Ep 41 - Jeff Richardson - Data strategy and leadership from a B2B CDO
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Episode Summary
“You’re going to have your data engineers and your data modeling on almost one side of the work and then your data visualization and your data translators.
They’ll sit in the middle and bridge that gap between your data engineers and modeling, and then your end users. People who need those insights to go make decisions.”
Being a Chief Data Officer is a challenging role but Jeff Richardson has been up to the challenge at Bentley Systems. In this episode Jeff talks about his approach around data strategy and leadership, the importance of data literacy and data translators, and more.
Jeff is a seasoned data and analytics executive leader with a cloud-first focused on evolving technology and trends. Over a 17-year career, he has crafted a results driven strategy for growth and delivered outcomes which have helped Bentley achieve a leading position in cloud technology, record revenue, and significant user growth. A prolific speaker on the topics of cloud and data and analytics, Richardson can often be found at conferences and networking events in the Greater Philadelphia and mid-Atlantic area (and now virtually!). He holds a bachelor’s degree from Providence College, where he was also a Division I swimmer, a master’s degree in Statistics from Central Connecticut State University and recently completed a business capstone program at Yale University.
More about Jeff
Bentley Systems: https://www.bentley.com/
Jeff’s LinkedIn: in/jeffrichardson
Data Able Ep 41 – Data strategy and leadership lessons from a B2B CDO
Jeff Richardson, Chief Data Officer of Bentley Systems
Machine Generated Transcript via Descript
Dave Mathias: [00:00:00] Hey everyone. Welcome to another episode of Data Able Dave Mathias with you here today with a special guest Jeff Richardson. Welcome Jeff. Jeff is the Chief Data Officer of Bentley Systems. And Bentley is in the B2B space, doing software. And, Jeff will talk more about that.
but Jeff is a seasoned data and analytics executive leader with a cloud first focused, evolving technologies and trends. And boy, there's a lot of that happening more and more nowadays, right? Jeff.
Jeff Richardson: [00:00:30] Oh yeah. My God everywhere.
Dave Mathias: [00:00:32] And so Jeff, you want to tell a little bit more, give the audience some background because they see here to date officer they're excited.
They're like, Oh, that's how do I become a chief data officer? But how do you tell us a little bit about your journey to get where you're at today?
Jeff Richardson: [00:00:44] Sure. So since most people confuse Bentley with other companies with the same name, very quickly, Bentley systems is a CAD software provider. that makes software for building infrastructure assets. For anything in the world that you would think of that is a fit physical asset. So we are the leading provider of software solutions to people like engineers, architects, geospatial, engineers, that build things.
So if you've driven on a road in the United States, you've probably driven on a road that was built using software that we made. So at Bentley, my role of chief data officer is globally responsible for all of our data assets, our data architecture, and how we use information at the company. being a software company that sells.
Data sells software to users. Those users create data. We then take that data in the company. We do things with that. my role is a very broad spanning multifaceted, role in the organization. So I'm responsible for governance and architecture, but also monetization and insights analysis, even down to like corporate reporting and financial reporting.
Dave Mathias: [00:01:47] I was excited when I heard Bentley and I right away the, my mind went to the nice car. I'm sure a lot of people thought about that. And so thanks for the clarification now. Okay. So data governance and all those other things that you're responsible for, how do you. Keep the sanity between all the different demands that are on an organization now for using data.
how do you go about, just on your team to make sure that you're hitting on all the resources?
Jeff Richardson: [00:02:13] Yeah, so data governance is really an interesting topic right now. globally. So Bentley does business at 160 countries. So we have hit every single landmine of data governance in the world right now. We have a dedicated legal team that does compliance and Bentley as an organization that does business in Europe has a data protection officer.
And then in my team, I have dedicated resources who work with them on compliance. So it's very interesting. Most people don't realize the depth of the laws we've created around the world now. But if you do business, for instance, in Singapore, legally obligated to keep all of the data that you do business with Singapore residents.
In Singapore, physically, it can't leave the Singapore for boundaries. So we have to figure out ways to utilize data centers, Singapore, to hold Singaporean data on Singapore customers. Same thing in Sweden. A couple of years ago, Sweden had a huge data breach and their government, and they have now passed a law that any data on Swedish residents.
So if we sell software to a Swedish person or they're doing work on Swedish infrastructure assets that manage Swedish users, Swedish people, we have to keep that data in Sweden, physically in Sweden.
Dave Mathias: [00:03:19] That's a great insight. And I actually do wonder, because obviously we've had things like the California consumer privacy act, Where? Yeah. yeah. And so the question is, at some point. we run into those issues where people say, Hey, Yan, California residents. You need to keep your data in California.
And not that I've heard anyone. I haven't heard anyone talking about those things, but I think there is that real caution of, okay, this data is going everywhere. We need to figure out a way and it's sorta like a little hodgepodge. whether it's at the state or whether it's at the federal level, there's been discussions here in the United States.
What are your thoughts on that? do you have any perspective on where the direction of data privacy is going on? Data governance for US residents?
