I have been paying some attention to the link between machine learning and the rest of the company’s operations as many of us are getting back into the flow of things at work following a Christmas vacation. In my current work at DataGrail, which I’ve been settling into since November, I’ve learnt how crucial it is for machine learning tasks to grasp the business’s operations and requirements.
How will machine learning impact business?
For those of us who are simply interested in machine learning for research reasons, my ideas may not be immediately useful. But I feel it’s useful to concentrate on our relationships with the business we’re a part of if our role is machine learning in service of a firm or organization rather than merely enhancing machine learning in and of itself.
By understanding the needs and goals of the business, we can align our research in machine learning to address specific challenges and opportunities. This approach allows us to develop practical applications that have real-world impact and drive value for the organization. Ultimately, by focusing on the relationship between machine learning and the business, we can create a symbiotic partnership where both sides benefit and thrive.
Where did you come from?
By this, I mean to say, why did someone select to use your expertise here? Why was a new headcount asked for? Hiring new workers is costly, especially for technical roles like ours. Nowadays, it’s not certain that someone will come in to replace a job that someone else has left, and there is likely a compelling need for you to be considered. How did you persuade the person in charge of the budget that you needed to acquire someone who was adept at machine learning?
You were able to persuade the person in charge of the budget to hire a skilled machine learning expert by emphasizing the growing importance of data-driven decision-making in today’s business landscape. You highlighted the potential competitive advantage that could be gained by leveraging machine learning algorithms to analyze vast amounts of data and extract valuable insights.
Additionally, you showcased the potential cost savings and efficiency improvements that could be achieved through automation and predictive modeling. By demonstrating the tangible benefits and long-term value of having an expert in machine learning on the team, you successfully justified the need for a new headcount.
What are the ideal results people want to see from having you around?
They want some data science or machine learning productivity to happen, and it could be challenging to satisfy those expectations if you don’t know what they are. You may also discover anything about the organizational culture from this inquiry. Once you know what they believed the value would be of bringing in a new ML headcount, is that thinking realistic about the effect ML might make? Understanding the expectations and desired outcomes of implementing data science or machine learning is crucial.
It allows for a better understanding of the organizational culture and the potential impact ML can have. By determining what they believe the value of hiring a new ML headcount would be, it becomes possible to assess whether their expectations align with the realistic capabilities and potential benefits of ML. This insight is essential for effectively meeting their needs and managing their expectations.
What is this firm all about?
Besides these assumptions you are entering into, you should build your own independent opinions about what machine learning can do in your organization. To do this, you need to take a look at the organization and talk to lots of people in different functional areas. (This is in fact something I spend a lot of my time doing right now, as I’m tackling this subject in my own position.)
What is the firm seeking to do? What’s the equation they believe will lead to success? Understanding the organization’s objectives and their definition of success is crucial in forming your independent opinions about the potential impact of machine learning.
By talking to people from various functional areas, you can gain insights into the specific challenges the organization is facing and how machine learning can be applied to address those issues. This comprehensive approach will enable you to assess the feasibility and effectiveness of implementing machine learning in your organization and make informed decisions.
Who is the customer, and what is the product?
Somewhat linked to this, you should also query about data. What data does the company have, where is it, how is it kept, etc. This is going to be really crucial for you to adequately assess what sort of efforts you should devote your attention to in this organization.
We all know that having data is a prerequisite in order to do data science, and if the data is disorganized or (god help you) absent entirely, then you need to be the one who speaks up to your stakeholders about what the reasonable expectations are for machine learning objectives in light of that. This is part of bridging the gap between corporate vision and machine learning reality and is frequently neglected when everyone wants to move full speed ahead in establishing new projects.
1. The importance of data organization in the field of data science: Highlight how disorganized or absent data can hinder the progress and success of machine learning projects.
2. The role of a data scientist as a bridge between corporate vision and machine learning reality: Discuss how it is crucial for data scientists to communicate with stakeholders about realistic expectations based on available data.
3. Overcoming challenges in establishing new machine learning projects: Explore the common neglect of considering data quality and availability when organizations are eager to initiate new projects, and provide suggestions on addressing this issue.
4. The need for effective communication skills in the field of data science: Emphasize that being able to articulate limitations due to inadequate data is essential for successfully aligning corporate goals with practical machine learning objectives.
