What is Self-Service?
The Gartner definition of self-service is “a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support”. Other definitions offer up the idea that self-service is the platform that allows business users to utilize data to spot business opportunities, without requiring them to have a background in statistics or technology. This approach allows business users to make data-driven decisions in real time without having to rely on information technology (IT) staff or data scientists to create reports.
The problem with this is of course that consumers of self-service platforms are far more diverse than the providers of that capability. The personas amongst those who utilize self-service platforms include business analysts, hard-core programmers, data scientists, and business executives. Self-service means something different to each persona, and many are diametrically opposed to the other. For the business analyst, self-service means being able to mash data and a SQL-like language into an intuitive set of steps to deliver insights. For the programmer, it means providing the ability to create their own scripts in their language of choice. For the data scientist, it means being able to intelligently mix and match state of the art algorithms to understand underlying data patterns. For executives, it means interactively working with a few dashboards to fully understand the state of their business.
Under these circumstances, how would a provider-centric self-service platform work? It wouldn’t!
Simply put there are at least eight items that need to be considered by the purveyors of an analytic platform when providing self-service capabilities.
What Self-Service Analytics Platforms Should Include
Simply put there are at least eight items that need to be considered by the purveyors of an analytic platform when providing self-service capabilities. Each of these topics can be explored further, however, when integrated thoughtfully into an analytics platform they are able cater to the widest net of analytic professionals. More importantly, our quest for driving business outcomes in a self-service manner becomes a bit more perfect.
The capabilities are:
Analytics is a team sport. Never in our hands-on careers have we seen an analyst doing all their work and that’s the end of their story. Typically, when analysts work alone, the things they are working on can be called science experiments. True collaboration comes when the analytic environment is structured in a manner where all participants speak in a common language with one another.
Data catalogs provide a cross-organizational view of all the data sources and objects within those sources with a clear marking of definitions and hierarchies. A crisp catalog now makes it easy for analysts to pick and choose their variables that they use in analytics.
Security and Governance:
In this day and age, what is the use of a data-driven practice that does not guarantee security? Especially in the context of collaboration, security becomes important to the business. We are not just referring to access privileges that are applied across the board, but instead a persona-based sensible application of rules that ensure data fidelity, confidentiality, as well as context-based access.
Pre-built analytic functions across multiple genres (e.g., machine learning, sentiment analysis, graph, statistics) make it easier for the citizen data scientists to quickly invoke functions in a nested manner to render insights. The alternative is to code these in a non-repeatable manner, therefore wasting time and resources.
Integrated BI Tools:Not all solutions have their own built in BI tools. However, when an analytics platform integrates well with a BI tool it makes it easier for users to quickly deliver compelling visualizations and dashboards that, in turn, make the operationalization of business insights a given.
Analytic workflow tools, such as KNIME, include a graphical interface that enables users to create data flows, execute selected analysis steps, and review the results, models and interactive views. This, again, caters to a group of analytic personas that want the rigor of analysis without having to deal with the unstructured scripting environments that others may prefer.
Gone are the days where one data source claims to speak to some existential truth. Nowadays, we connect to many enterprise data warehouses, data lakes, cloud data pods, flat files, and unstructured data repositories, to name a few. To do analytics separately in each of these siloed systems and then attempt to piece together the outcomes is a disaster of apocalyptic proportions (I’m only slightly exaggerating). What we need is to have the ability to connect to various sources at will so that we can complement analytics with all data that the business needs and generates.
Flexible UX/UI Design:Underpinning a strong focus on self-service analytics is an absolute focus on usability and experience. A design that is customized based on user personas exponentially increases analytics adoption. Adoption that is instead focused on simply giving anyone access to the platform without regard to their skills and aptitudes is likely to fail. In the worst-case scenario, a bad user experience turns off the entire organization from a path of intelligent decision making.