Jeff Richardson: [00:04:01] So for US residents were, we seem to always be a little bit behind other countries there. it's like the California laws are driving the US policy. I think. Globally my personal opinion on this is that what we're seeing now is a handful of reactionary measures to some serious events that need to be dealt with.
but globally, none of this scales very well, right? every large company now deals with data that's global, right? Everything that you do is it from a company that deals with things in many countries. So you can, there's no longterm play where. You can have these isolated country based laws or even state-based laws about information, Something as silly as Facebook as a global company, like. Anyone can deal with the Singapore and thus Swedish and the California laws, but when every country does that in the future, I don't think that's going to scale very well. but I find this to be a very interesting double-edged sort of discussion because it's forcing us now to take a more global approach to data.
So we're rolling out a new identity system in our company and the data laws in Europe and in Singapore specifically forced us to rethink how we architected that. So where we might've in the past. Stored most of that data in Eastern Europe, Eastern United States cloud public cloud vendor, like Azure or AWS, we now made decisions because of those laws to have it be globally scaled in multiple data centers, multiple regions with a more robust fail over system and DDR system and data residency systems.
So they drove innovation. Accidentally because of these policies. But if you take that any further, if there were six or 10 countries like that, I actually don't know how we would have built a system that managed to actually work for us following 10 countries where we had to be data resident. So the laws are driving a certain degree of innovation now accidentally.
but I do think eventually we're going to get to a point where there's just gotta be one, one set of rules. That's not maybe country specific.
Dave Mathias: [00:06:02] Yeah, that's a great point. And I do wonder always to where, especially when we get large companies, large technology companies that have the resources that can think about how to expand and have all playing all these different. Realms and in different ways, but if you're a startup or even an established company that might be, a few hundred people, so you that's, you've got a good point product out there, but you're working at you have it in a number of countries.
How do you, be able to have the systems in place to be able to handle that? And maybe there'll be. More companies that are just trying to do that service as if these laws stay the way. If the direction is that goes the same way, where it's becomes more fragmented, because I think that will be business opportunities for new companies there too.
But ideally we get to a point where there's at least a somewhat agreed upon sort of minimum viable standards of data. Privacy across the world and that'd be the ideal state, like you're saying. And I think in the long run that'd be great for innovation, but it's good that I was glad when GDPR was passed, Where it's like, Hey, it's challenge, companies to start thinking about this in a more holistic context of thinking of like, how do I treat data? And certainly with CCPA going then that was just another sort of, realm there. So yeah. When you go to that next level as a person that's in that chief data officer role, how do you work with, product managers and innovation people and other business leaders, as they're trying to, they hear data governance and sometimes they might want to think about, Oh, this is cutting off options, as opposed to really creating new opportunities.
How do you, as a chief data officer, be part of that innovation holistically in the company, as looking at new opportunities and partnering together, how does that work?
Jeff Richardson: [00:07:42] Yeah. So I'm fortunate in our organization where we developed the chief data role, and we saw a lot of these government requirements come up after we had structured process to develop products. So we fit in nicely into that process. More organically than I think it would have happened if it was the reverse.
So we generally have two kinds of development teams, Everything you can break up into two groups. We have the teams who come to us ahead of time and look for ways to architect and build those solutions that fit those roles and then work as a partnership to go forward and build that. And that works out great.
That is a great paradigm. I love that. And then of course there are the other side of that, where we have to step in. Nearly after the fact and kind of act as the data police. So we get that. Let me inside Bentley, we have dozens, if not hundreds of development teams working on some of our 700 different products.
So we get both sides of that a lot. We're seeing much more of the partnership. aspect the partnership paradigm happening because people don't want to have to go back and re-engineer these solutions afterwards, because it's no longer a debate. It's not Oh, we're giving you advice on how to do this.
we are telling you what the global laws are. We'll help you get there. We'll act as a trusted advisor and a partner and a resource for you to go do that. But you really have to go do this.
Dave Mathias: [00:08:59] Makes sense. And you're using technology that helps like snowflake, which is, great for being that flexibility. so doing more on that front too, I know we were chatting about, and so one of the things and partly is I'm speaking at a college. What's later today. it's to a group of business analysts and it's regarding the data translator role and how has business analysts, it can be better data translators.
What advice would you have for such an audience as a chief data officer that they could be better data translators, as BAS.
Jeff Richardson: [00:09:31] Yeah. So I really do think that the business analyst role and the data partnership role between like structured organizations and like the business teams is going to evolve quite a bit over time. And the value add spots are going to be the data translators and the data visualization is going forward, in those business roles.
So where, before you, might've had more centralized reporting and analytics and storytelling and translation. That we're seeing that certainly evolved now into the third or fourth or fifth iteration, whatever that is now. So business people, business analysts that can translate data models and complex data structures into insights and analysis are going to be huge in the next.
Two to five years. my advice to those people would be learn the tools of that translation, but don't get mired in the details of the data modeling anymore. I see that breaking up into two, two groups of people. Now
Jeff Richardson: [00:10:26] you're going to have your data engineers and your data modeling on almost one side of the work and then your data visualization and your data translators.
they'll sit in the middle and bridge that gap between your data engineers and modeling, and then your end users. People who need those insights to go make decisions.
