Updated: Apr 28
Written by Craig Gittelman, VP Sales & Customer Success at Systech
Conceptualizing an enterprise methodology and strategy for data analytics seems elusive. Companies may understand that it serves a purpose, but are unsure as to what capacity, value, and scale. For the past 30+ years, I have specialized in data analytics, business management, and consulting. Most recently, I am pursuing a master’s degree in history, where I have come to identify a surprising but clear parallel between the two.
Like data analytics, Historians quantify the world around us. They study artifacts and information captured in an archive or a repository of some sort. They construct chronologies and narratives that add human interpretation to past people, places, and events and, in doing so, they try to tell a story that is complete, truthful, and enriching to those who learn from it.
Data Analytics is no different. We capture transactions and events that occurred in the past and store them in an archive — a data warehouse, or data lake. We perform analysis on that data and render interpretive stories in the forms of reports, dashboards or analytical applications that aim to deliver a modicum of value to some stakeholder connected to our work.
I am of the opinion that the challenge in data analytics is not a technical one. Agreed, the skills are complex, and the technologies are diverse but, as most of us would admit, when we know exactly what we want to do, why we want to do it, and the value in doing it, the actual process is, for lack of a better word, doable.
History advocates commonly lay claim to the mantra that if we do not learn from the past, history will repeat itself but, in all honesty, historians to this day struggle to define an accurate and complete picture of the past and what lesson it is we must learn. The same is true for data. Without a clear vision of purpose and impact, it is difficult to architect a sound framework to process events from the past, and even more challenging to apply insights gained from our data events to make more profitable and impactful decisions moving forward.
I propose in this blog that we entertain a discussion of the big why, what, how, and who questions of data analytics not from a technical perspective, but more from the pursuit of value in the enterprise landscape. Let’s see how history can lend a hand along the way…
Why pursue data analytics from a strategic and enterprise mindset? Think beyond simple corporate mantras of growth, profitability, and efficiency. How do we grow? How do we sell more? How do we reduce waste? How do we engage our customers more effectively? What of our partners and employees? How do we translate the more meaningless marketing speak to such operational objectives and in doing so, what role does data analytics play?
What should be included in our archive and, most importantly why? Is what is contained in the archive complete? What was omitted and why? Is it accurate? If accessible, to who?
How do we govern (data governance) the assemblage and management (data management) of the archive (data lake)? How do these rules affect what is stored? How do they influence the results (time to market), the free flow of information (data democratization), or, conversely, are they able to protect us from any false narratives (inconsistent data or erroneous query results)?
Who has access to the archive and/or its interpretations? This goes to the question of how we apply these insights and learnings from the past and gain perspective on the present? This goes to the further question of objectivity and bias in both the archivist (technical data management), the historian (analysts), and the end consumer (the business).
Contrary to popular belief, the work of historians and data analysts alike are more similar than one would think. In drawing this parallel between the value that each expert brings to their field, I can’t help but wonder if it may also be pertinent to reflect on the role each will play in shaping the future for good, or evil.