Data-driven business decisions: the foundation for growth

Data is all the rage these days, with storage expected to reach 175 zettabytes by 2025. That’s 1,000,000,000,000,000,000,000 bytes. That’s an absolute lot.

That’s according to Deloitte, and emphasized by Avinash Tripathi, a leading figure in analytics who has observed that explosion of data throughout his long career. We sat down with him for an email Q&A and he had plenty of insights on this.

Currently working as VP of Analytics at the University of Phoenix, Avinash brings more than 20 years of experience in using data to inform decisions.

What he sees is a shift in the way businesses run operations. In short, we’re going data-first.

A data-first society

First off – our business communities are evolving.

“The advancements in computing and increased access to data have paved the way for the rise of technologies such as AI,” Avinash says.

“Organizations are recognizing the immense value of data, investing heavily in data infrastructure, governance, and talent.”

It’s no longer about traditional data storage restricted to limited tools and prohibitive costs of storing data. Business communities are moving forward to more modern, sophisticated analytics platforms both in the way they approach customers and employees.

“Today,” Avinash says, “the changing landscape of customer demands is pushing for data-driven approaches.”

He adds that compliance is a growing consideration in an increasingly regulated data ecosystem – necessitating a data-driven approach.

Despite the optimistic landscape, companies face practical challenges.

“Despite the investments in data-driven initiatives, research by McKinsey suggests that only a small percentage of organizations (8%) effectively scale up their analytics capabilities.”

This, however, could be an opportunity for the right organization to get ahead. Avinash reaffirms this – with a caveat.

“This underscores the need for data governance, talent development, and a strategic approach to maximize the potential of available information.”

“This underscores the need for data governance, talent development, and a strategic approach to maximize the potential of available information.”

The value of data-led management

What should companies do with all this, then?

The first step is to recognize the sheer value of vast data collections and analytical tools in today’s competitive environment. There are a number of ways how data has value for organizations:

More accurate projections of behavior

“Companies are amassing vast troves of customer information,” says Avinash. “These resources help them identify patterns and trends that may go unnoticed using traditional methods.”

A classic example is seen at Lyft, which matches drivers to customers through behavioral data – escalating its profile as a carshare service beyond the traditional taxi even back in the late 2010s.

That’s the kind of customer intent data that Avinash is referring to, where data provides a deeper understanding of customer behaviors, market trends, and operational efficiencies, enabling businesses to tailor their decisions more closely to their ideal customer’s needs and pain points.

Using data to make decisions also reduces risk in ensuring that decisions are grounded in real science – again, in the Lyft example, knowing exactly where/when a customer is looking for service is an objective advantage over pursuing the same result via sheer speculation.

“Utilizing real-time data to inform decisions has additional benefits compared to other traditional approaches. The accuracy derived from analyzing data sets it apart from relying solely on intuition and opinions.”

“Utilizing real-time data to inform decisions has additional benefits compared to other traditional approaches. The accuracy derived from analyzing data sets it apart from relying solely on intuition and opinions.”

An ever-evolving feedback loop

Companies can also gain a competitive edge when they can use historical data to refine business strategies.

“By monitoring the impact of their decisions, evaluating results, and learning from mistakes, businesses can refine their strategies for better outcomes and continuous growth.”

This continuous loop of feedback and adjustment keeps businesses competitive and adaptive to changing market dynamics, Avinash adds.

“The scalability of data-driven decision-making makes it suitable for businesses of all sizes, fostering growth and creating opportunities for improvement.”

How to use data in hiring decisions

When asked for how data can be applied in recruitment, Avinath highlighted three examples.

1. Refining recruitment practices

First, recruitment practices can be updated.

“During the hiring process, recruitment analytics can influence screening by employing skills-focused tools to sift through and assess applicants,” says Avinash.

Read more: Data-driven recruiting 101: How to improve your hiring process

He adds that data-driven tools like gamified assessments and simulations can support the suitability of a candidate for a role, resulting in more accurate and efficient hiring processes.

All the usual recruitment metrics, including time to hire, time to fill, cost per hire, etc., can illustrate the opportunities that leveraging data can provide in streamlining recruitment operations, Avinash says.

“Data-backed tools … can provide comprehensive analytics on candidate sourcing, engagement, and conversion rates and help optimize hiring.”

2. Planning the workforce of the future

Data analytics can also aid in future-proofing talent management – predicting potential outcomes in a company workforce that can be minimized or even eliminated using data intelligence.

