Presented by McKinsey & Company
Encapsulation and report back from the Techonomy Labs
Kirkpatrick: I hit every one of them, but most of you probably didn’t. And I certainly didn’t see all of each one of them. But as they’re coming up, I want to just tell you one other thing. You know, we publish a lot of content at Techonomy. We put out a newsletter every two weeks. If you go to our website, the first thing you’re going to see, if it’s the first time you’ve been there, is an invitation to sign up for the newsletter. I hope you’ll do that. And I also just wanted to point out that Annemarie, who was on the last session, from Microsoft and Karoli Hindricks from Estonia, who spoke earlier and Peter Hirshberg and also Mark Hatch of Tech Shop, all have written articles on our website in the last few days and weeks that you can go and read that amplify some of the comments they’ve made here—and I think I’m leaving one other person off, another speaker who wrote something for us. So that’s something I’m just proud of and—Peter, I mentioned. There’s somebody I forgot. But anyway, they’ll tell me and I’ll tell it later. But please, let’s hear what happened at the labs.
Chui: David, thank you. My name is Michael Chui. I’m a partner at the McKinsey Global Institute, and I’m joined by my colleague.
Haas: I’m Stacey Haas. I’m a partner here in our Detroit office. I’m a lifelong Detroiter. I’ve actually lived on both coasts and always come back to Detroit, so I’m very happy to be here.
Chui: Terrific. Well, we have the nearly impossible task, to report back the discussion that happened during the labs earlier today, but we’ll do our best to at least capture some things. Thank you to all of you who participated in them. Feel free to jump out and yell at us and tell us the things that we missed as we try to synthesize some of the things that were covered.
So I’m going to start first with the session on mapping Detroit’s information ecosystem, which really should be just mapping a lot of the people who are in this room in some sense. So I’m sure I’ll do an incomplete job here, but let me just describe some of the things that we did talk about there. First of all, one early point was that the context around the information is an important part of the information itself and, therefore, to a certain extent, the context around Detroit is incredibly important.
So a little bit about Detroit’s information context, as described during the discussion. First, it had been described as a city that was historically data rich but information poor, and unique in certain ways, with lots of challenges, but some at the same quantitative levels as other cities in the Midwest, amongst other places, and perhaps at a higher scale. That being said, tremendous opportunities, and Detroiters were described as having an insatiable appetite for data and the applications that they can engender.
So with that being said, what are the potential benefits of using data and information that we heard described? Some of these benefits are already being captured. Number one, just simply improving the service quality, amongst other things, of public services, whether it’s filling in potholes, whether it’s dealing with trees, etcetera, and this question about how can we make civic services—the metaphor was “make our ears bigger”—how can we listen better and improve, in particular how Detroiters interact with city government. It included things like the ability to establish a policy rule, so exhaust data, or the data exhaust of city operations could be made available to citizens in a reliable and timely manner. Creating better conversations, grounded in shared truth, and that’s the data and information. And then the ability to create personal data jackhammers was another metaphor, giving access to civics data, for example, for elections to citizens. And then allowing people not only to have access to the data and get better services, but to really engage in the civic discussions, for example, as a platform for a community to discuss how they want to be policed, so being able to take crime data, being able to interact with your local police officer, in Bagley, amongst other places, and being able to have that discussion. And then the fact that actually engaging the data is an indication of engaging in the community and that’s powerful as well. And also just as another contextual factor, particularly powerful in a time when local newspapers are in decline.
So a few things about what people found to be successful as they did it. First, there are a whole bunch of tools to just make processes better that are being used in other places and can be used here, whether it’s lean or six sigma, etcetera. There’s a phrase I’ve heard outside here—Jen Pahlka said it, right? “Government for the people only works if government works for people.” So being able to actually make the government work well.
There was also a great discussion about the value—you know, we tech geeks, right, are always about minimal viable product, how can we decrease the cycle time, increase the speed of innovation, etcetera. But there was a discussion about the value of moving slow; the value of taking the time to engage with the community and how well spent that time is; doing qualitative research in addition to the quantitative research; not only finding out what people are searching for, but why—and the only way to find that out is to actually ask them and have a discussion; having a participatory planning process. And then, you know, this idea that by default, if data is made public, it’s more valuable, and integrating data from multiple sources.
So a few points from that discussion. Hopefully that’s helpful. Stacey?
Haas: Great. The session I’ll give you a debrief on was developing and retaining diverse talent—although we also had quite a lot of discussion around how we attract the talent. And overall, we said that we are in a race for talent, but yet, somehow we still have a diversity issue and challenge. Four themes I’ll share from the conversation. The first one is actually, the term ‘diversity’ has baggage. We had a lot of discussion that there is a classic definition, which is race, class, gender, but one of the panelists suggested, I think, we should flip the term to be inclusion, because diversity as its own term is really holding us back and we’d be better of thinking about how we can be inclusive and really thinking about experiences and that that best work environments and cultural environments are where we’re very inclusive of different people’s experiences.
