August 29, 2021
Improve Business Outcomes Using Analytical Decision Making
Big data has moved from far-off promise to new reality. B2B companies now have access to more data than ever–with many getting buried by a deluge of data. But what happens to that information? Do you let it pile up and degrade, or are you putting it to work?
The thing about data is that there’s this “use it or lose it” element that will make or break your entire strategy, something many brands are learning the hard way amid a global pandemic.
As datasets become more complex, organizations struggle to identify which insights are most relevant and what actions represent the best path forward. Even when we’re not dealing with once-in-a-lifetime crises, data-driven decision-making plays a critical role in retaining customers and edging out the competition.
In this article, we’ll examine how big data unlocks opportunity, enables real-time action, and uses data analytics for business decision-making.
Importance of Developing a Data-Driven Culture
According to AdWeek, nearly half of all 2020 insights are now irrelevant. While businesses still can’t predict with 100% certainty when the next earthquake, hurricane, or pandemic will strike, the ability to make decisions based on accurate, real-time information, allows them to change course when the game suddenly changes.
In a recent article published by Boston Consulting Group, advanced analytics help companies gain the “uncertainty advantage” by addressing three key areas:
- Detection. The ability to analyze a wide range of datasets, assess risks, and uncover opportunities that may seem counterintuitive.
- Multivariate modeling. The ability to use simulations to identify a variety of futures and prepare plans for different scenarios.
- Contingency planning. The ability to anticipate disruption/emergencies and develop contingency plans in advance, including training employees for how to respond to a wide range of potential scenarios.
According to a 2019 Deloitte survey, most executives don’t believe that their company is “data-driven,” and 67% say that they’re not comfortable accessing or using data. Of the 37% of survey participants with the strongest data-driven cultures, nearly half of those companies significantly exceeded their business goals in the past year, making them twice as likely to do so than participants with weak analytics cultures.
In another report, McKinsey researchers found that strategic use of customer behavior data was instrumental in helping organizations outperform their competitors. On average, those companies achieved 85% higher sales growth and 25+% higher gross margins than those struggling to make sense of their data.
While there’s no doubt that data-driven decision-making is a critical success factor for many businesses, many aren’t sure how to best leverage the latest tech. How can businesses learn to use analytical decision-making to improve business outcomes? The short answer is, it’s a combination of strategy, tools, talent, and most importantly, culture.
In these next few sections, we’ll walk through the steps toward becoming “data-driven.”
Look at Business Objectives & Prioritize Initiatives Based on Impact
As we’ve mentioned before, making the most out of your data initiatives starts with a laser focus on specific business objectives.
Goals might include any of the following:
- Understanding consumer behavior
- Driving performance gains
- Reducing indirect expenses
- Managing risk
Keep in mind, even if you plan on targeting multiple objectives, you’ll want to implement and measure each effort separately. Sure, it’s all “big data,” but it doesn’t make sense to measure your fraud detection efforts in the same report as your marketing campaign or operational performance.
Identify Relevant Data
Depending on your goal, you can find relevant data in sources like BI platforms, social listening tools, IoT sensors & equipment, CRM software, customer feedback, security logs, surveillance footage, and unstructured text.
By identifying what data you have available, information can be used to inform your decision. Are you currently leveraging that data, or is it trapped in departmental silos? What is preventing you from using that information?
If no data exists, you’ll need to consider what you’ll need to put into place to make this happen. For example, what tools will you need to fill in the gaps? Alternatively, you might look outside of your organization, and incorporate third-party or publicly-available datasets to generate big-picture insights.
Build & Test Models
At this stage, you’ll start building models to test your data and answer the business questions you identified earlier. Test different models like decision trees, random forest modeling, or data graphs to determine how to best display insights to support users’ goals and answer critical questions.
Collect feedback from the people who will be using these solutions on the job. After all, you’ll want to ensure that end-users have access to tools and insights they can immediately understand and use, regardless of data science experience.
Gartner recommends looking for tools with explainability features, at least for specific functions, pointing out that showing users the factors behind an AI-based recommendation can help build trust and encourage adoption among skeptical end-users.
