DATA EXCHANGE/DATA FUSION
Data exchange and data fusion are emerging as preconfigured integration environments that cut out much of the upfront work that slows down data analytics applications and functions. “Data exchange allows companies to pull data that was generated by their technology partner platforms into their own systems so they can use it in their business intelligence or analytics tools,” said Sammy Kolt, chief product officer for SmartSense. “Data fusion happens when multiple streams of data are brought together into one database for additional analysis. External data streams like weather and traffic can help inform and impact business-critical decisions that need to be made. Internal data streams bring about compound effects when electricity, temperature, energy, inventory, and financial datasets, as an example, are combined. This is all made possible when vendors and platforms go beyond proprietary reporting, open their systems up, and offer access to their underlying structured data.”
Data exchange and data fusion help mitigate the workloads of data science teams, Kolt explained. “Companies are no longer only looking to use the out-of-the box reports provided by their vendors. They want to be given access to the data so their teams can do additional analysis with it.” Data science teams will need to be prepared for this approach. “Roadblocks happen when your data science team is not well-defined or well-designed,” he said. “Create a data science team that focuses on both the technology side of things and business analysis.”
Companies “that have positioned themselves as platforms have already figured out that they increase value for their customers by embracing data exchange,” said Kolt. “There’s a clear correlation between the data science maturity in certain verticals and how far along they are with this concept.”
DIGITAL ASSET MANAGEMENT
Digital asset management platforms have been on the rise, “as the need to quickly access the right assets at the right time within a single repository has never been greater, and the volume of those assets is never higher,” said Alan Porter, principal content architect at Hyland. Digital asset management “brings together collections of data, images, files and associated material, eliminating siloes around those assets and, with proper metadata inputs and tagging, makes those assets far more retrievable through the platform.”
In its early days, digital asset management “was primarily used by marketing departments to manage content and assets,” he added. “Fast-forward to today, and the technology has evolved to include product asset management, and is applicable across an organization—not just marketers, but product teams and more—to connect content, data, and other assets.” With huge, burgeoning asset repositories, “employees can often waste substantial man-hours locating and retrieving required files—or producing poor-quality output by making do with whatever resources are at hand due to the difficulties trying to find appropriate content.”
AI now plays a key role in the performance of these platforms as well. “On top of that framework, an AI engine capable of learning the connections between disparate datasets makes those assets far more useful and functional,” said Porter. “For example, a TV giant uses its DAM platform for storing and retrieving stock footage, ensuring there is little to no duplication across the many productions the company works on simultaneously. The company can then locate and reuse iconic images such as a panorama of the Brooklyn Bridge or the famous Hollywood sign—identifying contract details to avoid licensing issues and saving it otherwise costly re-shoots.”
The effectiveness of DAM platforms “relies on accurate, complete, and detailed input of metadata on the front end,” Porter cautioned. “To provide the best results to users searching for a very specific asset, the platform needs the best data from which to base its work.”