“Predicting future workforce needs, finding the right talent, and effectively nurturing and retaining employees is a multifaceted and intricate challenge.”

Employee data – such as schedules, productivity and quality of work, time off and sick days, engagement survey results, exit interview feedback, compensation, collaborative tool usage, and much much more – can be analyzed using analytics tools.

This can then be turned into actionable insights to support business decisions as they relate to talent, Avinash says.

These insights can prove valuable in mitigating talent attrition and turnover, looming skills gaps, and losses in productivity.

3. Establishing a robust talent pipeline

Data also provides objective criteria for decisions to develop and promote your team members.

“Analytics can help pinpoint employees using measurable indicators,” Avinash tells us. “It can also play a role in cultivating talent pipelines by analyzing employee performance metrics.”

This underscores the fairness and objectivity that data-driven criteria bring to advancement decisions, reducing risks associated with bias – bias being a growing concern in today’s working world.

This highlights another benefit of talent data analytics: progress in diversity, equity, and inclusion.

“Recognizing and appreciating diversity and inclusivity is crucial when it comes to making decisions based on data,” Avinash says.

“Prioritizing fairness and inclusivity in data-driven processes for all employees helps create a positive workplace environment. Companies must recognize the limitations of data and ensure they integrate insights to prevent oversimplification.”

The risks of relying on data

The movie “Moneyball” is based on the true story of a baseball manager who switched to a data-first approach in building his team. As Brad Pitt’s character says in one scene: “His on-base percentage is all we’re looking at now.”

Data can provide a reliably objective foundation for employer decision-making, but does not consider the vast range of nuances and intangibles that an employee can bring to the table.

As such, Avinash warns against overreliance on the numbers especially in today’s workplaces which are more multifaceted than the game of baseball.

“Employees are much more than just statistics, aren’t they?” he asks. “Relying solely on a fixed set of metrics for evaluation has its limitations.”

In other words, soft skills are undervalued.

“Characteristics such as ethics and integrity, collaboration, empathy, resilience, and adaptability are essential but frequently difficult to accurately quantify.”

Avinash emphasizes a balanced approach that appreciates both tangible and intangible contributions.

“Neglecting these [intangible] traits could result in underestimating the true worth of an employee.”

A fine balance between human and machine

Many employers are cognizant of the need for balance. According to Workable’s poll on AI in Hiring & Work, 15% of hiring managers who use AI still take a fully human approach and 57% adopt a mostly human approach to that crucial final hiring decision.

“While data is valuable it is essential to remember that it is one piece of the puzzle, in understanding and managing individuals,” says Avinash, who notes that the reliance on data-first insights versus human expertise really depends on the situation.

Which begs the question: how do you know which situation calls for data and which calls for human involvement?

“Data analysis is preferred for operational tasks and decisions because it helps reduce risks and uncertainties, such as sales forecasts, staffing, and more,” says Avinash, who says that these can be high-impact decisions and should absolutely be grounded in data.

On the other hand, humans are still the experts at managing the human component of business or establishing overall strategy.

“For decisions that require strategic foresight, or a grasp of human capital, experience and intuition hold immense value,” says Avinash. “This is particularly evident in areas like employee relations and the voice of the customer.”

This means an ideally symbiotic relationship between data-driven methodologies and human insights, highlighting the importance of leveraging both to achieve comprehensive and fair outcomes in a data-driven world.

Data, data, everywhere – and not a drop to waste

Avinash isn’t just a self-proclaimed data expert – his resume includes overseeing data science, marketing analytics, and yes, AI, at the University of Phoenix. He also holds board member positions at Fast Company and at Evanta, a Gartner company. His past roles include numerous directorial-level positions in analytics at a range of companies primarily in the education sector.

His sage advice is that companies understand the advantages of advanced data analytics and ensuring a copacetic synergy between human wisdom and data intelligence in business operations.

It’s an exciting road ahead. If 175 zettabytes of data volume is predicted for 2025, imagine what’s coming after that. And companies need to get on board if they want to stay competitive.

“As these factors continue to converge,” Avinash concludes, “making decisions based on data will unquestionably be a factor in achieving success in contemporary society.”

“As these factors continue to converge, making decisions based on data will unquestionably be a factor in achieving success in contemporary society.”

Disclaimer: The opinions and views expressed herein are solely those of Avinash Tripathi and do not necessarily reflect the views of the University of Phoenix, its affiliates, or its employees. This content is provided for informational purposes only and should not be considered advice, an endorsement or representation by University of Phoenix or any other party.