Not to sound alarmist or anything, but machine learning (ML) initiatives can be risky. It’s true, they sound amazing, and you hear success stories left and right about the transformative power of ML. But, the hype sometimes drowns out the reality. The rush to get ML projects online is understandable, as they have real, tangible benefits, but sometimes it could pay to take a breath and think about how you’re going about it.
Don’t believe us? Let’s get down to brass tacks about the realities of ML for one second. According to a 2019 report, eight out of every ten organizations surveyed reported failed AI initiatives. These failed initiatives can come from a variety of reasons, as multiple studies have found. A separate report found that 96% said they have difficulties with data, while another survey found that nearly 80% of respondents faced serious barriers to entry from management and the inability to show the technology’s value to their organizations.
Additionally, there’s the issue of having the right resources — both in terms of the people and skills you need and the infrastructure to support your ML and data science ambitions. So, you should just steer clear of ML, and that’s your problem solved, right? Well, it’s not that simple. ML is becoming a necessary technology across industries, so if you want to keep up, you’ll need to jump in the water. However, that doesn’t mean you need to jump in blind. Here’s how you can make sure your next ML initiative is only the first of many successes:
One of the very first roadblocks you might face when trying to upgrade your organization’s analytics is convincing everyone of the value of a powerful but sometimes expensive resource. An executive might be more focused on ROI and impact than on potential insights, but your first task is to convince them that they can have both.
It’s tough to justify something your organization may have no experience with and which looks like a big red number on the company ledger. However, you don’t necessarily need to give them hard numbers to prove otherwise. In fact, focusing on just the cold hard facts might not be the way at all. Instead, you need to paint a picture of success — build a story of how ML can be a transformative tool for your organization. Show the C-level that your ML initiatives are all about delivering impact, and relate it to successes in your industry, as well as the potential avenues you could explore.
More than anything, the goal here is to create both excitement and find a champion in management who will help you drive the organizational change you need to launch your initiative with the full support it requires to succeed.
Something that can slip by unnoticed in the rush to catch up to any new technology trend is that innovation is an involved process. It’s not just about “building” a new ML model and letting it run or simply buying an out-of-the-box solution and getting instant insights. Even in the best of cases, deploying ML in your organization requires patience and technological capital to ensure success. It’s important before you invest serious money into ML and data science that you position your company for future success.
There are two key components to consider here: your technical infrastructure and your people. The first part is about how ready your organization is for the challenges that come with data science needs. How well developed are your data pipelines and streams? Are they collecting the right data? Is your internal database (or data warehouse, data lake, etc.) prepared to scale and offer easy access to data? How well can your organization’s network manage a cloud-based solution or even an on-premises platform? These questions are all important and require you to invest some capital at first — to avoid paying much more in losses later.
Next, you need to make sure your team has the right skills and resources to ensure an ML success. Here it’s about understanding if the right people to carry out a data science or ML initiative are already in your organization, and if not, whether you can offer them the resources to learn them. Maybe you have a great IT team, but they’re not experts in data. Could they adapt to a platform that helps them do the work? Or maybe you have developers who are ML enthusiasts who can lead the way and learn the new systems. On the other hand, if you don’t have those teams, you should focus on finding a fully managed data science platform that can help you get where you need.
One of the biggest areas of need when it comes to ML initiatives, and which is often overlooked, is data. It’s true that organizations today collect more data than ever before. You might already have a trove of data that comes from your CRM and a variety of touchpoints (points of sale, your website, social media, and more). It’s definitely a great start if all you’re looking for is a small pilot or a relatively simple model, but it may not be enough if you’re looking to scale or if you need to build more complex predictive models.
If your internal data isn’t enough — and it likely isn’t — you need to find the right external data that gives you the right coverage and context. Doing this on your own is possible, but it’s costly, lengthy, and complex. More importantly, you likely need more than one single dataset, and the manual process yields you just one at a time. In this case, you might need to consider a variety of solutions. You could seek out a data marketplace, which cuts out the trouble with finding the right data.
This way still leaves you with work to do — cleaning the data, finding the relevant features, and integrating it with your datasets. Alternatively, you could look for a platform that offers augmented data discovery tools, connecting you instantly to all the data sources you need. Whatever your needs, it’s crucial that you’re thinking not just about today but about how the processes you build today will impact your ability to expand and improve your models. How reliable are your data pipelines? How easily can you access new data in the future? Answering these questions is essential to making sure your models are relevant not just when they’re deployed but also weeks, months, and even years later.
ML initiatives are transformative for any organization. Unfortunately, that can be a good or a bad thing, depending on how you go about them. However, you don’t have to jump in blind and hope for the best. You can take your time to answer the important questions about your ML initiative and develop a strong plan that can help you ensure success. More importantly, you can find the tools and platforms that can help your organization embrace ML without creating bottlenecks and draining your teams’ resources.