Today, these two terms represent two of the hottest trends in tech – however, we can hardly talk about one without the other. All data is big and nearly all intelligence – artificial but combine the computing power and insights of the two and you’re in for an accelerated disruption of many industries that still rely on traditional methods of collecting, analyzing and using customer data.
AI in a nutshell
Even though artificial intelligence has been around for decades, it is as of late that society is realizing its true potential of ruling the world one day not too far off in the future, and making many human jobs obsolete. Without having big data at its fingertips, however, AI would simply be like a Ferrari without fuel – powerful but static. The way AI works is by recognizing patterns in data that could be about anything: customer behavior, market performance, industrial output, and by doing so – offering unprecedented insights and solutions based on the most advantageous outcome, given the circumstances. For this to work well, the parameters of the question or challenge at hand have to be well-defined and the data, upon which the predictive algorithms of AI need in order to function, must be high-quality, plentiful and well-structured. Lo and behold the data scientist – the underappreciated, lone nerd who engineers algorithms, scripts, hosting and database infrastructure to help individuals and companies make the best of the available data. At the root of his success sit both great data and the tools to make sense of it.
Bid data without AI
The challenge of the hard-working data nerd we described, before the emergence and advances in AI took place, was to cook the data and make something sensible out of it. Even if all imaginable valuable insights were collected, qualified, enriched and stored perfectly, any human or team of humans would be hard pressed to gather sophisticated intelligence out of millions of rows and columns containing nothing but text and numbers. Some graphical representations could be produced and smart rules set up for things like management or investor reporting, marketing automation or financial forecasting but, in essence, the data would remain raw until more robust, data-driven predictive analytics could comb through it during millions of iterations and detect certain patterns of statistical significance across groups of similar demographics, for example.
Turning data into intelligence
Naturally, the biggest advances in AI stem from data-laborious algorithms collectively dubbed as machine learning. AI needs to be fed copious amounts of data so that the mechanism behind it can build, train, test and retrain the machine’s “brain.” Due to the complex setup and running of this process, its utility so far has been restricted to investment-rich industries, like banking, financial services or politics. Entry barriers for smaller players are still too high, which is limiting innovation in some areas and fostering it immensely in others. In the lack of proprietary data, organisations new to AI are commonly training their AI on publicly available datasets, which isn’t ideal due to built-in biases spread even wider by the popularity of this technique. A challenge for all ecosystem participants, and mostly for those without deep pockets, would certainly include building more robust publicly available and inexpensive incubator-like infrastructure, in which even smaller players can get up-to-speed relatively quickly and easily.
Innovation in AI
The ability to make data work for you and your organisation is the basis of AI and it has translated into sizeable financial benefits for companies like Netflix, Google, Amazon and BMW, who recognised its potency early on in the game and were able to build their businesses around ubiquitous data.
Get it right and you can reengineer your entire value chain: improving design, production process, time-to-market and last but not least, costs. When a business is agile and, at the same time, able to recognize micro- and macro-trends before anyone else, the sky is the limit for its success: entice and predict purchases, drive customer loyalty, improve service, and drive out less dynamic competitors over time. Stay behind the data game and you risk being rendered obsolete in a few years’ time.
Baby steps towards making big data + AI work for you
If you still haven’t started dabbling into machine learning, our recommendation is to start gradually by building your datasets, collecting and measuring potential insights anywhere you can: on your website, social media channels, apps, offline events, etc. Doing this will be your first large step towards making sense of the data you’ve collected in a few years’ time when AI algorithms will likely be more sophisticated and widely available, even to smaller industry.
Copywriter: Ina Danova