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Data-backed decisions

Data-Backed Decisions

Data-backed decisions have an air of authority and legitimacy about them that shooting from the hip definitely does not. But today’s guest, Ruben Ugarte, author of Data Mirage, says too many companies fail to use their data properly. Is this happening in your organization?

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What You’ll Discover About Data-Backed Decisions (highlights & transcript):

Data Mirage* How to be more strategic in collecting data for data-backed decisions [01:31]

* Best way to think about data to improve data-backed decisions – a framework [02:37]

* How KPIs can be misleading [04:30]

* 3 Common problems that thwart data-backed decisions [05:56]

* What small business owners need to consider to improve data-backed decisions [08:35]

* What The Data Mirage teaches about data-backed decision making [11:31]

* How small business owners can get more disciplined and focused in their data-backed decisions [13:13]

* How to trust your data for better data-backed decisions [16:36]

* And much MORE.

Hanna Hasl-Kelchner:  [00:00:00.21] Data-backed decisions have an air of authority and legitimacy about them that shooting from the hip definitely doesn’t but today’s guest says that too many companies fail to use their data properly. They could be unlocking more insights and make better decisions. And when we come back, we’ll find out more.


Announcer:     [00:00:18.69] This is Business Confidential Now with Hanna Hasl-Kelchner. Helping you see business issues hiding in plain view that matters to your bottom line.


Hanna: [00:00:29.91] Welcome to Business Confidential Now. I’m your host, Hanna Hasl-Kelchner and today’s guest is Ruben Ugarte. Ruben is an expert in data decision-making and the founder of Practical Analytics, and the author of Data Mirage: Why Companies Fail to Actually Use Their Data.


[00:00:48.81] He has worked with businesses at all stages on five continents and helped them use their data more effectively to really improve their decision-making in ways that significantly boost performance, increase profitability, dramatically lower costs and make their teams world class. Now, what’s not to like about that? So, let’s have him join us now and learn more.


[00:01:10.80] Welcome to Business Confidential Now, Ruben.


Ruben Ugarte: [00:01:13.93] Hi, Hanna. Thank you for having me. I’m excited to be here.


Hanna: [00:01:17.58] Well, data plays such an important and critical role in helping companies make strategic decisions, whether it’s to compete more effectively in the marketplace, create new products and services or just even hit other types of growth products.




[00:01:31.14] So, I’m really curious to learn more about improving data-backed decisions and I’m wondering, in your experience, how strategic do you think most companies are when it comes to even having a strategy for collecting their data?


Ruben: [00:01:44.82] Well, you know that, of course, varies significantly by industry, you know. I have done a lot of work with technology companies and I find they tend to be very data-driven a lot, their decisions are data-backed, and that may be drastically different from what you may see in a completely different industry, maybe one that hasn’t been as affected by technology.


[00:02:08.67] I would say that most companies have a path and idea of how they want to use data within their strategy. Every company has data that, you know, that is clear. Many of them are drowning in it, but they haven’t fully figured out, you know, this is the kind of specific role that we think data will play in the strategy in developing new products, finding new customers and innovation, in general.




Hanna: [00:02:37.14] Well, let’s dig a little deeper into that. What’s the best way for businesses to think about their data and especially key performance indicators, KPIs, when it comes to making better data-backed decisions or even collecting the right data?


Ruben:                   [00:02:53.55] Yeah. So, I always recommend to start with a simple framework, you know. I call it the “time zones” of data and it just breaks down the maturity of a company into three time zones, time zones we’re all familiar with: the past, the present and the future. In the past, you’re trying to understand what happened,  


[00:03:14.46] you know, what was the performance last quarter? How did that last marketing campaign perform? In the present, we’re trying to understand what is going on. Maybe we just launched a new product and we’re trying to monitor how the product launch is going or maybe we have an active campaign, a marketing campaign or sales campaign that we’re also trying to monitor. And in the future, we’re trying to predict what might happen and this is really the realm of data science and AI machine learning.


[00:03:44.82] So, this as a starting point, companies can then determine where they are. You know, most companies typically have a grasp as to what happened in the past. They may have a vague idea to what’s going on in the present, you know, it may be quite delayed, right? They might not know how the product launch is going until, you know, a month later and then maybe even fewer companies might have something for the future.      


[00:04:09.96] Now, within the framework, then we can start to place different KPIs, you know, KPIs about the past, about the present, about the future. We can talk about resources, you know. We may need data analysts for the past, but we may need data scientists for the future. So, it starts to give us a sense as to where a company is doing well and where a company can start to invest time and money.




