- "Data to drive execution" is one of the three strategic priorities of market-leading CEOs. Reaching the goal involves improving analytics maturity, of which prescriptive analytics is the final stage.
- 44% of Chief Data Officers surveyed by Gartner name return-on-investment from data analytics as their key objective. At the same time, prescriptive analytics is believed to have the most positive impact on business value compared to other analytics methods.
- Prescriptive analytics is a major trend in business intelligence, with forecast mass adoption across all big data projects.
The report assesses the potential for prescriptive analytics continued adoption within large enterprises based on its stated importance by market-leading CEOs and data and analytics trends for large companies. It also takes into account overall adoption forecasts and drivers since large enterprises are the primary market for prescriptive analytics.
Prescriptive Analytics and Data Maturity as Priorities for Large Enterprises
- A survey of 1,300 market-leading CEOs and a discussion panel with a small group of top CEOs, conducted by SBI's Research Institute, found that the most successful CEOs name "data to drive execution" as one of their three strategic priorities.
- At the same time, one of the key themes around data analytics is improving analytics maturity. The four stages of maturity are descriptive (what happened?), diagnostic (why did it happen?), predictive (what will happen next?), and prescriptive (what should we do?).
- Top CEOs believe that using prescriptive analytics helps companies grow faster than the market, especially if the capabilities are extended to frontline managers and individual contributors.
- The report also notes that increasing analytics maturity grows commercial effectiveness by 3-7%.
- Multiple other sources, including Forbes, Forrester, Gartner, and INFORMS, name prescriptive analytics as the final stage of data maturity.
- In a global survey by BCG, the companies across all regions aimed to improve their data maturity significantly between 2019 and 2021. The average target was a 26% improvement.
Data and Analytics Trends for Large Companies
- Additionally, several of 2021 data and analytics trends for large companies identified by Gartner point toward growing importance of prescriptive analytics, even though it is not explicitly mentioned.
- The first trend is "smarter, more responsible, scalable AI," which "will enable better learning algorithms, interpretable systems and shorter time to value." If organizations start to refine their AI capabilities, they are likely to increase their adoption of prescriptive analytics since insufficient machine learning capabilities is the main obstacle to its effectiveness.
- It is worth noting that only 20% of organizations have a mature ML strategy, even though it is necessary to capitalize from prescriptive analytics. Since companies are increasing their machine learning budgets, it is likely that the growth of ML and AI will result in growing use of prescriptive analytics.
- The second trend is composable data and analytics, with improved user experience and better connection between data insights and business actions. It can also point toward the continued adoption of prescriptive analytics since it revolves around offering solutions and proposing business actions.
- Another trend potentially related to prescriptive analytics is "engineered decision intelligence." As noted above, prescriptive analytics offers data-driven business decisions, which makes it an important part of an architecture that supports decision-making.
- A Deloitte survey among US-based executives from large companies found that 64% of them believe that business analytics will be one of the three most important topics in the coming years. Furthermore, 67% aren't comfortable with the data output they get from their current tools.
Prescriptive Analytics Providing Unique Value
- According to an article published in the British Journal of Management, which includes an overview of research on prescriptive analytics, it has been described as "the next step towards the development of business analytics, resulting in optimized decision-making for business performance improvement," "the most sophisticated type of business analytics, that is capable of generating the greatest intelligence and value for businesses," and "a critical advancement in analytics that can improve decision-making and process effectiveness."
- Strategic CFO includes several benefits of prescriptive analytics, which are directly related to increased revenue. They are improved evaluation of the effectiveness of investments, shortened planning cycles, automating redundant processes, integrating cross-functional teams, and providing a comprehensive view of the market.
- KPMG also believes that prescriptive analytics boosts profitability and increases competitiveness.
- The above findings suggest that using prescriptive analytics provides the most significant return-on-investment out of data analytics methods. At the same time, 44% of Chief Data Officers surveyed by Gartner name ROI from data and analytics as their top objective.
Predicted Adoption (Regardless of Business Size)
- According to SplashBI, the major future trend in business analytics is the mass adoption of prescriptive analytics across all big data projects for a variety of goals, including improved supply chains, cost reduction, better customer service, and increased revenue.
- Furthermore, Forbes believes that prescriptive analytics is still not mature, which means that its capabilities will likely grow significantly in the coming years. With its growth, it is likely that it will answer the needs of an increasing number of large companies.
- Data Science Central also emphasizes that prescriptive analytics is less mature compared to predictive and descriptive analytics. As it matures, new use cases in industries not typically associated with data analytics will emerge.
- DataPine mentions predictive and prescriptive analytics among 2021 trends in data and analytics. It claims that they are the most discussed topics by business intelligence professionals because companies will increasingly focus their business analytics on the future. Prescriptive analytics is the most effective method to do that, as it uses tools such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning to come up with the most appropriate business decisions.
- The growing popularity of prescriptive analytics, which is "changing the analytics game," has been described in similar terms by SelectHub.
- Additionally, KPMG notes that the pandemic unveiled the need for more widespread adoption of prescriptive analytics. It uncovered the gaps of decision-making processes within businesses, with the most commonly used forecasting methods not being useful in mitigating the risks.
- Apart from a survey by SBI's Research Institute, there is not enough information available on the potential adoption of prescriptive analytics among large enterprises, though they are repeatedly named as the primary market for it.
- However, sources such as SBI and Gartner believe that leading and large companies will work toward data maturity (of which prescriptive analytics is the final stage), more effective use of AI and machine learning, and optimized decision-making. Those factors suggest that the adoption of prescriptive analytics is likely to continue within large enterprises.
- Furthermore, since prescriptive analytics is more accessible to and more widely adopted among large enterprises, overall trends apply to this segment. They include predicted increased adoption due to the need for more refined analytics inspired by the uncertainty of the pandemic and forecast incorporation into all big data projects due to high ROI and a variety of benefits. Adoption is also expected to grow as prescriptive analytics matures and new capabilities emerge.
After searching market research reports, company whitepapers, company blogs, articles in business, tech, and data analytics media, and academic papers, we concluded that the information that is directly relevant to the potential for continued adoption of prescriptive analytics within large enterprises in the US is very limited. Therefore, we had to turn to a creative approach. We put the adoption of prescriptive analytics in the context of leading companies striving for data maturity, as well as predicted data and analytics trends among large companies. We also included predictions that aren't tied to businesses of a specific size. Since prescriptive analytics is primarily associated with large enterprises, we assumed that they are applicable.
Please note that due to low information availability, it was not possible to corroborate each finding with more than one source. We still prioritized findings that were supported by multiple sources. Additionally, some of the findings, as noted within the related bullet points, are global. Unfortunately, the vast majority of available surveys and reports on data analytics don't provide US-specific insights.