Written by Scott Castle, VP & GM of Analytics Solutions at Sisense

Over the past 2 years, data analytics has played a critical role in the way we’ve adapted and responded to the COVID-19 pandemic. In the UK, the government and public services opened up certain datasets to the private sector for the first time. In other cases, we saw individual public services pool their datasets, allowing for more sophisticated data analysis.

For corporations, the proliferation of data analytics, technology, BI and AI means we’re entering an era where entire businesses can unlock superspeed decision making and generate unheard-of value across all industries and at all levels. This potential to successfully ‘pair  knowledge workers and next-gen BI’ will remain untapped, however, if we don’t correct common mistakes we make when using data today.

Mistake #1: Ignoring data at ideation stage

One of the biggest mistakes we can make is to look for data, only after sourcing all our ideas. One example of this is in marketing, where creative minds brainstorm campaigns and strategies across an ever-increasing number of channels, but often before consulting the data and sometimes outright ignoring it. This is never how we should use data, and it’s a common occurrence in these departments.

What we must do is build a new mindset where insights from data help us define our ideas, strategies, and course of action. This doesn’t mean ‘blindly following what the data tells us’. Rather, analytics should work seamlessly alongside our natural creativity and expertise, making it ‘invisible’ where one begins and the other ends.

Instead of relying on traditional, standalone dashboards that require us to deviate from our existing workflows, we can receive insights from data, front and center in the apps we’re using, so we can easily leverage the data quickly where and when we normally make our key business decisions.

Mistake #2: Using spreadsheets and visually unclear data

One positive outcome of the self-service generation of BI was the push towards data visualisation, since we are more quickly able to understand visual representations of numbers than we are long lists of tables.

We can carry this trend forward into a new generation of BI, and continue to develop it further so that we design insights from data that are easily ‘consumable,’ ‘actionable’, and also ‘understandable’. There won’t be much progress if we infuse analytics everywhere but continue to present those analytics as tables of raw data.

This is even more critical when we consider the 1 billion ‘knowledge workers’ we can now empower with insights from data. These workers have the expertise we want to leverage but may not be accustomed to the language of traditional analysts and representations of data.

The more people we empower with data insights, the more care we must take to not misrepresent the data.

Avoid choosing flashy visuals for novelty’s sake; sometimes bar and time-series charts are exactly what we need. If available, work with your company’s in-house experts and analysts to design the right visualisations, in the right workflows, to ensure that analytics continues to drive meaningful business decisions.

Mistake #3: Over-Reliance on Data

It seems counter-intuitive to suggest that in providing easier access to data, we risk giving people the habit of over-relying on it. However, this is becoming more of a concern as we push people to make informed decisions in real-time, and in more extreme scenarios.

Current data models can’t always account for big disruptions, particularly at scale, and removing people from the decision-making equation can dampen the impact of data altogether.

An on-going example is the COVID-19 pandemic, which has upended many data usage habits, no matter the industry. Customer expectations continue to change daily, sometimes even within hours. This often affords us little time to dig through standalone dashboards or wait for analyst reports. Insisting on waiting for the data means missing critical decision windows.

People are ultimately the decision makers, and with a new and growing generation of knowledge workers, we must train them to take their knowledge, now augmented with analytical insights, to make powerful decisions in real time. Companies who work the other way around will be missing out, turning knowledge workers into mindless robots that only do what the data says to do.

Mistake #4: Spotlight on the wrong insights

At times, insights from data can be misleading and a distraction, even if the data itself is accurate. This is because companies often generate so much data today that we often codify certain insights into metrics and track them as if they’re our only focus.

How often a sales rep picked up the phone, how many parts an airline mechanic changed, even how many times people loaded a dashboard or shared an insight from data. Each of these metrics is useful, but they are not the numbers that help us make core, business-impacting decisions.

This is even more reason to focus first on how people make decisions. When we put the spotlight on metrics, we put our effort into improving those metrics while losing sight of the impact those metrics were supposed to achieve. Instead, companies should prioritise people’s existing workflows and design analytics to fit into them. When we look at workflows first, we discover what metrics people need to make informed decisions, and not simply vanity numbers that make us feel good.

Mistake #5: Overlooking the power of a data-driven culture

To truly unleash the potential of knowledge workers and next-gen BI to drive strategic growth, it’s not enough to simply ask users to avoid individual analytical mistakes. If we want to have a culture that constantly uses insights from data, we must build a culture that expects insights from data, in every decision, from every worker.

Here’s two ways how: First, invest in technological solutions that make it easy to infuse analytics everywhere in the company. Putting relevant insights from data where workers already are is the single easiest way to ensure that workers make analytically sound decisions. Asking users to turn to yet another app for information is one way to guarantee that they ignore it.

Second, we must set the expectation that everyone, from the top down, uses insights from data to make each decision. Executives must be seen consuming analytics without outsourcing the work to data specialists. Frontline workers should be given personalised insights to help them with their daily tasks.

This process can be difficult and doesn’t happen overnight. Like a muscle, building a culture requires practice, time, and repetition. We can’t simply reduce it to once-a-year data literacy initiatives or demanding that people bring more charts to meetings. It’s the most innovative companies that unlock actionable insights from data and make it the cornerstone of decision-making that will ultimately set themselves up to be more agile, scalable and future-proofed.

About the Author:

Scott Castle is an analytics infusion pioneer bringing over 25 years of software development, go to market strategy, product management and strategic partnership experience to his role as VP, Strategy at Sisense. Scott is passionate about turning data teams into superheroes that find unexpected insights in big data and disrupt traditional BI. Previously, Scott held technology positions at companies including Adobe, Electric Cloud and FileNet. Scott holds computer science degrees from the University of Massachusetts Amherst and UC Irvine.

About Sisense:

Sisense goes beyond traditional business intelligence by providing organizations with the ability to infuse analytics everywhere, embedded in both customer and employee applications and workflows. Sisense customers are breaking through the barriers of analytics adoption by going beyond the dashboard with Sisense Fusion – the highly customizable, AI-driven analytics cloud platform, that infuses intelligence at the right place and the right time, every time. More than 2,000 global companies rely on Sisense to innovate, disrupt markets and drive meaningful change in the world. Ranked as the No. 1 Business Intelligence company in terms of customer success, Sisense has also been named one of the Forbes’ Cloud 100, The World’s Best Cloud Companies, five years in a row. Visit us at www.sisense.com