It’s no secret that Big Data offerings have become one of the largest marketing bastions the world has ever seen.

In a fast-paced and ever-changing era, industries race against one another more than ever before to raise benchmarks, contexts, ROI, and ultimately profit margins in an interconnected world that never sleeps. Big data consulting services have been around for several years now, helping organizations reach their business goals by carefully absorbing and organizing trillions of bytes worth of data. As the process progresses and internet access continues to expand around the globe, the amount of data to process will only continue to swell.

In the midst of all these numbers and decimals, it’s no wonder that many companies have just barely dipped their toes into the water. And for good reason: no single employee, team, or task force is equipped enough to process it. The overwhelming amount of insight that big data offers is not compelling enough to embrace without some sort of strategy in place or results to be evaluated.

The advent of artificial intelligence, or AI, is beginning to level the playing field. Fed a continuous stream of information from any point of your big data, machine learning gives business owners several privileged insights to the progress and pitfalls in their structure and model. While not perfect, the pairing of big data’s sheer bulk with the intricacies of a predictive or prescriptive AI system is the first step towards becoming a data-driven company.

However, it’s important to remember that big data and AI are not perfect. As you begin the process of implementing these systems into your business, be aware of these four important categories that require some ‘humanizing’ optimization in order to make AI successful.

Context

The first and arguably most important thing to remember about machine learning is that it lacks awareness and context.

Artificial intelligence is only as powerful as the people behind it and the data they feed it. Consider the following:

• What variables must your unique situation take into account?
• What are your benchmarks?
• What is the end-goal?

Impracticality, expense, and manpower mean very little to a machine, which means it’s up to human beings to inject some necessary common sense to find an equitable solution.

Decide what is and what isn’t useful for the machine to analyze. Be specific about the kind of questions you want from your big data, and the AI will formulate the specific answers back in a coherent way. You will need to be ready to help the process with some intelligent queries and a good measure of trust.

Trust

Changes to the norm can be difficult, especially when dealing with new technology. The effect of artificial intelligence processing on big data is certain and measurable, but our understanding of the technology itself is cloudy at best.

AI programs arriving at various solutions with so little explanative background can make even the most seasoned professional nervous. After all, it’s not easy to trust an answer when we can’t fully see the equation. When algorithms continuously perform as expected and with successful results, we learn to build trust with the machine.

Instead of unquestionably following the advice of a strand of numbers, allow the artificial intelligence, data professionals, and context factors help to produce your ultimate end strategy.

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