Microsoft Power BI includes the Key Influencers visualization, which uses machine learning to create insights from your data. The training of the data is done within Power BI, making it quicker and easier to harness the power of AI. There’s no programming or selecting machine learning models – it’s all done for you. That means you have more time thinking about the business opportunity (or problem) and less time worrying about getting it to work. All of which means that Key Influencers is worth another look.
What problem does Key Influencers solve?
Before we start doing something more efficiently, let’s think why we would use Key Influencers at all. The visualization is designed for businesses who want to track important metrics. There are different names for these metrics, including:
• Key performance indicators
• Performance measures
• Objectives and Key Results (OKR’s)
• Balanced Scorecard measures including financial, customer, internal, and learning and growth
• Scorecards
• Lead and lag indicators
I’m sure there’s more – if you track metrics under a different name, put it in the comments and I’ll add it!
But whatever you call the metric; these are all ways of tracking things or processes that are important to your business. But tracking a metric is only part of the problem. The other half is knowing what to do to improve it, and that can be difficult. Everyone has a view, but it’s expensive to try all possible combinations.
A data-driven approach is more effective. By collecting data on what’s actually happened, you can cut through the decision-making process and get results faster. And that’s what the Key Influencers visual does. You provide it with data that includes actual results for the metric, plus lots of factors that may, or may not, contribute to improving it. Key Influencers uses machine learning to tell you which factors are important.
It doesn’t matter whether you want to influence the metric to be high (like sales) or low (like errors), the Key Influencers visual works in the same way. For example, if your metric is absenteeism, you might have data that includes work patterns, commute distance, job profile, etc. Some of these factors that may be contributing to the problem and Key influencers helps you understand which are important.
How does Key Influencers work?
The key influencers visual is actually two visuals in one:
1. Key influencers
2. Top segments
There is a tab at the top of the visual that allows you to toggle between the two. They both use the same set of data, however, and they both work by using supervised machine learning. This means that the model is trained using data where the outcomes, in this case absenteeism rates, are known. The model then learns the patterns and correlations between the outcome and influencing factors.
- The key influencers visual uses linear regression to show which attributes in your data correlate most strongly with your metric.
- The top segments analysis uses decision trees to segment the data into groups based on the attributes in your data.
In both cases a sample of the data is used to “train” the machine learning model, before processing all the data and generating the results. All of this goes on behind the scenes, within the Power BI engine. And it does it all impressively quickly. There’s a huge amount of power within this single visual!
What data works best with Key Influencers?
Supervised machine learning works by crunching as much data as you can find. However, it must include actual results relating to whatever you are analysing. For example, if you are analysing absenteeism, you need data for your chosen metric, say number of days absent in a given period (such as month or year). And related to each case of absenteeism, you also need the factors that might affect the metric, such as demographics, job profile, etc.
When data includes known results, it’s called supervised learning. The machine learning algorithms take these past observations and figure out why they happened.
Let’s talk!
Are you using the Key Influencers visual? What do you think? Leave a comment about your experiences – good or not so good! And if you interested in finding out more, let’s talk.
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