|Technology and data have transformed our lives, as we’re more connected and better informed than ever before. However, technology has also made us more anxious about security risks / Photo by: Konstantin Pelikh via 123RF|
Technology and data have transformed our lives, as we’re more connected and better informed than ever before, according to Bob Grove and Anjuli Bedi of the World Economic Forum, an independent international organization. However, technology has also made us more anxious about security risks.
Remember the 2018 Cambridge Analytica scandal? That’s an example of how our personal data can be easily gathered, leaked, and exploited for political gain. Nowadays, big data and other emerging technologies such as AI have the potential to change businesses for the better.
How Does AI and Big Data Work?
In Kevin Casey’s article in online publication platform Enterprisers Project, he said that big data “has just been getting bigger.” It is perceived as a desired state in organizations that are implementing and experimenting with machine learning and other related AI fields. Senior digital strategist at Anexinet Glenn Gruber stated, “AI and ML (machine learning) are now giving us new opportunities to use the big data that we already had, as well as unleash a whole lot of new use cases with new data types."
In fact, data can be in the form of videos, photos, and voice. In the past, people tried to minimize the amount of data, as they could not do anything much to it, and storing it entailed great costs, Gruber added. Big data and AI have a reciprocal relationship. AI depends on the former for success. At the same time, it also helps organizations unlock the potential in their data stores that were otherwise impossible and cumbersome in the past.
|In fact, data can be in the form of videos, photos, and voice. In the past, people tried to minimize the amount of data, as they could not do anything much to it, and storing it entailed great costs / Photo by: Konstantin Pelikh via 123RF|
Gruber explained that we want to gather as much data as we can to drive better insight into problems. The more we put data into ML models, the better they get. For Gruber, it’s a “virtuous cycle.”
AI and big data underscore and create new needs around governance, infrastructure, and data preparation, Gruber explained. However, in some cases, ML and AI technologies might play an essential role in addressing operational complexities.
How AI and Big Data Are Used In Various Industries
AI and big data can be used in tandem with improved customer experiences, analytics, improved efficiencies, and the like. Let’s look at how these two function in practical settings, per Casey's analysis.
1. Extract Structured Data From Non-Standardized Sources
Storing big data in a usable, cost-effective manner is a struggle. But that “usable” piece of data can be tricky when it comes to unstructured data, comprising 70% or more of enterprise data. It’s a chore for humans to transform unstructured data into usable formats. CTO at Exasol Mathias Golembek mentioned how AI can be applied to big data.
Organizations can train a model by making it learn from scanned invoices and the historical data of extracted structured data such as due date, recipient, invoice ID, and more. This process is done by humans since each invoice looks different. By using the historical data of invoices, it’s possible to create a model that could provide structured data just by scanning invoices.
This can be applied to finance, HR, and content management. With the help of AI, businesses can turn unstructured data into actionable information that can be employed in intelligent automation systems, allowing firms to make better decisions, chief innovation officer at ABBYY Anthony Macciola explained.
|AI and big data can be used in tandem with improved customer experiences, analytics, improved efficiencies, and the like / Photo by: Pop Nukoonrat via 123RF|
2. Improve Bureaucratic Processes
There’s complexity and bureaucracy in health, finance, and insurance services. Hence, these industries are experimenting with AI to cut red tape and improve processes and outcomes. In the financial sector, back-office operations at banks involve complex and labor-intensive data sets, as noted by general manager of data, analytics, and AI/ML at Persistent Systems Sameer Dixit.
When handled by robotic process automation and AI/ML, banks can save on time and costs when performing tasks like knowing their customers’ check, where it involves verifying a customer’s identity and address. In the mortgage industry, AI can be used to improve data analysis and automate “critical path processes,” according to director of product management at AI Foundry Arvind Jagannath.
With that, he said, “You can get mortgage processing down to just a matter of days.” This makes home-buying less stressful and faster for the buyer. For banks and lenders, they can process loans at a quicker rate. Plus, AI can process mortgages “with high accuracy rates.”
3. Utilize Audio Assets
“This call may be recorded for quality assurance and training purposes.” Every time you hear it, it makes big data even bigger, Brian Atkiss, director of advanced analytics at Anexinet, said. Companies usually store recorded calls for “manual review and compliance reasons,” often in compressed sizes to save on storage.
With advances in text-to-speech algorithms and AI, the recordings can be transcribed in real-time or near-time. Transcripts will become a “treasure trove of useful data” that firms can utilize to analyze customer experiences and boost operational performance. As for storage issues, the uncompressed files do not need to be stored. Instead, the transcripts of the call will be stored.
Big data and AI cannot be separated. They work hand-in-hand to help businesses offer better services to their customers. Moreover, employees will not be burdened with doing every single task manually. Presently, the future is looking good for big data and AI.