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This article appears in the issue January/February 2019 [Volume 28, Issue 1]
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Governing guidelines

Governance puts corporate information front and center as a highly valued asset by defining processes, controls, and metrics.

The data governance market is expected to grow from $1.31 billion in 2018 to $3.53 billion by 2023, increasing by a CAGR of 22%, according to another Research and Markets report.

One factor driving the need for greater governance is the astonishing rate of growth in data volume. However, the increase in business collaborations, along with the desire to enhance strategic risk management and decision making, is also fueling the expansion of the data governance market. Critical to the effort are tools that govern quality, lineage, access, and stewardship throughout the information lifecycle.

While there is a strong emphasis on sharing useful knowledge among more people, there has never been a greater need for dedicated information governance and protection.

In particular, the E.U.’s General Data Protection Regulation (GDPR), which went into effect in May 2018, is driving greater data governance awareness, with the risk of stiff penalties. Following closely on the heels of GDPR, state legislators in California recently passed the California Consumer Privacy Act of 2018, and additional states also have data protection laws that have been adopted or are in the process of being implemented.

If that were not enough, the frequent headlines about data breaches continue to heighten awareness about securing sensitive information.

Knowledge graphs

A combination of factors is contributing to interest in and expansion of the technology that allows people and machines to better understand connections in their datasets.

Google’s launch in 2012 of its own knowledge graph—powered in part by its acquisition of Freebase—is viewed by many as having helped to shine a spotlight on this technology. The knowledge graph is used behind-the-scenes to help Google to enhance search.

The ability for knowledge graphs to amass information and relationships and connect those facts allows companies to find context in data, which is important for extracting value as well as complying with new data regulations.

The concept of the enterprise knowledge graph (EKG) is fairly new and made possible by machine learning and big data technologies, including automated text analysis and graph engines, explained analyst Amy Stapleton in an Opus Research article. “An IA [intelligent assistant] that taps into an EKG can infer the context and intent of questions, generate direct answers, make recommendations, and automatically expand its understanding as the knowledge graph adds new content,” she noted.

Knowledge management enhanced with AI

Artificial intelligence (AI), machine learning, intelligent automation, and natural language processing form a combination of smart technologies enabling organizations to act more quickly and efficiently in ways unimaginable even a few years earlier. Whether organizing information, spotting trends, anticipating actions, or extracting insights from large quantities of structured and unstructured data to surface information faster, a partnership of machine speed and human intelligence are increasingly seen as critical to success.  

A new generation of database tools and platforms—led and enabled by machine-learning initiatives—is empowering organizations to find new insights and increase the value of their data, according to a different Unisphere Research report sponsored by Oracle. Machine learning can find things that humans would miss, and the beauty is that the more data that is fed into the algorithms, the better they become at identifying trends and patterns.

In knowledge management, cognitive computing and machine learning hold great potential as chatbots, cognitive search, natural language processing, and semantic technologies can speed up the ability of humans to find what they need to do their jobs.

Smart technologies are gaining ground with use cases spanning healthcare, business analytics, customer experience, retail, manufacturing, logistics and transportation, financial services, and a range of other fields—adding a diverse array of benefits, from fraud detection to cost-saving opportunities.

A McKinsey Global Institute report, which identified 400 use cases for AI across 19 industries, stated “early evidence suggests that AI can deliver real value to serious adopters and can be a powerful force for disruption.” In particular, the report noted, “early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the future.”

What’s ahead

In an era of digital transformation, companies are leveraging new technologies to enhance their knowledge management. Tom Davenport famously defined knowledge management back in 1994 as “the process of capturing, distributing, and effectively using knowledge.” Fast-forward to today, and the description has stood the test of time. However, many new solutions and technologies are expanding the range of what is possible in terms of making the right information available at the right time—in order to better serve internal users and customers.

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