People have been using statistics to make educated decisions ever since the first pharaoh thought that it might be a good idea to build a 481 ft tomb in the shape of a pyramid.
However, it wasn’t until the invention of the modern computer that we started to mine life-altering insights from the enormous mountains of data we generate daily.
Data science has helped businesses gain more customers, and create new products and better service. More importantly, data science has given us AI and machine learning.
Even though AI is still a nascent field, it has already impacted our lives in more ways than we could imagine. For one thing, AI is the engine behind autonomous vehicles, the puppeteer controlling the virtual assistant in your phone, and the diagnostician helping doctors save more lives.
AI is also helping us with data science, creating a closed virtuous circle that allows us to wade through colossal amounts of data that have been prohibitively large before. It is finding patterns that normal humans would have been able to spot, and it is automating the entire process, reducing the required human input.
However, this is just the tip of the iceberg; AI is bound to change the face of data analysis beyond anything we can imagine.
Here is a deeper look at how AI can enhance data science and analytics today:
#1 AI is integrating with other applications, making them smarter and automating decisions
Today, companies are using a host of advanced applications in an attempt to get an edge over the competition. Some businesses use enterprise resource planning software, ERP for short, to manage their day-to-day operations. Others rely on customer relationship management, CRM for short, to get the most value possible from their relationship with their customers.
Other companies may be using other applications, including project management software, business process management, and time management software.
That said, all these applications are bound to incorporate AI in the near future, where AI will help automate decisions. According to experts, AI will be able to automate decisions through robotic process automation, which is also known as RPA.
Once decisions are automated, real-life data will dictate how business processes need to be altered, and there will be no need for human intervention.
The benefits here are countless. Today, RPA helps businesses slash their costs by 2 percent, yet this technology is expected to reduce costs by a staggering 20 percent within the next two years.
Another benefit is that should AI be integrated with APIs along with other connective technologies, different platforms, such as ERP, CRM, AI, and analytics, will be able to collaborate with each other, anticipating market demand, optimizing operational processes, and maximizing business value.
#2 AI can find new insights in the data
Analyzing data and gleaning valuable insights from it is easier said than done. To begin with, some patterns may not be obvious, and even the most advanced statistician may have a hard time finding something.
Furthermore, if a company is dealing with real-life data, there is bound to be so much noise that any valuable data would be needles buried under heaps of haystack.
However, this is all changing with the help of AI. Rather than having to query a database or use SQL themselves, engineers can leverage AI to do this for them. Also, given the enormous computing power behind most AI systems, a smart machine will be able to pore through petabytes of data.
Additionally, some patterns may be too intricate for a human to spot, but an AI would be able to find it with ease. This is not to mention that AI can offer recommendations based on the insights it is extracting from the data. Best of all, AI can do all of this quickly and efficiently.
#3 AI can fetch data from disparate sources and combine it in one place
We discussed above the benefits of integrating AI with different platforms: the automation of decisions. Another potential benefit of this integration is that AI can combine all the data from the different platforms and unify it in one place.
This would require recognizing the different formats in which this data is stored as well as spotting any duplication of data and removing these redundancies; such a process would be arduous for any human, but it is child’s play for an AI.
Over and above, AI can get data from sources that would normally be hard to track, such as call data. The end result is having all the relevant data aggregated in one place, giving businesses a holistic bird’s eye view of their operations and their position in the market.
#4 AI increases the speed of data analysis
Data analysis is a laborious process: Seeing as any data collected from the real-world is bound to be filled with noise, a big part of data analysis involves “cleaning the data,” trying to sort out what is valuable and what is trash.
In fact, data scientists may spend up to 60 percent of their time cleaning data, and if the collected data is big enough, the process may take weeks or even months. During this time, the data scientists will be confined to doing exhausting manual labor.
However, AI is changing this. Machines can learn how to clean data, taking over this arduous process and doing it faster and more efficiently than any human ever could.
With the proper training, an AI could learn to spot outlier values, missing values, redundant values, and values that have different units.
None of this is to mention that once the data is cleaned, an AI can derive insights and make recommendations faster than any human.
#5 AI can be used for predictive maintenance
We are on the cusp of a new industrial age, Industry 4.0. Industry 4.0 is fueled by AI and machine learning, empowered by 5G networks, and made smart by IoT devices. This new industrial age will also be filled with technologies that leverage our new capabilities, including robots and predictive maintenance software.
We have already seen how AI can take decisions and make recommendations when combined with the right data. Yet, if we want to take things one more step, we can have AI make predictions based on the available data.
Accordingly, rather than relegating data analysis to diagnosing the past or describing the present, it can be used to predict the future.
Obviously, the predictions won’t be foolproof, but they will be reliable enough to be actionable. What’s more, AI can learn to make decisions based on its own predictions.
Putting it all together
Data science gave birth to AI, and AI has improved data science processes.
AI can integrate with advanced applications to make use of the data contained within said applications.
Additionally, not only is AI powerful enough to find insights that normal humans would never spot, but it also speeds up the analysis of the data by cleaning it for us.
And, at its most powerful, AI can use large swathes of data to predict the future.
