Here at eWEEK, we report about data analytics, artificial intelligence and machine learning quite a lot, because it seems like every new enterprise application that comes to the fore is using one of those ingredients to make it more valuable.
This is a direct result of the convergence of key IT factors eWEEK has observed during the last several years: unlimited storage, higher-performance/cooler-running processors, vastly improved networking, leaner/more efficient software architecture and smarter devices with intuitive user interfaces to run it all.
Emerging from all of this is the term “data intelligence.” Data intelligence is the analysis of various forms of data in such a way that it can be used by companies to expand their services or investments. Data intelligence can also refer to companies' use of internal data to analyze their own operations or workforce to make better decisions in the future.
Data intelligence is more than a shiny new solution; it is a strategy that enterprises must embrace to get the most out of its greatest asset–the data. Data intelligence empowers business users to access and share information while building greater trust in data outcomes and providing secure data governance.
In this eWEEK Data Point article, Datawatch CEO Michael Morrison offers his industry perspectives on the importance of finding and using data intelligence.
Data Point No. 1: Fostering a data-driven culture for business growth
Enterprises have a wealth of data at their fingertips, but seldom is it properly or correctly used to bring the company to the next level. By taking steps to improve data quality, collaboration and intelligence across the organization, firms are in a better position to call themselves a “data-driven” company.
Data Point No. 2: What is data intelligence?
Data intelligence is not a specific product or solution that an enterprise can purchase and implement. It is a strategy with a common, collaborative approach to producing trusted, re-usable, high value business ready data assets that are available to information consumers to use across the enterprise.
Data Point No. 3: The role of self-service in data analytics
According to a study by UCR, by the end of this year, the U.S. will face a 50 percent to 60 percent gap between supply and demand for deep analytical talent with the number of jobs exceeding 490,000, but there are fewer than 200,000 available data scientists. As a result, enterprises are enabling every business user with self-service analytical solutions that will allow them to access and analyze data for their tasks. This results in more individuals using the data for creating data-driven reports and business decision-making, but it also leads to multiple challenges.
Data Point No. 4: Challenges associated with data and analytics
While data use is being encouraged among all business users, there are still many challenges related to effective data management, governance and data access. A TDWI Pulse report earlier this year found that that less than half of respondents (44 percent) can find and access relevant data in a self-service fashion. Even fewer (28 percent) can access and analyze new data, including external data, without close IT support. As a result, business users cannot properly use data to make informed business decisions or increase their productivity.
Data Point No. 5: Building trust in the data outcomes
Additionally, the TDWI report found that data governance and trust was a troublesome issue in many enterprises as well. Only one in five (20 percent) reported that they can identify trusted data sources on their own, and only 18 percent can determine data lineage—that is, who created the data set and where it came from—without close IT support. Rather than relying on trusted data sources, most rely on tribal knowledge to find what they need, with 48 percent of users communicating and sharing data with each other through email and word of mouth (45 percent).
Data Point No. 6: Data intelligence strategies solves the issues
Data intelligence strategies solve the challenges related to data governance headaches and data access by making inter-departmental collaboration an essential part of the everyday work life. As business users share data and their analytical insights, they gain a greater understanding of business operations as well as increase their efficiency by leveraging the work of their colleagues rather than recreating a data set or analytical chart from scratch.
Data Point No. 7: Centralized data marketplace ensures access and control
This collaboration is made possible through the implementation of a centralized data marketplace that is managed by the IT team–ensuring data governance and compliance requirements are met. The beauty of the centralized data marketplace is that users can easily share and socialize their data, models and results, very similar to how social media sites operate. Commenting, liking, following and other interactions engage users and encourage further collaboration.
Data Point No. 8: Intelligence strategy results
As a result, enterprises are creating new levels of enterprise-wide awareness and building greater trust in their reporting and analytics. Business users are empowered to move their companies forward by uncovering bold new insights that lead to smarter business decisions and improvements. When a firm opts to add intelligence to its data management strategy, it is embracing the power of its data as a competitive differentiator. Those firms who use a data intelligence strategy are able to derive more value from their information with insights that can increase revenue and influence business decision-making.
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