Quick Take | Tips to Help Executives and Leaders Grow
The Quick Take podcast provides concise and actionable tips to help executives and leaders, like you, tackle the thorny and complex challenges that affect us daily. By leveraging their experience and relationship with other global leaders, our hosts provide suggestions that are based on their deep experience as leaders and coaches but also pressure tested in boardrooms everywhere because they asked their friends….(almost). Hosted by James Capps and Susie Tomenchok. Episodes release weekly on Thursdays.
Quick Take | Tips to Help Executives and Leaders Grow
Harnessing Data-Driven Leadership
Is your organization truly data-driven, or just data-drowning? Dive into the world of modern business intelligence as we explore how leaders can harness the power of data to make smarter decisions. From building company-wide data literacy to navigating the AI revolution, learn the essential steps to transform your organization into a data powerhouse.
In this episode, we discuss the following:
1. Importance of data literacy for leadership across all departments.
2. Evolution and significance of data in business decision-making.
3. The impact of AI on data interpretation.
CONNECT WITH SUSIE:
https://www.linkedin.com/in/susietomenchok/
CONNECT WITH JAMES:
https://www.linkedin.com/in/capps/
Welcome to the Quick Take podcast, the show where you get targeted advice and coaching for executives by executives. I'm Suzy Tominchuk.
Speaker 2:And I'm James Capps. Give us 15 minutes and we'll give you three secrets to address the complex topic of issues that are challenging executives like you today.
Speaker 1:Hey Quicksters, welcome to Quick Take. I'm your host, susie, along with James. Hey James, how are you?
Speaker 2:I'm super good. I've got lots of energy today hanging out with you Super, super fun.
Speaker 1:Super, super fun, all right. So we're going to talk about data-driven decision making right in leadership.
Speaker 1:Yeah, and as soon as you told me that, I was like oh, I have the best story. So I work with a team and they use data to look at the progress of the business every week, and what's really interesting is they're working on really refining the dashboards everybody looks at, and so the leader has been working behind the scenes to get a dashboard that she wants to look at every day. Meanwhile, her team is looking at a different dashboard.
Speaker 1:And so sometimes they look the same, and sometimes there's things that are off, and so what they do is, when things are off, they have to like do some investigation behind the scenes to figure it out, tie it all together, because they can't question what she's saying. Right, right Until they know if it's right or not. So it's so interesting. I'm sure this is something that it sounds so easy, right?
Speaker 2:Doesn't data deliver decision-making sound like just give me the data, but I do think it is so foundational. You know, it's funny because I think we've seen, in over the last 10, 15 years, this really strange waxing and waning about the importance of data for companies, where data for a long time was considered to be the new gold. That was where a lot of things were going to be driven. I think, you know, data-driven decision-making was part of that, and then I think that that that came out of what fell out of fashion, but then generative AI exploded. And then when people realize that, look, my entire generative AI or big data work is going to be predicated on me having good data, that that that had a little bit of a resurgence there.
Speaker 2:But I think to your point, though is, though, is, if a company doesn't think about this holistically, you do end up in these situations where you spend a ton of time trying to decompose some field and some table or some data in somebody's dashboard to try to understand what's going on. Try to understand what's going on. What you described just gives me so much anxiety and frustration, having been there where you spend a lot of energy trying to do what is wildly unnecessary. If you had a good data strategy.
Speaker 1:Yeah, for sure. And it is so much energy and so much doubt and lack of trust. Even the next time you look at it you're like is this really real?
Speaker 2:Right Yep.
Speaker 1:Yeah, so what are the tips that you have for leaders when you think about this challenge?
Speaker 2:I think the first thing I want to comment on is I gave a little bit of a history lesson on where data is. The truth is, it is here to stay and will only get more important. So it is critical that my first piece of advice is to create a data literacy program for your leaders. Help them understand the difference between data and information, help them understand how they can use data and it's easy for me, as a CTO, to get my head around this. We think about it a lot. Where is the data coming from? How do we maintain that data? But this data literacy needs to go from. You know technology, sales, marketing, legal. You know the number of conversations I've had about data and data provenance with my lawyer friends in the last six months. I never would have thought many of these organizations would have cared, but the truth is that data literacy needs to be something that everybody at a leadership level is really working on.
Speaker 1:And it's like stop what you're doing and do this Because it's not sexy, it's not fun, but we go from meeting to meeting and it makes me think of one of my kids is in a role where she has to pull data and one of her leadership, in in the middle of a kind of like an IM, came to her and said, hey, can you change the report to look differently by 30%? And she was like what? And that makes me think that her leadership doesn't understand all of the data.
Speaker 1:Like you said, the literacy is low, so then they're more reactionary in the moment because they're busy doing other things. They don't want to slow down and understand.
Speaker 2:Well, I think people need to understand the data that is available to them within the company. You know, I look back on our time in cable and the giant gap between at the time granted, this is before streaming the gap between who had cable and who was watching television was something that we could not actually understand. We could not. There was no data to prove that Cable measures, and still does measure, not viewers more so than homes past. Think about that. That's how cable was.
Speaker 1:Everybody what homes, did what homes?
Speaker 2:Was there a cable running next door to it? You know that literacy of the data that was available to your domain is super interesting. Um, and I think the second item is really not only the literacy of the data but creating a centralized and unified place for your organization to understand where the data is coming from, because I think, to your point, that company has to have a canonical understanding of these pieces of information sales accounts, receivable attrition, marketing, dollars spent. We all have to agree that this is the source of record. We all have to agree that we are going to get that data from these places.
