In today’s business world, data means a lot of things to a lot of people. It’s a means to an end, a treasure trove of insights, and even the currency of the future. What’s not always clear, at least to non-technical stakeholders in an organization, is why they should care about it. It’s something great in abstract terms, and definitely “something we should look into”, but when push comes to shove, it’s not something that most non-technical people in your office will really advocate.
And after all, can you blame them? We’ve become so numb to the concept of data — we know that it’s plentiful, that we collect tons of it, and that we use it for almost everything — that it’s hard to show really how valuable it is when used right. More importantly, it’s hard to demonstrate why it’s worth it to do more with it than most organizations already do.
If you already have analytics, the thinking goes, what’s the real ROI in investing in machine learning? Why should an organization spend thousands of dollars upgrading its data collection and warehousing? These are valid questions, and they have clear answers. The important aspect is not showing stakeholders why they’re wrong in hesitating but helping them see the value in the opposing point of view.
Data collection is a given these days. It’s impossible to avoid it, since every single interaction, transaction, touchpoint, and business action creates it. In this sense, there’s nothing special about having data. Every organization does — it’s just a question of how good they are at collecting it, and how much they want or need. The difference is that for many organizations, data is simply collateral of operating.
For these businesses, there’s little value in data beyond validating decisions already made or confirming that “they’re going in the right direction”. How then, could a data leader convince their stakeholders that there’s value in data beyond validation? The answer lies in demonstrating the business impact and ROI that truly utilizing data can have. Leaders must find a way to prove, tangibly, that embracing data and investing in better ways to use it has true value.
It all starts with a story — after all, we tend to remember more, and better, when we absorb information as a story than as cold hard facts. You can start showing the value of your data by seeing what you’ve already done with it and showing the real impact it’s had. Did it lead to smarter decisions? Did it improve procurement, or cut down on waste? This is where you make that shine.
Let’s say you’re the CDO at a retail company. Your organization collects tons of data from hundreds of touchpoints — point of sales, subscriptions, your website, email campaigns, ads online, and more.
You can show how well you’ve done in the past, and what’s worked for you so that you can do it again, or determine how your inventory levels and purchases should look based on the previous year’s sales data. You can determine the best stores to place your products based on where they sold the most last year, or plan your promotions better based on what worked before and what didn’t. You can also prioritize some marketing campaigns based on how well they converted and tested in the past.
That’s fantastic, you’re clearly showing improvements and making smarter decisions based on the data you have, and avoiding costly mistakes. You’ve saved your company money and helped them boost their bottom line.
Now, however, it’s time to sow the seeds of doubt.
“Data’s great, and all, but maybe there’s more to it than what we’ve done. What if we’re only scratching the surface?”
You’ve managed to do a few cool things, and even make the right choices in some cases, but what happens when the rules of the game change? What if the conditions from last year aren’t the same tomorrow?
This is the part of your story where it turns suspenseful. Data analytics and visualizations have helped but think of all that data sitting there, not being used for anything but saying if an action was the right one. Here, it’s time to start showing the ROI, the potential for the amazing things data could be if an organization embraces it and properly invests in getting the most possible out of it.
Back to our retail organization — conditions rarely stay the same from year to year, and simply looking at what worked last year won’t always give you the answers to what to do tomorrow. Moreover, you may not even be asking your data the right questions, and you might be unaware of it. A major event could cause a major shift in consumer preferences and behavior, or economic conditions could reshape the retail market.
Now isn’t the time to be shy. To demonstrate the value of the data you have, you need to start by showing how you’re hurting by NOT using it to the fullest potential. Here is where you begin filtering in hard facts.
For a retailer, going in hard on a promotion that’s unsuccessful isn’t just a blip, but could be a major loss. Buying a ton of inventory that’s not going to get sold isn’t just a mistake — it’s going to severely impact your bottom line in more ways than one. What if instead of one wrong decision about your product line, you make several, all based on the wrong assumptions?
You could talk about how machine learning offers greater insights and lets you plan ahead more confidently. Alternatively, you can contrast it by showing how uncertain situations and times require greater visibility.
Next, show the answers your data CAN provide if you embrace machine learning, AI, and other advanced analytics tools. Using historical data, you can build some powerful predictive models, but they’re limited because they can only train themselves on historic occurrences. What happens when the rules of the game change? It’s time to put data in the spotlight.
New data can give you a greater perspective and can offer greater context to your existing analytics. Even if you’re not a data scientist, there are platforms, data discovery tools, and freely available datasets that can help you prove your point. If you already have some predictive analytics in your organization, you can combine your datasets with external data and see what the results give you.
What if instead of simply relying on last year’s sales data, you could filter in information about weather conditions or about current events, or even how these two factors are affecting consumer opinions and behaviors in real-time judging by social media and web interactions? You could theoretically make decisions based not just on what happened, but on what could happen if one or two conditions changed while others remain the same.
What other datasets are out there? For our retailer, the answers would be surprising. As we mentioned above, everything from weather patterns to social media behaviors. But why not go a step further…literally? You could incorporate geospatial data about foot traffic by region, or the number of competitors around your store to understand where to maximize your efforts. You could go deeper and look at census data about the areas where your stores are located, using socioeconomic data and financial information to determine the right blend of products and services to offer at each location.
Data is a valuable asset and tool, but you can’t just rest on your laurels once it’s in your database. You need to do more with it, and that requires some investment and buy-in from your organization. You don’t need to break your budget trying to build a data science team, though. You can get by with the right tools and platforms. To make sure your data projects succeed though, you need to help your organization understand why they’re valuable, instead of just telling them.