. Automation is extremely useful here since it can do most of the heavy lifting for you. The data pipeline must also be carefully designed to avoid duplicate data which can lead to skewed results in AI and ML algorithms. The pipeline must follow a clear and logical design that works at the enterprise level. Data must be audited regularly to ensure data management is working correctly. Requirements for data accuracy must be clearly documented and expectations must be clearly outlined. Data governance teams and data project teams must be fully involved in any data projects.
The ROI of high-quality data
As with most business decisions, ROI must be considered. When you’re considering spending hefty sums of money on automated data solutions to transform your business into a data-driven enterprise, ROI naturally enters the discussion. Most companies understand the value of data science, but few companies understand the impact poor quality data has on their business.
One study by Gartner, which looked at 140 companies, found that on average, these companies estimated they were losing $8.2 million annually to poor data quality. This was the average across all companies, but the figures become more alarming as you delve deeper. A huge 22% of companies estimated they were losing more than $20 million a year.
This loss comes from both unrealized revenues and costs savings, as well as the poor outcomes that come from erroneous data, inconsistencies, duplicated data and missing data.
Only ever use artificial intelligence and data quality together again with NodeGraph
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NodeGraph provides you with easy access and transparency of your company’s data and its sources. Knowing the what, where, how and why gives you confidence that everyone is using the right data to make actual, rather than assumed, decisions. Don’t waste time, resource, and money when the only fully automated metadata extraction platform on the market can do the work for you! Enable easy sharing of data throughout the organization, automate documentation and testing and reduce errors.
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Read the full guide
Access the full guide and learn about the importance of artificial intelligence and data quality and the harm that can come from poor quality data, as they are used in AI and ML.
The guide covers
● An overview of the current data landscape
● The relationship between AI and data quality
● How bad data harms AI and ML
● How to achieve high data quality
● Data quality and ROI
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