The spread across sectors
Big data, or data of high volume, high velocity and high variety, offers organizations an opportunity to tackle problems they would have otherwise not been able to handle.
Why? Because big data can give you a unique view of a situation. It aggregates data from multiple sources — both internal and external and financial and non-financial — and, if analyzed correctly, can provide insights to help organizations make important strategic decisions.
The definition of big data in Figure 1 illustrates how enterprise data and financial data can be viewed as subsets of big data now available to a business. And it’s growing rapidly — data from outside the enterprise are proliferating on websites, in social media and other forms of communication across the internet.
1. Financial data Standard financial metrics, well-tracked and understood 2. Enterprise data The above, plus broader operational and transactional data that may be used to bolster analysis and forecasting
3. Big data The sectors that initially made the most use of data were those that had the most — retailers, insurance companies, banks and airlines. Now, businesses in almost every sector use data analytics to improve offerings, enhance the customer experience and make better-informed decisions.
Figure 1: Big data defined
Big data isn’t just about improving services and efficiency levels.
It can have applications in every aspect of the business model and across multiple sectors. Harnessing data thru data analytics can lead to the following enhanced business outcomes:
1. Farming — Tractors with sensors can collect data on seeding rates, crop yield and ground conditions, allowing farmers to predict production rates more accurately. Farmers can also analyze weather patterns to better prepare for poor conditions.(i)
2. Manufacturing — Manufacturers use advanced analytics to manage the risk of potential faults in equipment and product anomalies during production. They also are using it for forecasting, inventory management and production planning.(ii)
3. Retail — Retailers use data to forecast customer demand and understand their preferences, allowing them to become more proactive and better able to predict behavior. Big data also is used to create new digital product offerings.(iii)
4. Transportation — Players in the transportation industries (such as airlines, rail and trucking companies) use predictive analytics to manage their costs through preventative maintenance scheduling, inventory parts management and warranty claim management.(iv) The airline industry uses analytics to understand customers, predict demand and optimize pricing.(v) 5. Telecommunications — Telecommunications providers use analytics and big data to reduce customer acquisition costs, segment target subscriber audiences and rank prospective subscribers according to their propensity to buy.(vi)
6. Financial institutions — Banks and insurance companies use predictive analytics software for fraud analysis,(vii) while credit card companies use analytics to manage credit lines and collections.(viii) 7. Cities — City administrators increasingly use big data to understand their citizens’ needs and proactively plan for needs in transportation, policing, health care and more.(ix) 8. Education — Educators use data from student tests and assessments to determine patterns and performance levels. Such information helps them adapt courses and tailor the way they teach.(x) 9. Health care — To improve health care delivery and reduce costs, health care organizations use big data analytics to examine large amounts of structured and unstructured data to improve the efficiency and quality of care.(xi)
