The sheer size, variety, and speed of data traversing today’s networks are increasing exponentially. This highly distributed data is generated by a wide range of cloud and enterprise applications, websites, social media, computers, smartphones, sensors, cameras, and much more — all coming in different formats and protocols.
Whether it is in the cloud or at the edge, data generated by the Internet of Everything (IoE) must be analyzed to identify actionable insights that can be used to create better outcomes (such as from process optimization or improved customer engagement). Without this critical step, data remains just “data.”
There is often an immense gap, however, between the amount of data with hidden value and the amount of value that is actually being extracted. According to IDC, less than 1 percent of the world’s data is currently being analyzed. What good is data if isn’t analyzed to gain insights?
It’s no surprise, then, that in a recent survey conducted by Cisco Consulting Services, IT and Operational Technology leaders indicated that they perceive the Internet of Things (IoT) — a critical enabler of IoE — as being about much more than just “things.” When we asked them which area (people, process, data, or things) they needed to improve most to make effective use of IoT solutions, the largest number (40 percent) indicated “Data,” while “Process” (27 percent) ranked second. “People” placed third (20 percent) and “Things” finished last (13 percent).
These leaders understand that connecting “Things” is but a means to an end. The primary value that IoT creates is a direct result of the data that can be captured from connected things — and the resulting insights that drive business and operational transformation.
However, organizations often lack analytical capabilities due to an absence of both the skill sets (such as those possessed by data scientists) and tools to deal with the exploding size, speed, variety, and distribution of data.
According to IDC, 51 percent of CIOs are concerned that the digital data torrent is coming faster than they can cope, and 42 percent don’t feel that they have the talent needed to face this future. Gartner concurs, saying, “Few organizations will escape the need to connect smart objects with corporate systems and applications. Therefore, IT organizations must master the new skills, tools, and architectures required by the Internet of Things.”
Organizations must begin to prepare for the workforce of the future — one that can drive the transformational opportunities promised by IoE and data, with competencies aligned to industry-specific concerns and outcomes.
Already, we are witnessing tremendous interest by those looking to enter these areas of opportunity. For example, the online Big Data course taught by MIT’s Computer Science and Artificial Laboratory attracted more than 3,500 students from 88 countries for its inaugural session in 2013.
The greatest value, however, will come from employees whose knowledge intersects data science, design, and enterprise architecture. To deliver true value, data insights must link to specific business processes and outcomes.
To help enable this linkage, the role of chief data officer (CDO) is becoming increasingly common in organizations around the world. CDOs are “essentially responsible for determining how data can be used across an organization and the operational environment to drive better business outcomes.” Gartner predicts that 25 percent of large global organizations will have appointed CDOs (also known as “Big Data Czars”) by January 2015.
Analytics-driven insights will also drive the opportunity for significant process change and optimization. In many cases, these insights will foster transformative rather than incremental changes in business and operational processes. For example, respondents to Cisco’s survey indicated that IoT has the potential to fully automate up to 50 percent of their existing manual operational processes.
While the potential automation of nearly half of an organization’s manual processes will provide significant economic benefits, it will also eliminate many jobs — an impact already being felt across many global occupations. According to Gartner, digital businesses will require 50 percent fewer business process workers by 2018.
To address this “process automation” challenge, we must place renewed emphasis on education and training by following these steps
- Offer career training for those looking for a job in technology through programs like the Cisco Networking Academy, which teaches students to design, build, maintain and secure computer networks.
- Put technology in the hands of our young people at an earlier age by connecting every classroom in America to high-speed wireless broadband within five years.
- Inspire our young people to explore the incredible worlds of science, technology, engineering, and math (STEM) by developing and scaling new, innovative models for teaching these subjects.
Private and public sector organizations must master the “data” and “process” components of IoE to capture the insights required to drive improved outcomes. For most organizations, this will require new skills for roles that didn’t exist even a year ago. These competencies must be developed by organizations themselves — and by educational institutions cultivating the workforce of the future.
You said “The greatest value, however, will come from employees whose knowledge intersects data science, design, and enterprise architecture.”
Agreed, the correct way to approach the upcoming IoE opportunity is by assessing the people, process and technology parameters (specifically, in that order)?
However, while much of the current focus of a typical Big Data project is harvesting the raw data and analyzing the resulting insights, it’s unlikely that this exercise will lead to meaningful and substantive business outcomes — unless there’s an interpretation of those findings by someone that’s qualified to perform that essential task.
Experienced employees — people that know the markets, the stakeholder’s needs/wants, the challenges/opportunities, etc. — are able to apply the much-needed “context” to these findings that enable them to be truly actionable. But many of these employees are baby-boomers who are about to retire from the workforce and their knowledge and wisdom will not been retained. Therefore, who will be the interpreter once these people are gone from the workforce?
My point: the tacit knowledge — stored in people heads, but otherwise not codified — is the missing ingredient in the scenario you describe. In my opinion, this very apparent skills deficit is the most important “Workforce of the Future” issue that few companies are prepared to address.
You said “less than 1 percent of the world’s data is currently being analyzed.”. How about the rest?
David makes a very important point: “Experienced employees — people that know the markets, the stakeholder’s needs/wants, the challenges/opportunities, etc. — are able to apply the much-needed ‘context’ to these findings that enable them to be truly actionable.”
The best science involves deep theory (if not meta-theory and dialectical tension) as well as hypothesis testing over time, including experimentation, in a diverse community of practice.
The source of theory (or at least initial theorizing) for business analytics may actually have to come from people who are not themselves scientists—people who have expertise about the relevant business offerings, the markets they intend to serve, and individuals to whom they provide value. That would be an interesting development in scientific community if not in scientific epistemology itself.
The CISCO survey results that emphasize “Data” first then “Process” and “People” with “Things” last is consistent with abstraction hierarchies in familiar Enterprise Architectures. The level of abstraction that is even higher than Data in EA is the “Business” (and sometimes “Information”) layers. These are the layers of value and meaning.
Theory is required to comment business and information layers with the lower levels of abstraction addressed in the CISCO survey. In IOE/IOT, theory is where business and science intersect. New communities are needed to make this intersection principled, sustainable, and reliably productive.
This post sparks great interest because it hearkens to the past when those of us who worked on large data sets (using descriptive and or analytical statistical skills) and knew how to interpret the results were not called data scientists.
So much of what I see happening in the business sector today seems to be about finding new ways to describe old tactics. However, by calling these previously established skills “business analytics”, or the reporting of the data “data visualization” it gives those conveying degrees with these new titles more marketability.
None-the-less, I am optimistic that our collective re-discovery of data’s great potential, and the implications of IoT will provide opportunity for those of us who also see the philosophical theories (such as “scientific epistemology” referred to above) that can provide pathways for application.