Written by Craig Gittelman, VP Sales & Customer Success at Systech
Over 20 years ago, when I first joined Systech Solutions, I remember this idea of industry-wide adoption of surveys reporting “business intelligence” was going to be a leading driver of corporate initiatives in the coming year. I was happy to see it, as it signaled a strong market for the services I would be selling and delivering. The following year it made the list again. Then again, and again and, … um, again.
Imagine my surprise or possibly lack thereof that in Gartner’s published “Top Priorities for IT: Leadership vision for 2021,” analytics remains a top 3 issues for 78% of board members surveyed and AI a top 3 item for 69%. These issues ranked as the number one driver for the company in 36% and 24% of those surveyed.[1] Maybe it is just me, but I find something fundamentally troubling about a business priority that seems to persist for 20 years. Does this suggest in any way any sort of failure?
In 2017, Forbes reported 53% of companies were adopting big data analytics up from 17% the year before.[2] Small business trends reported in 2020 that 67% of small businesses spent more than 10k a year on analytics.[3] So sure, there had been a little progress. But in 2019, Harvard Business Review released a study that is more on point to the real challenge. While they acknowledge how some companies had evangelized the right ideas on applying analytics — becoming data driven and embracing AI — these same companies were falling short of their goals. The study stated, “we knew that progress toward these data-oriented goals was painfully slow, but the situation now appears worse.” [4]
They found that…
72% of survey participants report they have yet to forge a data culture.
69% report they have not created a data-driven organization.
53% state they are not yet treating data as a business asset.
52% admit they are not competing on data and analytics.
Perhaps even more troubling in their study is that Bean and Davenport found data-driven culture was declining in the prior three years, rather than growing.[5] But the question is why?
Bean and Davenport cited numerous challenges from the survey results but surprisingly, pure technical challenges were slim. Only 7.5% of respondents reported technical difficulties. 93% however pointed to people and process as the dominant obstacles impeding progress. More than 40% cite lack of organizational alignment and 24% report cultural resistance.[6]
The study is corroborated by further research. Gartner found that many companies were plagued with the challenge of achieving a unified strategy and difficulty in establishing approaches for embedding data and analytics in business processes. They also noticed that the shift in perception of data and analytics had occurred from an IT driven service to a shared objective and job of both IT and the business.[7]
The European Business Review identified two key challenges companies face in being data driven; making the data they are drowning in useful and available to all users and overcoming cultural resistance in the process.[8] BI-Survey respondents reported that issues of inadequate technical and analytics know-how, cost, and organizational maturity were the biggest stumbling blocks.[9] The end result is not a failure in the technology itself, but instead a failure by the organizations and their ability to process their vision, build deploy and adopt analytics effectively.
Understanding the Flexible Mind and Leaned Behavior
If I had to draw a parallel, I would have to point to a quote from Nobel Laureate John Forbes Nash Jr. (bear with me here), in which he stated “classes will dull your mind, destroy the potential for authentic creativity.”[10] While this quote has been victim to a lot of scrutiny, it challenges us to explore what really gives the mind its power and what then is the source of authentic creativity? How is learning achieved?
Concept 1: In a 2017 article published by the University of Illinois, Life Sciences editor Diana Yates positioned the theory that flexibility was at the heart of human intellect. Thankfully, she was referring to concepts of mental acuity rather than muscular elasticity. Otherwise, I would clearly be one of the dumber people on the planet.
Yates quotes University of Illinois psychology professor, Aaron Barbey, as saying that “… recently in neuroscience, there’s been a focus on understanding in biological terms how general intelligence arises. That requires studying the structural and functional characteristics of the brain.” Barbey continues to explain that we have long understood the brain is modular and that different regions support specific abilities but that “scientists have struggled to understand how the brain organizes itself and have tried to identify a structure or region that performs that function.” Barbey’s analysis concluded that “general intelligence requires both the ability to flexibly reach nearby, easy-to-access states… but also the ability to adapt and reach difficult-to-access states to support fluid intelligence.”[11] In other words, intellect must be able to tap into a wide range of functions and do so adaptively and agilely.
Concept 2: Marion Sipe, a freelance writer and novelist, explored a related concept of instinct vs learned behavior in one of her articles. In it, she defines: “the difference between an innate behavior and a leaned one is that innate behaviors are those an animal will engage in from birth without any intervention. Learned behavior is something an animal discovers through trial, error and observation.”[12] From my perspective, the distinction is important as both modes of operation produce outcomes and it is important to recognize that even higher forms of life (as measured by intellect) often operate in both ways. That said, I would argue that thought which embraces an awareness of outcomes over time and attempts to improve outcomes via new learned behaviors is the fundamental mode of operation among higher life forms.
These two concepts complement one another in that the achievement of improved outcomes requires a flexible mind that supports the concept of learned behavior. The more dynamically the concepts of flexible mind and learned behavior interoperate, the more effective and constant will be the experience of improved positive outcomes.
How this relates to Data Analytics
But how does this come back to analytics and, in context, what is Systech Solutions’ DAaaS? Come back to our beginning. Digital transformation and establishing data driven cultures remain a top priority in most organizations but one they struggle to achieve. We desire to move dynamically from long held and practiced innate behaviors to learned ones that produce highly desired outcomes. Data analytics — at its integral core — represents the organization’s metaphorical brain that can drive the evolution and showcase the inherent value in investing in it from an operational standpoint.
