The communication process between analysts and business users has been long discussed even before “data science” became a hot new job title. A recent article by Tom Davenport, a Fellow at MIT Center for Digital Business and Independent Senior Advisor to Deloitte Analytics, praised the concept of “light quants” and “analytical translators” within the workplace as an assistive force in helping organizations extract actionable insights from their data.
It would of course be great to have someone with heavy quant skills who is also a fantastic teller of analytical stories, but we are talking about a very small intersection of skills here. In fact, even if you once had strong communication skills, most graduate programs in quantitative fields will tend to drum those skills out.
In fact, some argue that all technical skills aside, domain expertise is still the number one needed trait to be able to extract relevant findings from analytics. The heavy lifting to perform data analytics, understand the business problem and domain, and finally communicate it in a relevant, consumable manner narrows the available workforce with these specific combination of skills greatly. Organizations are struggling to add the right people to augment their “data problems” – instead relying on teams of people with complementary skill sets to meet the business demand.
So how has the market come to address these problems?
Many attempts at solving these problems have been explored ranging from staffing consultancies as data advisors, rolling out IT systems to support ad-hoc query, analysis, and reporting, to self service business intelligence and visualization software to meet dynamic reporting needs targeted to business users.
These tools historically have been geared towards data savvy folks within the IT organization and have developed capabilities surrounding their particular skill sets. The shift towards self service BI and visualization as a means for communication to the true end report consumers, business users, has raised the bar in terms of ease of use, but also caused some concern over misinterpretation without an “analytical translator” by your side.
Put a collection of data and visualizations in front of 10 users and they will each draw their own interpretation. This is what makes self-service BI powerful but also dangerous. This happens for many reasons- self-serving bias, lack of analytical skills, lack of understanding of basic statistics, etc. If a picture is worth a thousand words then it is probably worth a thousand interpretations too. Information presented visually is open to interpretation. Give users the ability to choose which pictures they look at and they will tell the story they like best.
Insights from Narratives – a new way to do BI
Self service BI tools still fall short in meeting the business users’ need in that there is no accompanying narrative to consume information in a reliable and accessible manner. Narrative Science Quill is a new kind of “analytical translator” which arms businesses with automated natural language reports generated by an AI system.
With Quill, businesses have a better chance at working with existing IT and BI functions to communicate insights from data, reducing overall time to insight, and scaling analytic practices enterprise wide. Quill can augment analytic practices by providing a means for business users to approach data in a new way – driven by the narrative. As Tom Davenport explains:
Stories have always been effective tools to transmit human experience; those that involve data and analysis are just relatively recent versions of them. Narrative is the way we simplify and make sense of a complex world. It supplies context, insight, interpretation—all the things that make data meaningful and analytics more relevant and interesting.
Read about why stories are the last mile in big data and analytics in our point of view piece “Storytelling is the Last Mile in Big Data and Analytics”.