While the potential of AI and its impact on real-world endeavors like process automation and business analytics is already being realized, one important piece of the puzzle can be overlooked: data integration. Data integration is central to the advancement of these AI-driven applications and their promise to solve problems and make humans more efficient.
At its core, AI and associated machine learning are built on the ability of these programs to recognize patterns in data sets. Until now, the usefulness of these data sets has largely rested on their size. Better data integration ensures that they also have dimension in addition to their size and can be “acted on” by AI programming in increasingly complex and effective ways. But if data is siloed and cannot communicate with AI programs, or is able to transfer data but not in a form that is interpretable by the AI platform, that data is effectively wasted.
High Value Data at the Enterprise Level
Data integration and its ability to fuel future business dimensions are of particular interest to large enterprises constantly unloading and offloading new organizations and processes. As data-reliant AI becomes the focal point of business intelligence (BI), rich customer experiences, and smooth supply chain operations, these automated and human-centric workflows will become more of a focal point of business value. The value of acquisitions will hinge on a company’s ability to assimilate data. As such, it will become increasingly key to make data input sources interoperable with processing and output capacity.
The usefulness of this data has implications for engineers to managers to C-suite executives. For maximum utility, this data must be leveraged in the form of visualized dashboards that can powerfully but succinctly represent actionable information. But effective visualization starts from the database up and will require a holistic data integration approach to yield responsive output. These will need to expand on current extract, transform, and load processes (ETL) to make formats more dynamic and easy to access across an organization. Currently, ETLs are often exclusively the domain of IT specialists in most enterprises.
To receive full value from data, real-time streaming interfaces are emerging. These provide up-to-the-minute statuses on internal operations and external activities in the form of simple metrics. Newer tools in this domain rest on AI tools that can not only identify whether data is present but assist in helping predict when data is not appearing as it should. Future iterations of these dashboards will incorporate real-time database search, allowing for natural language queries to supply data insights within scaled-up cloud computing. These could even perform basic interrelated calculations by combining data seamlessly and dynamically in real-time.
Integrating Unconventional Data Sources
Data from alternative sources represents another data integration challenge of the future. Structured and unstructured data from four billion websites, social sites, satellite images, and transactional data all represent an untapped data source to be harnessed by evolving data integration techniques.
Among them is the integration of unstructured data distilled into structured via standardized metrics. Images from phones, drones, satellites, and security cameras all represent a trove of information. But putting it to use will require continued advancements in AI tools like natural language processing, machine learning, and computer vision. These will not only have to identify the disparate visual elements in images but then process those into formats more readily used by humans and machines. This is the process of transforming unstructured data into structured data. It will unlock opportunities to look at BI and consumer behavior in new and useful ways.