The Generative AI Journey Begins with Your Data

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise technology. In this feature, Qlik‘s Chief Strategy Officer James Fisher offers commentary on why the generative AI journey begins with your data and the steps to start.

The daily deluge of generative AI headlines, new vendors, and tech enhancements are enough to evoke anxiety in even the most steadfast executives. Many ponder whether to hop on the generative AI bandwagon quickly, which can sometimes result in missteps, or ignore the hype and potentially get left behind in a highly competitive race. What we found, unsurprisingly, in our own generative AI benchmark report is that 44 percent of enterprises have invested in the technology without any sort of strategy. While I cannot necessarily take away these conflicting feelings, I can assure business leaders that focusing on getting your data house in order will set your organization up to harness the potential of innovative technologies like generative AI – now and in the future.

Fundamentally, generative AI, like all AI, is all about data. Without high-quality and relevant data, generative AI can easily return results that are incomplete or false. Using any old data will not do. I fully agree with McKinsey, who notes that a modern data fabric is a key component for a successful approach to generative AI. This requires organizations to commit to an unwavering process of accessing high-quality, well-harmonized data supported by a scalable data architecture with proper governance and security measures in place.

Harnessing AI’s power will require several actions, such as deploying AI for advanced use cases with a trusted solution. As a start, below are four core areas to fine-tune when it comes to your data and any generative AI or traditional AI effort.

The Generative AI Journey Begins with Your Data

Establish Strong Data Governance 

Businesses are already required to comply with numerous data security and compliance regulations and processes. Poor data quality or uncontrolled data can compromise an enterprise’s decision-making, digital experiences, and operational efficiency and even stifle innovation. These issues are compounded once you add AI into the mix as leaders become concerned with how to maintain the security, privacy, and governance of an organization’s data and how to mitigate the risk of false conclusions based on inaccurate or incomplete information. Data must be organized and trusted to be used as a reliable source for both core operations and AI, so it is imperative to find a data quality solution that meets the needs of the business.

Ensure First-Rate Data Variety

The ability to bring data together from multiple sources – including, but not limited to, SaaS apps, mainframe, files, SAP – from multiple locations and in various formats in real-time is essential to using traditional and generative AI. It fuels the effectiveness and efficiency of AI applications by giving access to a diverse range of information, facilitating more complete and precise analysis. AI models will then be able to unearth more valuable insights, recognize deeper patterns and serve up more informed predictions. Synthesizing data from different sources also strengthens the overall quality and robustness of AI algorithms by lessening biases and other limitations that may arise from relying on a single dataset. This can be done by finding a data integration partner that can bridge connections and deliver information in real-time.

Strive for Connected Systems 

Generative AI will have a truly significant impact on the business when its outputs can be driven directly into operational systems through analytics to truly automate processes that trigger action for business units. Integrated AI-powered analytics can continuously analyze incoming data in real-time, providing immediate insights or recommendations based on pre-defined triggers or thresholds. These triggers can be created with the help of generative AI and then be programmed to launch precise actions or alerts when certain conditions are met. Imagine creating an AI model that analyzes customer data in real-time and detects a potential churn risk. It can trigger a pre-defined automated email campaign almost immediately to re-engage the customer. This is why a no-code automation solution can be the linchpin that generates sophisticated workflows, connected to all business applications and triggers action from each system.

Generate Easily Consumable Insights 

AI systems can generate large amounts of data and insights on their own, but it is critical for businesses to serve up these insights in a format that humans can easily comprehend, collaborate around in real-time and directly drive action from. Digestible formats span both text and visible representations such as graphs, charts or interactive visualizations within the context of other analytics. Representing the outputs of AI in a user-friendly format drives data literacy, making it easier for more people at multiple levels of the enterprise to engage with applications and rely on them for solving complex business issues. Whatever the analytics solution is for a business, it needs to marry dynamic visualizations and dashboards with AI and ML to generate the most meaningful insights that drive value.

Take a Data-First Approach with Generative AI

It is a transformative time in business and technology. Accenture underlines that “solving the data challenge is an urgent priority for every business,” making the race to leverage generative AI and new technologies one about data first. Prepare your business for the exciting future ahead and build your data house with a rock-solid foundation.

In my next piece, I’ll cut through the noise and outline some potential use cases for generative AI integration within analytics.