Trust in the data behind artificial intelligence and other data-driven initiatives is going downhill – fast. A recent survey finds fewer than half of business leaders say they have the data they need to pursue cutting-edge strategies – and their numbers have declined somewhat dramatically over the past two years.

The Current State of Data Trust

That’s the word from a recent Salesforce survey of 552 business leaders, which finds trust in the data that underpins data-driven decisions is falling. For example, only 40% trust the reliability of their companies’ data – down from 54% in a similar survey conducted in 2023. Likewise, 41% say their data is relevant – down from 50% two years ago. And, stunningly, a mere 36% believe their data is accurate – down from 49% just two years ago.

Challenges in Data Analysis

While 63% of leaders say finding, analyzing, and interpreting data on their own is key to their jobs, 54% of them aren’t fully confident in their ability to do this. This lack of confidence is a significant issue, as executives understand that subpar data collection, cleansing, and curation processes can lead to unreliable decision-making.

The Impact of Poor Data on AI Initiatives

Industry observers agree that data poses a problematic issue for AI initiatives of all kinds. Executives are aware that feeding low-quality data into AI models can result in poor decision-making. When it comes to automated processes, the stakes get even higher. “You better have data to back it up, and that’s a problem” said industry analyst Andy Thurai. “Executives are afraid their data is not complete, not wholesome, not timely, stale, not reliable, or not accurate.”

The Role of Synthetic Data

Complicating the situation is the fact that many enterprises now use synthetic data to train their AI models when not enough data is available, or to maintain security. The problem is that “executives’ confidence that the models are trained on real-world data is not there,” Thurai added. This lack of confidence can lead to hesitation in adopting AI solutions.

The Need for a New Approach

Part of the issue may also be the fact that applications designed for the 2020s are relying on technology designed in the 1990s. Enterprises “often struggle with a data infrastructure built over many years that utilizes many different technologies, and was built without a clear plan or direction,” said Dwaine Plauche, senior manager of product marketing for Aspen Technology. What is required is a shift in thinking for the handling and positioning of enterprise data.

Rethinking Data Infrastructure

Plauche advised that enterprises should rethink their data infrastructure as an internal customer service. “The goal should be providing internal customers or projects with needed data – a service-oriented mindset that is aligned with the organization’s strategy.” This approach can help to improve data quality and increase confidence in AI decision-making.

The Role of AI in Data Management

Of course, AI itself can help manage and overcome issues with data. “An adage among veteran data scientists is that good modeling is 80% data preparation and only 20% modeling and analysis,” said Richard Sonnenblick, chief data scientist at Planview. AI can help to identify and filter errors in data, lowering the chances of leading a team to false conclusions.

Using AI to Solve Business Problems

Sonnenblick advised that leaders should start with the business problem and work backwards. “AI for AI’s sake is unlikely to achieve useful results. Leaders should identify the desired outcome of a particular task that AI will support, such as decreased time to completion or a higher success rate. AI excels at identifying patterns in data that are too complex to be quickly recognized by humans, meaning it can provide powerful insights for reassigning tasks or reallocating resources to maximize margins or minimize downtime.”

Conclusion

In conclusion, trust in data is declining, and this is having a significant impact on AI initiatives. To overcome this issue, enterprises need to rethink their data infrastructure and approach to data management. By using AI to manage and overcome issues with data, and by starting with the business problem and working backwards, leaders can increase confidence in AI decision-making and achieve better outcomes.

FAQs

• Q: What is the current state of trust in data?
  A: Trust in data is declining, with only 40% of business leaders trusting the reliability of their companies’ data.
• Q: What is the impact of poor data on AI initiatives?
  A: Poor data can lead to unreliable decision-making and a lack of confidence in AI models.
• Q: How can AI help with data management?
  A: AI can help to identify and filter errors in data, and provide powerful insights for reassigning tasks or reallocating resources.
• Q: What approach should leaders take when using AI to solve business problems?
  A: Leaders should start with the business problem and work backwards, identifying the desired outcome of a particular task that AI will support.