Business and data executives are conducting reality checks on the best approaches for the employment of algorithms, autonomous operations, and AI. “Many businesses are being smart when it comes to adopting new technologies,” said Lyndsay Wise, director of market intelligence at Information Builders. “Instead of buying into the hype, they are asking critical questions for garnering the strongest ROI, resulting in a delay in broad adoption. For instance, organizations are realizing that strong data management is a core foundation for predictive and AI technology and are first focusing efforts on getting their data house in order. Others have realized that they don’t have the pool of data necessary to make the most of predictive technologies and are investing in building the right data streams.”
Most organizations by now “have worked with data long enough to know when they’re ready for a new trend or significant investment,” Wise added. “Organizations that are moving forward with predictive analytics, machine learning, and AI are doing so because they’ve dedicated enough time and resources to their data management and are confident they have the right amount of data needed. Data comfort is really critical here. You can’t implement data-fueled technologies if, as an organization, you’re not comfortable with data internally.”
A stronger data foundation will help deliver the value expected from data-intensive methodologies such as predictive analytics, machine learning, and AI, said Wise. “When implemented correctly, the opportunities are limitless. We can’t really predict the full scope of how AI and predictive technologies will improve organizations, but we know there will be significant improvements in efficiencies, cost savings, customer service and experience, bias improvement, and employee growth—to name a few.”
ECOSYSTEMS EVOLVE
Success in the data-driven organization means more than relying on corporate databases—innovation and growth now are tied to the development of connected ecosystems of partners, customers, and other constituencies. Expect to see a greater focus on these networks in the coming year. “Organizations already have started to pull the camera back and take a broader look at how their ecosystems drive their businesses, but it’s about something more than connecting or integrating,” said Frank Kenney, director of sales enablement at Cleo and former Gartner analyst. “It’s about driving value. In the next year and beyond, savvy organizations will deliberately focus on enabling their ecosystems to obtain different types of value out of relationships with customers, partners, partners’ customers, and so on. That means they are integrating not only their dynamic networks of people, partners, customers, systems, applications, and things but also the processes and interactions that drive those relationships.”
Managing and capitalizing on ecosystems “requires a modern approach to integration, one that can enable your business to take advantage—through functionality, through interoperability, through visibility—of all the ways value can be created in today’s era of digital transformation,” Kenney said.
Organizations aren’t quite ready to fully embrace an ecosystem-driven approach, however. “More work needs to be done in how we view and manage all the data interactions happening in our ecosystem, but it’s achievable,” Kenney said. “It’s important to set realistic goals and expectations, and simply mapping out how your intelligence network has expanded is a great first step to learning to use the data.”
MORE TARGETED SEARCH
Enterprise search is an area that will increasingly become part of organizational information management strategies in the year ahead, especially as both data and content are embraced as strategic assets enabling business growth. Jill Shuman, director of project engagement of the Copyright Clearance Center, foresees the rise of “a search solution that gathers information from multiple, disparate content sources all at once and presents results on a single page.”
Ideally, organizations “should consider creating a consolidated place to store both internal and external content, coupled with a single enterprise-wide search function,” said Shuman. “This allows employees access to everything they need in one basic search effort instead of having to check each disparate source individually. This approach saves time, money, and most importantly, protects organizations from loss of institutional knowledge, which could cost millions of dollars.”
As consolidated enterprise search emerges, companies will see “more productive staff, because they won’t have to duplicate the efforts of past employees—the information would be captured before the previous employee left and be easily available,” said Shuman. “An employee who is fully engaged with a company and its information feels more a part of the team.”
AI technologies are also playing a key role in enhancing both data and content search. “Coupling technologies such as machine learning and natural language processing with search is helping companies to glean greater value and insight from all their data, both structured and unstructured,” said Kamran Khan, a managing director with Accenture Applied Intelligence. “It increases the ability to find, analyze, understand, and present data more accurately and efficiently and provides a new turbo-charged way of searching that helps companies not only gain more insight from their data but it also improves the user or customer experiences that directly support many business goals and objectives. Examples of these might be an intranet or KM application, ecommerce site, or customer service portal.”
However, AI-driven information technologies are still immature, Khan cautioned. “While technical leadership might understand machine learning and NLP, there is still a knowledge gap with regard to fully realizing how these technologies work and how they can play a role in improving search and content analytics. These technologies are also not easy to implement. It takes time, money, and a deep understanding of content analytics to bring true cognitive search into enterprises.”
AI technologies are changing the way information is managed, said Khan. “Applying NLP and machine learning to search applications, for a smarter way of searching and understanding data will absolutely change the way information is managed. Right now, nearly 80% of all enterprise data is unstructured content which is rarely used in analytics. This number will only continue to grow as each enterprise’s data continues to increase.”