As data pioneers, we always encourage businesses to leverage whatever stats are available to them when making decisions. <\/p>\n
However, the way you do this matters a lot, and that has given rise to several terms.<\/p>\n
Though the difference between these buzzwords is subtle<\/strong>, each approach is significantly different and changes your company’s relationship with data.<\/p>\n Taking a data-driven approach where data drives everything you do might be the more objective approach, but you may miss the bigger picture as a result.<\/p>\n On the other hand, going all-in on the data-informed approach and allowing data to guide your business decisions gives room for too much flexibility in your data. <\/p>\n Just think about what might happen if you start cherry-picking data based on preconceived notions.<\/p>\n So, are we saying that both approaches are inadequate, and should you just do away with them? <\/strong><\/p>\n Not quite. Stick around to find out why.<\/p>\n If we took a data-driven marketing<\/a> approach, it would imply that we rely on data to make our decisions all the time. <\/p>\n In other words, every aspect of our business strategy depends on our interpretation of available data.<\/strong> <\/p>\n This approach is excellent where we can harness data for unique insights and information on your customers.<\/p>\n But, as you can already tell, the data-driven approach does not take personal experience into account. <\/p>\n \u27a4 It focuses less on the bigger picture.<\/strong> Simply put, it is all about the facts, where data has the final say.<\/p>\n Here’s a typical example: we have two copies of an ad, A and B. <\/p>\n From our view, ad A seems better, and we think that it might yield better results. However, instead of running ad A, we go on to A\/B test<\/a> both copies. <\/p>\n On evaluating their performance, ad B turns up with a better (lower<\/em>) Customer Acquisition Cost<\/a> (CAC).<\/p>\n We were pretty sure that ad A was the better one, though. It is such a shame that we will have to use ad B because we are strictly data-driven.<\/p>\n See what we mean?<\/p>\n Here’s how we think of it. <\/p>\n Since being data-driven basically takes decision-making out of our hands, it prevents personal bias. <\/p>\n Simply put, it tells us what to do, and we do it without gut instincts coming into play. <\/strong><\/p>\n Plus, we would not need to go through the nuances of decision-making \u2014 and some businesses love that.<\/p>\n As a follow-up from the first point, decision-making being out of our hands makes it easier for us to push back against stakeholders who may have their own agendas. <\/p>\n Who doesn’t like defending their position with cold hard facts and figures?<\/p>\n Another advantage of a data-driven approach is that it can help us identify trends<\/strong> that may signal future problems. <\/p>\n Rather than reacting to market changes, we can identify potentially problematic situations using data.<\/p>\n Placing sole emphasis on data to make decisions may cause us to miss the bigger picture.<\/p>\n Another major problem with the data-driven approach is that we may be relying on data that is not statistically significant. <\/p>\n This will undoubtedly prevent us from making informed decisions. <\/strong><\/p>\n In fact, if we are truly to go the data-driven route, we must collect large data sets over a relevant period to get an accurate representation of market conditions.<\/p>\n A purely data-driven approach is challenging. <\/strong><\/p>\n While 74% of businesses<\/a> aim to be data-driven, only about 29% are good at transforming their data analytics into action.<\/p>\n Both approaches have their pluses and minuses, but does one truly excel the other?<\/p>\n We find that the data-driven method is best for companies with a <\/strong>big data<\/strong><\/a> approach.<\/strong><\/p>\n It is our preferred method for “either\/or decisions”. <\/p>\n Being data-driven will work for you when you are focused on optimizing your conversion rate<\/a>, improving pricing<\/a> or performing A\/B testing.<\/p>\n Let’s take the example of A\/B testing. <\/p>\n When A\/B testing a single metric, your answer can only be A or B. This kind of decision can be data-driven, and personal experiences rarely come into play. <\/p>\n Your primary focus should be on choosing your success-defining parameters beforehand. Plus, where relevant, you also want to gather enough data to get significant results.