Dave Mathias: [00:10:41] That's great. Yeah. and I, that same exact perspective. So that's good to hear. So at least I don't have to rewrite my talk either. That'd be a lot of stress. yeah. And so when you think about, in your organization, how do you as a leader of the whole organization and chief data officer.
how do you help empower that data translator role, that data storytelling role, is, people have the skills you're able to provide. Some of the expertise in your teams are able to work good from a technical team with those people. How, what do you do to, make things a better success?
New York?
Jeff Richardson: [00:11:14] Yeah. So just to be totally upfront on this, we are learning how to get to that now. So we're not there yet. We are in that transition between very structured, centralized data storage, reporting, and delivery in one team to distributing that out among many teams. So we're trying to leverage. You mentioned snowflake, Azure synapse, lots of tools to build systems that are scalable to lots of users to get lots of access points.
And then we're working on more structured and distributed training for those data warehouses, those data models and things, to give those to users, to go leverage in multiple ways and users today have. 10 and 20 and 50 times more resources to leverage those data models and data sources than we had five or 10 years ago.
it's amazing. the barrier for entry to get to that point has just completely fallen off. It's gone. So you can do insightful analysis. You can do deep dives into data. You can do very basic machine learning, even to the point of like advanced machine learning. Now with no barrier to get there, as long as you have good access to clean data.
So my team is focusing on building very reliable data pipelines that build very reliable. Data stores to share. And then we're less focused now on building those end points, the analysis, because it's almost impossible for us to figure out what analysis somebody is going to want. Very rarely does someone come to us anymore with a very structured list of KPIs and they say, go build these exact KPIs and we're not going to change them for five years.
It's here's the dataset I need access to. I really don't know what I'm going to do with it, but I've got 20 different questions I might go ask. And, I don't think you want to help me answer all 20 of those questions every day for the next 10 weeks. Let me go play with that with an analyst now who has, power BI and ClixSense and Tableau as a tool set to go build visualizations and maybe a little bit of R to go do some regression in some, general machine learning.
Dave Mathias: [00:13:17] Yeah, that makes a lot of sense. you're leaving that last mile to the business. The those and the flexibility, especially in this agile state. And so as part of this, one of the things, that's oftentimes referenced or more recently in the last few years is the concept of data literacy or data fluency.
And what is, I know you and I had chatted on this, before, too on, cause you have a fairly skilled, group of people given what type of work you guys do and a fairly data literate, CRA crowd, but you might even have that overly. educated, overly focused, a grip control. Look a little bit more like deep seeing what challenges you face in your organization as respects, add and little perspective for the audience.
Jeff Richardson: [00:13:56] Yeah. So I feel like data literacy has two parts to it. it's more of a data competence and then a data literacy in the strictest sense. So can you use the tools that you want to use to answer the questions and then inside those tools, do you understand the data sets that you're getting to? So data literacy.
Five years ago might have been. Do you understand how to use click view or reporting services to get your answers that you wanted? And now data literacy has many facets. I just did a talk with Jordan Morrow, the, the godfather of data literacy, about similar topics. And what we're focused on now is making sure that people understand the data lineage.
Of the information they get, where it comes from and how they get it. and the flow of the information, as well as the definitions then of the information they're getting. But to your point,
Jeff Richardson: [00:14:46] I love the phrase the last mile there, that last mile of analysis and that last mile of data literacy is really on the business analyst, the business user, the data analyst that's embedded in the various groups that are consuming this now to really take that to their team.
Jeff Richardson: [00:15:01] So we might give them revenue metrics or customer metrics or other usage. And in data points clearly defined clearly structured with how do you get this? What's the Providence of that information? as long as we give them clear definitions of that, I feel like all were onus of data literacy.
Got them there. They need to take that to the next level. We don't leave them in the wind to go do that by themselves. We help them also educate. They're users as well. but really data literacy has turned into a much broader topic. I feel just in the last two or three years than ever before.
Dave Mathias: [00:15:35] Yeah, that's great. And certainly Jordan's great. Like you're referencing, I have the ability to talk or opportunity to talk with Jordan many times, but also present with him at the connections conference a couple of years ago. good to hear that. and last time we were chatting, I think that same day you were, you had a virtual presentation with him somewhere,
Jeff Richardson: [00:15:53] it was like an hour after that answer.
Dave Mathias: [00:15:54] Hour. And after that. Yeah. so one of the things I think, when talking to people in your role that often in terms a challenge, challenges, like how do you continue to show value? As cause there's a lot of expenses in that analytic space. People see a very expensive software and very expensive people that have.
So the different titles, but to make sure that you're showing that your areas as providing value there, it's a, it's not just a breakeven, it's really can be a profit center, for the organization. So how do you go about that as a chief data
Jeff Richardson: [00:16:27] a great question. Great point. So when we chartered a data office and started the data team and built the chief data officer role, we built a bunch of tenants that we had that we wanted to fulfill in that role. And one of those tenants was a monetization of data. So it sounds very capitalistic and almost, yeah, negative, but we have all of this information, this data, and in the past we treated it like a thing that we were going to collect somewhere.
And as we've evolved, we've realized we have to treat that data as an asset. It becomes as important as our intellectual property and the physical things that we have that we run our business on. And other businesses that sell, we sell software. So we have no tangible good, but. Other companies that sell physical things are also learning this, that the data that they have in collect is becoming a real asset for their company.
