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Analytics, The Unsung Hero (if done right)

As we look to integrate analytics directly to line of business solutions we are forced to completely rethink our approach to User Experience. Unlike our traditional customer, the Analyst who lives and breathes analytics, this new class of user is not impressed with or frankly interested in all the cool analytics we can do. They don’t care that we used a regression model or network analysis; and they definitely don’t want to see ultra cool 3-D multidimensional visualizations that will blow their minds. They have a life ;-)

For the average business user analytics is only a means to an end, and frankly not the most interesting part of that end. All they want is for us to share some valuable insight about their business in the context of what they are doing; thereby enabling them to do it better. It might be advising a purchaser what products to order (because stock is decreasing and demand increasing), a sales guy which customer to call (because sentiment is decreasing and contract renewal getting closer), or a manager which employee to promote (because impact is increasing, external influence is increasing, and attrition risk is increasing).

This shift is very hard for analysts to stomach for two reasons:

Firstly… analytics is tough! If we sweat blood and tears to generate some amazing insights we want to show that off to the world. No-one wants to make their work look simple or trivial, especially when it was so difficult to do in the first place. What we really want is to show the customer that amazing visualization which will impress the pants off them. However, simplify we must. We have to take a back seat to the business application essentially “hiding our light under a bushel”.

Secondly… it’s damn hard to simplify! Condensing analytic insights down into simple contextual business-relevent recommendations is way more difficult than presenting up the analytics in our traditional analyst-style dashboards. If you simplify the analysis and insight too much you loose the ability to make meaningful business decisions from the analysis. However, if you don’t simplify it then you loose the business user, and they are the person best positioned to leverage the insight.

So folks … we have to make ourselves invisible. We have to align our analytics to business problems and integrate ourselves into the fabric of doing business. No-one should see that we are there until we are not. It’s like mothers, no-one appreciates what they do around the house until they take a vacation and then everything goes to rack and ruin :) The more invisible we are, the better the job we are doing. Eventually people will move from talking about “analytics-driven business decisions” to just “business decisions” because no-one will be able to imagine making a decision that isn’t validated by analytics.

To paraphrase the great physicist Richard Feynman; “if you can’t describe a theory simply, then you don’t really understand it”.

Posted on May 7, 2013 at 11:49 in Analytics   |  RSS feed |   Reply   |   Trackback URL

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2 Comments to “Analytics, The Unsung Hero (if done right)”

    
  1. Ian Murphy
    October 3, 2013 at 09:56

    So immediate responses to your three statements are – it can be, it can be and haven’t we always.

    As you say, users have a life but I’ll stop from discussing the quality of that life :)

    More importantly, you cite some case studies that apply to most businesses. When trying to work out order levels back in the 80’s I had to work on large sets of printouts and graph paper. Did I see that as analytics? No. It was simply about managing warehouse space and how short we could make reorder times.

    Perhaps the issue here is that we need to focus on what it gives users and use their language rather than trying to sound clever by saying analytics and regression models in the same tone that others say water and sandwich

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    • Marie Wallace
      October 3, 2013 at 10:22

      Exactly! I will share a short anecdote to reinforce the point; us Irish just love telling stories :-)

      I was doing a project a while ago to build a recommender system for a sales system. The sales guys had 3 simple questions they wanted automatically answered for every opportunity that came in; suggest the best people to help them close the deal, the best content to share-with / respond-to the client, and similar successful opportunities they could learn from.

      After we had done our super-duper impressive analytics (and it was actually a work of art – content, semantic, and interaction analytics), we were able to not only suggest the people but also provide the evidence supporting our claim and show the entire network of interactions. We had created a lovely user experience that presented the results through a network visualization.

      The customer looked at us (like you might look at your cat when it dumps a rat on your doorstep) and kindly explained that they didn’t care about the pretty pictures and didn’t have time to go wading through graph visualizations. They just wanted the answers as a simple list of people with a link to the evidence and a suggestion for how best they should connect to that person. All our analytics had been dumbed down to a search list :-)

      But that is reality. Its not all about us (as much as we like to think we are rock stars), its just about getting whatever insight the client needs to them as simply and intuitively as possible.

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