Modern data management, the hidden brain of AI

The average adult brain weighs about 3 lbs. And uses 20 watts of power. It is a remarkably efficient machine. Nobel Prize-winning psychologist Daniel Kahneman refers to this functionality-finding function when describing the thinking of System 1 and System 2. He proved that we have a subconscious, and therefore less powerful, method of processing information. It functions more often than a high-powered, executive function.

Neuro-anatomy experts believe that memories are encoded with emotions, but those emotions are not stored individually. They are essentially references formed and stored in the limbic system. Basically, we remember an event and then have a lookup table for how we felt about it. It also has a powerful effect on how we make choices subconsciously.

This limbic system, located in the central brain, influences future decisions because it uses emotional memory as a framework for what we can do or do. Without it, we make sub-optimal choices because we lose the context for risk or reward.

Likewise, AI analysis without all the true data leads to a flawed future. Therefore, it is important to talk about how it is important to organize and present “all relevant data”. The handling of erratic, high-volume, unstructured data should be considered as important to AI as the limbic system is to the predictive function of the human brain.

However, there are other factors for making automatic decisions outside of the emotional memory system. Let’s explore the metaphor of the brain further. Kevin Simler and Robin Hanson argue in their book, Elephants in mind: hidden motives in everyday life, How unconscious we are about the nature of our own behavior. They make a case that we are like our primate “cousins” in acting according to social motives. Whether you consider this evolutionary biology or learned it in the original family, it is less important to understand that there is something else hidden in our human brain.

This blind spot could also explain why technologists often oversee data management as a cultural phenomenon. In general, pundits write about data management in only two dimensions. The first is technology-focused. It starts with byte size, throughput and access pattern. This is a platform mindset that can afford the acquisition, storage and availability of data. It has a strong bias for metadata (data about data) as it is the steering wheel with which to drive the car.

Another commonly used parameter is the process. This system-level view covers the entire pipeline, from editing at source to sorting and shuffling, to listing, rendering, and finally archiving. It is a farm-to-table point of view. Or rather, a farm-to-tupperware approach. It concerns itself with “how” while technology takes a “what” perspective.

The third, and arguably invisible, dimension lies in culture, or “angle.” Culture can be described as a set of behaviors that are anchored by a shared belief system and bound by group norms. Culture draws the puppet strings of process and technology. However, it is the most overlooked factor in data management.

Many organizations rush to deploy technology and tool processes without first realizing how they want their culture to mature. The way positive psychologists study the most successful people will serve them better to model themselves. Those researchers examine belief systems and behaviors that are common to true achievements.

While it would be appropriate to provide a few case studies to prove this point, for brevity, we will summarize the most successful findings in data management.

It starts with a change in the validation system around the data. In this new illustration, the data is not an artifact of what happened; It is an asset with tremendous economic implications. And unlike anything else on the balance sheet, it can add value over time.

With that in mind, find below a checklist of new behaviors associated with a change in mindset around data.

  • Data is federated into fabric, not centralized or siloed.
  • Knowledge is organized by context and tagged by both publishers and subscribers.
  • The model is constantly saved for learning and responsibility.
  • Transparency reduces legal and regulatory pressures.
  • The broader view of ethics extends beyond the initial concerns for privacy.
  • Machine learning automates data engineering tasks.
  • Knowledge workers become value-creation workers.
  • Top-down, data-driven decisions evolve into bottom-up shared insights.
  • Data is measured economically and not in terms of accounting.

So, if your organization’s goal is to exploit AI, don’t overlook the importance of modern data management and the fundamentals that make it. Start by benchmarking the current position to the desired position. Create a cross-disciplinary approach to bridge the gap. Game planners are heavily influenced by technologists, process engineers, institutional developers and economists.

If you would like to delve deeper into this modern data management philosophy, please check out this white paper written by Bill Smarzo, Dean of Big Data and a distinguished colleague of Dell Technologies.

This content was created by Dell Technologies. It was not written by the editorial staff of MIT Technology Review.

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