The best practices for creating a data-driven business featured in this article is based on a recent conversation on the Data Chats Podcast featuring Jason Foster, founder & CEO of Cynozure, host of the Hub & Spoken podcast, and co-author of Data Means Business: Level up your organisation to adapt, evolve and scale in an ever-changing world.
During the episode, Jason explored several best practices that will help a company develop a data-driven culture and launch a successful data project.
Best Practice #1: Data Projects Should Be Iterative
Building a data-driven business doesn’t begin by dropping a big data ecosystem in place and waiting for magic to happen. Instead, it happens by stacking one data project on top of the next and improving along the way.
An iterative approach to data projects recognizes that we might get it right or wrong at first, but either way, we’ll learn and refine.
Whether things get better or worse, you learned something. You’ve learned that the data wasn’t the right data or that the question wasn’t structured correctly. Or, you might learn that the answer was exactly what we thought it would be.
The iterative structure is a way to know that investments into new technologies or strategies will add value to the business.
For example, there was an online retailer called ManoMano based in France. The business was close to shutting down. They had one last chance to make it work, so they started by studying transactional behavior.
Instead of putting all of their marketing dollars into one campaign, they did 10 campaigns to see which produced the best results. Then they reinvested into the top-performing campaigns. Slowly, over time, they whittled down to the key marketing campaigns that led to their eventual success.
This is an example of looking for signals in data to prioritize resources.
Best Practice #2: Businesses Don’t Need More Data (Yet)
Having more data is not the goal, because the reality is data on its own isn’t going to tell us a specific answer. Becoming a data-driven organization is a journey that won’t happen overnight.
Every day businesses are creating data; it’s created when someone visits a web page, logs a complaint, signs up for an email or makes a purchase.
So, the first objective is to make sure that your data is dependable and looked after so it can produce its intended operational results.
Eventually, you want to be an organization that has a mindset equipped to harness data and use it in ways that help make better decisions.
Before the organization has a conversation about data collection, it needs to start by thinking beyond the data and answer the question, “how do we turn this into a valuable asset?”
Oftentimes, organizations distract themselves with technology to solve problems around their lack of data integration. Instead, they should look closer at the culture of the organization and the structures of the business to ensure they’re capable of acting on data insights. So, it’s not a technology problem, it’s a culture problem.
Best Practice #3: Data Should be Integrated into Every Aspect of the Business
Data shouldn’t be separate from the day-to-day business of an organization. But, all too often, there are data professionals and there are business leaders, and rarely does the work of these two roles intersect in meaningful and transformational ways.
But this type of work shouldn’t be siloed, instead it should be embedded together. The ultimate goal is to get to a place where people in the organization don’t think about data much, but it is absorbed into the fabric of the work.
Best Practice #4: Data Projects Start with a Business Problem
More often than not, data conversations begin around data collection, pipelines and models. These are all important, but they shouldn’t precede a conversation about business problems.
Data might be needed for a variety of big projects including transformational and strategic decisions. It can also be smaller projects like, “should we feature product A or product B in the next marketing campaign?”
Either way, you start with the problem and then backtrack to the right business questions to ask.
For example, trying to better understand the competitor landscape or where to find growth opportunities. You might also be trying to find gaps in the market or predict what customers would like to purchase next.
So if the problem is about competitors, you might scrape websites for competitor pricing. If it’s about customers, you might look at some kind of customer panel that exists in your industry.
If you’re trying to understand how to spend your marketing investments, then you might explore what’s been or is currently successful. You can also see who is currently purchasing your products the most. Then, find look-alikes in the market who have similar characteristics.
The type of data you need depends on the business challenge. That is why it’s so important that data projects start with a deep understanding of the most pressing business problems. Otherwise, you just have a bunch of data and you don’t know what to do with it.
Best Practice #5: Data Requires Responsibility
People can simply surface data that will support decisions that have already been made or ignore bits of data in its entirety. So, you have to be honest, open and collaborative within the business to get value from data.
If you’re going to ignore the data for whatever reason, then you need to capture that. Oftentimes, businesses will ignore the data consistently in the decision-making process but there isn’t transparency.
Additionally, data can expose people. You might think that a particular department or strategy is working because some data is acknowledged and other data is ignored. But, once everyone is looking at the data honestly, you might find the story you knew to be true wasn’t accurate. Then, the company might adjust its decisions based on new insights.
Best Practice #6: Build Champions of Data
Build an alliance with the cheerleaders of data in the organization. Then, work on uncovering a valuable insight and start communicating that success in the organization to win over the skeptics.
Telling stories and demonstrating use cases are effective ways to move an internal stakeholder from skeptic to positive promoter of the thing that you’re trying to achieve. Make a case for your data projects by having clarity on what you’re doing and why you’re doing it.
Best Practice #7: Data Doesn’t Replace Intuition—It’s Complementary
Organizations that are utilizing data to its fullest potential have a certain culture around data. This means, they aren’t making decisions based solely on data, but they are combining it with intuition and experience to make an informed or data-guided judgment.
You still might make a wrong decision, but you were doing it with good information and good intent in an effort to increase the odds of making the right choice.
For example, if you put politics aside and think about the COVID response, what governments were trying to do is look at the data (where are the spikes, what kind of cohorts are most impacted, etc). And then could make decisions about how to respond. In the early days of COVID, they weren’t able to do that. It was ad hoc and siloed. They didn’t know who had it, where it was coming from, or how to distribute testing.
So on one end of the spectrum, there are companies like Uber or Netflix who are absolutely making decisions based on data. On the other side, there are organizations that don’t even think about it. They still might make really good decisions, but they aren’t doing it by looking at the data available. As a result, they lack insight and understanding when it comes to what’s happening with their customers or products and how they could optimize to drive the business forward.
It’s all about data maturity, how embedded data is in the organization and whether or not it is used consistently in decision-making.
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