In the 1980s, the concept of a “digital divide” was interpreted solely as the gap between those with and without telephone access. By the 1990s, the digital divide was more widely understood as the gap between those with access to modern technology, like internet or broadband, and those who don’t or have restricted access — the “haves” and the “have nots.”
This divide has been viewed by most to be both a cause and an effect of poverty, and while progress has been made on this front, much work remains. improvements have been made to eliminate the disparity in access. Global access to the internet was below 1% in 1995, and now is well over 50% today.
That said, 50% is still a huge gap, and a daunting percentage to close. Even in higher-wealth countries such as the U.S., these gaps remain quite large. The Pew Research Center showed that lower income Americans still have significant difficulties accessing the internet today.
Impact on EDUCATION
The digital divide has especially far-reaching consequences when it comes to education. Recently, as schools across the nation closed their doors in response to the coronavirus (COVID-19), the disparity of access due to socioeconomics and its impact is particularly salient. As caregivers are tasked with providing “online schooling” for their children, many are not able to facilitate their children’s participation due to lack of access. Low-income college students home from school are unable to continue their studies online without adequate computer equipment and internet.
The Federal Communications Commission estimates that 21 million people in the U.S. do not have internet access, with nearly 30% of those living in rural areas compared to 2% living in cities.
A New Divide is Upon Us
In addition to the longstanding “digital divide,” a new disparity is quickly emerging: the “analytic divide.”
Just as the digital divide both created and reflected an education and income disparity, so too does the “analytic divide” separate groups along geographic and socioeconomic lines.
The Analytic Divide
Access to communication technologies is critical for access to information, but there is a new key capability dividing individuals and organizations — the ability to leverage data to analyze and automate processes — resulting in today’s analytic divide.
As large corporations continue to amass large quantities of data and sophisticated systems to analyze this data, there is risk across all sectors that growth and innovation will be unequally distributed.
For many businesses, the benefits of analytics remain out of reach.
A recent analysis of over 400 companies by the International Institute for Analytics (IAA) found that companies that demonstrated the highest levels of digital maturity experienced higher revenue generation (+12%), profitability (+26%), and higher market value (+12%).
Studies like this one from IAA make it clear that there is an increasing divide in the performance of companies that are analytically mature and those that are not. Outside of the quantitative data, we see examples in our everyday life, with companies such as Amazon, Netflix, and a myriad of other digital natives disrupting strong, mature companies that were once leaders in their space.
The strongest companies do not view analytics as a sport for the elite data science team only, but instead are democratizing analytics and ensuring that every employee is upskilling. These companies provide the tools, training, and encouragement to help raise the skills of all employees on their analytic journey.
And while the gap between companies has formed, the impact on individuals is also accelerating. Knowledge workers with analytic skills are commanding significantly higher wages, and the economic gap between those with analytic skills and those without are growing quickly. Today, according to a LinkedIn Salary Survey, a data scientist median base salary is $105k/year. Compare this to a $53k/year average salary for an accountant, $64k/year for a financial analyst, $58k/year for a logistics analyst, or $59k/year for a marketing analyst. The differences are not small.
Those within nearly all “knowledge work” professions are seeing that the ability to analytically attack problems, automate processes, and present data-driven solutions that are highly sought after. Knowledge workers can use drag-and-drop, automated analytics to quickly upskill without the need for a data science degree. As the number of data science jobs are expected to skyrocket to over 2.7 million, we’re facing a shortfall of 360k professionals to fill them. To note, only 39% of these positions require a master’s or Ph.D. degree.
Divide + Conquer
People, education, and culture lie at the heart of these challenges and their solutions. The gaps that define the digital and analytics divide are as important as the more obvious gaps in access to the technology itself. The good news for knowledge workers is that it can be very fast and easy to close this gap. With significant online content, those who are not gaining this knowledge in their classroom education can easily develop their capabilities. Furthermore, this can be done while still on the job. Some companies like Alteryx offer this training to customers for free while others charge modest fees.
The challenge in this fast-moving field is that learning needs to continually advance as new techniques and technologies enter. Learning basic techniques to automate processes, to find correlations, and wrestle data into the forms needed to answer questions are clearly the starting points; however, these are just the beginning, as the number of techniques in this field expand every day. Natural language processing (NLP) to gain meaning from free-form text to time series forecasting or market attribution analysis could all be next steps to go further. As an analytic professional for many years, I am still amazed at how much more there is to learn!
Companies can also work to close the gap by driving digital transformation initiatives. So many of these initiatives focus on a small number of specific projects that are considered key and transformational instead of ensuring that all their knowledge workers are becoming upskilled. While many key projects can deliver a huge return on investment, the studies by IAA show that analytically mature companies have this skill across all their functions and at all levels. The goal should ultimately be to drive analytics to every corner of the operation and to be used as a tool on all projects.
Where does your company stand on its analytic journey, and which side of the analytic divide are you on? Are you taking action to close the gap? Is everyone at the company going on the journey, or is it isolated to a few projects?