Artificial intelligence (AI) is changing the way that several industries operate. Automation, robotics, virtual assistants any other applications are creating disruption that changes the fundamental responsibilities of different job roles. For example, earlier in 2019, a Forbes report spoke about AI as a “job killer” with advances in automation handling many tasks that are currently done by humans.
The report goes on to talk about how the future of work is more about transitioning into new types of roles, rather than doing completely new jobs. One such role is clerical work which is highly susceptible to automation or manual manufacturing roles than can be displaced by AI-driven robotics. It is unclear as to what roles people will transition into but with machines taking care of the more laborious and tedious jobs, it is thought that AI will allow humans to be in jobs requiring expertise and strategic thinking.
Data Analysis is one such role and task that is changing through AI. This is in the context of the way analysis is done as well as the skills required by workers. In this post, we will look at how AI is changing data analysis and what the future looks like in the field.
How is AI changing the way we analyze data?
A business intelligence within a business has typically been responsible for creating reports using data by extracting information from a source or sources. The objective is to present information allowing other departments to make business decisions. Analytics has been the bottom line for businesses for some time now but used to rely on people looking at data to create their own insight.
However, AI applications have taken away a lot of that human element. Think of an everyday application like Amazon Alexa. Consumers are talking to the device all the time, giving commands to which, they are receiving ever more accurate responses. Data analysis through machine learning is the foundation of growth for something like Alexa. Every time Alexa makes a mistake in receiving a request, the data is analyzed and used to make the system smarter next time the command is asked.
In fact, humans will never actually even see the data analysis part. Machines are smart enough to process the data, analyze the trends and put a decision in place. In most cases, humans will have set some initial ground rules for the models (see supervised learning), but after that, machines can take care of everything themselves.
AI is driving the decisions through data and humans will review the results, rather than the other way around. The fact is that AI can process more data than a human ever could. This means it can ask more questions to ensure it produces the right results.
Machine learning software company Anodot discovered that 80% of anomalies that its platform uncovered were negative factors. This means companies were losing money because they had not been able to find problems in their data that a machine could.
Business Analytics vs Predictive Analytics vs Machine Learning
“AI is acting as an enabler to move us from a world of business analytics to one of predictive analytics and ultimately, machine learning”, says Deltec Bank
Traditionally, organizations use what we would call business analytics or business intelligence to make decisions. As a rule, this would involve taking your business data and looking at the patterns e.g. how many sales did we have yesterday or how many customers visited the website. Business analytics is backward-facing, giving a retrospective view after the event has happened.
With countless tools on the market like Tableau, Qlik or Sisense to name a few, many business analysis tasks tend to be automated and do not physically require a person to provide information. These tools plug into your business data and make it available to anybody who you want to provide permission to. However, it is all about looking at things we already know rather than asking more questions.
The deployment of AI systems is giving rise to a change in data analysis. The movement we are seeing is from more reactive business analytics to a proactive world of predictive analysis. Given that a lot of business data processes are automated, predictive modeling takes existing data and works out what could happen in the future. This is the concept of the Anodot solution that we spoke about earlier in this post.
The tools mentioned above all have predictive analytics capabilities as well, but the field tends to require knowledge of emerging trends like data science and machine learning rather than only analysis capability. In fact, even data science is starting to be automated now with companies producing what are known as AutoML systems.
Predictive analytics systems work with Big Data platforms like Hadoop, Spark or Cloudera (there are countless others) to process massive amounts of data and automate decisions, just like Alexa does. However, in the first phase, it will often by human-led rather than automatic. For example, if a previous marketing campaign led to a 20% increase in sales, human understanding of the past might predict that the same will happen again.
The problem with human-led predictive analytics is that there are simply too many questions to ask.
From Predictive Analytics to Machine Learning
AI and machine learning are a continuation of the concept of predictive analytics. However, this is where the AI makes assumptions, tests and learns autonomously. Back to the example above, where our campaign led to a 20% increase in sales. A machine would look at where those buyers lived, what they have in common, who are the 80% that didn’t buy and automatically make a better-informed choice.
A human couldn’t possibly analyze all that information at speed (usually real-time), investigating every possibility. Machine learning is a field that is likely to dominate the future of data analysis. It will allow humans to have time to be more strategic, review insights and the root cause of patterns in data.
Data analysis has always referred to reviewing data from past events but that is changing. Predictive analytics makes assumptions based on past data to predict what is most likely to happen in the future. However, modern technology is giving rise to AI-driven analytics. Machines can gather, process and analyze data far quicker and in more depth than humans ever could. This is giving rise to a change in what we call Data Analytics.
Disclaimer: The author of this text, Robin Trehan, has an undergraduate degree in Economics, Masters in international business and finance and MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries and/or employees.
About Deltec Bank
Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.