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? Based on my calculation 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 my math? Hint, I used ChatGPT for a little help.
The point is that 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. Modern businesses across every industry collect a wealth of data every day they are in business - both the information they intentionally track - sales transactions, delivery data, customer satisfaction - and what comes in through third party and tertiary means. The result? 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 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? The answer: no one.
Instead, think of it like your 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. 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.
Sound 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 - and what's the point of all this data if you just end up making the wrong decision?
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 is the essential tool you need to frame everything in a picture you can see. AI can't tell you the future, but it presents what previously happened so you can start making the tough decisions.
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 first have to understand how the question is built.
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.
Getting what you want is rooted in knowing how to ask for it. In AI, and in life, framing the right question the right way, to the right audience. Otherwise, you get what you get. For effective data analysis, especially deep pools of historical data, finding the right question may be in the scientific approach. Formulate a hypothesis, run experiments, analyze the results, and come up with the next hypothesis.
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 and informed the question, all analysis still falls victim to the human element of the final audience. You can present the 100% chance of rain and someone will still go outside without an umbrella.
When the analysis is strong enough, you can present the truth in a way to get the action you need. 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?
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.
Not just a future of what's right but acknowledging the threats and challenges of the here and now. We know the value that comes with 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.
So far, we have established tools to detect logins from unusual IP addresses or from VPNs - maybe it's just your VP of Revenue checking in on an international sales trip, or maybe it is a hacker logging in with a stollen password? How quickly could your IT department spot the login attempt? Likely, not quick enough.
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, and more. Wherever there is the volume of data, there is the potential to use autonomous AI bots to spot usual activity long before it becomes a problem.
Anomaly detection is only the beginning. We're wondering if you're asking about what's next? With AI that is informed on your historical data, anything is possible. Sometimes, though, we need a little help in coming up with what the "anything" might be, which is why we're creating the kind of AI that can predict and suggest applications that would benefit your business. Plus, we're powering our no-code platform with AI assistants that will show you how to build the app you need. No development, no code, no worries.
In 1984, John Gage of Sun Microsystems coined the slogan "The Network is the Computer”. Four decades later with AI, Data is the Application.
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. Our ethos? Keep it simple. You're already asking the right questions:
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. No hype here, AI can do wonders in consolidating, translating, and extrapolating the potential hidden in your data. Nextworld is creating the tools to put as few obstacles as possible between you and the AI that will unlock your business potential - predictively, and protectively.
Nextworld Advisory Board Member
Lyle is a seasoned enterprise software development executive and technology market advisor with over 38 years of experience in the field. Most recently the SVP of Product Development at Oracle, Lyle’s impressive career includes roles in enterprise applications development, industry technology, product strategy, product management, marketing, and program management at ChannelPoint, Siebel, and JD Edwards. Lyle has perfected his ability to communicate the value of enterprise applications and articulate unique digital technology visions. His insights and expertise will undoubtedly contribute to Nextworld's ongoing commitment to innovation and industry leadership.
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