As many of us head back to the office and get back to business after a winter break, I've been thinking a bit about the relationship between machine learning capabilities and the rest of the business. I've been settling into my new role at DataGrail since November and it has reminded me how much it is important for machine learning roles to know what the company is actually doing and what they need.
My thoughts here are not necessarily relevant to all machine learning practitioners; the pure researchers among us can probably move on. But for anyone whose role is machine learning serving a company or organization, rather than simply promoting machine learning for its own sake, I think it's worth reflecting on how we interact with the organization we're a part of.
By this I mean, why did someone decide to hire their skills here? Why was a new template requested? New hires aren't cheap, especially when it comes to technical positions like ours. Even if you're replacing a position for someone who left, that's not guaranteed to happen these days, and there was probably a specific need. What was the argument made to the portfolio holder that someone with machine learning skills needed to be hired?
You can learn several useful things by researching this question. On the one hand, what are the ideal results that people hope to see from having you around? They want some data science or machine learning productivity to happen, and it can be difficult to meet those expectations if you don't know what they are. You can also learn something about the company culture by asking this question. Once you know what they thought the value of bringing in a new ML template would be, is it realistic to think about the contribution ML could make?
In addition to these expectations you are in, you need to create your own independent views on what machine learning can do in your organization. To do this, you need to take a look at the business and talk to many people in different functional areas. (In fact, this is something I spend a lot of my time on right now, as I'm answering this question in my own role.) What is the company trying to do? What is the equation that you believe will lead to success? Who is the customer and what is the product?
Somewhat tangentially to this, it is also advisable to find out about the data. What data the business has, where it is, how it is managed, etc. This is going to be really important for you to accurately evaluate what types of initiatives you should focus your attention on in this organization. We all know that having data is a prerequisite for doing data science, and if the data is disorganized or (God help you) missing altogether, then you need to be the one to talk to your stakeholders about what reasonable expectations are. are for machine learning goals in light of that. This is part of bridging the gap between business vision and machine learning reality and is sometimes overlooked when everyone wants to move full steam ahead on developing new projects.
Once you have an idea of these answers, you'll need to provide perspectives on how elements of data science can help. Don't assume that everyone already knows what machine learning can do, because this is almost certainly not the case. Other roles have their own areas of expertise and it is unfair to assume that they will also know the intricacies of machine learning. This can be a really fun part of the job, because you get to explore the creative possibilities! Is there any indication of a classification problem somewhere, or a forecasting task that would actually help any department succeed? Is there a huge amount of data somewhere that probably has potential for useful knowledge, but no one has had time to dig into it? Maybe an NLP project is sitting in a pile of documentation that hasn't been kept tidy.
By understanding the business goal and how people hope to achieve it, you will be able to make connections between machine learning and those goals. You don't need to have a silver bullet that solves every problem overnight, but you'll be much more successful integrating your work with the rest of the company if you can draw a line between what you want to do. towards the goal everyone is working towards.
This may seem like a leftist question, but in my experience, it is very important.
If your work is not aligned with the business AND is not understood by your colleagues, you will be misused or ignored, and the value you could have brought will be lost. If you read my column regularly, you know that I am a big proponent of data science literacy and that I believe DS/ML practitioners have a responsibility to improve it. Part of your job is to help people understand what you create and how it will help them. It's not Finance or Sales' responsibility to understand machine learning without being educated (or “enabled” as many say these days), it's their responsibility to provide the education.
This may be easier if you are part of a relatively mature machine learning organization within the company; Hopefully, other people before you have addressed this literacy. However, it's not a guarantee, and even large, expensive machine learning functions within companies can be isolated and indecipherable to the rest of the business – a terrible situation.
What should you do about it? There are several options and it depends a lot on the culture of your organization. Talk about your work at every opportunity and be sure to speak at a level that laypeople can understand. Explain the definitions of technical terms not just once but many times, because these things are challenging and people will need time to learn them. Write documentation so people can refer to it when they forget things, on whatever wiki or documentation system your company uses. Offer to answer questions and be sincerely open and friendly about it, even when the questions seem glib or misguided; everyone has to start somewhere. If you have a basic level of interest from your colleagues, you can set up learning opportunities like lunch and learning or discussion groups on broader machine learning-related topics than just your particular project at the moment.
Furthermore, it is not enough to explain all the interesting things about machine learning. You should also explain why your colleagues should care and what this has to do with the success of the company as a whole and your peers individually. What does ML bring to the table that will make your job easier? You should have good answers for this question.
I've framed this somewhat as how to get started in a new organization, but even if you've been working on machine learning in your business for a while, it can still be useful to review these topics and take a look at how things are going. Making your feature effective is not a one-time deal, but requires ongoing care and maintenance. It gets easier if you keep it up, though, because your colleagues will learn that machine learning isn't scary, that it can help them with their work and their goals, and that your department is helpful and collegial rather than obscure and isolated.
Remember:
- Find out why your company has engaged machine learning and ask the expectations behind that choice.
- Understanding what the company does and its goals is vital to being able to do work that contributes to the company (and keeps you relevant).
- You need to help people understand what you're doing and how it helps them, because they won't magically and automatically understand it.
