Now, let’s be clear right off the bat that we don’t think dashboards are irrelevant or going to be wholly eradicated from use in the enterprise any time soon. Giving more people the ability to access data (and leverage it from a single point of truth) is definitely a benefit of dashboard data visualizations, but is it the only way? And is it the most sustainable over time? Here, we take a look at the potential shift from dashboards (which tend to be more exploratory) to more tangible solutions.
According to Gartner, “As a result of the shift to more dynamic, in-context data stories for insight monitoring and analysis, after more than 20 years of rising use of visual exploration-based dashboards and reports as the primary way users monitor and explore data, the percentage of time users spend in predefined dashboards will decline.”*
For analysts, this has some interesting implications. Already inundated with data in their day-to-day tasks, they will no longer need to start in a predefined dashboard and explore further (either manually or in an augmented way, to do things like build more views to analyze changes within the data). With data stories — fueled by augmented analytics and techniques like NLP — analysts will be able to seamlessly explore the root causes of changes in the business’s key performance indicators.
How Is This Done in Practice?
According to Jeff McMillan, Chief Analytics and Data Officer for Morgan Stanley Wealth Management, one of the keys to an efficient, more intelligent organization is a focus on empowering people to make the best decisions. Essentially, this means ensuring that even the most junior person in the company has all the information (or a way to get all the information) he or she needs to best serve clients they are engaging with. By augmenting decision making (rather than simply automating it, like in the case of a predefined dashboard), there’s room for human insight and intuition to play a role — and make an impact.
In any project in Dataiku, data analysts (for example) can put a user-friendly interface on top of a data science project so that anyone they need to share their insights with can do so autonomously — without bothering the analyst. And, the analyst can sleep well at night knowing said viewer won’t mess around with their analysis or make rogue changes.
In the video below, discover how to create and share webapps and Dataiku applications that enable business users to consumer reports, generate insights, and draw business conclusions — without the need to fully understand or interact directly with the project flow:
One Dataiku customer, a multinational bank and financial services company, has developed a data marketplace that people across the organization can use when they need to get answers from other datasets. For example, an analyst trying to understand the cost of property can use the balance sheet from the data marketplace and plug in lease data.
The model represents a unique take on a self-service data program where the center of excellence owns the core structured intelligence of the company, but enables other teams to build experiences on top of that data, relevant to their specific function or line of business. As a result, people from various teams around the organization can use the apps within the enterprise-level data marketplace to get their answers to day-to-day business problems, which not only gets more people using data on a regular basis, but does so in a way that is set up for long-term scalability.
*Gartner - Top 10 Trends in Data and Analytics, 2020. Mark Beyer, Adam Ronthal. 11 May 2020