At any given moment, a single BI dashboard can have anywhere from 25 to thousands of people viewing it-all with different roles, responsibilities, and various levels of expertise. A few things to consider:

  • Rarely do two people interpret a visualization and come to the same exact conclusion.
  • Rarely do two people have the same exact goals.
  • Rarely do two people require the same exact insights.

As more and more viewers consume a dashboard, the stakes of providing clear and relevant insights exponentially rise. So what’s at stake?

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Creating a dashboard that accounts for all of these factors is incredibly challenging-especially one that is engaging, understandable’ and speaks to viewers in a one-on-one way. With enterprises heavily investing in data visualization platforms, it’s critical to have one source of truth within a dashboard so viewers can derive understanding, eliminate misinterpretation, and extract the full value out of these tools. This conundrum we currently find ourselves in is the reason why, according to Gartner, “despite easier-to-use BI and analytic tools, BI is not broadly deployed in most organizations, with only 32% of employees using any BI tool.”1

Yes, BI and analytic tools may be easier-to-use, but that doesn’t necessarily mean that insights are easier-to-understand.

What happens when your company neglects your data visualization platforms and refuses to use them as a decision-making tool? If people aren’t finding answers from a dashboard, where do they turn for insight? 

The reluctance of organizations to use dashboards as an everyday, decision-making tool mostly impacts analysts and their data teams. The lack of adoption and engagement forces analysts to manually explain dashboards with written reports, phone call follow-ups, or video walkthroughs. Instead of spending valuable time manually translating their findings into easily understandable insights, they could be expanding data analysis to other areas of the company, constructing projections for future business with predictive analytics, or creating more dashboards for external clients that produce more revenue.

This manual grind of communicating insights from a dashboard will only serve as a short-term solution-one that is impossible to scale.

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But what about some of those browser extensions that plug into your dashboards-don’t they explain insights and eliminate the need for analysts?

Having so many demands on the layers of insights needed from a dashboard is the reason why pre-configured, plug-and-play solutions, even though they claim to be advanced, will always fall short. They may provide some level of value a few weeks into initial deployment, but once you realize that you need a deeper and personalized understanding of insights, you’ll quickly discover that blanket insights from a chart explainer plug-in are not useful. Knowing rudimentary insights like min, max, sum, and average may make your company data-aware on a very basic level, but it will never transform your organization into a data-driven company. Sure enough, companies inevitably see through the holes in band-aid solutions like this and go back to being 100% dependent on analysts.

Analysts are the “glue” that make BI work. And it’s to no surprise-they have the most domain expertise and live every day deep within the data. It’s imperative that we provide analysts with tools to augment their reporting and share their own unique expertise without artificially limiting them.

So, how can analysts leverage their expertise to communicate in a one-on-one way with every customer or colleague, while simultaneously spending less time actually doing that?

Role-Based NLG

Automated Insights’ Wordsmith platform and its ability to produce role-based natural language generation (NLG) provides analysts with the power to generate contextual and personalized insights, and scale their expertise across an entire organization. Remember that diagram above with all the different possible viewers of a dashboard at any given time? Wordsmith can generate written analytics right beside your data visualizations depending on who is viewing the dashboard-all that are relative to individuals, org structures, and your company’s overall strategic goals.

[LIVE DEMO] Try our demo of role-based natural language generation

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Role-based NLG allows analysts to generate written analytics right beside dashboard visualizations that are completely unique and relevant to org structures, job functions, and individual goals.

Wordsmith gives your analysts the complete control, flexibility, and power to create custom insights at scale-directly in your data visualization platform. By augmenting their reporting responsibilities, NLG can enable analysts and data experts to focus more time on revenue-generating tasks.

Becoming a truly data-driven organization hinges on your firm’s willingness to arm analysts with tools that give them complete control over how the data’s story is told. NLG has the unique power to bridge the gap of insight communication and transform your data visualization platform into an end-to-end, decision-making tool.

1. Survey Analysis: Why BI and Analytics Adoption Remains Low and How to Expand Its Reach Published: 30 June 2017 ID: G00326220 Analyst(s): Cindi Howson | Rita L. Sallam