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Executive Edge: Making humans, not machines, smarter with big data

Amit MehtaBy Amit Mehta

Today’s real take-away in getting value from big data analytics is the capability of making humans, not machines, smarter first. This may be heresy to proponents of big data analytics, but it’s time to re-think big data’s potential. Good insights from smart analytics are of scant value if there is an insight deficit in big judgment enablement.

Insight deficit? It’s the ability to find and analyze relevant information to drive actions and decisions effectively. If humans can do their daily jobs more efficiently, not only will their daily quality of life improve, they will have more time to apply big judgment that steers clear of overlooking opportunities or underestimating risks.

Lessons from Consumer Applications

Let’s draw parallels from consumer apps. Apple’s legendary Steve Jobs discovered how to make consumers “smarter” by combining the ability to make phone calls, take photos, send text messages, look for driving directions, search the vast Internet and more in one platform. The aptly named smartphone has accomplished nothing less than changing the world by making us more productive and enhancing our quality of life.

Once Apple “hooked” consumers on smartphones, Jobs and his Silicon Valley experts made users even smarter by reducing all possible daily inefficiencies that occur (let’s call it obstacles to human efficiency). With the smartphone, users theoretically became more productive and, as a result, have more time to think, be more inventive and launch new, innovative initiatives.

Apple, of course, does not hold a monopoly on changing the electronic marketplace. To the contrary, the consumer application world is filled with similar success examples such as Facebook getting users hooked to its platform via better user experience, then gathering human intelligence via human generated data and now actively applying actionable insight to deliver, for example, more focused advertising to users among other new experiments they are conducting with artificial intelligence (AI).

Enterprise User Engagement Will Drive Big Data Adoption

Enterprises must draw parallels from these consumer world successes if they want to see big data become a competitive differentiator and adopted companywide. How can this be done? Simply by finding all human efficiency problems, i.e., finding all possible dead times, idle times and wait times in the current enterprise workflows from data to decisions. Let’s look at some examples of human inefficiencies in the enterprises: upload/download data, prepare data, aggregate data from a variety of sources, make Excel charts, make management presentations, and separate facts from fiction in operational and business decisions.

Subsequently, as with an “aha” reaction of discovery, if big data can be applied to eliminate or reduce these human inefficiencies, it will free users from drowning in data, from being busy in essentially a make-work way and from being less productive. Once enterprise users are liberated from the above and become more productive in their respective enterprises, their quality of life will improve. Virtually overnight, they will have enough time to apply real insights and intelligence to make the machines for which they are responsible function even smarter. Once users are engaged through this scenario, cognitive and other intelligent analytics could be added as humans will most likely generate good judgment via human data in such a platform effortlessly.

Data Decisions Workflows: Addressing the Insight Deficit

Given that perspective, what should an organization’s executives and management look for to make humans, not machines, smarter first? Data decisions workflows have three primary pillars: data quality, actionable insight and human capability. Let’s define, then analyze, the effects:

  • Data quality: ensuring all data from a variety of sources/data lakes is of good quality regardless of real-time or historical.
  • Actionable insight (information): turning good data into information/insight that is useful and can be trusted.
  • Knowledge worker capability (engineer): individual competency, which can vary based on their experience and expertise.
Figure 1: The three pillars of data decisions workflows must intersect.

Figure 1: The three pillars of data decisions workflows must intersect.

All three must intersect (see Figure 1) to leverage real value from big data analytics and to reduce insight deficit, help enterprise users enable big judgment and eventually increase human efficiency.

As shown in Figure 1, let’s look at three scenarios to explain the point:

  1. If data quality and actionable insight are excellent but human capability is low, they will overlook opportunities or underestimate risks.
  2. If data quality and human capability are excellent but actionable insight cannot be trusted, knowledge workers cannot translate information to action.
  3. If data quality is poor, but human capability and insight are excellent, knowledge workers will not trust the analytics.

Hence all three must intersect to ensure the highest business performance and make humans smarter, which will set the foundation to apply real complex machine learning/AI.

Real Change Management

Since the above is not rocket science, why are central data analytics enterprise groups seemingly so opposed and focused on only applying data analytics to machines first? The simple answer is, “Machines can be fixed, machine data has been gathered for years, they are relatively expensive so optimizing them in theory makes sense.”

Humans, on the other hand, have their perspectives and competencies that make everyone unique. Each individual has their own habits, which need considerable influencing. That is particularly the case when organizational change is required.

Typically, individuals offer resistance to change and need “hand holding” to move from old to new ways. And that’s simply not easy. The dilemma in making humans smarter is: Data analytics groups budget for data scientist hiring, but will they budget to hire personnel who specialize in influencing habit changes? If “yes” to the latter, the results can be dynamic across the business world.

Amit Mehta is CEO of Houston-based Moblize (

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