Hanna: [00:04:30.99] So, at these different stages, do different KPIs play different roles? Help me understand that a little better.


Ruben: [00:04:39.06] It would, yes. You know, you may have a – what’s commonly referred to as far as leading versus lagging KPIs. You may have a KPI, such as sales, which might not take into account what happens or what needs to happen to get there. So, your KPI, your major KPI, may be very past-driven, you know. You’re looking at something at a time period that took place in the past: total number of sales, total number of new customers.


[00:05:05.51] Then, you may need more KPIs to understand the present, to know if you’re going to be on target to that sales quota or to that new customer quota. And then, in the future, you start to look at perhaps less KPIs. You can start to look at what may be better referred to as just projections, you know. You may be trying to have a sales projection, a sales quota model as to how the quota might unveil, how a different budget might play out, but nonetheless, there’s different kinds of KPIs, and you can start to get a gauge


[00:05:38.69] as to how well you perform based on the other stages, right? And you may discover that, “Hey, we do really well in the past, but we’re never quite sure if we’re really going to hit the quota.” So, you may be missing, you know, things in the present and then maybe even your future, the way you project is not accurate yet.




Hanna: [00:05:56.39] Well, let’s zero in on some more accuracy. When it comes to common problems organizations encounter that keep them from unlocking more insights from their data so that they can make better data-backed decisions, what are some of those issues that you see?


Speake3:         [00:06:13.58] Well, let me give the top three that tend to affect almost all companies in some shape or form: one is overwhelmed, having too much data and not knowing how to convert that into insights and action; two would be silos, so      


[00:06:28.72] the classic example of the sales team and the marketing team having their own data, but they don’t talk to each other; and then, third would be a lack of trust. Someone, a team, a company has data, but they don’t trust it. They don’t think it’s accurate, they don’t think it reflects what’s going on, so they just don’t use it.


Hanna: [00:06:50.21] So, when you’re consulting with these various companies, in which of these three do you find you spend more time?


Ruben: [00:06:58.85] Well, I find that – it differs based on the company’s size. You know, I think smaller companies – and let’s put smaller as any company under a hundred employees. At those level of company, it tends to be more focused on the lack of trust, you know. Even though they have a small amount of data, there is also a high level of distrust in the data. It might be quite disorganized, they may not have the right technical infrastructure, so numbers always look quite funky.


[00:07:32.37] As the company gets larger, then some of the other two problems come into play. You know, silos, for example. It’s primarily really a political or people issue. You know, you have two departments that don’t get along or are not aligned. You know, for example, I’ve worked in financial companies where a lot of our data strategy plan was just simply vetoed and shut down by the legal department.


[00:08:00.15] So, you have sort of different regulations that tend to appear as the company gets larger and now you need processes and ways of handling a significant amount of data. And then, of course, as a company collects more and more data, and it gets larger and larger, there’s simply more information coming at people and coming at teams and they start to get to a point where they have accurate data. They have a lot of data, they have lots of quality data, but now they have too much. [Laughter] And, you know, it’s not quite clear how you sort through it all, how do you make decisions, what to focus on, what to ignore.




Hanna: [00:08:35.55] Well, let’s take this down to an even smaller level for the entrepreneur, someone who definitely has less than a hundred employees, maybe half that, maybe even much less than that. How should they start thinking about data? What to collect? How to review it? How to make better data-backed decisions? Because otherwise, they’re just shooting from the hip. They might as well be throwing darts in the dark.


Ruben: [00:09:03.91] [Laughter] Yeah. Yeah, and you know the odds of the dart game might be a little better. [Laughter] Well, I think it really starts with the outcome, right? You know, one of the things I argue in the book is, you know, data doesn’t need any big stacks. Everyone understands and is on the same page as to why data can be helpful, but you also don’t want to become the end in itself where you worked really hard to have this really amazing data and reports and dashboards, but your business is not growing, how you’re not getting any customers, you’re not increasing the sales or customer satisfaction.


[00:09:38.74] So, it starts with the outcome that this entrepreneurial company might be working towards. Let’s say, you know, they’re interested in expanding the customer base. So, from there, there’s going to be a series of questions or hypotheses that the team or company might have around that. They may be unsure as to what markets could be similar to what they already serve and they may want to explore. [00:10:04.76] They may not be sure if their current pricing model is a good fit for the markets or if they need to develop new products, you know. Now, these kinds of questions I find are readily available within companies and those become the starting points for data.


[00:10:19.28] So, from those questions which are, you know, tied to some kind of outcome, something tangible, we can then start to determine – okay, if we want to understand markets or new markets, then we need some kind of data around market research, best type of markets, trends in markets. Where can we get that? Do we have that? Do we have to go to an external provider?