And you don't just let an asset sit on a shelf. You figure out how to grow that asset. Either. Turn it into something that you can monetize and use to grow your company or something that you can use to appreciate the value of the asset itself. So again, one of the core tenants of our group was data monetization and turning that data asset into a larger asset.
We also focus on operationalizing things in the company. So reducing cost, reducing time spent in various places where we can operationalize data-driven tasks and help people reduce low value, add work, and give them opportunities to do more insightful work, more analysis. By taking away that silly burden of like data movement.
and then the last thing we do obviously is we were always trying to drive insight and analysis and give people new information to make better decisions.
Dave Mathias: [00:18:06] Of course. Yeah. Got to make those better decisions. And so one thing tied of this is. Is the valuation of data. So when using in the prioritization and just trying to think of like where to devote resources, do you do anything? And this is something most organizations are do, but, and we haven't chatted on this before.
It's do you do anything regarding that data evaluation, as you're trying to assess where efforts are spent or, and if so, how do you go about doing that?
Jeff Richardson: [00:18:32] so we absolutely do. So one of the data sets that we collect, is our usage data on software. So our data, our software model. Our business model is very similar to how you might do business with Microsoft Azure or AWS, where we invoice our users for the consumption of the products that they use sort of real time.
So it is a pay for what you use model. So we get streaming events of analytics, telemetry from this usage data. So the, we are constantly working to value and figure out ways to safeguard that information, and prioritize which information we get that we need to run our business on. So that becomes a critical payload of information.
We call it. And where that goes. And then we have obviously all the other information that we collect as a business, which becomes less critical to the day to day operations, but then might become critical to decision making and products. And then, those streams just keep breaking off into different value marked sort of sets of data.
Dave Mathias: [00:19:33] Great. That makes a lot of sense. And I know you and I, when we were chatting before your, you would assess your organization as a pretty upper end of sophistication of how data is being used in your organization. Certainly sounds that way. Is that fair?
Jeff Richardson: [00:19:47] Yeah. again, I've been very lucky in the role that I'm in the organization. We were a very, early adopter of cloud technology. So we sell software that we had hosted in our company to our users. So we were a cloud provider at some level in the past. and I want to say nine or 10 years ago, we very quickly jumped on the public cloud bandwagon.
we were one of the early adopters of many of Microsoft Azure services and AWS services. And we have continued to evolve that relationship as we grow. And we are always on the cutting edge of those cloud services, which is good and bad, obviously, because, Being on the cutting edge of any technology is always a bit dangerous, but, w we've been very fortunate to be a very cloud focused and cloud first organization.
Dave Mathias: [00:20:33] that sounds good. It sounds like you're happy. Guinea pig. Yeah, there you go. so one of the things I think is always a challenge is how, when you're working with other areas too, say between like analytics and business teams is how to make sure that everyone's getting credit together. And everyone wants to start to say, sales are up, things are up, it's out.
It's gotta be the B2B sales team or things like that. How do you work as a chief? Data officer in that realm of trying to, get, make sure that, you're getting credit for the stuff that you're able to add value, but at the same time, I'm making sure that the businesses getting credit when, with the wins too, is there, how do you, that sort of sharing of credit and in turn, like more resources that come about because of that?
Jeff Richardson: [00:21:20] That is a great question. and that is a constant sort of struggle, a discussion that we have throughout the organization, certainly with resources, because as you become more successful, In operationalizing, automating and streamlining work, it looks like you need less resources. And really you're just moving the work to different areas.
we're very fortunate again, in that we have lots of skilled technical people, so they don't assume that, the hidden machinery underneath the beautiful city, they live in, just works without people working on it. They know that underneath there are people who are getting their hands dirty.
Our business teams and our data teams really do work in concert and our executives are very good at understanding the visibility between how those groups work. So we generally try to partner with all of our business units and dedicate resources in those groups. So that we have a very visible partnership with them.
And the work that's being done is easily shared and easily separated between their groups and our groups. And we try not to speak in the terms of theirs and ours obviously, but you ended up doing that regardless. but by embedding resources in groups, and then. The opposite of that. We have resources in those groups that dotted line work through our data office.
It becomes more of a partnership and a shared service than it does, like a really hard it line where there's a fence and they throw requirements over the fence. by having this shared service sort of partnership mentality, we find that creates more of a synergy between those groups
Dave Mathias: [00:22:49] Yeah. Yeah, that's good. That's a nice type of perspective then. And so tied with that then. Cause then we were talking about data governance. We talked about some of the data privacy, but even data ethics. And where does the role of. How would data ethics be described? How would you describe it in your organization and as a person trying to really spearhead, that, what do you mean ethics and organization has?
How does that work when you're working with business and things like that? Because there's a, sometimes people will look at ethics as a hindrance or just like data privacy as a hindrance to innovation. how do you work with business to one is w. Maybe tell a little bit about what your data ethics practices are at your organization and also how you work with the business to make sure that, things are not just followed, but championed.
Jeff Richardson: [00:23:40] Absolutely. So we don't collect enough personal information to really fall on the spectrum where data ethics becomes is a serious issue for us. So again, yeah, we're mostly B2B and in the end users of our software, we know their first and last names and where they live and the software that they're using.
so it is some personal information, but we, it's not too far along the data ethics realm, certainly. When people are using our software to develop things, like roads and bridges or buildings or nuclear power plants, there is a day ethics discussion that happens, but that is all very locked down in the products.
