Our society has been moving in a more digital direction for years, but 2020 kicked us into high gear when it came time to finding new digital solutions for work, education, socializing, and entertainment. With the forced digital transformation of many companies, we’re seeing a strong emergence of certain technologies and practices. Overall the ‘value of data’ has increased to many companies. Artificial Intelligence (AI) and Machine Learning (ML) in particular have come into focus for many as what is “next up” for advancement.
TC Riley, host of Diving Into Data, shared his top tips and tricks that illustrate that AI and ML span many levels and that you don’t need experts to implement these techniques.
Set a Defined & Simple Goal
Riley explained that your project should have a very simple, focused goal. He says that one of the easiest ways to derail an AI or ML project is by trying to tackle too much on your first attempt. Make each goal achievable and clear.
Work Toward an Impactful Result
That being said, you also need to ensure that simplicity doesn’t result in a watered-down expected result. Riley urged listeners to work for something that will have a measurable impact, and to ensure you’re looking at a core competency of your business. Your efforts should always produce actionable results.
Clean Your Data
Riley explained that ML is an incredible tool, but the opportunities it provides for data analysis are only as good as the data inputs. Take enough time to properly clean and refine your data before launch.
Avoid “Scope Creep”
He warned that analysis projects do tend to creep outside their initial scope. Make sure you stick to the predefined goal and project scope. There will be more time for future projects down the road.
According to Riley, “If you’re a business leader control what you can control. The biggest requirement to me of a business leader right now and what they can do, is understanding data and being able to make informed decisions with any and all external factors that may be into play. You need leaders who are able to appreciate the data, but also appreciate what the data can’t show or the unpredictable elements of that data,” Riley said.
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