Key Trends in Data and Analytics

Whereas analytics has a reputation for being rooted in the wizardry of algorithms and “pure math,” Southekal recast the practice as driven by finding the most relevant questions for a particular business and then using those questions – in conjunction with data – to gain insights. Without “visibility” into business goals and KPIs, analytics will not be valuable, he said:

“A VP of an oil and gas company asked me, ‘Can machine learning and data analytics help me get more oil from the ground?’ The answer is no. To get more oil from the ground, you need a pump jack. But to get more value out of the pump jack, you need analytics, because it gives that kind of visibility into the factors that will help you get more value out of your pump jack, and ultimately get more value to the business.”

Key Components of Analytics

Southekal identified what he believes are the four building blocks of analytics: data, algorithms, ethics, and assumptions. “It’s not just about technology,” he explained. Because the business world is constantly changing – with mergers and acquisitions, new data regulations, product launches, and so on – data can never truly be “real time.” A successful data analyst must be able to make assumptions about missing data, discern good-quality data from bad, and calculate just how much data is needed to make decisions in a timely – and profitable – manner.  

“There is no perfect data available,” said Southekal. “If you’re looking for this unicorn in your company to have perfect-quality data, it doesn’t exist. Most of the time, 70-75% of data is good enough.”

In other words, the best analytics strategy involves using less data to do more – not simply amassing a large quantity of data.

Three Trends in Data and Analytics

Once organizations understand what business problems they want to solve with analytics, the next step is to adopt these top trends, said Southekal.

1. Focus on advanced analytics.

The most basic type of analytics, called descriptive analytics, uses historical data to answer the question, “What happened?” In contrast, advanced analytics – which includes predictive and prescriptive analytics – tackles the questions of “What will happen?” and “What can I do to make it happen?” These latter two dimensions comprise advanced analytics.

Advanced analytics allows decision-makers to be proactive about their organization’s future and think ahead about managing resources and remaining solvent. As the recent global supply chain crisis revealed, a robust predictive analytics model can be the key to an enterprise’s survival. “Today, in the post-COVID world, companies are concerned about liquidity,” said Southekal. “They’re concerned about cash flow; they’re concerned about working capital.” 

2. Embrace data democratization.

A second key concern in contemporary analytics is data democratization, which increases the availability of cutting-edge analytical tools to all levels of business operation. In a nutshell, our current era of affordable, end-user platforms and software empowers all businesspeople with tools once reserved just for data scientists, making every person a “data person.” 

Moreover, the “intimacy” and small scale of democratized analytics can often make for better, faster decision-making than the top-down bureaucratic mechanisms of larger corporations, giving analysts a competitive edge in maximizing results – and profits.

3. Consider ESG-based EPM dashboards.

Finally, Southekal shed light on the ubiquity of ESG dashboards in today’s business culture. ESG dashboards are at-a-glance tools that provide metrics for an enterprise’s performance, good and bad, in three areas: environmental, social, and governance. 

By looking at multiple factors and not just a company’s bottom line, these dashboards offer a holistic window into business operations, revealing elements that might not be obvious. Of special appeal is the dashboard’s analysis of a company’s carbon footprint, which can help companies like the server-reliant giants of big tech improve their environmental performance. “Whether it’s Shell or Microsoft, or whether it’s banks, everybody is talking about ESG today,” said Southekal.

Putting the Trends to Work for Your Business

The key to harnessing all these powerful elements of data and analytics comes down to building a strong business case, which Southekal likes to call a “reflective methodology.” He breaks this strategy down to three steps. First, define what the company is doing, a process that may involve stakeholder interviews and raw data analysis. Second, analyze why these outcomes are relevant, usually by ferreting out root problems and inefficiencies at the same time as positive factors are mapped out. Third, realize what the best course of action entails, given the idiosyncratic contours of the business under scrutiny.  

Depending on the nature of the enterprise, the best industry practices may place greater emphasis on the data end of the spectrum (important to oil, gas, and other tangible commodities) or upon personal insights (consumer-driven, more personalized services). A strong business case should be able to realistically predict yearly profits and provide stakeholders with a robust picture of the company. Good analytics must be built on a foundation of relevant data, which for Southekal means looking at the transactional operations of a company at all levels, since these data points are the areas at which things get done.

Southekal also emphasized the role of personal integrity and leadership in implementing analytics. Education is key in building “citizen data scientists” who can implement self-service analytics and in training analysts competent in 10 areas of data literacy ranging from Data Stewardship to Data Governance.  

Above all else, effective analytics relies on individual leaders committed to making ethical, non-biased decisions – on time. “When you want to do analytics projects, you have to keep the momentum,” stressed Southekal. “That means think big, start small, and act fast.”