[00:10:40.40] Because we go through this sort of logical sequence of “What do I want to accomplish?” “What are the questions I have where data could help clarify?” “Where do I get that data?” “How do I visualize that?” “How do I analyze it?” “What do I want to learn from it?” Effectively, it’s what it really comes down to. They start with some clear way of “What do you want to do?” “What do you want to do with the data?” and they go after it.


[00:11:09.59] And I will say, this is different for larger companies. Larger companies tend or collect, you know, the entire universe and then just start to put resources in terms of people and technology to try and analyze and find interesting insights. The smaller companies have to be much more disciplined. It can’t just be random. It has to be much more targeted.




Hanna: [00:11:31.37] So, tell me about your book The Data Mirage. What’s the mirage part?


Ruben: [00:11:35.97] You know, I think that the mirage in the title came as I was reading executive surveys around data over the past, about 10, 15 years and they’re all very similar, right? They were – you know, the surveys match what was going on in the industry. This is sort of to say “broad” business


[00:11:53.90] industry executives knew that they needed to invest in data, in technology, in people. They were investing in it, so you can see this trend of “We know we should be doing this. We know we should be investing. We are investing. The amount of money is increasing,” and yet, there was always this disconnect because there was always a question in the survey that said, you know, “How well do you think the team does that.


[00:12:17.78] Converting data into insights at finding those actionable ideas but then will lead to, you know, data backed-decisions?” And there was always a disconnect where that number was small, you know, 30%, 40% of people, of executives thought they were doing as well. So, I felt that that reflected what I was seeing in my work where

[00:12:41.57] I did have to do a lot, you know, to sort of pitch why companies should invest in data and yet it wasn’t quite clear that they were really getting as much value out of it as they should. As you know, they were investing into it.


[00:12:55.70] So, I thought that that was sort of a symbol, a mirage. You start seeing this, you know, amazing location in the middle of the desert. You might be thirsty. This might seem like a great destination, but you can’t seem to really reach it. It always is out of reach, it’s always just a few steps ahead of you.




Hanna: [00:13:13.82] Interesting. So, how do we really improve data-backed decision making especially for the smaller companies that don’t have a ton of resources? I mean, you said earlier that they need to be more disciplined and more focused. So, how do they do it?


Ruben: [00:13:32.99] Well, you know, for smaller companies – there’s a framework in the book that I find helpful for companies who are coming up with a plan, and it’s a simple framework called the Three Ps. It stands for people, processes and providers. And it just simply guides you through questions on how to come up with a data plan or a data strategy which is, I think, always the first starting point for any company that wants to really make a difference in this area. You’re just really trying to determine, you know, who are the people who are going to be involved in this.


[00:14:08.24] Do you need to hire people or is there people already in your staff that may have the inclination or the skills? You may already have technical people, you know, data analysts, engineers and so on. You may have people already in your company who are comfortable with data, that they understand numbers and statistics, and probabilities, and so on.


[00:14:28.70] Then, you look at a process, you know, how you’re actually going to convert data into insights and this becomes a question of: are you going to have a weekly meeting to discuss the latest numbers, to look at opportunities within the data or look at trends they should be aware of? How are people going to access the data?


[00:14:50.57] Is it going to be just dashboards? Is there going to be a – you know, an offline export that then can be taken into Excel? Is it going to be an e-mail digest? So, you have to get it further towards the granularity as to how things are going to happen on a daily, weekly basis. It’s not just a “We’re going to have data. It’s here, you know, come and get it,” sort of like the Field of Dreams strategy, right? You build it and they will come.


[00:15:13.91] You really want to be quite clear as to how they will actually come. [Laughter] And then finally, the technology. And this is where you might be looking at software tools to visualize data or software tools to store data and so on. It can be very complex, but for small companies, the technology is really not as important as the people in the process.


Hanna: [00:15:35.33] Interesting. So, the three Ps – people, process, providers – is really the key to improving data-backed decisions.


Ruben: [00:15:44.78] Yes, exactly. And the thing is, you know, a company is already doing something along these lines. You know, you probably already have people, you have technology, you have data, right? A small business will have a CRM, may have a payment processing provider that processes the payments. You might have a marketing tool, like a marketing email tool, a website, whatever it may be. It’s already there. You already have pieces, you just have to find the gaps, right?


[00:16:12.80] You may have data, but no one’s looking at it. So, you may need either a better process, a better way of reviewing the numbers in a way that’s not burdensome, like it doesn’t become a full-time job or you may just need people. You may need to run through some training for your people or bring someone in. That starts to give you a sense as to where you should focus your time and what’s working. 




