The proliferation and importance of effective data management has the industry taking note. In fact, in a recent International Data Corp. FutureScape 2016 report, IDC Chief Analyst Frank Gens said, "We'll see massive upshifts in commitment to DX initiatives, 3rd Platform IT, the cloud, coders, data pipelines, the Internet of Things, cognitive services, industry cloud platforms, and customer numbers and connections." I couldn’t agree more.
Organizations are increasingly using cognitive computing, to quickly respond to changes in their business, gain insight into customers, industries and markets, and overall improve productivity. Looking ahead to 2016, we predict semantic technology will prove to add value and increase business agility in 10 key areas.
Cognitive computing enhanced business applications. People expect to be able to ask a PC or a smartphone for what they want (driving directions, the nearest ATM, etc.) and receive an intelligent response. This is the new norm due to all of the available artificial intelligence technologies. These same expectations are driving cognitive computing systems into organizations. The core of a cognitive system is comprised of the ability to mimic human understanding; therefore, semantics is and will remain a major enabler of these applications.
A full-circle information management solution. User created information -- text documents, emails, social media posts, etc. -- is a valued asset inside an enterprise. Strategic activities that leverage this unstructured information, such as operational risk management, marketing intelligence, or customer support, require our information systems to perform difficult tasks that are made even more challenging when they must be performed at scale.
Putting the meta in data. As a company’s knowledge base grows, the implementation of a consistent and effective metadata strategy is becoming a vital aspect of information creation, sharing, and distribution. Semantic technology is a critical cornerstone of any metadata strategy because of its essential role in various phases of the metadata process. Not only does semantics support creating pragmatic taxonomies based on an analysis of the available content, it also identifies relevant dynamic tags for each piece of content.
Enhancing the return on information assets. Finding the information you need quickly is important to the success of your business (and to limit frustrations). The key to helping companies get the most from their knowledge assets comes from combining content categorization and knowledge collaboration. This is essentially the business value of the effective metadata strategy outlined above. In this case, however, we are talking about the impact it could have across the enterprise on common information management applications (and major investments) such as SharePoint or Google Search Appliance.
The difference is meaning (also in the Internet of Things). Things are changing. Our interactions with the devices we use in our daily lives are becoming more focused on communication. As the “Internet of Things” becomes an increasingly common component of our daily lives, the transition of simple data collection to communication will logically follow. People do not communicate via data, systems do. Data is an abstraction, understanding is communication, and to understand and communicate one must know meaning. No technology is stronger at executing this than semantic technology.
Do more than just listen to social media. OSINT (Open Source Intelligence) means more than just monitoring all of the available information: it is essential for defining specific criteria in order to establish priorities and achieve goals. It includes a constant selection process to find and monitor reliable sources, while providing the ability to leverage any and all available information to gain intelligence on the competition. Companies will increasingly rely on semantics to help identify risks and gather meaningful details from even the weakest signals from all of their various information streams. The result is reduced operational risks and the ability to anticipate market developments in near real time.
Let’s API. API functionality is everywhere. One prevalent example is that API functionality is behind the social icons (Twitter or LinkedIn, for example) on a page that allows you to share articles. These icons are links that call on the APIs associated with each service to share information. APIs are important because they dictate how developers realize new applications. Although not widespread (yet), intelligent APIs such as semantic APIs are being increasingly used to link, find, and understand unstructured information within corporate intelligence, CRM, human resources, data management, knowledge management, and social media monitoring applications.
Compliance and e-discovery. Organizations must manage a wide range of information for regulatory compliance or e-discovery, encompassing a diverse range of formats and information types that are not easily reconciled. Semantic technology helps process structured information across different formats and repositories to enable a standardized view of all information assets, mapping relationships between data, and providing a foundation for information extraction and reporting. As compliance requirements increase, this technology will help minimize the economic burden.
The programmer of the future is a linguist. So much of our content is language driven, making it essential that technology be built with an understanding of words -- their roles, their meanings, their contextual variations. The linguist helps technology make sense of text, which in turn enables programmers to decode jumbles of text, to make sense of translations, and to enable discovery of patterns and structures where hidden.
Knowledge and skills mapping. Specialized knowledge such as in-depth information specific to customers, projects or sectors, as well as that related to important applications or procedures are, in reality, priceless enterprise assets that often go uncaptured. Locating specialized areas of expertise often relies more on relationships than on corporate procedures or structures. The larger the organization, the more difficult it is to track or manage them. Organizations will increasingly use semantic technology to build an internal information bank of subject matter expertise. This will enable the real-time matching of required skills and knowledge for a project with the people or teams who are best suited to address it.