What shirt should I wear to that important meeting? Which tasks should we prioritize as a team? To whom do I delegate? Do we go full remote or hybrid for our work setup? Should we invest in that new tech tool?
Decisions drive our day to day; and decisions drive businesses forward. They can make you or break you. Sometimes, they can even drive you crazy. Upon interviewing various U.S. executives (We spoke to over 50 C-levels to ensure the design of our product truly benefits executives!), one of them summed it up perfectly:
“We may be a little too data crazy, but that’s the best way to make decisions.”
There is a strong desire among organizations to base decisions on better data and its analysis, and for good reason. Data-driven decision making affects the company’s bottom line.
We now know that data-driven organizations are 23 times more likely to acquire customers. Online retailers such as Amazon and Alibaba routinely trace and analyze the purchase histories of user groups when attempting to enhance product and marketing decisions (Dawar & Bendle, 2018; Kaplan & Haenlein, 2020). Zara achieved speedy growth in annual revenue with fast fashion through analytics assisted by deep learning (Ghemawat, Nueno, & Dailey, 2003).
Thanks to advancements in artificial intelligence (AI), particularly in machine learning, business leaders can now reap the benefits not just of data-driven decisions but of decisions based on big data and deep learning.
What is Big Data, Machine Learning, and Deep Learning?
Before we move forward, a quick definition of terms:
- Big data pertains to extremely large and complex data sets, normally from newer sources. It’s often explained with the 3 Vs: High Volume, High Velocity, and High Variety. Imagine data so massive that it overwhelms traditional software. It’s a treasure trove of information for businesses, should they find a way to analyze it.
- Machine learning is a subfield of AI that uses data and algorithms to imitate the way humans learn, that is to learn and improve with more experience. As the AI takes in more information and uses its algorithms to make sense of it, it gains more experience and improves its accuracy. So in machine learning, the more data, the better and more accurate the analytics through time.
- Deep learning is a subfield or method of machine learning that is inspired by the human brain. It uses multiple layers of algorithms (structured as neural networks), which are basically logical rules the machine follows in order to analyze data. The AI developments we’re familiar with, such as Alexa and Siri, chatbots, and self-driving cars, are thanks to deep learning.
We can now mine big data for gold.
We have the data. As early as 2020, the quantity of voice and text generated by organizations through communication tools like Slack and G-Suite rose by over 300 percent. Not to mention other business data in the countless tech tools that measure success and growth indicators. Thanks to deep learning, we have the technology to make sense of big data.
Decision-makers in organizations can draw on AI’s processing capabilities to learn and augment their decision-making with new insights into emerging phenomena and predictions, based off of enormous quantities of raw, unbiased data (Ghasemaghaei, 2018).
Now, we’re seeing the desire to make the most of this resource to create success for organizations in the fields of marketing and sales, as well as human resources and people management.
Moving into Human Resources (HR) and Workforce Intelligence
Probably the most illusive and complex component of any business is its human capital. Each person is a universe of emotions, skills, issues, talents, and potential that can be difficult (or a breeze, depending on the person and the day!) to manage. Plus, with the current mass exodus of workers leaving their jobs in 2021 and the potential cost of turnover, more and more managers are grasping for ways to better understand their workforce through objective, measurable data. [Link previous sentence to download page of Great Resignation eBook; and article on the cost of turnover]
HR departments in Google, Best Buy, and Cisco employ deep learning to augment decisions aimed at fostering productivity, engagement, and retention of talented employees (Davenport, Shapiro, & Harris, 2010; Tambe, Cappelli, & Yakubovich, 2019). We’re also seeing a rise in AI tools that help organizations hire the best candidates for the job, relying more on data and deep learning rather than instincts or gut feel.
More and more companies are realizing the advantages of better data in making better decisions not just for their business, but also for their people, which as we know ultimately benefits the company in the long run.
Advantages of data-driven decision making for people management
Data allows pinpointing new challenges and discovering the reasons behind it. Once analyzed, data offers widespread advantages, such as:
- Shedding light on an employee or department’s performance tendencies
- Foreseeing trends or repeated events, therefore designing a response that saves time, effort, and resources
- Offering performance measurements, providing proof of areas or tasks that require more attention or resources
Ultimately, the hope is that decisions based on accurate and timely workforce intelligence will help companies reduce the rates of burnout and turnover, which can cost the company 200% of an employee’s annual salary, as well as increase employee engagement, which improved productivity and profitability.
As more and more executives rely on data to make decisions, we’ll be able to clearly see its impact on businesses. A survey by BARC research already shows that organizations using big data report an 8 percent increase in profit and a 10 percent reduction in cost. In the same survey, 69 percent reported better strategic decisions.
Gut feel and the barriers to data-driven decisions
Yet, 45% of executives still rely more on instinct than facts and figures to run their businesses. Why aren’t we supercharging our decisions with reliable data and analytics? Much like AI’s neural networks, perhaps we just need more time, training, and experience.
A Qlik/Accenture study shows that workers confident of their data literacy skills comprise only 21% of those surveyed. Plus, 74% said they felt overwhelmed or unhappy when working with data. In the field of people analytics, a study published in 2019 stated that the main barriers to its adoption are data quality and availability, management understanding, and staff knowledge.
We seem to be in the precipice of change and evolution, wherein we are logically aware of the benefits of investing in data and analytics but are intimidated by having to learn and adopt something new. Why not take a queue from AI and do what humans do best—learn and improve with new experiences and new technology so we can move forward with better data and better calculated decisions.