We live in a data-driven world. For instance, if you activate location services on Google Maps and, a year later, go to your timeline, it can tell you where you were on the same day the year prior. If you turn on Facebook Activation services, it suggests friends you should request when you go somewhere.
Successful enterprises are extracting information and intelligence from all the data being collected to identify their target customers and sell products and services to them.
In the current disruptive market environment, data is driving change to business models. Pay-per-use, enabled by cloud platforms, has become a well-established practice. For example, the emergence of Uber has disrupted the taxi business. Focus has shifted from a system-centric approach to a user-centric approach. Even traditional businesses, like insurance, provide self-service for buying and managing policies around the clock online, on mobile devices and even through social media channels. Products and services enabled by new technologies such as big data, artificial intelligence (AI), blockchain and augmented and virtual reality are being leveraged by financial technology companies to introduce creative products and services.
The disruption in the marketplace is making it imperative for organizations to buy what they can, build what they must and outsource the rest to stay cost-competitive. A focus on innovation has become critical for businesses that must develop new products and services to differentiate from the competition. Adopting digital transformation strategies by automating from customer-facing to back-office activities is a top priority for businesses.
Data generated by people and systems needs to be the foundation for getting the strategy right. Organizations that have managed to leverage data to extract information and intelligence for competitive advantage are finding success.
How Is Intelligence Extracted From Data?
Data is text, audio and video. For example, consider the data of the stocks listed on a stock exchange. If we were to add to that data the additional indicator of whether a particular stock moved up or down from the close of the previous day, it would become information — something that is of interest to us, and something we can analyze. When we add context to information, it becomes intelligence.
If we were to look at the stock market data of 30 stocks representing a stock market index, that is just data. If we were to add the indicator that the stock market index in Singapore was down by 407 points at the close of business on a particular day but the Dow Jones was up by 330 points on that same day, then that is information. You can use that information to strive to predict how the market would open the following day in Singapore. That is intelligence.
How Do You Extract Intelligence And Information From Data?
That is the subject of business intelligence (BI) in an organizational context. The goal is to get clean, accurate and meaningful information. The process of extracting information is analysis and may be undertaken through processes such as online analytical processing (OLAP) and data mining. OLAP is about pulling data into data models with a data warehouse as the back end to aggregate and slice it.
So, What’s New? Big Data, AI and Machine Learning
BI with OLAP and data mining have been around for a while -- what has changed over the past decade is the growth of big data techniques and tools. Big data caused an explosion in the use of more extensive data-mining techniques. The characteristics of big data are, typically, described in terms of three V’s — volume, velocity and variety. More recently, a fourth term, veracity, has been added to the list. What has made big data so attractive? Technologies today make it possible to hold the data on commodity computers, which keeps the costs down, and certain algorithms such as MapReduce may be used to mine for the information sought, but the more compelling driver is the value to a business.
Let’s look at three ways a combination of conventional BI techniques, coupled with big data, can add value.
First, identifying target customers is valuable to any business. Imagine that you are part of a car company’s marketing team and have a list of customers who have purchased automobiles. You can look at their social data and create a shortlist, with one or two degrees of separation, of additional potential target customers. Then, social network analysis provides a look at the connections between people in many fields and commercial activities. Another analytics technique — regression analysis — may also be used to examine the demographics of those individuals shortlisted and, based on their age, predict the kind of car they are likely to buy.
Cross-selling products and services is another way businesses try to increase their revenue. In continuing with the same car-selling analogy, if you know that people with a certain demographic profile who purchase a specific type of vehicle are more likely to buy another type of car as their second, that has value to the business. You can predict this connection through a technique called association rule learning, which involves discovering correlations between variables.
Lastly, understanding customer perception is critical to the success of a business. How are the brand and car models perceived in the market? You can use a technique called sentiment analysis to find out.
Where Is All This Headed?
Big data and AI techniques are being used to complement and supplement one another to extract greater intelligence. Almost all vertical market segments are using AI to make their offerings to clients more intuitive. In the car-sales scenario, if we were to ask which model of a brand a particular customer is more likely to buy, we could derive a good answer using AI and big data.
Data is strategic, and organizations that manage data holistically stand to gain. That means information technology plays an increasing role, through new technologies, techniques and skills, in providing a competitive edge to organizations. Most importantly, innovation is key to the survival of a business — as companies must continually innovate or perish.