Speaker 2:I have worked with a company that has had that very problem where budget decisions were made based on two different data sources. So you can imagine the huge epiphany and disaster that occurred in first quarter when the realization was that the revenue generation estimates were not based on the same number, that are the same data source as the cost of goods sold data source. So suddenly there was no chance of success for that budget to come together. I think it's critical that you agree to where that data is coming from.
Speaker 1:Yeah, oh, my gosh. But I have to say and maybe this will tie into your last one, or we need to talk about it but you get so energized by this and these could just like come off your tongue about all the different things, but for me I'm going oh, where would I start what? I don't have time for this. This is so much. I'm overwhelmed. Like how do you talk to somebody that doesn't have this understanding of the power of this and stopping and spending the time to really understand it?
Speaker 2:Well, I think the truth is is that the industry has caught up with the reality of this. I think a great example is Salesforce. Salesforce as a product is working to be that thing for your company, right. It is a tool that has your sales data in it. It has your customer data in it. It can have what soft serve ice cream you wolfed down at lunch in it. Does that ever happen to you, susie? Does that?
Speaker 1:You know that's a different episode. It is a different episode. We'll come back to that later.
Speaker 2:We'll come back to that, but there are tools that exist to enable you to do this. I think that if you simply decide that this is important, then you bring in the people that can help you. You don't need to say you know, we're going to do this by doing these things, but certainly understanding the importance of this and then deciding where that data is and then how you. You know it's the how and the what will come. The why has really got to be the first thing you decide.
Speaker 1:Okay, all right, that's fair. All right, what's your last tip?
Speaker 2:Well, and the third one is really just on top of that is you do need to build and leverage analytics tools, right? You can't use Excel exclusively as your tool, like, I think the best way to think about this is you know, for many years there was a big data was a word that we used a lot, data lakes and the ability to store data. The fact of the matter is is we have gone from maybe having hundreds of millions of rows of data that are being consumed and creating reports to hundreds of billions of rows of data, to hundreds of billions of rows of data. Now, with generative AI, you can look at whether it's ChachiBT or Grok or Gemini. They're looking at all the data, yeah, all of the data BlackRock is looking at.
Speaker 2:When we evaluate a company, we look at everything ever published about that company, ever, wow, yeah. So the truth is, is that that the level and the amount of information that is available far exceeds, perhaps, the tools or the systems you've put in place? So, even if you've done the first thing, which has created an environment for your employees and your leadership to have data literacy, secondly, you have created a central repository. Your ability to analyze that data is much different than it was five years ago, and you've got to start looking at these more aggressive and more modern analytics tools.
Speaker 2:Wow, that's so much, and AI isn't going to solve the data source problem, so you have to make sure that you have the basis.
Speaker 1:Your reality will continue to get skewed.
Speaker 2:Yeah, and I think that's the funny thing about it, if you've been following the AI story at all. The concern is the data that you provide to your engine is skewed. Why is that? Because data is, by definition, skewed right and so, and it may be skewed in a way that you're not aware of, or maybe what you are aware of, and suddenly those things matter. Hallucinations in AI do occur, so because they stitch together things that aren't real. So data literacy is what we're talking about here Understanding the nuances here, getting to being ensuring that the data you're using is your data is right, and then using others. Data actually tells the stories and you understand where it's coming from. So this is no longer just something that is unique to the technology basement. This is now critical to every level of the business.
Speaker 1:Yeah, so it's building the foundation so that you can really leverage the things to give you the power to look at it a lot of different ways.
Speaker 2:Yeah, that is so true, 100% true. But even if you hear us talking about this and you're thinking to yourself you know what, we don't need to be a data-driven company, we just do this. Look, the truth of the matter is you are a data-driven company and if you don't know how you're being driven by data, then you're actually not. You have the possibility of you making poor decisions. Data is key to how you're spending your dollars, how you're staffing, who you're hiring, where your products are being consumed. All those things are data-based, and it is key that you start to appreciate the criticality of this and how people are using data around you.
Speaker 1:Love it, love it, all right. So what are the three tips?
Speaker 2:First one is to solve that problem I just described. Create data literacy of your C-suite. Create data literacy of your direct reports, your management. Help them understand how important this is today. Help them understand the vendor data that you have. Help them understand where the vendors are getting their data. Start teaching, get your people the education, the understanding around data literacy. Second, create a book of record, a canonical understanding of where your company's important data is. What data do we consider to be factual? What is our data sources of record? What do we want to consider the important data and what do we consider not as accurate or trustworthy? And then, third, start appreciating the fact that you need to have tools and systems in place to start consuming very large pieces of data. The role that data plays in your day-to-day business is only going to get more acute, and without advanced tools, I think you'll get left behind.
Speaker 1:Yeah, so such an important thing. It may seem not sexy to start with, but over time and when you really get people talking about it, it will really inform your future.
Speaker 2:Yeah, Take that first step, create the data literacy and it'll blow your mind. You'll really realize how important this is and that this isn't just something that you should be talking about occasionally. It's really really a big deal.
Speaker 1:Yeah, Foundational Thanks James.
Speaker 2:Thank you.
Speaker 1:Thanks for listening to this week's episode of Quick Take, where we talk about the questions that are on the mind of executives everywhere. Connect with us and share what's on your mind.
Speaker 2:You can find us on LinkedIn, youtube or whatever nerdy place on the internet. You find your podcasts. Our links to the show are in the show notes.
Speaker 1:We appreciate you.
