The other day I received an email from a CEO within our Work-Bench community:
“Even though we are still tiny, as we evolve our team and employees begin to grow into management roles for their first time, I’ve started to think a lot about management training. As a first time CEO, this is definitely new territory for me as well.”
This is one of the most common painpoints I hear, across the fastest growing VC-backed enterprise startups we work with and the globally distributed Fortune 1000 corporations in our corporate network: managing people is hard.
And yet there are still surprisingly few technology products that have served this people manager and leadership development training space, estimated to hit $13.8B in 2020 (here), which to date still revolves largely around in-person trainings and workshops.
Given the rise of AI across a range of verticals: financial services, life sciences, healthcare, energy, transportation, heavy industry, agriculture, and materials — we have yet to see AI broadly applied to the people manager training space (though there are some early startups tackling this space, as detailed in my slides above).
At Work-Bench Ventures, we invest in early stage enterprise technology startups. HR is an area where we have been spending significant time. Our first portfolio company exit was True Office, which transforms outdated training content into interactive courses, using game mechanics and motivation techniques so that users actually enjoyed learning. The NYSE acquired the company a year after we invested to enhance its role as thought leaders in the Governance Risk Compliance space. We’ve explored the broader HR space as a major focus area with the help and guidance of Tom Carroll, a founding board member of Work-Bench and Chief HR Officer of R.R. Donnelley, a Fortune 500 communication services provider. Tom and other thought leaders in the New York ecosystem have been instrumental in helping us understand the landscape and challenges that accompany the space.
Much like the evolution of systems design, IT-enabled process change ebbs and flows over time. We see this in history as each technology revolution brings with it a refactoring of business operations.
With the rise of client/server computing in the 1980s, and the introduction of database servers and visual development tools like PowerBuilder, “business process re-engineering” became all the craze during the 1990s.
By 1993, 60 percent of the Fortune 500 developed IT systems to automate mundane tasks like insurance claims processing or AP invoice/purchase order reconciliation, channeling the mandate of technology-led business transformation in Michael Hammer’s infamous 1990 HBR article, “Don’t Automate, Obliterate.”
In the setup to this article, we discussed how Big Data Security Analytics (BDSA) is an evolution beyond the limitations of classic Security Information & Event Management (SIEM) solutions. Namely, that Big Data approaches are differentiated by their ability to provide analytics from unstructured data sources and huge, disparate data sets (IBM and others refer to this as the 4Vs: Volume, Velocity, Variety, & Veracity).
Big Data solutions have other traits that enhance their effectiveness, better unlocking insights than legacy solutions. For example, many solutions are capable of certain types of machine learning – suggesting or executing a particular course of action based on historical actions, rather than as a result of formally coded rules. As another example, Big Data solutions will often consume not just event-based sources, but also intelligence feeds or contextual reference data (e.g., threats, vulnerabilities, asset inventories) for better overall insights.
An Evolution Beyond Security Information & Event Management
Limitations of SIEM
Depending on which company or startup we speak with, Security Information & Event Management (SIEM) is either dead or will live on forever. Quite different answers. In our minds, Big Data Analytics represents an evolution – not revolution – beyond the aggregation, alerts, and response facilitated by a classic SIEM solution. Big Data approaches differ from SIEM in two key ways: 1) unstructured data is acceptable, and 2) huge datasets are no longer a challenge. Of course, #1 and #2 resulted from new technologies we've spoken about before, which were created for purposes other than security.