With the rise of generative AI (Gen AI), enterprises are entering a new paradigm when it comes to business and data analytics, and it is poised to bring challenges. Business and data analytics continues to grow, but the satisfaction level is only moderate. As data analytics are only going to get more complicated with Gen AI, it is important to meet the challenges of data analytics head-on.
Figure 1 from our full report, Business and Data Analytics Adoption and Customer Experience, shows that the adoption, investment, ROI success, and TCO success rates for business and data analytics are high. However, the satisfaction rate is moderate.
This new phase of business and data analytics combines data with Gen AI to not only make predictions but also generate new ideas, create products, improve business processes, communicate with clients and end users, and otherwise uncover data like never before. It is becoming more popular to use a combination of predictive and descriptive analytics for future outcomes. Altogether, these trends are improving business and data analytics results across enterprises.
However, challenges remain when it comes to wider adoption of business and data analytics tools. For instance, our research shows that a common challenge occurs when a company finds that its data is not as clean or well-structured as anticipated; this presents problems for analytics solutions. Inaccurate, incomplete, or outdated information can lead to poor decision-making and ineffective business strategies. Data integration is also a major issue as data silos still exist across applications. Moreover, some companies are not making efficient use of data and struggle with data visualization. This becomes infinitely more difficult as the use of Gen AI increases within the enterprise.
“Like with any data analytics project, ‘what you put in is what you get’,” said Waynelle John, research analyst for Computer Economics, a service of Avasant Research, based in Los Angeles. “Generative AI relies on vast data pools, leading to the inclusion of inaccurate information. The moderate satisfaction rating is a sign that these challenges are impacting data analytics projects.”
Fortunately, the market is responding to these challenges. For instance, vendors specializing in data analytics as a service have touted new solutions for processing unstructured data. Moreover, the desire to better leverage data locked up in transactional systems, the falling cost of storage, and the emergence of easier-to-use business and data analytics tools are helping drive investment in these solutions. As new solutions are introduced to address common challenges, we can expect an improvement in the levels of satisfaction with these technologies.
Our full report examines the adoption trends for business and data analytics technology of all types, providing insight into how many organizations have the technology in place, how many are implementing it, and how many are expanding investments in new capabilities. We also recommend steps for successful implementation.
To give additional insight, we look at the economic experience of those who have adopted the technology. We examine the ROI experience in terms of the percentage of organizations that report positive and break-even ROI within a two-year period. We also balance the potential ROI against the risks measured in terms of the percentage of organizations that exceed budgets for TCO.
This Research Byte is a brief overview of our study, Business and Data Analytics Adoption and Customer Experience. The full report is available at no charge for subscribers, or it may be purchased by non-clients directly from our website (click for pricing).