We don't have visibility to that information. So the data ethics conversation stops there internally with our personal data. We've been leveraging global rules. And again, you'd mentioned, you were glad when GDPR came along, I am as well because does create a framework for ethics you can leverage and fall back on.
We've been able to leverage that for many different areas to create a. A reasonably good framework for an ethical way to deal with communication with users, collecting information on them, sharing that information and where people look at that as a hindrance or some kind of like speed bump along their road to, wonderful innovation.
We try to give them. Options and alternatives that give them equally beneficial, equally valuable information that doesn't violate those kinds of ethical gray areas on the edges there. so we've been trying to shift it to, yes, I know you want this, but here is an alternative to that. that's less of a gray area.
or when we have to, we do go back and fall back on the, these are the hard and fast rules. We are going to follow these rules. we will be the police of this. If we need to.
Dave Mathias: [00:25:19] Yeah. yeah. To have those hard guard rails in place. Yeah, you're trying to softly get people. one thing we had chat a little bit as COVID, obviously we're in Cova times right now chatting. but the world has changed a lot in, yeah. the responsiveness, the agile newness, pushing people had five, 10 years ahead in their businesses and how they're and certainly they're using your software.
How do you see the world with an, the B2B software space? How are you working to. Provide clients more value, and the chief data office as a chief data officer and your company in general, how do you see software being used differently now in this, COVID and post COVID realm?
Jeff Richardson: [00:25:59] Yeah, so we, we saw an immediate shift in how companies were handling their digital transformations. we provide software that helps, the giant, the largest EPC firms, engineering, architects, the people that build things, the constructors, we give them the software, they use to go build those things.
And we've been working with many of those companies over the last. 15 years on their various digital transformations, moving to the cloud, moving to more global mobility, moving to streamlined, edge devices and all the various things that you can bucket into the word salad of digital transformation.
COVID. Pushed many of those companies much faster into how do you get to true cloud where they didn't want resources stuck in their offices? They couldn't go to anymore. And how do you get true global mobility of their users? So if you have 10,000 architects and CAD designers around the world building.
Giant BIM models, multi gigabyte, multi terabyte, dim models. How do you access to that information when they are now working instead of out of 10 offices around the world, out of 10,000 homes with questionable internet access, questionable hardware, right? Questionable service all around. So we have been.
very fortunate to have software solutions to help them to go do that. And we are seeing a tremendous shift in how those companies want to deal with that. Many of them were saying, yeah, we know we'll go to the cloud in five years. in March 80% of those companies said we're going to the cloud in April.
Dave Mathias: [00:27:24] Yeah, it's amazing how that, both it's something where you probably been crossing your fingers, that this would happen and all of a sudden, whoops, now it's here. Yeah. Now you've got to deliver. yeah, so that's a good, that's a great thing. one thing we were talking about you in the chief data officer role, and I w.
I think that's something that's always helpful is to learn from our successes and our mistakes. If you're going to say, what was the thing that in your life you would say the biggest success that you had that contributed to you getting into the role that you have now? what is the, whether it's something that was totally locked and it just, happened or something that you consciously chose.
What was that thing that you would most attribute to? How, where you're at today?
Jeff Richardson: [00:28:05] so about 14 years ago, which seems like forever ago in real person time. I made a significant effort to formalize the way that we stored information in our company. and I built our first real. Internal accessible data warehouse for our colleagues. in the past data, warehouses had always been something that sat behind like a Cognos or a BW BI system that only experts could really get to.
And I really pushed to get, because at the time I wasn't in our it team, so I wouldn't have had access to that. So I really pushed to get that built for our company, for people that just have access to, and. Over the years, that's evolved into many other systems, many technologies and things, but that.
Kind of cemented my position as the person that brought information back to everybody in the company in a more open way. And it changed our culture of how we deal with information in the company. and I completely lucked into that because that is like the paradigm now that every company works off of.
And I just happened to have frustrations at the time that I wanted to get around. And I had read like an Inman book on data warehousing and I was like, Oh, we can do this and we can share this. And it was fine. And I just lucked into that a little early. And then built a name around that and then got to build on that into the role that I'm in now.
Dave Mathias: [00:29:25] Nice. and you're probably young enough into your career that you're like, whatever, like I'm going to push this. I'm frustrated and why not try to do it?
Jeff Richardson: [00:29:32] I had nothing to lose at the time and everything to gain. I, of course at the time, I didn't even realize that I just knew I was frustrated and wanted to solve a, so we just did that. And then we built a very small, I think it had about 20 objects in it, data warehouse, it had some customer data, some revenue, data, some data on materials and things, and people could answer questions.
All of a sudden that in the past was a big deal to go ask a question. And then it was just a little easier and then SQL became more ubiquitous as data tools became more ubiquitous. It just cascaded into this thing. And now we talk about our data warehouse almost. really, we talk about our data warehouse more than we talk about any other enterprise system in our company.
Dave Mathias: [00:30:11] Awesome. that's a great story. That's a great thing that people can learn from his second one is to take some risks when you're younger and understand when the upside is potentially upside. And two is just find a problem that really frustrates you and frustrates others. and maybe that's where you have put your reputation on, you won't necessarily know where it will lead you, but, oftentimes good things come about for that.
