Q: What is Big Data and analytics?
Every day we create an estimated 2.5 quintillion bytes of data, which is equivalent to stacking books from the Sun to Pluto and back again. This data comes from everywhere, such as sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals. Big Data is about taking advantage of all available data, structured and unstructured, and analyzing this information, both static and in-motion, to make a decision at a business moment.
Q:How does data and analytics technology allow businesses to gain important customer insights?
Improving the customer experience by better understanding customer behavior patterns and preferences drives almost half of all active Big Data efforts. Most organizations want to drive top-line growth and Big Data can offer a wealth of opportunity if organizations know how to use it. Transactions, multi-channel interactions, social media, syndicated data through sources such as loyalty cards, and other customer-related information, have increased the ability of organizations to create a complete picture of customers, which has been a goal of marketing, sales and customer service for decades. Big Data and analytics can provide deeper understanding of clients and insight in new ways to engage with existing and potential customers.
Q: What are the characteristics of big data?
We characterize Big data as the 4 Vs: Volume, Variety, Velocity and Veracity. The are:
- Volume is the accelerated growth of data. Most organizations are transitioning from collecting and analyzing terabytes (1,000 gigabytes of data) to pedabytes (1,000 terabytes of data).
- Variety is about the unstructured data such as images, audio, video, social media and other information that are not organized or easily interpreted by traditional databases. Unstructured data includes all digital data made by people or machines, such as sensors, web feeds, networks and service platforms.
- Velocity is about harnessing data in motion in real-time data. To pinpoint meaningful insights in a short period of time, organizations need to consider both “data at rest” and “data in motion,” which tells businesses exactly what is going on by the second and allows them to take on-the-fly actions for immediate results. For example, knowing from your smartphone that you happen to be near your favorite cafe could trigger a real-time offer to entice you to come in and order an iced latte.
- Veracity is about trust and confidence in the data and the decisions made. Trusting the facts requires putting the right foundation of capabilities to manage data quality, maintain master data across multiple sources, optimize the use of information across the organization, protect privacy and security of information, and make decisions while fully aware of risks.
Q: What are the trends that are coming together to connect big data and analytics with businesses and their customers?
I see three important trends that are converging to usher in a new era of big data, They are ones that will fundamentally transform how businesses operate and how they engage with customers, suppliers, partners and employees to make better decisions.
The first big change is the digitized world we find ourselves in. Today, because the world is becoming increasingly instrumented, companies and individuals can use telematics and sensor data to track the real-time status of a bullet train racing across the countryside, hourly energy usage in a business or at home, or how much time a shopper spent in the electronics department of a store.
The second trend is how social media is changing what we know about each other. People around the world are communicating and volunteering more and more information about themselves, and interacting in ways that were unimaginable only a few years ago.
And the final trend is the quantum leap in technology, which now enables organizations to capture and analyze these new streams of data, regardless of what type, how much, or how fast they are moving, and make more informed decisions based on that information. Often, the data may be stored in the cloud.
Q:What are some industries that can make use of Big Data and analytics software?
Every industry and profession will be remade by using data, creating new buyers and new markets for IT. Departmental spending already accounts for 61 percent of information technology (IT) spending. We see this phenomenon creating a massive $175 billion IT solutions market in 2014, growing at 7 percent annually.
Here are examples from different industries:
- Imagine what big data and analytics could do for retailers in terms of capturing and analyzing changes in markets, trends and consumer preferences at holiday time.
- Farmers could better understand the impact of weather, seed types and soil quality on how they plant, grow harvest and sell their crops.
- Banks could draw new levels of data from key metrics and past performance, anticipate performance gaps, and reach and interact with customers in new ways.
- Communications providers could predict customer behavior and reach out to customers most likely to go elsewhere, and then keep them satisfied, loyal and coming back for more products and services.
At the same time, the automotive industry could analyze braking patterns to warn drivers, hospitals could analyze 100,000 real-time patient data points per second to enable healthier outcomes, and the energy and utilities industries could analyze years of climate data to optimize wind turbine placement.
Q: What is the best way to find out about customers' habits and opinions?
Companies have long been involved in the analysis of how a company performed over time by analyzing all the available data. In the past, this used to be merely descriptive analytics, which looks at the reasons behind past success or failure. With the availability of big data, we entered the new area of predictive analytics, which focuses on answering the question: “What is probably going to happen in the future?”
However, the real advantage of analytics comes with prescriptive analytics, which goes beyond future outcomes to answer the question: “What’s my best action?”
Highly intelligent cognitive systems take analytics a step further. Instead of needing to be programmed, they use natural language processing and machine learning algorithms to help make key decisions using extraordinary volumes of fast-moving big data.
These four types of analytics should co-exist. One is not better than the other; they are just different. All of them are necessary to get a complete picture of an organization, its customers and the problems that it's trying to solve by using all of the available information and data.
Q: How can big data, analytics and predictive technologies be improved?
Businesses benefit as they apply more sophisticated analytics across more disparate data sources in more parts of their organization. We recently analyzed more than 2,000 companies that were using data and analytics. The leading companies take advantage of multiple data sources, including both structured data from their sales and operations, and unstructured data from mobile devices, social networks and the sensors in the physical world.
The leaders apply increasingly sophisticated analytics -- descriptive, predictive, prescriptive -- to understand what is happening, what is likely to happen, and what is the best course of action. The results are striking. Leading companies are:
- 2.5 times more likely to enable new services for their customers.
- Nearly 2 times as likely to offer smart products and services.
- 2 times as likely to act in real time.
- More than 2 times as likely to personalize offerings for the individual.
Real-time use of data increasingly will become a competitive differentiator, especially when you think about geo-spatial location data, time data and sensor data. Two-thirds of companies say that the time they take to make operational decisions has been compressed over the past year. Real-time insight is now an expectation. Fifty-eight percent of businesses can access their operational metrics in one minute or less. These companies have higher performance across key operating metrics, such as cash flow, inventory turns and reduced operating costs.
Enterprises will need cognitive computing capabilities as data continues to grow in all dimensions. Analysts predict that -- in just three years -- 10 percent of computers will be learning rather than processing.