<\/p>\n On the other hand, we find that the data-informed approach works best for complex projects<\/strong> that combine various inputs. <\/p>\n For example, if we were going to update our eCommerce<\/a> store, we would have to focus on more than just data. <\/p>\n Inputs like customer feedback, personal experience, competitive data, and stakeholder input, amongst others, will come into play.<\/p>\n A data-informed approach will also be best for processes that rely heavily on creativity. Here, a combination of data and personal experience will also come into play.<\/p>\n There\u2019s something oddly comforting about the messiness of complex decisions. In these situations, data can only offer so much before you need to take a breath and step back from the numbers\u2014sometimes the best insight comes from the half-remembered comment a customer made in passing, or the gut feeling you just can\u2019t shake. Sure, data can illuminate patterns you might not see, but you\u2019re always working with incomplete information. Embracing that imperfection doesn\u2019t mean you\u2019re being careless; it might actually save you from chasing the perfect answer that doesn\u2019t exist.<\/p>\n It\u2019s also worth acknowledging how team dynamics come into play, especially when you\u2019re weighing stakeholder input or personal experience alongside the numbers. One person might champion a data-backed decision, while someone else draws on years of industry know-how and pushes for a different direction. If you ignore either side, the process gets weaker, not stronger. Honestly, the strongest decisions I\u2019ve seen tend to happen when teams aren\u2019t afraid to let their quantitative and qualitative biases argue a bit\u2014then land somewhere that feels right for the moment, even if it\u2019s not what the formula spits out.<\/p>\n In this drawn-out data-informed vs data-driven battle, you could go full-on data mode and make those stakeholders eat their words, or you could take the informed route by combining multiple inputs.<\/p>\n But, what if we said you could do both?<\/p>\n \u27a4 We think that finding a balance between the data-driven and data-informed methods<\/strong> is the ideal way to go to give your company a competitive advantage.<\/p>\n However, as we have already mentioned, the method you will use will depend on the circumstances.<\/p>\n Generally, whenever you need to make decisions based on quantitative input, you can use a data-driven approach. <\/p>\n If the decision requires some elements of qualitative input, you can follow the data-informed route. <\/p>\n Whichever you choose to do is up to you. <\/strong>In the end, you have to understand that there is no clear winner between both approaches; it all depends on the particular setting.<\/p>\n Here is an additional method: the data-inspired approach. <\/p>\n This method involves using intuition, creativity, and past experience to spot trends in a variety of data. <\/strong><\/p>\n It entails bringing different sources together in an attempt to find common ground between them. <\/p>\n When it comes to generating new ideas and developing innovative strategies, there’s might be a place for the data-inspired approach in your business decision-making process.<\/p>\n But be advised: “inspired data” must never be seen as concrete data. <\/strong><\/p>\n It is risky to draw serious conclusions from this data \u2014 although, depending on the situation, you should bet on it, since it could shed light on previously obscure areas.<\/p>\n It will improve the amount and relevance of the insights you get to work with.<\/p>\n In the end, it’s not really about the “data-informed vs data-driven” dispute. <\/strong><\/p>\n It is all about recognizing those yes\/no situations, those complex scenarios that require multiple inputs, and those moments of pure inspiration to determine which route to take.<\/p>\n Eventually, whether you go the data-driven, data-informed, data-inspired route or combine all three, there’s no denying that data has a role to play in your decision-making process, and rightly so.<\/p>\n That’s why you need to take conscious and deliberate action to make the most of your data collection<\/a> and utilization process.<\/p>\nWhat is a Data-Driven Approach?<\/h2>\n
Pros of a data-driven approach<\/h3>\n
Cons of a data-driven approach<\/h3>\n
Data-Informed vs Data-Driven: Which One Is the Best?<\/h2>\n
So, Which Approach Should Your Business Pick?<\/h2>\n
Bonus Topic: Meet the Data-Inspired Approach<\/h2>\n
Wrap Up: Leverage all Data Mindsets based on Specific Circumstances and Expectations<\/h2>\n