Embracing machine learning in a sustainable way is not as easy as it sounds, but tips from this expert can help.

Once considered peripheral, machine learning (ML) is becoming a core part of businesses around the world, regardless of industry.

Now, the question is no longer whether your company should have a ML strategy, but rather, how can your company get it in motion as quickly and effectively as possible, in areas such as personalization, supply chain management, and forecasting systems.

Whether your company is just getting started or in the middle of your first implementation, here are the four steps you should take to have a successful ML journey. 

  1. Get the data in order
    When it comes to adopting machine learning, data is often cited as the number one challenge. We found that more than 50% of time spent in building ML models can be spent in data wrangling, data cleanup, and pre-processing stages. Therefore, prioritize investing in the establishment of a strong data strategy to avoid spending excessive time and resources on data cleanup and management.

    When starting out, the three most important questions to ask are:
    • What data is available today?
    • What data can be made available?
    • A year from now, what data will we wish we had started collecting today?

    In order to determine what data is available today, you will need to overcome data hugging— the tendency for teams to gatekeep data they work with most closely. Breaking down silos between teams for a more expansive view of the data landscape while still maintaining data governance, is crucial for long-term success.

    Additionally, identify what data actually matters as part of your ML approach. Think about best ways to store data and invest early in the data processing tools for de-identification and/or anonymization, if needed.
  2. Identify the right problems
    When evaluating what and how to apply ML, focus on assessing the business problem across three dimensions: data readiness, business impact, and machine learning applicability.

    Balancing speed with business value is key. Instead of trying to embark on a three-year ML project, focus on a handful of critical business use cases that could be solved in the upcoming six to 10 months.

    Start by identifying places where you already have a lot of untapped data and evaluate if ML brings benefits. Avoid picking a problem that is flashy but has unclear business value, as it will end up becoming a one-off experiment.
  3. Champion an ML culture
    In order to scale, you need to champion a culture of ML. At its core, ML is experimentation­. Therefore, it is imperative that your organization embrace failures and take a long-term view of what is possible.

    Businesses also need to combine a blend of technical and domain experts to work backwards from the customer problem. Assembling the right group of people also helps eliminate the cultural barrier to adoption with a quicker buy-in from the business.

    Similarly, leaders should constantly find ways to simplify the process of ML adoption for their developers. Since building ML infrastructures at scale is a time and labor-intensive process, leaders should encourage their teams to use tools that cover the entire ML workflow to build, train, and deploy these models efficiently.

    For instance, one accounting and tax software firm wanted to simplify the expense sorting process for their self-employed customers to help identify potential deductions. With the right ML framework in place, the time to build ML models into their software products decreased from six months to less than a week. Furthermore, their ML-driven software now helps customers claim back thousands of dollars on average in deductible business expenses.
  4. Develop your ML team
    Developing your team is essential to foster a successful ML culture. Rather than spending resources to recruit new talent in a competitive market, hone in on developing your company’s internal talent through robust training programs. 

Welcome to the Golden Age of ML!