Data Solutions

Is your business ready for machine learning?

date: 16 December 2021
reading time: 5 min

Machine learning (ML) is a hot buzzword these days, and many companies want to immediately apply it to their businesses. But do they know what it really means, and are they ready to take full advantage of it?

“Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”

That’s an IBM definition. Machine Learning is a complex concept, so before deciding to leverage any ML solutions, it’s wise to take a step back and ask yourself a few questions first.

The basics

  • Do you have any data to work with? If so, is your data structured or unstructured?

    You need to be fully aware of the kind of data that you have in order to know how it should be applied. Structured data is clearly defined, stored in tabular forms and easily searchable (like customer phone numbers or transactional information). Unstructured data is not organised, and is stored in its native format (like audio files or images).

  • Do you understand your data and the processes that you want to optimise?

    You should be able to separate right from wrong and know which pieces of data are actually relevant to the processes that require optimisation – processes that should also be known inside-out themselves.

  • Does your data storage architecture provide seamless data usage?

    You should have convenient yet secure access to data, permitting easy collaboration between data engineers.

  • Have you already reviewed your current reporting system?

    Maybe all that you need to begin with is simple analytics instead of complex machine learning.

Addressing a challenge

  • Can you identify a business issue that could be solved with the use of ML?

    It’s essential to know exactly what you need machine learning for and the effects that you expect. This will be helpful later on, when the time comes to measure the results of your investment and make any adjustments to your strategy.

  • Do you have sufficient data, or maybe you need some external data sources?

    Very often, companies need to reach for public government data or use social media analytics tools to gather more of it. This isn’t exactly rocket science, but it adds another layer to your business and technological process.

  • Are you at a sufficient level of expertise to solve this task? Do you have enough resources?

    Maybe you will need to collaborate with an external IT partner or hire additional experts to evoke the full potential of ML. Plus, a tandem of data and machine learning engineers are often needed in order to implement ML solutions correctly, so you need to take this into consideration as well.

  • Should this be a one-off like a discovery experiment, or a solution that will be repeated?

    If it’s the latter case, you will need to think about how to maintain the solution, which can sometimes be trickier and more time and resource-consuming than the ML solution itself.

  • Are you able to build infrastructure for the solution?

    This is also an HR-related question. It’s very likely that your IT team is not capable of creating an efficient infrastructure on their own, especially if they have little to no experience with ML.

  • Can you take the risk of failure?

    The initial phase of the ML solution is always an experiment. It requires multiple attempts at parameter tuning or making several changes to the original model. Due to the very specific nature of the ML solution development process, one would need to be conscious of the fact that there are situations in which we may not be able to receive a complete answer to our original question, though this doesn’t mean that we can’t still benefit from the insights that we’ll have gathered along the way.

What if you don’t have all the answers?

You might not be able to answer some of the above-mentioned questions. For example, you may not fully comprehend the nature of your processes, lack sufficient data, or not have all of the resources needed to implement a desired ML solution. But should this hold you back from investing in this kind of technology? Not necessarily.

Nowadays, there are many ways to bridge certain gaps:

  • If you need to enrich your data, because the pieces that are at your disposal are insufficient, you can turn to external sources. Companies like Google or Facebook offer access to the data that they are constantly gathering, and you can either use this as a complementary source or build your own solution on top of their data with the help of an IT Partner. Of course, this would only apply to a certain group of specific problems.

  • If your objective for using ML is not well-defined, thing about workshop with technical and business experts to understand your data better and identify possible ways to utilise ML in your organisation.

  • If you don’t have the in-house resources needed to build a solution, there are outsourcing companies with experience providing either small parts to a solution or entire solutions on their own.

Of course, you may also come to the conclusion that machine learning is not something that you need to implement at this very moment.

Maybe some classic methods in analytics will suffice, but you will still need to learn how to use them more effectively.

A thorough evaluation of your situation is a must, in order to avoid putting all your resources into something that is not going to bring any additional benefits to your business.


There are many cases in which a business is ready for machine learning. Answering the questions that I laid out above is critical in terms of getting the lay of the land and seeing how many requirements need to be fulfilled. This will allow you to estimate your costs and compare them to the expected gains from implementing ML, both in the short and long run.

However, if you are struggling with your evaluation – feel free to reach out to us, and we will help you see the bigger picture as well as all the finer details.

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