Information Management & Barbie

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women in tech

Information Management & Barbie

last updated:

But looking back over my years in this industry, I know women have had an outsized impact on the sector, despite their lack of numbers or full recognition.

I hope the inspiration for this blog was not simply that I watched the Barbie movie at the weekend and got to see the ‘real world’ through Ken’s eyes, but it probably played a part.  The actual trigger, though, was talking last week with another conference organizer asking for my recommendations for speakers at their events who might reflect a bit of diversity. Each time I am asked (often), I come up with the same list of names, which is depressingly short.

I am a clear-cut privileged white guy; damn it, I even have an English accent! It may seem odd that I am writing about the need for more women in leadership positions in our industry. But it shouldn’t be strange when you think about it; we can all see something is amiss. And it’s not just the shortage of women; our industry does not now, nor has, embraced diversity in any form. I’m stating the obvious here, but we need to do much better and take account of the elephant in the room. But what are we supposed to do precisely, as pointing out a lack of diversity doesn’t do much beyond, once again, stating the obvious? And the truth is, I have no idea. But looking back over my years in this industry, I know women have had an outsized impact on the sector, despite their lack of numbers or full recognition.

Ginni Rommety (IBM), Meg Whitman (HP), Ursula Burns & Anne Mulcahy (Xerox), and Safra Catz (Oracle) are some of the prominent big names that come to mind. But in our world of information management and automation, we have the legend that is Whitney Bouck (Documentum, Box, HelloSign), Bernadette Nixon (Alfresco, Algolia), Tori Miller Liu (AIIM), Olivia Bushe (FlowForma), Annemarie Pulcher (Papyrus), Lana Bailey-Tamoro (caso) and the indomitable Galina Datskovsky (Vaporstream, ARMA, CA etc). Though there are more, there are sadly not as many as there should be. It feels good to name-check these incredibly successful women, but it doesn’t change that they are few and far between in the bigger scheme. If you think the situation is slowly improving, you would be wrong. The number of women in tech leadership positions is falling, not rising, in 2023; it’s heading in the wrong direction.

Frankly, I am totally out of my wheelhouse here, and beyond wanting to see more diversity and recognition of individuals’ skills, I am unsure what can be done. But I feel we may have the opportunity to recognize a problem publicly. Mad though it may sound, the current interest and exploration of the use of AI in the enterprise may prove to be a minor but pivotal point to improving the situation. Beyond the hype and outrageous marketing, enterprises (and, to some degree, vendors alike) embracing AI face the same challenge. The data that AI trains on is riddled to its core with discriminatory bias. Indeed that is part of the focus of a raft of new and proposed regulations, such as the recent EU Artificial Intelligence Act.

As an interesting side note, 68.7% of people studying for MLIS (Master of Library & Information Science) graduate programs are women. Yet, these very skills are in short supply in KM & Information Management.

My point is that AI is hot right now and will remain hot for some time to come, and aside from all the debate about hallucinations, black boxes, and killer robots, there is, at the core, something critical, the need for good data. Good AI, trained on good data, can help to reduce recruitment bias. But without the work and effort to clean that data up and recognize its flaws and biases, it could not only perpetuate discriminatory hiring practices but compound the situation and make it worse. If nothing else, we can jump on the bandwagon and try to improve the situation. If nothing else, there is now clear-cut empirical data (as if there wasn’t enough already) to prove that discrimination is a living reality for over half of the workforce, be that at entry-level or leadership positions.  Ultimately all that is being asked is that everyone is treated equally, not that preference is given to one group over another. And the data tells us that is far from the case today.

But I can’t leave the industry analyst world out of this discussion, as although they may fare a little better than in the vendor community, women and people of color are woefully underrepresented here too. Ironic, really, as some of the best analysts I have ever encountered, analysts that inspired me were women. We, too, can and should do better. Of course, the problems of discrimination, sexism, or any other ‘ism’ will not be solved by cleaning the data on which AI systems are trained. But it is an opportunity to bring to light and reflect on the past and where we want to go in the future. Will we do that? Probably not, but I live in hope.

I can’t know what it is like to be a woman in the tech sector, but I know nobody likes to be treated unfairly. So, yes, there was a lot of name-checking here, but these names don’t always get the recognition they deserve.

Sincerely Alan (I’m Ken’s friend….)

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