Michelangelo di Lodovico Buonarroti Simoni, born in 1475, is one of the most impactful artists of all time. His works are iconic and enduring: the marble sculpture of David, the painted ceiling of the Sistine chapel, St. Peter’s Basilica in Vatican City, just to name a few. Additionally, he was a noted poet and engineer of his day. It is a marvel that in each of these masterpieces, his tools and media were entirely different – how was he so successful in so many different major endeavors? From Michelangelo, we learn that the transferable power of artistic vision – independent of technique or medium of expression – can be applied across multiple art and technical forms. Can that same ability to envision be applied in other areas such as business or analytics? We think yes – before we get to that, let’s take a slightly deeper look at Michelangelo’s methods.
Michelangelo once remarked, ‘every block of stone has a statue inside it – it is the task of the sculptor to discover it.’ Discovery in this process is essentially knowing what pieces of the stone are important and should stay, and which should go. There is a similar problem in business analytics as with sculpting – there is generally much more data than you actually need to tell the critical story. Keeping the better parts is a matter of a) being prepared to ignore much of it, and b) understanding where you are going with your own analysis. This, in our view and experience is greatly enhanced by developing hypotheses right from the beginning. Develop a prototype of your own finished analysis: make hypotheses, take the time to imagine and envision where the analysis will go, draw an entire storyboard. Random chipping away at the rock without such a vision would never result in a masterpiece. Accordingly, take the time before analysis to determine the most important answers, string them together into a cohesive narrative, and then apply yourself to carving away the rock. For Michelangelo, this was an iterative process – alternating between action and critical examination. Through years of experience, he could easily recognize when he had reached the figure within the stone.
While not the immediate or most-likely place to turn for a comparison, an approach for analytics can also benefit from a Michelangelo-like mindset. Let’s note the two big ones here:
- Each set of data has actionable insight in it, it is the task of the analyst to discover it
This attitude is a required beginning for analytics. It takes an expectation of insight to do what it takes to find it. Whatever the data type, whatever the tool – a good analyst will make valuable discoveries. Similar to Michelangelo’s ability to cross across media, our own ability to make observations should not be limited to our favorite platform – or even format of the data, whether qualitative vs. quantitative, or the other forms data can take.
- Our own internal vision will always over-rule tools
Michelangelo also said, ‘a man paints with his brain and not with his hands’. In analytics, this is accomplished by ensuring that the tools and methods are always subject to the overall objectives. When done well, the primary narrative becomes the figure that emerges. The primary narrative is the story that each figure, method and tool contribute to. It is sometimes enticing to assume that our advanced software tools are sufficiently powered to both define the key problems and answer them – they are not. Rather, we must use our own vision to be the driver, especially where the problems are not yet clearly defined.
Like painting, sculpting, or drafting, analytics comes with its unique tools and required technical skillset. These will always be necessary – but most critical is our ability to recognize when we have ‘uncovered the figure.’ Michelangelo did not have laser-guided rock saws for his sculpture, or a projector when working on the ceiling of the Sistine chapel – instead, he created with his brain. Perhaps this is what we are most impressed with by Michelangelo – his ability to see: whether the beauty is buried deep within stone, a bucket of paint, or bricks and mortar. This goes well beyond good tools. In our role in making sense out of data, it is similarly more than just methods or tools. We must see the critical stories the data is telling no matter the source or type of that data. To do this, we need to seek out our hypotheses from the beginning and remember that analysis is much more than the carving itself – this is a work that requires us to recognize the figures within and let them loose.