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.
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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.
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.
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.
An often-neglected key insight created through the marriage of big data and machine learning is the gift of strategy. Artificial intelligence through big data services may help to formulate a strategy or assist in highlighting patterns from numbers, but it lacks the knowledge of what to do with it all. That’s where you come in.
Use the information gleaned from your data to construct a strategy in several different ways.
- With the ability to push enormous amounts of data into easily recognizable formats, AI can help to produce databases of information that is easily accessible to the organization. This is an excellent way to look for noticeable patterns and create a winning strategy.
- Unstructured data, or numbers that don’t quite fit into the average spreadsheet, can be reconfigured by AI into new formats and onto specific platforms. This allows you to take many different angles into consideration while monitoring your implemented strategies.
- Emails and infographics, videos and Facebook post all can be processed for easy implementation into a coherent data set. The machine can’t understand the importance of this in your business strategy, but you certainly can.
- Human beings, not machine learning, possess the innate ability to predict the things that current technology is incapable of. It’s important to not rely solely on the abilities of artificial intelligence in a modern business model.
Be Rational, not Rash-In-All
Big data is powerful, but the ability to compound it all with machine learning is even more so. The misuse or misfiring of certain machine learning can lead to some significant legal troubles for the unprepared business.
Before jumping into the deep end to try your hand at applying artificial intelligence to every possible department, thoughtfully consider the effects it will have on your business and your customers. What legalities or protective measures will you need to have in place in case of hacks or breaches? What areas of your business need AI processing, and which ones do not?
Be responsible about where and how you use the power of machine learning.
Finding Your Key Insights
Big data with AI implementation is not for the faint of heart. At the end of the day, you will need to deliver insights your business needs the most to continue optimizing performance. Failure to correctly measure the right indicators (or failure to measure them at all) can spell disaster for your company and render the offerings of your big data system completely worthless.
Big data analytics services with AI involvement provide excellent data measurement and management for large industries around the world. By pairing numbers and statistics with real-world problems and high-level machine learning schemas, successful strategy implementations are highlighted while hiccups and flaws become glaringly obvious. Instead of creating a guessing game built on outdated models or ‘fishnet’ marketing, AI will continuously offer solutions to current business structures and provide pathways to greater insights about consumers, products, services, and the relationships between them.
Big data is here to stay, and the increased demand for artificial intelligence only promises a brighter future for businesses. After all, the big data you produce will eventually become nourishing food for your continuously growing machine learning algorithms. Are you ready for the revolution?