It seems like every company is rushing to add an AI-powered chatbot, agent assistant, or forecasting/ prediction tool. But are you ready to take the steps needed to ensure that these initiatives provide the value you expect? One of the most problematic areas surrounding AI projects is the data used to train the algorithm. This is typically due to data maturity, which can be broken down into data quality and data quantity.
Data quantity is pretty self-explanatory; you need enough data to create statistically significant results. Data quality is a little more challenging concept, and it can directly impact your required data quantity. Data quality revolves around the accuracy and completeness of the information. With high-quality data, you typically start to see significantly statistical results with a lower quantity of data. In contrast, if the data is poor, you will typically not see any significant results. Just providing huge amounts of random data does not help with AI. A great saying to remember this is, "garbage in, garbage out."
This blog will mainly focus on data quality since it is the easiest to boost and affect your needed data quality. Let's look at some basic evaluation metrics surrounding data quality and go through a few basic tips that can help you improve your data and processes.
You have very little understanding of data quality concepts, and you ignore or are unaware of most of the issues causing low data quality. You don’t have any structured approaches for improving data quality, and your basic belief is that all of your information or data is correct without any cleaning. You don’t have anyone directly responsible or accountable for the quality of data.
You are enforcing basic data quality controls through mandatory fields, validations, and formatting. You perform simple data quality cleaning on an ad hoc basis. Most organization members see data quality as IT's responsibility despite any formal or even informal division within the department.
Data quality has become a notable concept within your organization but is not a priority. You have simple data quality tools that assist in efforts to clean data. Your data has a high enough quality to be used for making most business and process decisions. Critical issues and areas of improvement have been identified, at a high level, for future remediation. You have created organizational guidelines and clear ownership of data collection, processing, and correction.
Data quality has become a priority within your organization. You have implemented tools and processes to quickly identify any trouble areas and remediate them as quickly as possible. You have regular audits and assessments of data quality processes. You have established formal owners and roles surrounding all areas of data collection and quality, and you have taken dashboards or other tools into use for constant monitoring and evaluation.
You have rigorous data quality controls, including monitoring, auditing, and real-time evaluation of trouble areas and issues. Data quality is one of your organization's strategic initiatives with dedicated resources. Your data is now being assessed based on subjectivity, along with accuracy, completeness, etc. Your organization is continuously looking for new data sources and methods to continue to expand your data quality.
If your organization's data maturity is lower than you were expecting, don't worry. With a few strategic actions and initiatives, you can quickly improve your data quality. Here are 5 tips that will help most organizations enhance the quality of data.
Even with low data quality maturity, organizations can still have high data quality. Before you start changing any processes to improve data quality maturity, don't forget to check what are the areas with the most significant problems and in need of immediate changes.
Nothing fixes a problem faster than making it one person's problem. But don’t forget, you need to give that person or team responsible enough power to make the necessary changes and corrections to improve the data quality.
Most people hate mandatory fields. They never have the right options and only cause headaches and ultimately misuse. This isn't because the idea of mandatory fields is flawed; only the fields are not optimized. Talk with users to find core problems and create actionable solutions to fix them.
Review the most and least used or common values to find quick wins and improved accuracy. Typically, the most used values are too broad, and the least used are either irrelevant or confusing. There is no magic bullet for data quality, only continuous optimization.
A great way to standardize your data quality audits and be quickly alerted of issues is through continuous monitoring. Using dashboards and agreed-on KPIs will help your data quality team speed up reviews and solve minor issues before they become major problems.
When evaluating your organization or systems readiness for AI, data maturity (quality and quantity) is only one area that should be reviewed. If you want to learn more about some of the other key areas associated with AI adoption such as organizational maturity, value, direction, and risk, be sure to check out our guide "Tips for excelling with AI in ITSM," where we go through these core concepts.
Patrick works as a Product Manager at Efecte, focusing on portfolio development and strategy. He works closely with key stakeholders and other members of the product unit to identify potential areas of development, new concepts, strategies, and technologies to enhance our solutions.
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