AI can help solve complex problems, automate tasks, generate insights, and create new value. However, I also know that AI is not a magic bullet that can work with any data. In fact, data quality is one of the most critical factors for the success of any AI project.
Data quality refers to the accuracy, completeness, consistency, timeliness, and relevance of the data used for AI. Poor data quality can lead to inaccurate predictions, unreliable outcomes, biased decisions, and wasted resources. According to a report by IBM1, bad data costs the U.S. economy $3.1 trillion per year.
One of the biggest challenges for data quality is data preparation or scrubbing. This is the process of cleaning, transforming, and enriching the data to make it suitable for AI. Data scrubbing involves tasks such as removing duplicates, correcting errors, filling in missing values, standardizing formats, defining response standards, and adding labels.
Human-collected data adds one more challenge to data scrubbing. Did the human who collected the data tell the truth? Truthful data isn’t something we worry about as much with closed systems as we do with humans, people have reasons to tell lies or cut corners at their jobs. We find in our world that pencil whipping is a real issue. Pencil whipping is completing checklists by just answering the questions without reading the questions or checking to see if your operations meet standards.
The old adage applies, Garbage in Garbage out. This is especially true with AI and predictive analytics. If you are using bad data to predict you are going to get bad predictions every time.
Data scrubbing is not only tedious and time-consuming but also expensive. According to a survey by CrowdFlower2, data scientists spend 80% of their time just getting data ready for analysis. That means only 20% of their time is spent on actually building and deploying AI models. Considering that the average data scientist makes $200k a year2, that means you are spending $160k a year just paying a data scientist to go through your data.
That’s a huge hidden cost of AI that many organizations overlook or underestimate. Imagine if you could reduce or eliminate that cost and free up your data scientists to focus on analyzing data vs scrubbing data. How much more impact could you achieve with your AI initiatives?
That’s where OpsAnalitica comes in. OpsAnalitica has AI-ready data that can be used out of the box in your AI models with very little to no scrubbing. AI-ready data is data that meets the quality standards and requirements for AI applications. It is accurate, complete, consistent, timely, and relevant.
How does OpsAnalitica achieve AI-ready data? By using its unique technology called Data Accuracy Scoring. Data Accuracy Scoring is a method that automatically evaluates the quality of each data point based on various criteria and then classifies each data point as accurate or not accurate.
Data Accuracy Scoring allows data scientists to easily identify and filter out inaccurate or unreliable data with one button click. It also helps them prioritize and select the most relevant and trustworthy data for their AI models. Data Accuracy Scoring is only available from OpsAnalitica.
By using OpsAnalitica’s AI-ready data with Data Accuracy Scoring, you can save time, money, and resources on data scrubbing. You can also improve the performance, reliability, and fairness of your AI models. You can unleash the full potential of AI for your business and industry.
If you would like to learn more about Data Accuracy Scoring and OpsAnalitica’s AI offerings, please schedule a Free AI Strategy Session with us today. We will help you assess your current data quality situation and show you how you can optimize it for AI success.
Data quality is not only an AI problem but also an AI opportunity. Don’t let poor data quality hold you back from achieving your AI goals. Let OpsAnalitica help you turn your data into a competitive advantage with AI-ready data and Data Accuracy Scoring.