MORE DATA INTELLIGENCE ACROSS THE ENTERPRISE
There will be a redoubling of efforts to make data-driven intelligence across the breadth of enterprises, John Wills, -field CTO at Alation, predicted. “Data intelligence supports the entire employee population, both non-technical and technical, by helping them find, understand, and apply data in their function within the business,” he explained. “An effective data and analytics strategy should also include predictive and prescriptive elements. The aim should be to use analytics, not to simply look back and understand what happened, but use it to optimize future outcomes.”
With greater employment of forward-looking data intelligence, businesses will be able to “inventory and maintain a curated knowledgebase that shows who and how data and analytics are being used,” Wills said. They will also have “accessibility and transparency for a broad number of roles across the enterprise, including technical and non-technical roles that can use techniques such as asking natural language to find what they need.”
To achieve more powerful data intelligence, Wills advised that businesses “self-assess their use of data by inventorying current descriptive, diagnostic, predictive, and prescriptive analytic capabilities for each key business process. They should establish predictive models that show the most likely performance given no change as well as scenario analysis.”
INTELLIGENT AUTOMATION
The support for greater data intelligence comes out of rising levels of artificial intelligence and machine learning. This will ultimately make data managers’ jobs a bit easier. The volume and type of data required to support data storage and management “is completely different to what we’ve seen before,” said Kurt Kuckein, senior vice president of marketing at DDN. “It’s not that it’s much harder, it just needs different tools and platforms. These tools and platforms have matured tremendously over the past 2 to 3 years, and 2023 will be the time for organizations to really begin to benefit from custom-developed AI and analytics applications using standardized tools and platforms.”
In response, there will be “more intelligent automation throughout enterprises,” Jim Sears, vice president of professional services at Boomi, predicted. “Intelligent automation can be leveraged to ensure that automations enable up-to-date information across an organization, don’t break, and can even improve on themselves.”
With more intelligent automation, “data platforms will continue to simplify workloads despite scale, and coupled with AI and machine learning becoming more accessible, will enable getting insights from data that are actionable,” said Vara Kumar, co-founder and chief product and technology officer of Whatfix. “Understanding customer and employee needs through their behaviors on the application will become commonplace.”
AI and machine learning are “gaining maturity for data analysis, especially for customer experience,” Steve Zisk, senior product marketing manager with Redpoint Global, added. “This is not simply chatbots, but deeper analysis of customer goals and desires, machine-learning selection of products and messages, and even machine-learning-based automation of interactions and processes.”
In another example “intelligent automation for integrations can recommend to users which applications to connect in order to minimize data silos or improve flow of information,” said Sears. “This helps alleviate headaches that come from the use of inaccurate data. It can also help free up time for leaders in data-driven enterprises to look at data from a larger lens and create solutions for customers instead of spending time on keeping data connected and accurate.”
Successful data-driven organizations “can take advantage of systems developed with AI and analytics, so that adjustments can be made at every stage of the data journey to improve the speed and efficiency of data ingestion, processing, transfer, and long-term storage,” said Kuckein. “Companies wasted a lot of time trying to get their existing infrastructure to support AI, but they are realizing the need for new data repositories that are optimized for AI, and they need to be integrated with the business. That integration process has matured significantly in recent years, and there are now many more bridges between AI platforms.”