The term “big data” refers to large sets of data that may be structured or unstructured. To handle these big sets of data, companies must organize and analyze efficiently. If companies successfully harness the power of big data, they are able to reduce costs and time, and make smart, informed decisions about their customers, products, and markets. Usually, the term “big data” implies some sort of analytics that turn data into value: big data can be used to identify fraud in real time, optimize call center strategies, manage power grids, analyze consumer response, in additional to many, many other things.
 
By analyzing transaction patterns and claims, companies can analyze both broad patterns and individual ones. This kind of analysis can quickly spot suspect behavior that indicates potential fraud. Aggregating call center data can identify common problems, frequent customers, and staff responses. By analyzing this data in real time, companies can make changes before issues develop into bad situations. Social media analysis can give companies quick, valuable feedback on the efficacy of ad campaigns and products, and can alert them to potential issues and problems. With this information in hand, companies can adjust quickly.
 
In order to make the most of big data, many companies use data sets in conjunction with another. This kind of data combination can produce statistics that would have otherwise been overlooked. Companies interested in big data will also need robust storage capabilities: such storage will provide the foundation for big data analysis. Big data can be a powerful tool, and many companies have learned how to harness that power.
 
Macy’s, the department store chain headquartered in Ohio, uses data on customer demand and inventory to analyze purchasing trends. From these conclusions, Macy’s is able to adjust prices in real time for tens of millions of items.
 
Bristol-Myers Squibb, a biopharmaceutical company headquartered in New York, switched from an internal system to the cloud. This gave the company the storage and processing power it needed to run clinical trial simulations faster and reduce the number of subjects needed for trials. These reductions in time and manpower allows the company to process and analyze clinical big data more effectively.
 
The computer company Dell, based in Texas, handled big data by analyzing social media activity alongside transaction numbers and record. The company pairs social media usernames and email addresses with transaction details, and from this offers personalized deals rather than general ones. This kind of targeted marketing has paid off: it has doubled the percentage of advertisement response.
 
Wellmark Blue Cross/Blue Shield, a United States company with a bulk of its members in South Dakota and Iowa, used big data (by combining web log data with other call logs and member data) to find that half of their calls were coming from under 10% of members. Once the company identified this problem, they enacted new member education tools to reduce these customers’ need to call.
 
Morton’s The Steakhouse, a chain restaurant based in Texas, saw a tweet from a customer requesting a steak dinner at an airport. Though the tweet was a joke, Morton’s matched the customer’s Twitter profile with internal data to figure out what that customer frequently ordered. Then, Morton’s ascertained the customer’s flight time, and sent the customer a steak. Though Morton’s does not conduct such activities on a large scale, this situation proves the company’s ability to harness big data on demand, and actually get tangible results.
 
Red Roof Inn, a hotel chain headquartered in Ohio, uses flight cancellation data and weather data to target specific customers through mobile advertisements. This campaign makes it easier for delayed travelers to book a room.