Hanna: [00:16:36.38] Very good. Now, you mentioned earlier that smaller companies, in particular, sometimes don’t trust their data. Do you have any tips for how to improve that trust factor?


Ruben  [00:16:47.87] Well, lack of trust is tricky, you know. It’s pervasive. Once it starts to happen, it can undermine everything. And trust, like really in anything, you know, it’s built one report at a time and it’s lost one report at a time. So, for companies that are trying to maintain it or perhaps repair it.


[00:17:07.16] It starts by, you know, counterintuitive or doing less. So, if you have, you know, 10 reports, 10 dashboards, but you don’t trust any of them, they’re also worthless. So, you really want to come down and do less. You may have only one report, one dashboard, a few KPIs that you know are trustworthy.


[00:17:25.19] As someone went through, they double checked, they randomized, they double-checked all the pipes, you know, the – from a technical perspective and they know that these KPIs are trustworthy, and from there, you can, well, react more and more.       


[00:17:38.63] The second thing is, of course, to have someone who can do some of this double checking. So, it may be someone internal in your team, maybe an external set of eyes who might want to do this double checking for you, almost like an audit, if you don’t think that your team is able to do it.        


[00:17:54.41] But just have someone who could say, you know, “This calculation makes sense.” “This number makes sense.” “I can see this KPIs are being pulled from this specific location,” and so on. And then third is, perhaps, managing expectations.          


[00:17:54.41] Some of the issues that small companies run into is they have been running on numbers. They’re [Laughter] – you know, they’re effectively sort of napkin math. You know, at some point someone said, you know, “We have X number of customers and then X number of activities happen so – or you know, our return rate is 10% and no one ever challenged that.”


[00:18:28.20] But at some point, they get real data and they see that the return rate is actually 20%. So in that case, you know, it’s about re-managing expectations, confirming that this is the real number and so on. That process takes time. But trust, it’s really a people issue. You really need someone in your team or someone that can help you validate that what you’re seeing is correct and they should trust them.


Hanna: [00:18:55.00] Makes sense. Now, your book Data Mirage: Why companies Fail to Actually Use Their Data, what would be the most important takeaway you want a reader to have from that book?


Ruben: [00:19:07.05] I think after going through all this, you know, strategy and tips and tactics and so on, I think what I want the readers to takeaway is, you know, data is important. They can help you make those, you know, data-backed decisions, but it has its appropriate place in the company, you know? You want to do good enough in it.


[00:19:27.40] For most companies, they’re not going to become this data-driven machine, like a Google or Facebook or some of these other technology companies, to figure out why you need to level up, where you can improve and then move on. And then, you move on to the real aspects of running the business, of building products and serving customers.


Hanna: [00:19:48.34] So, the tail doesn’t wag the dog.


Ruben: [00:19:50.59] Exactly. [Laughter] Yeah. [Laughter] Yeah. Unless, you know – unless your business is data and I think you would know it.


Hanna: [00:19:56.00] Yeah.


Ruben: [00:19:57.00] If this is the case, then this is really just a tool in the many sort of arsenal that business has to be able to run a successful business.


Hanna: [00:20:06.22] Exactly. But it’s a tool that needs to be used properly and you want to maximize it. So, I really thank you for being able to share that information on how to improve data-backed decisions in your business because I think it is important. People want to make the right decisions and justify it. And granted the data may be changing over time and it’s a little bit of a moving target, but at least it has some basis in reality and in data.


Hanna: [00:20:35.83] So, if you’re listening and you’d like to contact Ruben, learn more about his work with data-backed decisions or his book The Data Mirage – he’s even got a popular blog, we’re going to have information about that in the show notes on


[00:20:52.12] And if you know someone who could benefit from today’s conversation, please consider sharing the link with them and leaving a positive review on your podcast app or


[00:21:07.63] You’ve been listening to Hanna Hasl-Kelchner and Business Confidential Now.


[00:21:11.71] Have a great day and an even better tomorrow.

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Guest: Ruben Ugarte

Ruben UgarteRuben Ugarte is founder of Practico Analytics, providing expertise in data analytics. He has worked with companies on five continents and in all company stages, helping them to use data to make higher quality decisions, boost performance, increase profitability and make their teams world-class.

He maintains a popular blog with more than 100,000 readers. His new book is The Data Mirage: Why Companies Fail to Actually Use Their Data (Business Expert Press, January 22, 2021).

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Connect with Ruben on LinkedIn, Twitter, and YouTube. Also read his popular blog.

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