Putting Big Data to Work

Even companies that are fully committed to big data, that have defined the business case and are ready to mature beyond the “science project” phase, face a daunting question: how do we make big data work?

The massive hype, and the perplexing range of big data technology options and vendors, makes finding the right answer harder than it needs to be. The goal must be to design and build an underlying big data environment that is low cost and low complexity. That is stable, highly integrated, and scalable enough to move the entire organization toward true data-and-analytics centricity.

Data-and-analytics centricity is a state of being where the power of big data and big data analytics are available to all the parts of the organization that need them. With the underlying infrastructure, data streams and user toolsets required to discover valuable insights, make better decisions and solve actual business problems. That’s how big data should work.

How Big Data Works, Big Data Technologies, Big Data Examples & Hadoop
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Big Data as business engine of opportunity

So where do you start? Think of big data as an engine. To boost performance, it’s a matter of assembling the right components in a seamless, stable and sustainable way. Those components include:

  • Data Sources: operational and functional systems, machine logs and sensors, Web and social and many other sources
  • Data Platforms, Warehouses and Discovery Platforms: that enable the capture and management of data, and then – critically – its conversion into customer insights and, ultimately, action
  • Big Data Analytics Tools and Apps: the “front end” used by executives, analysts, managers and others to access customer insights, models scenarios and otherwise do their jobs and manage the business.

At this level, it’s about harnessing and exploiting the full horsepower of big data assets to actually create business value. Making it all work together requires a strategic big data design and thoughtful big data architecture that not only examines current data streams and repositories, but also accounts for specific business objectives and longer-term market trends. In other words, there is no one single template to making big data work. We’re not talking about COTS here.

Given that big data will only become more important tomorrow, these infrastructures should be viewed the foundation of future operations. So, yes, capital outlays may be significant. However, many forward-thinking organizations and early adopters of big data have reached a surprising – and somewhat counterintuitive – conclusion: that designing the right big data environment can actually lead to cost savings. Speaking of surprises: these cost savings can be pleasantly large and harvestable relatively soon.

It’s critical to note that with flexible frameworks in place, big data technologies and programs can support multiple parts of the enterprise and improve operations across the business. Otherwise, there is real risk that even advanced and ambitious big data projects will end up as stranded investments. Gartner estimates that 90% of big data projects be leveraged or replicated across the enterprise. Tomorrow’s big data winners are in that 10% today, and long ago stopped thinking small.

Attributes of Highly Effective Big Data Environments

  • Seamlessly Use Data Sets: Much of the payoff comes through the mixing, combining and contrasting of data sets – so there’s no analytics-enabled innovation without integration
  • Flexible, Low Cost: The target here is low complexity and low cost, with sufficient flexibility to scale for future needs, which will be both larger-scale and more targeted at specific user groups
  • Stable: Stability is critical because the data volumes are massive and users need to easily access and interact with data. In this sense, infrastructure performance holds a key to boosting business performance through big data
Given that big data will only become more important tomorrow, these infrastructures should be viewed the foundation of future operations.

Big Data & Hadoop: A Technology to Know

Hadoop is a file system that allows the storage of any type of data, most of which would have been discarded in the past (because making it usable would’ve been too difficult and expensive). The value of big data and Hadoop comes through on-the-fly modeling of data that might actually be useful and which, when integrated with existing big data and analytics environment, can enrich business insights.

Big Data Integration: the most important variable

Limited reusability is, to a large extent, a function of poor integration. In fact, integration may be the most important variable in the equation for big data success.

Forrester Research has written that 80% of the value in big data comes through integration. The big picture idea is that the highest value big data is readily accessible to the right users, and robust and clearly defined business rules and governance structures. Deeper data sets – legacy transactional data and long-tail customer histories – may only need reliable storage and robust data management, so data scientists and data explorers can review and model it when it makes sense to do so.

Big data integration is also about thinking big. In this instance, “big” actually means holistically, inclusively and multi-dimensionally. Dots must be connected, islands of data bridged and functional silos plugged into each other (if not broken down entirely).

High degrees of integration. Well designed ecosystems. Unified architectures. Data and analytics centricity. That short list doesn’t necessarily require every component or technical detail to make big data programs function. But certainly these are difference-making attributes that ensure big data programs work effectively.