By this logic, if we consider data analytics the organizational brain of a data driven enterprise, then by Yates and Barbey’s academic opinion, achievement of the higher intellect hinges on the development of that brain’s flexible mind; a concept that requires a level of functional understanding of its parts in data analytics terms. In other words, in the world of complex data and technology that drive the function of data analytics, how easily can the organization access and leverage both the easy and the difficult to access states to help the company learn new behaviors? In what context do we consider these “states,” for data analytics?
By this logic, the organization’s analytical brain is fundamentally made up of its data, its technology, and it people. Data represents the organizations memory and stored knowledge. Data analytics technology is embodied in the brains modular structure; those parts that perform the functional processing of the data for some end use. The people represent the neural networks that access the brains modular functions to operate those processes and used stored data to achieve an outcome. To come full circle, this is where the disconnect with creating data-driven organizations is born… this is the part that inhibits the cognitive process to vision, build, deploy and adopt data driven behavior developed by data analytics practices.
To make things more convoluted, new concepts and technologies emerge every day. Data architectures have evolved tremendously as have techniques in data ingestion and pipeline build. Companies are leveraging different approaches to narratives they build in their reporting and analytics and looking at novel ways to integrate those ideas back into the business. Data Science has pushed us from descriptive reporting to predictive and prescriptive capabilities at a whole new level; Automation abounds.
The ability of companies to absorb all this, to do so quickly and to reap the benefits gleaned from new learned behaviors is a direct function of the incredible strength generated by the flexible mind in creates.
DAaaS — A Structural Innovation Driving Transformation
More than just providing another much-needed anacronym to our discipline’s technical lexicon (sarcastic wink), DAaaS (Data Analytics as a Service) is an interesting solution and engagement model that’s objective seems to pursue helping organizations achieve a flexible mind that will ideally facilitate improved outcomes through data analytic-supported learned behaviors.
Consulting as a practice is intended to augment team strength and skills. It can simply facilitate or — at times — bring innovation. But these skills often remain difficult to access areas in structured consulting settings. DAaaS works in a flexible sort of neuro-network approach that enables the central structure of the brain to call up the modality needed in any given setting and is designed to do so in a way that meets and overcomes the tactical barriers the organization has seen in other approaches to staffing. Where traditional modes of engagement may expand capability in some respect, these innate and tired models remain limited in their ability to empower that most flexible mind. DAaaS, some could say, is the stuff of the future but far from Borg style assimilation, it is aimed at adaptation and innovation on a massive scale.
The Systech Solutions, Inc. Blog Series is designed to showcase ongoing innovations in the data and analytics space. If you have any suggestions for an upcoming article, or would like to volunteer to be interviewed, please contact Olivia Klayman at oliviak@systechusa.com.
[1] “Top Priorities for ITL Leadership Vision for 2021”, Gartner, Accessed March 24th, 2021, https://www.gartner.com/en/information-technology/trends/top-priorities-2021-data-analytics-challenges-gb-pd?utm_source=google&utm_medium=cpc&utm_campaign=RM_NA_2021_ITDA_CPC_LG1_DAR-PD-G&utm_adgroup=119523142495&utm_term=challenges%20in%20data%20analytics&ad=505169164766&matchtype=e&gclid=Cj0KCQjwo-aCBhC-ARIsAAkNQiul3O00buCJF7k3wppIXAZxsfZcR1PtMAkRs2AuypuLQU7867nSZ80aAhhDEALw_wcB
[2] Louis Columbus, “53% of Companies are Adopting Bi Data Analytics,” Forbes, published December 24th, 2017, accessed March 25th, 2021, https://www.forbes.com/sites/louiscolumbus/2017/12/24/53-of-companies-are-adopting-big-data-analytics/?sh=51bd4e6239a1
[3] Michael Guta, “67% of Small Businesses Spend More Than $10K a Year on Analytics,” Small Business Trends, Published March 12, 2020 in Small business News, accessed on March 25th, 2021.
[4] Randy Bean and Thomas Davenport, “Companies Are failing in Their Efforts to Become Data-Driven”, The Harvard Business Review, published February 5th, 2019, accessed March 25th 2021, https://hbr.org/2019/02/companies-are-failing-in-their-efforts-to-become-data-driven.
[5] Bean and Davenport, “Companies Are failing …”, The Harvard Business Review
[6] Bean and Davenport, “Companies Are failing …”, The Harvard Business Review.
[7] “Top Priorities for ITL Leadership Vision for 2021”, Gartner
[8] “3 Challenges Companies Face in Becoming Data Driven”, The European Business Review, Accessed March 24th, 2021, https://www.europeanbusinessreview.com/3-challenges-companies-face-in-becoming-data-driven/.
[9] “Insufficient Skills are Curbing The Big Data Boom,” BI-Survey, Accessed March 24th 2021, https://bi-survey.com/challenges-big-data-analytics
[10] “A beautiful Mind Quotes”, Rotten Tomatoes, Accessed March 24th, 2021, https://www.rottentomatoes.com/m/beautiful_mind/quotes/
[11] “Theory: Flexibility is at the heart of human intelligence,” Science Daily, https://www.sciencedaily.com/releases/2017/11/171120085456.htm
[12] Marion Sipe, “What is Innate and Learned Animal Behavior,” Sciencing, Published April 24th, 2018, Accessed March 25th, 2021, https://sciencing.com/innate-learned-animal-behavior-6668264.html