Jeff Richardson: [00:30:29] Very few people get mad at you for solving problems.
Dave Mathias: [00:30:32] Yes. Now the other side, the flip side of that is we all have failures like it or not. We were all we're. None of us are perfect, but, so what is the biggest failure? Whether it's something in school realm or whether it was in professional life or something like that you learn something from, that was a key component of your success now.
Jeff Richardson: [00:30:52] so I'd love to get into a funny anecdote on any of the other failures in my life, which are myriad and fantastic. but the first thing that comes to mind is our first foray into a really structured, modern, analytical visualization tool. so again, we had. Numerous tools in the past that were I T driven and sitting on those warehouses and were very structured and not very agile.
and when we rolled out our first BI tool, 10 years ago now, we had chosen ClickView as the tool that we were going to use. And at the time there was that big competitive soup of all of the other competitive tools at the time, Tableau power BI and the power pivot Microsoft suite was just coming out.
there was. The Cognos business objects things, and what we picked I'm very happy with. It was the right decision at the time. That was fantastic. and it's treated as well for years, but we should have done a better job of. Getting buy in and like ownership for more groups in our company, because what we immediately saw was, my group wants to use this and my group wants to use this.
And it created this useless argument and schism over who made the better bar chart, in a tool which. Just defeated the whole purpose of sharing information and getting that out. and at the time I wish I had been able to realize where we are now, where data modeling data storage, that pipelines of information becomes one part of that.
And the visualizations become much less important to control. You need to govern the data and how people understand the data and data literacy, but visualizations were going to become a commodity no matter what. And we're there now. So I wish I had known that then to just have a different sort of sentiment around how we did this, it would have just saved us a couple of years of, going back and forth over who made the better, Mico chart or whatever.
Dave Mathias: [00:32:47] do you think as part of the, is part of that lesson? Am I getting interpreted correctly as also bring those others into the table instead of just making decisions on something like this in a little bit more of a, less inclusive way, would you, is that one thing you're getting at there too,
Jeff Richardson: [00:33:03] Exactly. Yeah. Having ownership, not even just buying in, but ownership from all of the various groups, which is always hard, You're trying to get a whole bunch of ducks to swim in the same direction. but that would have been, a much better way to do that. And we've, I've seen this evolve in it over the last 10 years.
Now. It's much more common to get that ownership of other groups. And have them almost request of you the thing. And then you can implement that thing at the time. We knew what solved our problem best. And we were like, here's the best solution for you? And then immediately, of course, you're going to get someone who says no.
Dave Mathias: [00:33:34] Yes. I've seen this. This is better. It makes that better bar chart. So yeah. No, that's great advice. and certainly somebody like, no matter what level you're at, is something to think about. And how do you bring that? Get that buy in. From the start, I would say, early on yeah.
Conversations, don't bring people in late and early. and if don't want to be part of the conversation and then certainly, and you can still keep them updated. At least they felt like they were asked and contributed. yeah, there's a lot of ways to do it.
Jeff Richardson: [00:34:03] And even expanding on that. The other big takeaway that I've learned there is really try to look 10 years, five years, 10 years into the future and figure out what becomes a commodity and what becomes a core essence that you need to really focus on. And if I look back five, 10 years ago, I would have absolutely known that the Providence of data, right?
The accuracy of that information, that was never going to go. If we really had a core focus on that, but of course we were going to commoditize. The delivery of information, that last mile of information, there was no way that wasn't going to happen. So looking forward at what is the next thing that will be commoditized?
for instance, like I look right now where we are like, even like a year ago, machine learning algorithms and the languages of machine learning that is going to become a commodity over the next five years. So it doesn't make sense to try to push down. a hard structure on, you have to use R or you have to use, PSW, or you have to use some tool from machine learning.
That's all going to be commoditized. Every single tool is going to have regression and neural networks and all of that machine learning built into it. By default, the core there is going to be again, the data ownership and how you get that information into those tools.
Dave Mathias: [00:35:16] So as part of that is, okay. So data there's a, the proprietary data that you have versus all the more public data are data that's available to many. It might be vendors are providing it to many people, et cetera. how do you go about determining where to spend effort to build proprietary sort of data and modes, versus, just trying to leverage, partners with and with data.
Jeff Richardson: [00:35:42] We, I always try to bring in external data, into our sets to make them richer and to make them more useful. And honestly, we don't really have a good balance there as to how you justify what you do there. Cause the integration of that information into your systems is always the hard point. how do you make sure you match external lists or external research to your data in a way that you can join that and leverage that in some kind of model?
That makes sense. For the most part we have taken sets that we have found value in over the years and then cemented those into our culture of data. and that's worked really well. And we take the approach right now of taking the spaghetti and throwing it on the wall and seeing what sticks with other external sets of information.
And it, it hasn't been very structured and it hasn't been a great, repeatable process so far.
Dave Mathias: [00:36:33] Okay. So if you were saying the next couple of years in your role as CDO and where your organization's using data, what would you, where do you see the big efforts, the big wins happening? where what's the next two to three years look like?
