Is poor data quality letting down your AI?
The most successful companies in the future will be those that optimize their AI investments. As companies begin their journey to AI readiness, they must develop robust data management strategies to handle increased data volume and complexity, and ensure reliable data is available for business use. Poor quality data is a burden for users trying to build reliable models to extrapolate insights for revenue-generating activities and better business outcomes.
It’s not uncommon for business users to prioritize access to the data they need over its quality or usability. The simple truth is that if an organization has poor quality data and uses it to feed AI tools, it will inevitably produce poor quality and unreliable results.
Chief Product Officer, Ataccama.
Why data quality is important
Data quality is critical because it acts as a bridge between technical and business teams, enabling effective collaboration and maximizing the value derived from data. Depending on the data source and governance requirements, this poses a time-consuming challenge for data scientists, who can spend up to 80 percent of their time cleaning the data before they can even start working with it.
Merging data sources is a huge task. The work involved in combining and transforming multiple data sets, such as raw data from regular business activities, old data in various formats, or new data sets acquired after an acquisition or merger, should not be underestimated.
This is important work for business development purposes. Data is critical to better target marketing and sales, drive product innovation and market expansion, improve customer service, and even create an AI chatbot or agent to enhance the brand experience. It is also critical to ensure compliance with the latest regulations and to prepare for likely future requirements in key areas including data privacy and protection. So companies need to know which data contains sensitive information to secure it and prevent leaks or breaches.
But not all data is equal and organizations need to be able to distinguish the high-value data that is business-critical from the low-value, low-risk data that does not require management or protection. The only way to do this is to ensure that the data is clean and of high quality.
Cultivating a data-driven culture
Being data-driven means developing an organization-wide culture that understands value from data and actively strives to inform all decision-making to ensure better business outcomes. It’s less about having the data and more about knowing how to optimize it.
This requires a high level of maturity and dedication to develop this ability over time. One of the key challenges for organizations becoming more data-driven is effectively connecting technical and business teams. This is not a new problem, but many companies have not yet successfully addressed it and it is hindering their ability to become data-driven.
Data teams are often focused on building a foundation for data governance and setting up various tools and processes to help their organization. However, business teams may conclude that the data they receive is too technical, not of the right quality, not in the right format, or simply not the right data they need. The data team may not understand the business context of the request and therefore what data is needed, and this unintentional misalignment is a huge challenge for organizations to overcome.
The result is that companies end up with data teams that do their best to build robust data governance systems, but business teams remain dissatisfied and underutilize the data. This is where accelerating data transformation with AI-enhanced data quality initiatives becomes critical. Business users need solutions that allow them to work independently with data: change formats, enrich them and solve problems automatically through smart algorithms. This provides the reliable data base needed to implement successful AI projects.
Successful AI starts with data management
However, despite the current hype around AI, Gartner estimates a loss of confidence in generative AI projects due to poor data quality as one of the main reasons. It is predicted that at least 30 percent will be abandoned at the proof of concept stage by 2025. .
Ensuring data quality comes from setting up an organization-wide data governance strategy. This allows the company to focus on the desired outcomes of using AI and generative AI, rather than rolling out AI regardless of the state of the data that will be used to train AI. However, AI is also a tool that will help bring the data into a state of AI-readiness by reducing the manual oversight and labor traditionally required to transform and cleanse data by automating processes and rules. It can also help profile and classify data and detect anomalies, contributing to the overall health of data sets.
GenAI can capture data in non-standard formats, including tables, images and even audio, to ensure data quality rules are universally applied. AI also enables non-technical users to self-serve and find the data insights they need by using natural language to process queries, supporting the creation of business value for an organization across all its departments. This process of data democratization is central to the success of all AI initiatives, as limiting its adoption and benefits to engineering teams will severely limit their impact.
Ultimately, quality is more important than quantity when it comes to AI training data. Any poor quality recording will add confusion to the LLM, increasing the risk of hallucinations, and when poor quality data is used consistently, the reliability of the results will decrease. Today, an inflection point has emerged with the rapid advancement of AI toolsets, the exponential increase in data, and digital and AI regulations, meaning organizations have an opportunity to fine-tune their data strategy. With competitive advantage, market expansion, customer experience and business growth all on the line, the winners will be those who prioritize this transformation now.
We list the best data visualization tools.
This article was produced as part of Ny BreakingPro’s Expert Insights channel, where we profile the best and brightest minds in today’s technology industry. The views expressed here are those of the author and are not necessarily those of Ny BreakingPro or Future plc. If you are interested in contributing, you can read more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro