In The Pursuit Of Knowledge: Making Sense Of Data In The Modern Enterprise

2019.11.08

Joshua Kwah

PARTNERSHIPS

‘Knowledge’ appears at many levels in modern business. Its foundations lie in the raw material of data — quantitative statistics and qualitative facts that exist and relate to one another in enterprise data systems. Data scientists are responsible for combining these raw materials — that is, creating “information” — analyzing their relationships, then producing interpretations as knowledge — that is, actionable insights — for business users.

“If data and information are the ‘what,’ insight is the ‘so what.”

Forbes

Companies who can process data, assemble information, and produce knowledge in a practical and scalable way are poised to become leaders in their industries. As we will find, data science and the production and management of knowledge have become essential across modern industries today.

4 Industries and the Knowledge That Defines Them

According to Forrester, today’s enterprises “use no more than 20% of their data for insights, and less than 20% of knowledge workers use enterprise Business Intelligence applications, still preferring spreadsheets and other shadow IT approaches.” Here is a look at four industries where knowledge, and the use of knowledge base technologies, are driving transformation for business leaders.

Finance

The financial sector generates enormous amounts of data. This is most often structured data from transactions, customer logs, and financial product performance metrics, but also includes unstructured data, such as social media and website data at increasing rates. MDPI reports that “with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested” within the financial sector. Knowledge bases help these companies manage complex financial products, ensure regulatory compliance, and gauge profitability, according to Forbes.

Accounting

As we recently reported, accountants are increasingly called upon for high-level advisory services several steps removed from the redundant accounting services of the past. Leading accounting firms are using knowledge graphs to visualize risks for clients — even adopting natural language processing AI to improve rule-based approaches and analyses for their clients. For example, cross-checking financial statements for inconsistencies and audit compliance is now a task engaged by AI in leading accounting firms and departments.

Legal

Law firms, too, now operate in the data economy, acknowledging BI as a strategic asset. “CMOs in the legal industry in particular cannot discuss strategy or identify new business opportunities without the necessary insights that come from client data and relationship knowledge,” the American Bar Association finds. Emerging and innovative technologies help legal entities establish standardized data processes, such as categorizing information for appropriate roles. Knowledge bases help them reduce redundancies and even determine case law precedent.

Manufacturing

According to Boston Consulting Group, data technologies are helping manufacturers to integrate data pools across functions and even with suppliers and customers. Knowledge bases help them track resources, streamline production processes, and mitigate compliance issues; use of AI “can reduce manufacturers’ conversion costs by up to 20%, with up to 70% of the cost reduction resulting from higher workforce productivity” as well.

Future Knowledge and the Innate Wealth of Unstructured Data

As industries refine their data strategies, historically cumbersome unstructured data will grow in its importance to boost strategic initiatives.

As we found previously, unstructured data makes up 80% to 90% or more of all digital data today.

Organizations that analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity gains over their less analytically oriented peers by 2020.

IDC

But while this data requires human curation to become actionable, combinations of AI disciplines like Natural Language Processing in addition to machine learning will increasingly lend themselves to the successful and timely execution of human-driven tasks of this kind.

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