Whoever learns the fastest wins.
When you discover an insight before your competitors, you have an opportunity to exploit that insight for a competitive advantage. Unfortunately, discovering those insights is easier said than done because the world does not stand still waiting for you to have your “aha” moment. It’s constantly changing and growing more complex, and that process can turn your competitive advantages into disadvantages and make your insights irrelevant.
If you aren’t able to detect those changes, then you can’t adapt. Instead, you will continue to rely on a collective set of beliefs and assumptions about the world to make decisions, a mental model, that no longer accurately reflects the actual world you are in (assuming it was accurate to begin with). This is why Neo wasn’t able to make the jump the first time in the Matrix; he still believed in a model of the world that told him it was impossible to make that jump. For Neo, working with the wrong mental model led to a missed jump and a little pain. For you, working with the wrong mental model means making bad decisions.
It is extremely difficult to consistently make good decisions if your view of the world, your market, your customers, etc., is wrong. So how do you make sure that your mental model remains accurate and that you make the best decisions possible?
Our improving ability to quickly gather and analyze data makes it significantly easier to test our assumptions and update our mental models on an ongoing, and timely basis. It wasn’t so long ago that you had to make most decisions with a dearth of data. There wasn’t a Glassdoor to tell you what it’s like to work at a company. There wasn’t a Foursquare Analytics to tell you who is visiting your retail stores. And there wasn’t a Google (and Bing) crawling over, organizing, and make the world’s information available to you via computers that you carry in your pocket.
It’s no surprise that more and more companies are moving towards being more data-driven (I pity companies that can’t make the shift or choose not to). I saw the shift at Microsoft, where analytical skills were becoming a key part of the marketing function. And at my last job, when I spoke to the people leading marketing at other startups, it became incredibly obvious that we were all trying to hire the same “unicorns,” marketers that were able to combine creativity with strong analytical skills. We were looking for people that could make use of the available data to continue improving what we were doing, and hopefully, uncover those insights that would give us an edge.
To remain competitive, you need to become data literate, but it can be challenging. There are a lot of mistakes that you’ll make along the way. I’ve made a lot of them myself, but I’ve found that there are ways to mitigate, or avoid the most common mistakes.
- You don’t consult multiple data sources (your data variety is too low): worst-case scenario, you are relying only on your gut and intuition. Depending on the magnitude of the decision and your experience and relevant knowledge, this isn’t always a bad choice. But it’s very common to be overly confident in what you think you know and to not seek information that contradicts your beliefs. This one is easy to fix. Take time to audit all of the sources of data that you have at your disposal and look for ways to add more sources. These sources can be product data, mentors, peers, customer research, books, etc. For example, at my last job, I consulted a group of marketing directors at other similarly-sized companies via Slack often.
- You are looking for data, but you can’t get to it quickly enough (your data velocity is too slow): data is worthless if you can’t get to it when you need it. There is a bias towards moving quickly and failing quickly (that I think is dangerous to be dogmatic about), even when taking a bit of time to pause and plan could easily help you avoid failing and reduce the time and resources required to figure out if something is worth pursuing. But that’s the reality that we live in, so don’t wait for a critical decision to come up to collect and analyze data. Instead, you should systematize the process of gathering and analyzing data. Do it on a regular basis, so that you are constantly updating your mental models and improving the information at your disposal to make decisions quickly. For example, you can make it a habit of contacting a subset of customers periodically to learn more about them (qualitative research) and augment it with regular customer surveys (quantitative research). The point here is to pack a parachute in advance so you can pull the chord when you need it; don’t wait until mid-fall to start thinking about that parachute.
- You don’t know how to work with or interpret the data: Once you have the data, you need to know what to do with it and how to manipulate it to answer the questions you have. Becoming proficient at Excel is a good starting point for this, and there are plenty of resources and online courses that you can consult for this. I highly recommend the book Data Smart as a starting point. The first chapter in the book covers a lot of the things you learn about working with data in Excel at top consulting firms; even just learning how to use a pivot table puts you ahead of the pack. And the remainder of the book covers practical scenarios that you can apply at work, like using cluster analysis to help identify customer segments and building better forecasts. Once you are ready for something more advanced, you can start to learn R, a free analytical software package that is common in data science. There are other options like Stata and SPSS, but your company may not have the resources to pay for a license so you can use them. On the other hand, R will always be available. If you work in marketing, the book R for Marketing Research and Analytics is a good place to start learning how to use R.
- You aren’t sure about the quality of the data that you are using: data quality issues are ubiquitious across many companies. If you are lucky, the quirks in your data don’t impact the outcome of your analysis. But it’s better to be safe than sorry. There are a few ways to address this. First, make sure you are using the same data set that everyone is using (if there isn’t a centralized, single source of truth at your company, take the time to build it). Second, there are ways to quickly scan the data to look for potential problems, like using conditional formatting in Exel to highlight data points that are outliers in your data set. Beyond that, you should periodically stop and ask yourself if what you are seeing in the data makes sense. If something doesn’t seem right, take the time to investigate it further.
- You spend too much time in the data: it’s easy to spend too much time analyzing data and finding “fun facts” that you can’t actually apply to your work or the problem at hand. In some cases, it is worthwhile to do some exploratory research, but the trick is to try to base it on a hypothesis instead of boiling the ocean for the sake of boiling the ocean. The best advice I’ve gotten for avoiding this is to take time to define the questions you need to answer or hypotheses that you want to prove or disprove before you start looking at the data. That allows you to be very targeted, doing just the analysis that you need to help you make your decision.
- You draw the wrong conclusions from the data: analyzing data and figuring out what it is telling you is an art and a science. Two people can arrive at very different conclusions and recommendations given the same data set. The best way to avoid this is to talk through what you are seeing with your coworkers or colleagues periodically. Ask them to look for holes in your logic or other conclusions that you might be able to draw from the same data. The more you do this, the more likely you are to come across any errors in your analysis and to discover other ways to help disprove your conclusions (confirmation bias is everywhere).
If you aren’t using data regularly to update your mental models and help you make better decisions, then you are putting yourself at a disadvantage. The pace of innovation and change demands that you continue to adapt to remain competitive. And by taking advantage of as much data as possible, as often as possible, you ensure that your decisions reflect the actual world you are competing in, and not the world that you think you are competing in.