Adding Hadoop to your Big Data Mix?
SAS provides everything you need to get valuable insights from all that data.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
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.
Sports data analytics company SciSports has developed a camera system called BallJames to capture big data from all the players on the field who don’t have the ball – an extension of traditional approaches. This real-time tracking technology automatically generates 3-D data from the video of 14 cameras placed around the stadium to record every movement of the players. BallJames generates player data such as precision, direction and speed of the passing, sprinting strength and jumping strength – that’s a lot of big data.
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:
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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.
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.
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.
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.
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.
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.
Oliver Schabenberger, Executive Vice President and Chief Technology Officer at SAS, describes the characteristics of a modern analytics platform. He cites the diversity of data management and analytics challenges such a platform must handle – for small data as well as big data. The modern analytics platform, he says, should be able to process structured and unstructured data and accommodate simple analytics to complex machine learning problems.
It’s important to remember that the primary value from big data comes not from the data in its raw form, but from the processing and analysis of it and the insights, products, and services that emerge from analysis. The sweeping changes in big data technologies and management approaches need to be accompanied by similarly dramatic shifts in how data supports decisions and product/service innovation.
SAS makes it easy to understand what your data has to tell you. Interactively explore billions of rows of data in seconds.
This category includes data that reaches your IT systems from a web of connected devices, often part of the IoT. 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.
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.
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