We entered the data era many, many years ago but the Covid-19 pandemic has ushered us into what can becalled a culture of analytics. When I say 'culture of analytics', what I mean is that data has become the linchpin of how we do business. Analytics today is bigger, better, and fundamentally more important than it’s ever been. If we used to look at numbers in a weekly cadence before, we now—and I’m being only a tad hyperbolic—start and end the week with data.

I've been fascinated by the pivotal role data analytics played during the pandemic and the many ways it has changed the business landscape since then. Join me as we rewind a little to understand how analytics emerged as a hero of the pandemic.

None of this is meant to bring up unpleasant memories but if you find mentions of Covid-19 triggering ,please skip ahead to the last section where I list out five mindset shifts that will help businesses navigate the analytics landscape of today.

A throwback to early2020

March 2020 was not the best of times, possibly not the worst of times. But, it certainly felt like the most uncertain of times. As people ,businesses, policy makers alike floundered in the face of the virus, we were all desperately looking for understanding - in numbers, metrics, and graphs .Many of us remember the early dashboards online,  showing the spread of the virus - and the‘ flatten the curve’ graphs, imploring us to stay home.

Business leaders sought answers in numbers too. At the time, I was heading Data Analytics and Growth Strategy at Fresh works and had a ringside view of the sudden, amplified importance of data. Leaders across verticals tasked analytics teams with monitoring key metrics closely and highlighting any extraordinary situations. Analysts had to scramble to put things together for management meetings and war rooms. Apart from tracking internal metrics, we had our eyes peeled open for what was happening outside and how it could impact our business. As the months rolled on, this ability to monitor things closely gave us at least some semblance of calm amidst the devastating waves.

For three pandemic-era years, data was everywhere. Its omnipresence permanently changed how we perceive data-driven business intelligence and how much we value it. Here’s how analytics has evolved, the way I see it.

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More data, more consumers: ergo more analytics

Lockdown restrictions meant that businesses went online at a staggering pace. Ecommerce sales shot up and customers left their digital footprints everywhere, leading to huge spikes in available data. As the market moved almost entirely online, competition began to heat up and consumers were inundated with ads.

In this noisy environment, only high-quality analytics and precise targeting could stand out from the competition. Anybody who had so far been nonchalant about building a data culture was now at risk of getting vaporized out of existence.

To tackle this, we began taking a closer look at customer usage of products and following that up with real conversations with customers. The insights so gathered helped us make more accurate forecasts about the revenue pipeline and qualify our decisions. What the pandemic set into motion is now here to stay - data analytics at the very forefront of understanding consumer behavior.

Expansion of data sources and looking outside-in

As lockdowns and economic uncertainty set in, a series of cascading changes ensued. We started hearing terms like force majeure. Everyone was panicking, even governments across the globe. Firms started renegotiating their contracts. Customers started to ask for billing related reliefs.

Since so many outside factors led to disruptions within businesses, internal data was no longer sufficient to forecast future trends. Some GTM leaders who kept their ears to the ground were able to foretell turns from their conversations with customers, even before data could demonstrate them empirically and quantitatively. Feedback from customers and customer facing teams became an important method to build strong hypotheses hinged in reality.

This period taught me the tremendous importance of having outside-in views and tying this back to the trends we were witnessing internally. Without expanding our vision and the data sources we were looking at, we’d just be flying blind.

Preparedness for diverse scenarios

For business leaders who’d experienced the crises of 2000and 2008, those scenarios were  already playing out in their minds. But even as people were anticipating the sales cycles to elongate and new business to slow down, something extraordinary happened— closure rates began increasing even as ARPA was reducing. Businesses understood the importance of digital transformation but in the face of economic uncertainty, they wanted to be frugal with their spending.

As for the outside world, pandemic waves were impacting all regions, but neither equally nor simultaneously. Businesses had to be accordingly agile when it came to revenue planning and marketing spends. They realized they had to accept the entropy they were seeing, and model for all the possible outcomes they could think of. This required a certain level of preparedness on the analytics front.