Jeff Richardson: [00:36:46] So one of the things that I out of COVID I've taken out of this is going to be data sharing and data partnerships, and access to large stores of information that we use to run our business and that our users and customers use to run their business. Nobody wants to live in this world anymore, where they can't make very quick, real time decisions.
Everything is faster, it has to be reliable. It has to be automated. And what we're seeing with our customers is they want access to their stores of information. Very quickly and very reliably. And of course we do as well. We want that from our cloud providers. We want that from the people that we interact with on all kinds of levels to make decisions.
So we're seeing a much, Much more concerted push to automate and move and share information between organizations. We're going to leverage for some of that because they are, they provide us platform to securely share large amounts of information between ourselves and our vendors and our customers with their, I think they call it the marketplace and they may have rebranded it recently, but I'm hoping that we can leverage that in the future so that we can give large EPC firms access to the billions of records of data we have on their billable users and hours of what they'll eventually be charged for.
They want that. Now it's very hard to give them that reliably. So hopefully we can leverage tools more to do that in the future, less barriers between how you access information.
Dave Mathias: [00:38:05] Awesome. That sounds, yeah. Sounds very innovative and more, and certainly tools like snowflake are making that easier. When you now, one thing we talk a lot about is data storytelling. And as a chief data officer, you need to be able to tell a good data story. If you're looking at the data storytellers in your life, who's your favorite storyteller and who's your favorite data storyteller.
There may be different, or they may be the same person. so just storyteller versus data storyteller.
Jeff Richardson: [00:38:34] So my favorite storyteller out of like anyone who can just spin a tale, I would have to say is Simon Sinek.
Dave Mathias: [00:38:42] Sounds great.
Jeff Richardson: [00:38:43] his books, his the way he presents information, the way he tells stories, I just, I could listen to his podcast. I can listen to his videos, read his books for ever. I think behind me, It's two or three of his books as it is.
but as far as data storytelling goes, I usually fall back and default to a Mico Yuk.
Dave Mathias: [00:39:01] Yeah.
Jeff Richardson: [00:39:02] I love her podcast. I love the way she talks about showing people value, information and storytelling and the way she teaches storytelling to other people.
Dave Mathias: [00:39:11] Awesome.
Jeff Richardson: [00:39:12] I find a lot of value there.
Dave Mathias: [00:39:14] Great. Great. And of course I got asked this question, you referenced it when I'd sent it to you as okay. If you were going to be a data visualization, what kind of data visualization would you classify yourself as?
Jeff Richardson: [00:39:23] Oh, it's such a difficult question. There's so many, I think I would eventually end up with some kind of. Radar charts, something I like reaching out into lots of different areas and having like little points of expertise in different spots, but like a broad kind of view. And, also I've never been able to figure out a good use for a radar chart, which I think speaks well to like where I find myself now, like all over the place.
I'm trying to learn a lot, trying to figure out where I fit in different, situations and organizations. that's a great question. That's a crazy question. I love it.
Dave Mathias: [00:39:56] Okay. one thing we were chatting about is this week you've been working, you're taking this MIT course, and you it's around sort of operations. And it's got a lot of math around it and bottlenecks and trying to understand, some different things. But we were chatting on that a little bit.
What is the sort of the thing that you've taken the most out of that class and then maybe. As a tail add on question is what type of learning would you recommend to somebody if they're wanting to eventually be in that chief data officer role, where would you, what's something you had come across that you thought, Hey, this is somewhere you should go to take this class or read this book or do something like that.
Jeff Richardson: [00:40:32] Yeah. So the course that I'm in now on its operational excellence, I believe is the name and really it's about automation and how to streamline processes in any industry. So they focus on things that have inputs that have processes have outputs. and what I did not realize there is a very deep and well-researched science behind all of that.
There are. Structured mathematical formulas for how to deal with various different operational processes. in situations, there is a vendor, there is a mathematical function that will tell you how many newspapers to put in a newspaper stand, depending on a handful of them, of cost metrics. And that's it.
Formula is very well known in certain areas. I had no idea that there was actually math behind that, but there was really a news vendor, mathematical formula.
Dave Mathias: [00:41:19] yeah. One, one book. I like his algorithms and I forgot the author of it, but it's a great book. And it's, it talks about some of those different formulas in different contexts where you're like, Oh yeah, some things were like, okay, I know this, but some were brand new.
how about on a learning front? anything that you have come across. and it could be something where you wish you would've read this or taken this before you, years ago, but something you've come across that you would recommend to folks to learn. So that they'd be more successful if they wanted to be a chief data officer.
Jeff Richardson: [00:41:48] So specifically in my role, right? you're learning on so many different levels, at least I'm trying to, both business strategy, technology compliance. it's very. Broad. so places where you can go on now and get little vignettes of learning and lots of it areas like Pluralsight. that's been very valuable.
we were lucky enough to have Pluralsight logins through the organization I work for. So that becomes great. But what I've been finding myself doing more and more now, to plug your podcast and other podcasts is fine. Finding leaders in this area, leaders in the data, lytic space, the CDO space, listening to podcasts, and then writing down everything they reference.
And then reading those books, listening to those podcasts, watching those videos. So people like Cindy Howson, people like yourself, up in through people like Tim Ferriss, who like loosely is on the edges of data in some spots, figuring out what they talk about and then going and reading those books.
