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The data-curious knowledge worker is tomorrow’s trusted business adviser

by Alex Smith, iManage RAVN
Article ImageChange was already in the cards for most enterprises, but the onset of the COVID-19 pandemic in 2020 significantly accelerated it.

Rapid adoption of digitized processes became the order of the day, and 2021 is continuing that momentum, creating enterprises that are now awash with ever-increasing sources of data.

What does this new world mean for knowledge workers?

It means that business problems are going to be increasingly ‘digital’ in nature, making data-enabled knowledge workers the trusted business advisers of the future. Their ability to cultivate a ‘data curiosity-led disposition’ – to understand the way various technological systems work; what data they hold; and what techniques to use to discover important information, patterns, and connections in that data – is what will differentiate these workers as they carry out their daily work.

Finding the Story in the Data

A data-curious mindset starts with an appreciation for all the new sources of data that are a part of today’s enterprise. Corporates that weren’t already using messaging and collaboration platforms (in addition to more established communication channels like email and social media) are certainly doing so now that remote work is par for the course.

In addition, there are Zoom calls, not to mention recordings of Zoom calls and transcripts of Zoom calls. There are even AI tools that can review a recorded sales meeting and then pull out action items and next steps, creating a summary report.

Simply put, we are leaving more and more of a digital trail behind by nature of the various systems we use. There's a lot more data that can be discovered, and that's where the curiosity aspect comes in. The data-curious knowledge worker knows to look at the full sweep of data to see what kind of story it’s telling.

For example, an organization might think they are compliant with their regulatory obligations because of various training programs that they've run and compliance checklists that they have in place. But once they start really digging into the systems – not just one ‘official’ repository of data where people might be saying and doing the right things, but all the ancillary data repositories as well – they might find a different picture.

A data-curious mindset, then, is largely about using the data to validate assumptions and guide decision-making. One can see the same interrogation of data and validation of assumptions being used to ensure efficacy of a litigation strategy.

A data curiosity-led disposition will also give knowledge workers broad capabilities that sit outside of the traditional sphere of their profession, be that law, accounting, or financial services. Think here of data forensics in a corporate M&A scenario: In this situation, blended data and blended skillsets will be necessary to make sense of the endeavor from every angle, and this will increasingly become the norm.

A toolkit approach

AI is a broad umbrella, so what techniques does the knowledge worker need at their disposal to dig into the data and advise effectively?

The data in some business systems – for example, billing or timekeeping – will often be relatively structured, so there’s no need to normalize it or discern meanings in the language using NLP (natural language processing) tools. Trying to uncover patterns in unstructured data like emails or instant messenger threads, however, will definitely benefit from NLP.

Regardless of the specific tool used, parsing the data to seek answers to questions often leads the data-curious knowledge worker to uncover other questions and dig deeper for answers.

For example, seeing an email thread suddenly go cold might prompt the data-curious knowledge worker to wonder if the conversation has migrated to some other less formal communication channel or to see if the content has made its way into some written documents. Working with the data requires a toolkit approach, where different tools that can handle structured and unstructured data can be selected as appropriate.

No coding necessary

All this focus on data shouldn’t obscure the fact that the knowledge worker is still at the center of the process, and that what they are fundamentally doing is problem solving.

In other words, having a data curiosity-led disposition and facilitating the practical application of AI in business doesn’t require learning to code. Instead, it requires knowledge workers to understand how people communicate within the organization, where data gets captured, how data flows between the systems, and so on.

By understanding these ‘behind the scenes’ aspects, knowledge workers can more effectively tackle whatever problem or opportunity is in front of them, whether it's a tax audit, a piece of regulatory work, or an M&A scenario. Data can help the knowledge worker better home in on what really matters.

In this way, being curious can significantly improve the day-to-day work of knowledge workers. It’s not just knowing where to look to find important data – it’s knowing to look at the data in the first place. And that’s an advantage that only comes with a data-curiosity led disposition.


Alex Smith, Global Product Management Lead for iManage RAVN, has over 20 years of experience in product management and service design, including new and emerging technologies such as artificial intelligence, semantic search, and linked data, as well as content management. Prior to iManage RAVN, Alex has held positions at Reed Smith LLP and LexisNexis UK.

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