5. How to assess efforts and prioritize tasks in an organization as a data scientist: Provide insights into developing strategies for evaluating different initiatives, allocating resources, and identifying key areas where attention should be focused within an organization’s context.
Once you sense these answers, you will need to explain your opinions to the table.
Don’t assume everyone already knows what machine learning can do, as this is probably not the case. Other roles have their own areas of specialty, and it’s illogical to suppose they would also know about the complexities of machine learning. This may be a really intriguing component of the task because you get to explore the creative possibilities!
Is there a hint of a categorization challenge someplace or a prediction job that might actually help some departments succeed? Is there a giant pile of data laying somewhere that obviously has enormous insight potential, but no one has had time to snoop around in it? Maybe an NLP project is waiting in a pile of documents that haven’t been kept clean.
By understanding the aim of the firm and how people intend to achieve it, you will be able to develop ties between machine learning and those goals. You don’t need to have a silver bullet solution that’s going to solve all the difficulties overnight, but you’ll have a lot better success integrating your work with the rest of the business if you can draw a line from what you want to accomplish to the target everyone is working towards.
How well is your role understood by the rest of the business?
If your work isn’t both aligned with the business and understood by your coworkers, it’s likely to be exploited or overlooked, and the value you could have made will be lost. If you read my column regularly, you’ll know that I am a major supporter of data science literacy and that I believe practitioners of DS/ML carry responsibility for creating it.
Part of your task is letting people realize what you create and how it is going to aid them. It is not the role of finance or sales to comprehend machine learning without being offered training (or ‘enablement, as many say these days); it is your obligation to provide the education.
By taking the time to educate your coworkers about the potential of data science and machine learning, you empower them to make informed decisions and leverage the insights you provide. Without this understanding, your work may be undervalued and underutilized. It is through education and training that you can bridge the gap between technical knowledge and practical application, ensuring that the value of your work is properly recognized and utilized within the organization. Don’t underestimate the power of sharing knowledge and enabling others to harness the benefits of data science.
This may be easy if you’re part of a reasonably established ML group within the organization; ideally, this literacy has been addressed by others before you. However, it’s not a given, and even vast and pricey ML divisions within corporations could be compartmentalized, secluded, and indecipherable to the rest of the organization—a poor scenario.
What should you do about this?
There are a range of choices, and it depends a lot on the culture of your organization. Talk about your work at every opportunity, and make sure you communicate at a layman-understandable level. Explain the explanations of technical terms not only once but many times, as these issues are challenging and people will require time to learn.
Write documentation so people may refer to it when they forget anything, in whatever wiki or documenting system your organization utilizes. Offer to answer questions and be genuinely open and pleasant about it, particularly when queries seem trivial or erroneous; everyone has to start somewhere. If you have a basic degree of interest from colleagues, you may make up learning opportunities like lunch and learns or discussion groups on bigger ML-related themes than merely your individual project of the moment.
In addition, it’s not enough to just discuss all the fantastic things about machine learning. You also need to 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 is ML bringing to the table that’s going to make their job easier? You should have strong replies to this question.
I’ve framed this in some ways as how to get started in a new firm, but even if you’ve been working on machine learning in your business for some time, it may still be good to review these issues and take a look at how things are going. Making your career effective isn’t a one-and-done sort of event; it takes constant care and maintenance.
It gets simpler if you stick at it, however, as your colleagues will discover that machine learning isn’t terrible, that it can aid them with their work and aims, and that your department is helpful and convivial instead of being esoteric and guarded.
By consistently demonstrating the value and benefits of machine learning to your colleagues, you can help dispel any reservations or misconceptions they may have. Show them how machine learning can streamline processes, improve accuracy, and ultimately contribute to the overall success of the business.
Foster a collaborative and supportive environment within your department, encouraging open communication and knowledge sharing. This will help create a positive perception of machine learning within the firm and encourage others to embrace it as well.
- Find out why your firm has recruited for machine learning and analyze the assumptions underneath that choice.
- Understanding what the firm does and its aims is vital for you to undertake work that will contribute to the business and keep you relevant.
- You need to help individuals comprehend what you’re accomplishing and how it benefits them, because they won’t suddenly realize it.