For us at Fresh works, this included the level of automation, ability to do text analytics, cost analytics and ROI calculation for initiatives, ability to drill through the data to a level of granularity that enabled precise action. Some examples of granular data that we looked at:

1. New pain points at a user level (both admin and agents)

2. Vertical-level impact on new and expansion revenue

3. Elasticity of lead generation and change in AdWord bids

The pandemic woke us up to the fact that it’s important to plan for a range of scenarios, not just the most likely prediction. By modeling a range of possible scenarios, we could better prepare for whatever ensued. This is why you’ll find that the data models of today are built for better, deeper scenario analyses than ever before.

More flexibility in data models

Before 2020, analytics tools were built for stability rather than agility. But the pandemic changed that. The sheer, tearing hunger for real-time information pushed data analytics to become much more flexible than before.

At Freshworks, we found that target setting posed new challenges. There was a widespread realization that annual operating plans for the current year would have to be revised, at least once, if not multiple times. Therefore, it was absolutely necessary that the data was transparent and reliable, and that iterations were quick.

With the post-pandemic headwinds during the immediate recovery period, the political upheavals since then that have created economic ripples globally, and the onset of the generative AI era, it’s safe to say that this demand for agility in business intelligence is here to stay.

Machine learning models that used to take weeks or months to produce actionable insights now crunch away in much shorter time spans. As Robert Tischler, of German research and consulting firm BARC says, "Now we see people wanting to build things they can change quickly. If the environment changes, the analytics must change, and if it takes weeks and months to write requirements for new reports, new dashboards, that's totally meaningless."

AI-boosted analytics

Analytics service provider Forrester annually surveys its clients to gauge AI adoption. In the years before 2019, AI adoption would go up by roughly 2 percentage points every year.  In 2019, 54% of the respondents had adopted AI, while in 2020, that skyrocketed to 68%. A 2021 PWC study found that 52% percent of companies accelerated their AI adoption plans because of the Covid crisis.

Before the pandemic struck, it was the norm to focus on historical data collection to predict trends. But the pandemic skewed numbers so much,  and so pervasively, that data collected from the year 2020 has basically left analysts and strategists nonplussed. This is why many businesses have begun looking to the new kid on the block, augmented analytics.

Augmented analytics is essentially predictive analytics that employs machine learning and AI-assisted insight generation to aid human analysis. With augmented analytics, capabilities like automated data preparation and NLP are increasingly becoming a key part of business intelligence platforms.

Data lessons to carry into the future

The pandemic acted as a crucible of change, forever altering how businesses see and use data. Here are five of my biggest takeaways from helping to navigate a business through this time:

1. Embrace a data culture: Whether it’s monitoring key metrics closely, identifying trends, or just making more informed everyday decisions, actively work on building a data culture in your organization. Create a robust, agile data environment with strong analytics capabilities—and no, this does not have to be an expensive, clunky business intelligence platform. Here’s why. ‍

2. Adopt an outside-In perspective: Look beyond internal data and closely monitor external developments to anticipate potential impacts on the business. ‍

3. Prepare for granular analysis: Ensure that the data analytics tools or platforms you use can help you drill down to the level of granularity needed to take precise actions and strategic adjustments.‍

4. Embrace agility: Be ready to switch gears and change your strategy based on emerging trends and changing circumstances. Data insights can guide you here, provided you’re looking at the right metrics and sources.

5. Prioritize customer empathy:  Use data analytics to understand your customers’ needs and concerns in real-time, fostering a more compassionate approach to decision-making. Transformative decisions can be made only when data intelligence meets human empathy.

The next big thing (or the next big disruption) might be just around the corner but a fresh approach to data-driven business intelligence can help us deliver resilient, sustainable growth for our businesses.‍‍