So algorithms to live by is going to be the next book that I go read.
Dave Mathias: [00:42:44] Hope you enjoy.
Jeff Richardson: [00:42:45] Yeah, I'm sure it's going to be fantastic. the last podcast that I listened to, you referenced the book, how to not be wrong, which is actually a very similar book. If I go look at the recommended things on Amazon there, those are right next to each other.
That was a great book on learning how to, how mathematics and structure around that can teach you ways to think about problems.
Dave Mathias: [00:43:05] Yeah. There's so many great books out there. one book that I read last year, did you have the chance to, Annie? Duke's a book thinking in bets.
Jeff Richardson: [00:43:12] I did not. Oh, she's the,
Dave Mathias: [00:43:13] player. Yeah.
Jeff Richardson: [00:43:14] player, the world
Dave Mathias: [00:43:15] yeah. And she's out of Philadelphia area too. So in fact, I met her last year when I was out in Philadelphia in person and she's working on her second book.
I don't think it's out yet. I think it's coming out this fall. but it's a really good book. And I think the more, one of the things that she talks about of course is like we, so it's a leveraging some of the concepts around behavioral science and. We're very, we tend to get stuck in our decisions.
And so thinking in bed says as really saying, okay, what do what, don't, how confident am I in these things? And what's the probability and you're almost never going to be a hundred. You should never be a hundred percent certain, but if you're 80% certain, you make the wrong bet.
Okay. how can I was my probabilities off, right? Okay, let's learn how to do that, but you don't feel so stuck to the decision. And so I think the more, I think the children, people with data, as they want to think that they can get to a answer that is going to be a hundred percent correct all the time and now, and I think.
Analytics will not get you there. It will get to a certain level of confidence and then you have to make decisions and you have to weigh a lot of trade offs. And that's the human part. That's not the, Hey, let's just stick it in an algorithm. And we'll just tell us how to like, run our lives on what we're done, So
Jeff Richardson: [00:44:21] down to decision science and the Condamine traverse
Dave Mathias: [00:44:25] yes. Yes.
Jeff Richardson: [00:44:26] the way to deal with how you think.
Dave Mathias: [00:44:28] Yes. Exactly. Exactly. so I think the more that data people can leverage those things in a part of it's like you're doing is you're grabbing information from all these sources to be better versed in that realm. So
Jeff Richardson: [00:44:38] that I've been doing that for a couple of years now. And it's really broadened, if you think about the spiderweb that you will learn from those kinds of things, right? you can figure out where to press on to go learn more things that are becoming more and more popular. ironically, I just listened to a podcast on graph databases, which is the next thing I'm going to go dig into.
And it really becomes like how graph databases work, where you constantly have over overlapping, nodes of things that intersect, right? Those become more and more prominent. Those become more important in your life. So the more and more you hear it, people that you respect talking about XYZ thing, that's probably the next big thing that you should go research.
Dave Mathias: [00:45:14] So on the graph database front, what makes you excited? I know you're just starting in that realm.
Jeff Richardson: [00:45:19] I had originally learned about these. We were out in Seattle about a year and a half ago at Microsoft headquarters talking about, how to architect a data solution for, a very distributed, set of alerts globally. and they had recommended, we look into a graph database to try to store.
That information to share it back as the repository of where it would store. So that was the first time I heard about that. and this was on my list of things to dig into more. And I just was searching for podcasts the other day, and I think it was on data crunch. they had a interview with, somebody who's writing a book that's coming out.
I think this week on graph databases. I very much think that will be in the forefront of data storage. Obviously many companies are using it now. Facebook's been using it for about five years now for their search. I think now as tools become more ubiquitous and it's less. the, again, that burden to get into that has dropped significantly. We're going to find that many companies, many organizations, many datasets lend themselves better to a graph structure than a relational structure. And in my head, I can think of five problems we have at Bentley that would immediately be easier if we could put them into, reasonably reliable, easy to turn on graph database systems.
Dave Mathias: [00:46:27] Cool. I know we're up at the bottom of the hour, love to, yeah. So love to have you share, how would somebody that listens to this podcast and wants to get ahold of you or follow what you're doing? Things like that. How would they do
Jeff Richardson: [00:46:41] Yep. So the best thing right now would be LinkedIn. I am working on building a social presence, find me on LinkedIn. just Jeff Richardson. I think my LinkedIn URL is just slash Jeff Richardson. message me connect. I love talking to people. this was fantastic talking to you is, was excellent.
so I love to network and talk to people about. Anything with data analytics or really anything in the general nerd space. She's watched the umbrella Academy recently. I just finished that. I would love to go talk to somebody about that now that I'm done.
Dave Mathias: [00:47:09] I'm up. So three right now on that, I'll be honest. And I started watching it because of your
Jeff Richardson: [00:47:13] That's right. We talked about that
Dave Mathias: [00:47:14] Yeah. Yeah. so because your recommendation, I was like, okay, I'm gonna start watching some, I just finished up so three. after I'm done watching, then we'll want to grab a chat. Okay.
have a good weekend ahead and a great talk with you and, till next time,
Jeff Richardson: [00:47:27] Thank you very much. Thanks a lot.