Big Data for Dummies
Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you’ll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You’ll learn what it is, why it matters, and how to choose and implement solutions that work.
Learn to:
- Leverage big data tools and architectures
- Explore how big data can transform your business
- Integrate structured and unstructured data into your big data environment
- Use predictive analytics to make better decisions
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Chapter 1 Grasping the Fundamentals of Big Data
In This Chapter
- Looking at a history of data management
- Understanding why big data matters to business
- Applying big data to business effectiveness
- Defining the foundational elements of big data
- Examining big data’s role in the future
Managing and analyzing data have always offered the greatest benefits and the greatest challenges for organizations of all sizes and across all industries. Businesses have long struggled with finding a pragmatic approach to capturing information about their customers, products, and services. When a company only had a handful of customers who all bought the same product in the same way, things were pretty straightforward and simple. But over time, companies and the markets they participate in have grown more complicated. To survive or gain a competitive advantage with customers, these companies added more product lines and diversified how they deliver their product. Data struggles are not limited to business. Research and development (R&D) organizations, for example, have struggled to get enough computing power to run sophisticated models or to process images and other sources of scientific data.
Indeed, we are dealing with a lot of complexity when it comes to data. Some data is structured and stored in a traditional relational database, while other data, including documents, customer service records, and even pictures and videos, is unstructured. Companies also have to consider new sources of data generated by machines such as sensors. Other new information sources are human generated, such as data from social media and the click-stream data generated from website interactions. In addition, the availability and adoption of newer, more powerful mobile devices, coupled with ubiquitous access to global networks will drive the creation of new sources for data.
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