Four Hurdles To Creating Value From Data
Data provide organisations the new platform in the 21st century and beyond for innovation of products and processes — there is no arguing that. From identifying bottlenecks in departmental processes and reasons behind marketing campaigns under performing, to using data to inform and improve products and services in ways that were not previously possible, everyone knows data is important. However, that is where the good vibes end. Knowing that the future of your organisations rest on data versus being fully aware of what it entails are two very different stories.
When we consider reports such as this, this and many others, they tell us exactly the same thing, which is that investment into all things data have been increasingly steadily — so there’s nothing news worthy about that. Instead what is troubling and something that we should be paying more attention to is that the same businesses that have invested into data are not getting the most bang for their buck. More respondents than not in those surveys shared that they face challenges translating their investments in data into business outcomes. Do you want to make a guess as to what the main hurdles might be? It is not technology if that is what you are thinking.
I thought if we hired a couple thousand technology people, if we upgraded our software, things like that, that was it. I was wrong. Product managers have to be different; salespeople have to be different; on-site support has to be different. — Jeff Immelt, CEO of GE in 2015
An overwhelming number of business leaders (over 90%) in a particular survey responded that issues pertaining to people and awareness are the main hurdles getting in the way of their organisations getting value from data. That should not come as a surprise as data are not a thing that you can buy and setup as if you are rolling out a new ERP system across an organisation. If you think that your experience with introducing SAP or the likes in your companies is a nightmare, wait until you try making data work for you. The gist of the challenge that lies ahead is reflected nicely with what the CEO of GE, Jeff Immelt told McKinsey a few years ago, which is quoted above. At the end of the day, data are only as useful to organisations as the people inside those organisations who not only know how to appreciate it but more importantly what to do with it.
Executives and members of the rank and file questioning the return on data initiatives [1]
This long 3-part article is suitable for readers of all levels regardless of your backgrounds. In particular, this article reaches out to top-level managers who have information, data and/or technology as your remit, or owners of businesses who have questions about investment into data and data science or are struggling to get ROI for those investments. The intent is for this article to hopefully shed some light on the pertinent areas and spur the right kind of thinking and conversations.
A quick glance through the four main hurdles
In this 3-part article, we dig into what I would consider the four main hurdles around people and the necessary awareness that prevent organisations from getting the best out of data. Now more than ever, many decision makers or people with the cheque book are finding themselves in a predicament of what to do with their investment in data. On one hand, you like everyone else knows that data are the future but on the other, data just do not seem to deliver. The response is of course not to cut investment. Instead, the answer to the getting data to work for you is about who (and to a lesser extend what) you invest in, and more importantly, the order in which that happens. The four hurdles, which we will discuss in detail in two other separate posts are:
- Lack of appreciation of the different types of data and what each entail
- Products, data assets and budgets that are not ready
- Inability to connect the dots from data solutions to business outcomes
- Unaware of the challenges surrounding retention of data professionals
The first hurdle pertains to people’s awareness of the different types of data, the very specific roles that they play in data solutions, and the strategy around the acquisition and management of those data. In other words, not all data are created equal. While the concept of big data has been grabbing the headlines in recent times, “small” data are important too but in different ways. We question whether the people in roles that will ultimately shape the data culture and investment into data have both the necessary appreciation for such things as well as the experience to know what to do with them. Do they know what are the main things to consider, the pitfalls to avoid, and the right questions to ask. Are they prepared to have the answers or at least know who to pull in when it comes to planning and preparing for data related matters in the business.
The second hurdle examines the readiness of a business’ data infrastructure to provide the things that data professionals need to solve problems and improve products. Here, we look at whether the right data are captured in the right way, processed and made available in the right forms for the people who need to work with them, and whether the necessary tools are available or not. On the question of whether the data are right, it is somewhat intertwined with the state of the products or aspects of the business that you want to improve with data. Often, if a product is still starting out or is confused about who its users are or what problems it is trying to solve, then data solutions can do very little good. Strong product management and design are critical here. In fact, I have dedicated an entire article on this topic.
The third hurdle looks at the relevant people’s ability in a business to connect the dots between data solutions to problems and ultimately business outcomes. You can have the biggest team of the smartest data scientists in the world and they would not be of any good to a business if what they do are not completely aligned with what the business needs solving. You should consider whether as a business, you have the right people, whether internally groomed or attracted from external, who will make sure that the best solutions for the problems within some constraints can be derived. It does not matter what we call this type of new, emerging role but the required competency definitely sits outside of what one would typically consider for a product manager, project manager, data scientist, or any other more common roles for that matter.
Once you have started amassing the right data around products that have good fit, have the right people to come up with solutions that are right for the problems and to actually work on the data, this fourth and last hurdle makes us reflect on our ability to motivate and retain those data professionals. I am not suggesting that data professionals are a special breed that requires special treatments and pampering. No, not at all. Instead, the intent is to make us be more mindful and consider certain factors relevant to retention of such roles that come about due to the traits of the people who would generally fill them and the market forces at play with such professions. They have to be factored into the talent retention strategy of any organisations that want to leverage data.
A story, a fairy tale
Before we end this first part of the 3-part article, I would like to put some colour over the lessons that we hope to discuss in detail in the subsequent parts. A few days ago, I dreamed up a story, more like a fairy tale where every startup, every business hopes that they are those characters in the story. This is not a story about only those with the deepest pocket can fair well in the space of innovating using data. Rather, more often than not, it is the decision on who the money is spent on and the timing of the investment that holds them back from being competitive with data. We will discuss those as hurdles that organisations need to get over in the upcoming posts but before that, here is the fairy tale.
Once upon a time, there was a business that operates an online marketplace for pets adoption called Pets4U. The product solves pressing jobs to be done, is easy to use, enables paying customers and users to self-serve and has virality built in. One day, the founders needed to start visualising certain top line metrics to express the health of their business to investors. At the same time, they also appreciate that their business’ ability to compete will rest on the data they have and the value they can create from it. Instead of using simple, out-of-the-box solutions, the co-founder CTO got the relatively small engineering team to implement customs tracking of how the users interact with their product. The raw data after being processed and stored in an easily queryable form is then used by the in-house data analyst to report on the metrics that the founders needed. The investors are happy, everyone is happy. After a while, the CTO feels that the business needed someone who can put the raw and the processed data that they have been accumulating to better use beyond just reporting on the metrics. The business went to market for someone who can lead their next phase of investment in data, as the founders are mindful that the challenges that lie ahead are beyond any of their experience. They hired a Director of Extracting Value From Data who is a scientist by training, who was hands-on in the past and spent the last 10 years attracting and retaining talent across many organisations. She in turn brought in the necessary people including data curators, data scientists, machine learning engineers and so on to get the job done. Pets4U went on to become the largest pet adoption marketplace in the world with a market cap of over $5 billion. Everyone lives happily ever after.
In the subsequent parts, we will look at how a story such as the one above could come undone through executives tripping over one or more of the four hurdles. The second and third parts are here:
