Getting Your Company Started with Big Data & Machine Learning Applications
So, you’ve laid the groundwork for success with big data and machine learning at your company. You understand that getting analytics projects to production is a major challenge for most companies. Also, you’re willing to dedicate the resources, time, and attention to doing things right.
Congratulations! There are some awesome insights available to companies that use statistics and machine learning to solve business challenges. Nevertheless, you’re probably still not quite ready to dive into the deep end of machine learning applications. Instead, this post will focus on the early wins you should aim for on your way toward becoming a data-centric company.
Early Wins with Big Data and Machine Learning
Company executives often fail to understand the complexity of modern machine learning and big data applications that are in production. While it may seem that “everyone is doing AI,” in fact most data science projects fail or balloon out of control as the scope of work becomes apparent.
To mitigate these risks, it’s best to have realistic expectations for the early results of your data projects. Here are some early wins that will set you up for success down the road.
Well-built & Clean Data Pipeline
Chances are, your company has access to a lot of different types of data about your customers, product, industry, and more. However, it’s also likely true that this data exists in many disparate places.
Most companies have a CRM, sales funnel, email marketing platform, advertising conversion rates, website usage data, accounts payable/receivable, general ledger/accounting, headcount/hiring data, and more. All those sources contribute to the business’s bottom line and may contain valuable insights.
Moreover, data from different sources might correlate and drive new insights about the business if you could see them side-by-side. The challenge, of course, is getting all the company’s data into one place in a standard format for comparison.
A data pipeline is the infrastructure that extracts, transforms, and loads all this data from various sources for use across domains. It’s the infrastructure atop which all data science and machine learning applications are built.
Capturing, cleaning, and storing data is the domain of data engineering. Since it’s foundational to everything else you’ll do with the data, the data engineer you choose may be your most important hire.
Visualize Your Data
With all your data cleaned and in one place, the next easy step is to visualize it. Ask your data engineer to produce some reports from the data they’ve collected. As you start to see graphs and charts, you’ll get a sense for what types of questions your data may be able to answer.
This step is worth spending some time on. Visualizing data can be really powerful, and the conclusions you can draw from graphs are more intuitive than complicated statistics or machine learning results. Many companies find that fine-tuning their reports helps them stay on track toward goals or glean new insights into their customers.
A good data engineer can set these reports up to run automatically on a regular basis once you’ve found some metrics you’d like to track consistently. What’s more, they can also often create online dashboards with up-to-the-minute data, giving round the clock insights for the leadership team. Dashboards are a big early win for many clients getting started with big data applications.
Low Hanging Fruit
The key point of this article is to make sure you secure low-hanging fruit before moving on to larger data science projects. As you become more familiar with the data you have access to, you’ll be better positioned to identify areas where advanced statistics and machine learning applications might be useful.
There are a number of well-established algorithms, regressions, and analysis techniques that fit many common applications. A good data engineer will know the most common of these, and they can help implement early iterations. As you dig deeper, you’ll eventually want to hire a data scientist who can help design more complex and specific analyses. However, in the beginning, look for the easy, seemingly simple insights. They can be surprising and have a profound impact on your business.
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