- 82% of enterprises are prioritizing analytics and BI as part of their budgets for new technologies and cloud-based services.
- 54% say AI, Machine Learning and Natural Language Processing (NLP) are also a high investment priority.
- 50% of enterprises say their stronger focus on metrics and Key Performance Indicators (KPIs) company-wide are a major driver of new investment in analytics and BI.
- 43% plan to both build and buy AI and machine learning applications and platforms.
- 42% are seeking to improve user experiences by automating discovery of data insights and 26% are using AI to provide user recommendations.
These and many other fascinating insights are from the recent TDWI Best Practices Report, BI and Analytics in the Age of AI and Big Data. An executive summary of the study is available online here. The entire study is available for download here (39 PP., PDF, free, opt-in). The study found that enterprises are placing a high priority on augmenting existing systems and replacing older technologies and data platforms with new cloud-based BI and predictive analytics ones. Transforming Data with Intelligence (TDWI) is a global community of AI, analytics, data science and machine learning professionals interested in staying current in these and more technology areas as part of their professional development. Please see page 3 of the study for specifics regarding the methodology.
Key takeaways from the study include the following:
- 82% of enterprises are prioritizing analytics and BI applications and platforms as part of their budgets for new technologies and cloud-based services. 78% of enterprises are prioritizing advanced analytics, and 76% data preparation. 54% say AI, machine learning and Natural Language Processing (NLP) are also a high investment priority. The following graphic ranks enterprises’ investment priorities for acquiring or subscribing to new technologies and cloud-based services by analytics and BI initiatives or strategies. Please click on the graphic to expand for easier reading.
- Data warehouse or mart in the cloud (41%), data lake in the cloud (39%) and BI platform in the cloud (38%) are the top three types of technologies enterprises are planning to use. Based on this finding and others in the study, cloud platforms are the new normal in enterprises’ analytics and Bi strategies going into 2019. Cloud data storage (object, file, or block) and data virtualization or federation (both 32%) are the next-most planned for technologies by enterprises when it comes to investing in the analytics and BI initiatives. Please click on the graphic to expand for easier reading.
- The three most important factors in delivering a positive user experience include good query performance (61%), creating and editing visualizations (60%), and personalizing dashboards and reports (also 60%). The three activities that lead to the least amount of satisfaction are using predictive analytics and forecasting tools (27% dissatisfied), “What if” analysis and deriving new data (25%) and searching across data and reports (24%). Please click on the graphic to expand for easier reading.
- 82% of enterprises are looking to broaden the base of analytics and BI platforms they rely on for insights and intelligence, not just stay with the solutions they have in place today. Just 18% of enterprises plan to add more instances of existing platforms and systems. Cloud-native platforms (38%), a new analytics platform (35%) and cloud-based data lakes (31%) are the top three system areas enterprises are planning to augment or replace existing BI, analytics, and data warehousing systems in. Please click on the graphic to expand for easier reading.
- The majority of enterprises plan to both build and buy Artificial Intelligence (AI) and machine learning (ML) solutions so that they can customize them to their specific needs. 43% of enterprises surveyed plan to both build and buy AI and ML applications and platforms, a figure higher than any other recent survey on this aspect of enterprise AI adoption. 13% of responding enterprises say they will exclusively build their own AI and ML applications.
- Capitalizing on machine learning’s innate strengths of applying algorithms to large volumes of data to find actionable new insights (54%) is what’s most important to the majority of enterprises. 47% of enterprises look to AI and machine learning to improve the accuracy and quality of information. And 42% are configuring AI and machine learning applications and platforms to augment user decision making by giving recommendations. Please click on the graphic to expand for easier reading.