The Age of Self-Serve Analytics

Subscribe to our monthly newsletter to get the latest updates in your inbox
In the last few years many organizations have witnessed a rapid adoption of self-serve analytics tools by non-technical users. Gartner’s Magic Quadrant report from a few months ago makes it pretty official that the age of self-serve Analytics is upon us. While these changes are creating new opportunities, they also present new challenges and concerns. Exploring these could ideally help build a healthier and more responsible socio-technological culture within an organization. On one hand, the self-serve organizational environment is an accelerant to being better informed and making better decisions. Having constricted information is subpar. On the other hand, knowledge to properly interpret and apply a critical eye to data is crucial and the lack of it is dangerous. Too much info can be equally detrimental. It can be used to confirm biases as opposed to searching for the truth. Lack of accountability, standards, and ethics in tech exacerbates these issues further. The story can often be factually accurate while the distortion   transpires through emphasis or subtle framing of the presentation. Nowadays data seems to be the new shiny thing and we, as society, are leaping to new heights of BS through twisting numbers. As Danah Boyd writes, “I believe in data, but data itself has become spectacle . I cannot believe that it has become acceptable for media entities to throw around polling data without any critique of the limits of that data, to produce fancy visualizations which suggest that numbers are magical information.” While her point refers to the socio-political environment this is true for any domain (and I really mean any domain, think the long vilified eggs and butter that made it back to ‘healthy’ and, hopefully, to your fridge, again). Widespread access to data-producing technology is intensifying the distortion of data. Therefore it is only understandable that the gut reaction of many organizations is to block or severely limit a self-serve basis. However unsurprising, I believe this MO is not solving the underlying issue but is providing a short-term, band-aid approach to the crisis and negates long term benefits. It is not a self-serve basis that deserves the finger pointing but the human condition that we “often tend to lack an appetite for the truth and suffer a fatal weakness for flattering denials.” (Alain de Botton) The most efficient way to transform behaviors is to create culture and transform context. If companies choose to catch the wave of self-serve they need to articulate—clearly—what values they believe are important and instill a sense of accountability and responsibility in people interpreting the data. So what are the ingredients for nurturing a responsible self-serve culture within a company?

Trainings

I believe trainings are one of the main components of the organizational culture that is supportive of self-serve analytics. The trainings’ focus should be customized based on the audience and goals. Separate between tech trainings and data-savvy trainings. Focus less on how to use the tools and more on how to interpret and contextualize data. Throw some enthusiasm in the mix. Trainings that succeed in inspiring people, simply, are more efficient.

Mentorship programs

A system where someone data-savvy is reviewing report/analysis and, most importantly, conclusions and stories built around data, pointing out things that don’t gel. Anytime numbers are drawn they should be run by someone.

This ‘designated leader’ can be an analyst or an analytically-savvy SME that receives a more personalized and rigorous training that can be passed on.

Community

A place (physical or virtual) where adopters can help answer one another’s questions. Slack rocks for this!

Governance

Some form of control and incentive. For example, making it a requirement to take a course or basic training before getting access to tools. Phased approach to what data is available.

And last but not least, self-serve is not a polarizing choice between all or nothing. It is about avoiding complete reliance and dependence on central analytics while balancing that out with centralized key insights and messages that come from Analysts. Self-serve comes as a compliment to an Analytics team and one of its awesome benefits is freeing the precious bandwidth of analysts from FOAM: Frivolous Ongoing Analytics Minutiae. (Brent Dykes)