The second conversation or theme was really about, in order to this we have to be much more deliberate, and that if we think about the world, there’s kind of the education system and people as they grow up and then there’s the marketplace. There’s large parts of the education system that are now not perfect, but starting to get diversity more, doing it better. On the women’s side, we now have, you know, over half of our graduates coming out of college are women, but it’s not translating into the marketplace. And so a lot of conversation that we have to be deliberate, more deliberate, if we’re going to fix the challenge.
But a lot of recognition that it’s hard. You know, a lot of startups, if we take that as an example, working day to day to actually just keep the lights on and having to think about how they create diverse environments and cultures is, you know, unfortunately just not on the top of their list of things to do, because they’re just working to pay their bills.
But the flip to that was, we said, at the other end, if you wait too long, until you’re big enough to think about diversity, it’s too late. Your culture is already embedded in the organization and it’s too late to change. And so as difficult as it may be to think about that early in an organization’s creation, it’s actually really important to do that.
We also said—the panel talked a lot about, it’s really up to leadership of organizations and companies to make this successful, and leaders have to be very diligent. One of the panelists talked about actually giving out grants and a big part of giving out the grants was they required every one of the organizations and companies to self-select diversity metrics. And it wasn’t a one-time thing. They did it and then they had to keep after the organization every time they talked to them to see how they were doing on their metrics, how they were progressing and keeping it going. And they’ve been very successful. I think they had three times the national average in terms of companies that are owned or founded by a diverse population. And so there’s a lot of opportunity, but we have to actually keep leaders at it and be fanatical.
Last example here about being deliberate is using outsiders to help evaluate roles. We had a discussion about, people tend to hire and look for people that look like them, or look like who was just in that role previously, and using outsiders to actually look at your roles and help you can be very valuable.
Third theme, we talked about policy and whether policy can actually be helpful in diversity. And I think the thought here was, policy in its traditional definition, I think the panelists generally agreed is actually not going to be the solution, but that there are—if we took an expanded definition of policy, beyond just kind of legislature, it actually can be very helpful. So creating more access to capital and how we do accreditation for education. You know, one example here I think the group liked a lot was that American GIs, when they’re coming back, actually can’t use the GI Bill funding for coding type education and that’s an area where policy actually could be quite helpful.
Last theme, a resounding from the entire panelist group that in the end, a lot of it is going to come down to whether we create the right self-belief in individuals, a diverse set of individuals, that they actually can do and be a part of this environment, do this type of work, and be successful. We’re teaching coding a lot. There’s a lot of options out there, particularly for young girls and women who are growing up, but it’s not enough to just teach it. We’ve got to teach all people that they can actually participate in this, and that starts from little children all the way up. But that self-belief in the ability to be a part of this type of culture and group was the most important thing.
Chui: Terrific. I’ll report back on one other lab, which is technology changing or affecting the organization of work in the future. Three themes here. The first one is just what are the fundamental changes, and, again, we’re all living this, to a certain extent. But some of the things that came out was a little bit of the disassembly and reassembly—so in some ways the economy is fragmenting. For instance, the ability to fragment a single job into many different tasks that now can be assigned through a platform, for instance. That these platforms for independent contractors are becoming a larger part of our labor force, or the dynamics around our labor force. The fact that technologies such as social media can amplify the power of small enterprises in terms of sharer voice, and that’s extremely powerful when something becomes viral, for instance. But even large institutions and organizations and enterprises being able to recut themselves; rather than just being on a physical site, technology allows you to be cut by functions or by teams instead of just purely by geographies. And so there’s a lot of different ways in which technology allows organizations to be, to a certain extent, deconstructed and reconstructed in new ways.
The second theme was just the effect of data and these technologies on organizations with regard to—to a certain extent, you might call it labor and management. Certainly in some ways, the technology provides more power to management. What we’re seeing is the ability of these technologies to provide more power to labor, for example, in terms of transparency. An example was given about a larger and larger percentage of a large company, for instance, the employees are on a platform that allows them to share concerns about, for instance, should men be able to wear facial hair in the store, right? And, you know, that might be trivial, but it’s—again, it’s a different way of thinking about organizing, in addition to even traditional organized labor.
And then finally, some thoughts about the long-term effects of automation. That’s another topic that we’re actually studying at the McKinsey Global Institute, but it’s not only physical work, but also knowledge work. And clearly, an increasing amount of labor in fact can be automated by some of these technologies. I think the overall finding there is that’s going to happen. So what does that mean? More than ever, this trend as well as others means that skills will be far more important than experience. It’s not going to be what’s on your resume, it’s what you’re going to be able to bring to whomever you’re working for, and whether it’s an employer or someone through a platform.
And then a question about if in fact we see more automation, if in fact we can unlock more time, what do we do with it? Can we open up more space for entrepreneurism, for people to otherwise do things that they’re excited about, or spend time that they otherwise would have had to spend time doing on something that was automated? A lot of questions and an interesting discussion to come on that.
So hopefully that at least gives you a little bit of a flavor for the discussions you all had. Hopefully that will continue as well, and thanks again for creating the time for—