Many analytics platforms offer free trials or guided demos, which allows you to let teams try out various tools to find the right fit. It’s also important to consider the end-user in context. For each use case, consider who is using this solution, what they ask, and what they need to know to arrive at the right decision. Keep in mind; you’ll want to avoid presenting more information than is necessary.
Analyze Data Analytics Insights & Draw Conclusions
Once you’ve captured and cleaned the data, you can start to analyze that information to help make business decisions. Are the models you’ve selected an effective way to present that data? Do they offer clear next steps for what you should do next? If not, where are you running into information gaps?
Ultimately, the goal here is to eliminate the habit of relying on instinct, experience, or best practices when evaluating the best path forward.
Plan the Strategy
The next step is to put your insights to work. Here, the goal is to establish clearly-defined objectives around what needs to be done and why, who is responsible, and what kind of outcome you expect to see.
Define your milestones in clear terms, avoiding goals like “increase revenue,” “be more productive” and vague deadlines like “it needs to be done by next year.”
Using data analytics to improve business relies on setting deadlines, measuring progress, and continuously improving the strategy.
To measure the success of your big data initiative, you’ll first need to look at the data you originally collected and used to inform your decision. You’ll then want to make sure that the KPIs you’re tracking sync back to the objectives defined in your initial plan. Then, when you pass the deadline for your goal, compare your benchmark to the new data.
What’s changed? How did insights from data analytics impact your business? Data should help you answer critical questions and drive positive change.
Look for areas where decisions led to “wins” and keep an eye out for instances where data led you down the wrong path. You’ll also want to consider the time it took to arrive at a specific decision. If you arrived at the “right” conclusion but took several hours to get there, you might need to refine a few aspects of your strategy. Here, you’ll want to find out whether there’s an issue with the data integrity, how insights were presented, or a matter of employees lacking proper training.
Leading with Big Data Analytics
While there’s no shortage of insights hiding in your average corporate dataset, business leaders continue to struggle to leverage data to improve decision-making.
Of course, many of the challenges are cultural, caused by skeptical leadership, departmental silos, and a lack of data literacy across the entire org chart.
A recent McKinsey survey found that 61% of business leaders felt that the time spent making decisions was a waste of time.
In a later report, analysts identified three practices to help business leaders improve their decision-making capabilities, based on three different types of decisions. Here’s a quick look at how they broke it down.
- Big bet decisions. Infrequent, high-impact decisions. Big bets come with high-stakes, and decision-making typically stays within the C-suite and stands to shape the company’s future direction. These decisions are broad in scope and may include mergers, acquisitions, and resource allocation.
- Cross-cutting decisions. Cross-cutting decisions, like big bets, tend to focus on the big picture but happen more frequently. These decisions consist of a series of smaller decisions made by interconnected teams. Examples might include improving products, updating brand messaging, or raising prices.
- Delegated decisions. Delegated decisions are smaller in scope than big bets and cross-cutting decisions made by individuals or teams. These decisions typically don’t require input from other departments, such as a sales director updating their team’s training schedule or the HR developing a new recruiting policy.
According to BI-Survey researchers, senior management is the driving force behind big data analytics adoption. A recent survey revealed that in 61% of organizations with integrated big data initiatives, senior management was the driving force for organization-wide adoption.
We’ve all seen situations where middle management attempts to spearhead digital transformation efforts within their own department. They might get the green light to adopt new technologies but face barriers when it comes to realizing their initiative’s full potential. When leadership makes big data a priority, change can happen on a holistic level. Silos come down, training becomes mandatory, and new processes gradually replace legacy workflows.
Still, while the top brass must champion initiatives, Amazon’s SVP of HR makes an important point—technology is “everyone’s job.” As such, leaders must think critically about their approach to “cross-functional” and “delegated decisions” that require input from multiple players. We’re beginning to see AI fill in the talent gap, but it’s worth noting there’s a significant learning curve when it comes to using data analytics for effective decision-making.
HBR points toward TD Bank Group’s “Data and Analytics Academy for the Non-Analytics Executive” to bring employees up to speed. The firm offers an immersive training program where participants work through a series of exercises: framing the problem, identifying the data sources needed to address the problem, then applying analytics solutions to find the best path forward.
Data Analytics for Business Decision-Making is Now a Top-Priority
It’s no secret that those companies getting big data and analytics right stand to see a positive impact on the bottom line.