How using AI can break big data down to byte-size chunks

A screen showing 2 graphs and sets of important looking statistics | Data Nextworld

There will never be less data than there is right now. By the time you reach the end of this article, there will be significantly more data for the world to reckon with. How much? Approximately 6.94 petabytes, or roughly 3.47 trillion pages of printed text – given 2.5 quintillion bytes of data are generated daily and the average reading time on this article of four minutes. Want to check the math? Hint – I used ChatGPT for a little help.

Every photo uploaded, every credit card tapped, every “send” button pressed – all results in data. Not to mention the terabytes of information that are generated, tracked and traded across millions of networks (internet, infrastructure, communications, weather instruments, automotive cars, surveillance cameras…just to name a few).

Among this mess of data, your company. Most organizations are treading water at the top of a vast and deep data pool. Day-to-day operations require you to sift through the recent information at the top, while valuable opportunities for growth and improvement sink to the depths. Getting anything useful out of excessive data is a challenge, but with the right tools you can find an incredible amount of value.

The value of data

The more relevant data you have, the better chance of making an accurate and beneficial decision. Yet, approaching the ‘deep pool’ of data is an overwhelming thought. Who has that kind of time and manpower?

Instead, think of it like a messy house. Saying “I will clean my entire house today” is overwhelming. But tackling the stack of mail on the kitchen table is doable, and so is coming up with a system to instantly process, save, or discard the mail you are sent every day; it’s all about taking small steps. The same goes for data – a system that keeps data organized and easily accessible is key, as is a means to determine what information is irrelevant or redundant.

Sounds easy enough? Well, allow us to complicate things further: ‘irrelevant or redundant’ is contextual. One person’s trash is another’s treasure, and that other person likely works within your organization. Redundant data can still serve as the foundation for future database needs.

Furthermore, understanding the difference between what is known and unknown is vital. Your assumptions can bring you to erroneous conclusions – what’s the point of all this data if you end up making the wrong decision?

Tomorrow’s win is behind you

Data cannot predict the future. It can map out a trend and suggest what might happen based on what has happened, but the future is still unwritten. Data tells you what happened – past tense. It is the historical record of what your business has accomplished, even if you don’t remember everything. This is where the value truly lies.

When it comes to deep pools of data, AI can’t tell you the future, but it presents what previously happened so you can start making the tough decisions.

Fortunately, data is far from insular. Context matters and your internal data can be best defined and enhanced by outside data. Even if you feel your historical data is lacking, accessing data sources from beyond your company can elevate your results and change how you think about your business. From publicly available data to private collections, aligning the right variables can put you on an optimistic path.

While many consumer AI applications are focused on ideation and generation, enterprise applications require AI to provide intelligent answers. But to get the answer you’re after, you have to ask the right question.

Asking the right question

The scientific approach can’t be underestimated: formulate a hypothesis, run experiments, analyze results and come up with the right question.

The answer is only as good as the question, and the answer needs to be presented in a way that is clear and concise. No matter how solid the data or informed the question, all analysis is still dependent on the ‘human element’ factor of the final audience’s interpretation. You can present a 100 percent chance of rain and someone will still go outside without an umbrella.

When the analysis is strong enough, you need to present the truth in a way to get the action your business requires. If the analyst and the decision maker are two different people, this could be as thorough as footnoting every result, or as simple as presenting just two options. Do you want to be wet or dry?

Informing the autonomous AI

The prediction capabilities of AI are always going to take the limelight – who doesn’t want to paint a picture of what the future could hold? However, smart business owners and managers know the value of having someone mind the shop while others are exploring what’s possible.

AI needs to be as protective as it is predictive

The value comes when using historical data to inform and educate autonomous AI tools that are deployed to protect a company’s processes and information systems. With the right information on hand, anomaly detection tools can quickly identify unusual or unexpected events and alert the right people to address the situations.

With the right framework, anomaly detection can alert any occurrence outside of established parameters, so long as there is historic data to educate the AI about what is ‘normal’. Sales activity, website volume, sales spikes or drop-offs, customer service inquiries; wherever there is a high volume of data, there is the potential to use autonomous AI bots. The bots will be able to spot unusual activity before it becomes a problem.Anomaly detection is only the beginning. With AI that is informed on your historical data, anything is possible. Sometimes, however, you may need a little help in coming up with what the ‘anything’ might be, which is why AI tools are being created that can predict and suggest applications to benefit businesses. Powering no-code platforms with AI assistants, can also show you how to build the app you need. No development, no code, no worries.

Better answers to old questions

The ‘big data’ movement of the 2010s left a lot of companies buried by a pile of information that did not take them anywhere. What was once an investment now feels more like a burden. AI is sold as the obvious solution, but it also seems like the most costly and difficult to implement. The key for success is to keep it simple. You’re already asking the right questions: how can margins be increased? How can shipping times be expedited? What are the biggest instigators of customer churn? What is the common factor among your biggest losses?

The answers that might have guided your operations until now were likely the result of the data your analysts had easy access to. They did the best with the technology at hand and their human capacity. AI has the power to quickly process massive amounts of data and provide enriched answers that are quickly updated when new data comes in. It can also guide you in ways to modify your queries so you can field better results.

AI within your business isn’t a passing fad, it’s not going anywhere (except, maybe forward, and upward). Your data collection will only grow, and there will never be enough time to “deal with it.” Right now, your competition is already rolling out AI tools to get the answers they need to keep their edge. Furthermore, companies in other industries are using the same tools to find ways to become your next competitor. AI can do wonders in consolidating, translating and extrapolating the potential hidden deep within your data, with the potential to unlock your business’ potential, predictively and protectively.