Business Intelligence, Decision Intelligence

Tackling Macroeconomic Changes with Better Analytics and Decision Intelligence

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Organizations today are finding themselves in an often precarious situation as it pertains to the macroeconomic environment, be it slowing company growth, layoffs or hiring freezes, or higher overall costs affecting consumer behavior. 

In particular, about two years ago, Gartner predicted the top seven macro factors expected to shape the 2020s: the COVID-19 pandemic, a market crash and recession, systemic mistrust, weak productivity, environment concerns, a shortage of talent, and advancements in technology. Yup, the past few years have been a doozy, to say the least.

Looking at today’s landscape, it’s certainly not all doom and gloom, of course. According to a recent Forbes report, despite rising food and housing costs in November and an expected hike in interest rates for 2023, inflation actually “eased more than expected” for December, representing the smallest 12-month increase since December 2021. In food, apparel, and home goods markets in particular, price declines in November presented “a more encouraging report than most expected,” wrote Forbes.

During this often unexpected roller coaster of macroeconomic conditions, organizations must be prepared to answer these types of questions:

  • How do you make the best decisions for your business during the inevitable ups and downs? 
  • How quickly can you react to change, both good and bad?
  • If your organization isn’t hiring, how can you upskill your current team to become more productive? 
  • How proactive can you be ahead of the next big change?

With better decision intelligence, you can tackle these challenges head-on, setting up your organization to build a better foundation for the future. 

Why intuition-based decision-making falls short

Are you using data to drive decision-making at your organization, or are you relying more on intuition and experience? 

Without the right data insights, you can only rely on intuition and experience for so long, especially if the current environment isn’t anything you’ve experienced before. You might be coming up short when it comes to answering beyond what happened: Only by analyzing all the data you have available can you truly get a full, detailed picture of what’s happening across the business, a deep understanding of why things change, and a precise set of recommendations to drive business outcomes.

A problem is that many organizations have only a rudimentary understanding of historical data and consumer behavior instead of granular-level details, so they end up making decisions based primarily on what’s happened in the past: e.g., What countries usually perform best? What tone of messaging resonates well on each social media platform? What usually happens to sales around the holiday season each year?

However, these intuition- and experienced-based decisions are inherently more subjective and can be rooted in biases, which doesn’t translate to agile, adaptive decision-making in new environments. It also often equates to pointing at the same reasons for why things are happening—but perhaps those weren’t the right reasons to begin with.

In this new environment, you need more of the right data to inform you of what’s happening, breaking up any patterns of inaccurate or incomplete information.

Using data to proactively spot changes

If you are using data to drive decision-making, is it up to par?

Using BI tools, you can support decision-making with dashboards, which deliver data surrounding, for example, revenue by a few key parameters: location, brand, or product line.

But what happens when you want to identify a downturn in sales or an uptick in customer churn using a combination of these parameters: i.e., information that could include literally thousands (or millions!) of data points? 

Let’s say you’re a company that sells cleaning supplies. You know you had a massive Q2 2020 in terms of sales—and likely had trouble keeping up with demand—but in a post-pandemic world, what can you do to drive sales and retain customers going forward? You’ll need to look at the more granular metrics beyond just the obvious driver of sales that occurred in an entirely different environment.

If you experienced a significant downturn in sales for transportable, antibacterial, unscented wipes in the Denver metropolitan area in the third week of 2021, you could be looking at a host of potential reasons. Did recession fears cause buyers to shift to a more generic brand? Was there a local delivery issue affecting the supply chain? Did marketing face cost cuts and have to shift significant dollars away from advertising during this time? Using dashboards with aggregated data, you may never know, or using pure intuition, it may just be a conjecture based on hypotheses. 

With so much data for you to investigate, dashboards can’t provide a complete view into critical performance trends or predict what to expect with sales volume moving forward. There are far too many different combinations of data points to analyze to expose granular findings that you can take action on—traditional and manual analysis approaches using dashboards simply cannot keep up.

Instead, organizations are increasingly modernizing their data stack to gather, store, transform, and analyze their data. Using AI-driven decision intelligence, companies can continuously monitor changing metrics to always know what’s going on. Instead of combing through dashboards or making an educated guess, they can use machine learning models to automatically identify key drivers most likely to impact improvements—no matter how many data points there are.

Doing more with less

In addition to keeping up with the multitude of economic changes affecting business, organizations are also forced to adapt to internal changes related to attrition, layoffs, and hiring freezes—they’re trying to figure out how to do more with less.

With the flexibility afforded by most companies’ work-from-home policies, more doors are opening for job applicants who were once limited by their physical location. For instance, if a rural bank’s top-notch, highly sought-after members of a data science team are suddenly able to take remote jobs for a company based in a big city, that bank loses top talent in its data analytics practice until it finds more highly skilled replacements looking to move to the more rural location.

Many organizations rely on skilled data scientists to perform deep analyses of data. However, they can only keep up with so much demand, especially with limited personnel, which can leave many unanswered business queries on the table. In turn, this can force organizations to once again rely on just intuition or experience to answer questions, or burn out the data experts trying to keep up with demand—all of which can affect overall business outcomes. 

This is why it’s crucial to enable more teams in the business to uncover insights on their own without relying on the data experts. Organizations are turning to self-service business intelligence tools, for example, which bring more autonomy to data analysis. Instead of having to wait for data scientists to build models or execute the automation of analysis, data analysts are upskilling and making use of self-service BI to perform their own advanced analysis using AI and machine learning, which would otherwise be siloed for the data scientists.

Doing more with less is increasingly becoming more commonplace for organizations grappling with layoffs and other attrition problems, but with the right tools, more people are enabled to get the answers they need, when they need them, to drive the right answers for decision-makers. When you enable more people with data literacy, you’re better equipped to make your current team more effective, even through hiring limitations.

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Establishing a foundation for the future

We’re in the midst of hyper technology change when it comes to data, automation, and AI—the stakes are extremely high for organizations to become more data-driven. 

In fact, according to McKinsey, nearly every employee at an organization by the year 2025 will be making use of data to improve their work. 

“Rapidly accelerating technology advances, the recognized value of data, and increasing data literacy are changing what it means to be ‘data-driven,’” the firm said.

During the next economic change—or whatever may come over the next two years—data-driven decision-making is what will differentiate laggards and leaders in their respective markets, and those that push analytics access throughout their organization will reap the rewards. Thanks to the increasing adoption of AI-driven decision intelligence tools, businesses are able to get the right insights to decision-makers quickly and more easily. 

To establish a better foundation for the future, organizations must modernize their approach to business intelligence, moving away from dashboards and intuition-based decision-making and into a new way of engaging with data—no matter what internal and external changes they face along the never-ending roller coaster of macroeconomic conditions.

Ready to embrace the changes?

Nobody can truly predict the future, and no dashboard has all of the answers. This is why organizations are using decision intelligence to augment human expertise and make better decisions backed by data, no matter what’s happening in the world.  

Tellius helps organizations across industries—including financial services, pharmaceutical and life sciences, retail, healthcare, and high technology—accelerate their journey from data to decisions. Tellius enables them to quickly understand reasons and key drivers for business behaviors, get instant answers using natural language, and simplify complex data analysis with machine learning automation.

Learn more by checking out our eBook or trying a 14-day, risk-free trial of Tellius.

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