Done right, the business opportunity presented by your big data can be quite compelling. The insights you unlock from your data can help deliver great customer experiences, optimize operations and improve IT economics. By putting data-driven decision-making at the heart of the enterprise, you can leverage the right data to drive competitive advantage and innovation throughout the organization.
As we saw in the first post of this four-part series, at the outset IT and business leaders must partner to understand where business challenges lie and how the organization can benefit from big data technologies. Next, we looked at how analytics starts the business conversation: It provides the means to determine the use cases for which you need to harness data to solve tangible business problems.
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Now, think of all the different places you’re pulling data from today. How do you make sure those platforms, repositories and databases are managed? Are they secure and working at maximum efficiency? Is the data organized, available to be analyzed and acted on by business users?
Key to data availability
The platforms you use to work with your structured, unstructured and semistructured data play a pivotal role in collecting, managing, analyzing and storing information. An effective data management platform enables the data availability you need to build actionable insights.
Unfortunately, the cost of administering and monitoring databases — and sustaining high availability — easily saps IT budgets. To streamline database administration and reduce costs, you can automate key tasks, from maintenance and performance tuning to backup and disaster recovery.
Also, you can gain operational efficiencies by improving the performance of your data management platforms, even in the face of rapidly expanding data volumes and escalating application interdependencies. Ideally, these platforms should store data cost-effectively, process it quickly and provision large amounts of it for integration and analysis. These efficiency gains translate into investments you can make to further your analytics capabilities.
Advancing analytics
The open Apache™ Hadoop® platform has emerged as a major technology for managing and analyzing large, complex data sets. In comparison, traditional relational database management or enterprise data warehouse tools often are unable to efficiently handle huge volumes of diverse data.
Hadoop enables distributed parallel processing of high-volume, high-velocity data across servers that both store and process the data. Because it supports structured, semistructured and unstructured data from disparate systems, the highly scalable Hadoop framework lets you store and analyze a tremendous amount of data.
Early adopters typically used Hadoop for batch processing. Prime use cases included data warehouse optimization and extract, transform and load (ETL) processes.
Now, Hadoop has become a viable option for organizations across many industries, depending on their existing environment. IT leaders are using Hadoop to enable predictive analytics and answer business-critical questions that have been beyond the capabilities of basic spreadsheets, databases and business intelligence (BI) tools. Hadoop facilitates customer analytics, churn analysis, network security and fraud prevention — many of which require interactive processing and analysis.
For example, to help global manufacturers efficiently manage product quality, Omneo implemented a Hadoop-based software solution. Using the solution, Omneo customers can quickly search, analyze and mine all their data in a single place — billions of records in seconds — so they can identify and resolve emerging supply chain issues. The resulting visibility helps manufacturers put out more consistent, better-quality products.
Information security service organizations also must deal with enormous amounts of data. Dell SecureWorks is on deck 24 hours a day, 365 days a year, to help protect client IT assets against cyberthreats. To meet its data processing challenges, Dell SecureWorks deployed a Hadoop-based solution that processes billions of events every day. Now, Dell SecureWorks can put more data into its clients’ hands so they can respond faster to security threats than ever before.
Internet of Things
An emerging source of data that’s well suited for the Hadoop platform is the Internet of Things (IoT). The rapid expansion of IoT has led to a proliferation of connected devices and machines with embedded sensors that generate massive amounts of data. To derive meaningful insights quickly from this data, you need superfast, interactive processing and analytics.
The Apache Spark™ engine is poised to become the foundational technology driving the analysis of IoT data. Spark uses in-memory computing to deliver high-performance data processing. It greatly accelerates the performance of applications in Hadoop clusters.
Dell sees Spark as a game-changer for interactive processing, driving Hadoop as the data platform of choice. Spark offers the flexibility and tools to analyze streaming data quickly and efficiently, from running machine-learning algorithms to graphing and visualizing the interrelationships among data elements — all on one platform.
Integrating Hadoop into your big data strategy
Hadoop is a powerful technology with a lot to offer, and it can be tempting to start exploring the technology as part of a one-off, IT-driven innovation project. But the key to really capitalizing on its power is to tie it to your overall business strategy. How you decide to deploy Hadoop should be linked to outcomes, not to tools or tasks. The Hadoop solution should adapt to you and your existing infrastructure so you can innovate without disruption.
To help advance Hadoop adoption, Dell has teamed up with Cloudera and Intel to deliver three turnkey solutions designed to optimize big data management and analytics. These end-to-end solutions include hardware, software, services and support that help take the complexity out of managing and analyzing data for a variety of use cases: an easy, cost-effective solution for getting started on a proof of concept (POC); a robust, cost-effective and scalable enterprise-level solution; and an in-memory appliance for near-real-time predictive analytics or analysis of streaming IoT data. We believe that implementing new data capabilities on these types of field-tested, low-risk technology solutions allows organizations to focus on driving innovation and results.
Making it real
How do you make sure a particular big data solution meets your business needs? Because of the cutting-edge, complex nature of big data technologies, taking a solution for a test-drive may be just the answer. Find out how you can get hands-on experience with a big data solution in the last post of this four-part series.
Spark the conversation
Hadoop has matured and evolved over the years, but some industry experts wonder whether it’s ready for prime time. If you’re at the 2015 Gartner Business Intelligence & Analytics Summit, join the discussion at the session “Will Hadoop Jump the Spark?” on March 31 at 2:00 p.m. Also, drop by our booth #425. Let us know how Hadoop fits into your big data strategy and what your challenges are today.
If you can’t make the summit this year, please check out our webinar “How the Internet of Things Is Driving the Adoption of Apache Spark.”
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