BIG DATA

Big Data History and Current Considerations

While the term “big data” is relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old. The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs:
Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
Variety. Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
At SAS, we consider two additional dimensions when it comes to big data:
Variability. In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.
Complexity. Today's data comes from multiple sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.

Why Is Big Data Important?

The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:

• Determining root causes of failures, issues and defects in near-real time.
• Generating coupons at the point of sale based on the customer’s buying habits.
• Recalculating entire risk portfolios in minutes.
• Detecting fraudulent behavior before it affects your organization.

Who uses big data?

Big data affects organizations across practically every industry. See how each industry can benefit from this onslaught of information.

Banking

With large amounts of information streaming in from countless sources, banks are faced with finding new and innovative ways to manage big data. While it’s important to understand customers and boost their satisfaction, it’s equally important to minimize risk and fraud while maintaining regulatory compliance. Big data brings big insights, but it also requires financial institutions to stay one step ahead of the game with advanced analytics.

Education

Educators armed with data-driven insight can make a significant impact on school systems, students and curriculums. By analyzing big data, they can identify at-risk students, make sure students are making adequate progress, and can implement a better system for evaluation and support of teachers and principals.

Government

When government agencies are able to harness and apply analytics to their big data, they gain significant ground when it comes to managing utilities, running agencies, dealing with traffic congestion or preventing crime. But while there are many advantages to big data, governments must also address issues of transparency and privacy.

Health Care

Patient records. Treatment plans. Prescription information. When it comes to health care, everything needs to be done quickly, accurately – and, in some cases, with enough transparency to satisfy stringent industry regulations. When big data is managed effectively, health care providers can uncover hidden insights that improve patient care.

Manufacturing

Armed with insight that big data can provide, manufacturers can boost quality and output while minimizing waste – processes that are key in today’s highly competitive market. More and more manufacturers are working in an analytics-based culture, which means they can solve problems faster and make more agile business decisions.

Retail

Customer relationship building is critical to the retail industry – and the best way to manage that is to manage big data. Retailers need to know the best way to market to customers, the most effective way to handle transactions, and the most strategic way to bring back lapsed business. Big data remains at the heart of all those things.

How It Works

 

Before discovering how big data can work for your business, you should first understand where it comes from. The sources for big data generally fall into one of three categories:

Streaming data

This category includes data that reaches your IT systems from a web of connected devices. You can analyze this data as it arrives and make decisions on what data to keep, what not to keep and what requires further analysis.

Social media data

The data on social interactions is an increasingly attractive set of information, particularly for marketing, sales and support functions. It's often in unstructured or semistructured forms, so it poses a unique challenge when it comes to consumption and analysis.

Publicly available sources

Massive amounts of data are available through open data sources like the US government’s data.gov, the CIA World Factbook or the European Union Open Data Portal.

 

After identifying all the potential sources for data, consider the decisions you’ll need to make once you begin harnessing information. These include:

How to store and manage it

Whereas storage would have been a problem several years ago, there are now low-cost options for storing data if that’s the best strategy for your business.

How much of it to analyze

Some organizations don't exclude any data from their analyses, which is possible with today’s high-performance technologies such as grid computing or in-memory analytics. Another approach is to determine upfront which data is relevant before analyzing it.

How to use any insights you uncover

The more knowledge you have, the more confident you’ll be in making business decisions. It’s smart to have a strategy in place once you have an abundance of information at hand.

 

The final step in making big data work for your business is to research the technologies that help you make the most of big data and big data analytics. Consider:

• Cheap, abundant storage.
• Faster processors.
• Affordable open source, distributed big data platforms, such as Hadoop.
• Parallel processing, clustering, MPP, virtualization, large grid environments, high connectivity and high throughputs.
• Cloud computing and other flexible resource allocation arrangements.