As enterprises seek to take advantage of ever-larger stores and sources of information, data analytics continues to take on greater importance. Modern CIOs are tasked with determining how best to capitalize on an organization’s data to improve decision making, fine-tune marketing programs, respond to changes in customer demand and seize upon new business opportunities.
But before technology managers can put big data to strategic use, they must first make some tactical decisions around IT infrastructure. One decision involves how big data is stored: Hadoop clusters versus traditional network-attached storage and storage-area network installations, for instance. The process of collecting, processing and analyzing large data sets also calls for a lot of processing power. Enter framework offerings like Hadoop's MapReduce and Apache Spark. NoSQL databases, in-memory analytics tools, distributed computing systems and converged infrastructure appliances rank among the other considerations when building a technical architecture to support a data analytics strategy.
In this CIO Essential Guide, we’ll explore the potential benefits of data analytics and the technologies that enable this capability. Read on to learn big data basics and strategic guidelines, get tips on architecting the right data infrastructure, understand the advantages and complexities of Hadoop, discover real-world analytics success stories and peer into the future of data analytics strategy.
This Essential Guide on enterprise data analytics is designed to give IT leaders strategic management and decision-making advice on timely topics.
1Big data basics-
Understanding big data opportunities
How can your business reap the benefits of big data? It starts with a strong understanding of the analytics tools and management approaches available to you. In this section, learn what makes a balanced big data project, how big data instruments will fit into your business goals, and how one data processing engine is making waves.
In big data analytics projects, analytics managers must find the right balance between giving data scientists room to innovate and ensuring that their work has practical business value. Continue Reading
A BI manager at Time Warner Cable advises implementing big data tools only when you know how they will fit into your company's larger business goals. Continue Reading
With its in-memory feature, Apache Spark has the potential to improve data processing speed by an order of magnitude over MapReduce. Continue Reading
2The scoop on Hadoop-
Get acquainted with the processing powerhouse
You can't talk about data analytics without discussing Hadoop; it's a crucial processing technology that has changed the analytics game and become an integral part of IT ecosystems. In this section, get acquainted with the evolution of Hadoop, discover the benefits of the framework and ponder next steps.
Hadoop has become such an integrated part of IT systems that chatter about the technology itself is beginning to wane, The Data Mill reports. Continue Reading
At Spark Summit East, Databricks tried to distinguish its Apache Spark offering from MapReduce, the data processing engine attached to Hadoop. The Data Mill reports. Continue Reading
Hadoop clusters can help analysts and business users avoid the data inconsistencies created by individual spreadmarts, while still giving them a place to do self-service data analytics, says consultant Wayne Eckerson. Continue Reading
While SQL-on-Hadoop engines appear similar on the surface, expert Rick van der Lans explains the important differences in order to help you choose what's right for your company. Continue Reading
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Big data startup and experts' stances on Hadoop
In these videos, get the lowdown on Hadoop from IT experts and hear from a rising big data startup that's using analytics to change the travel business.
Cambridge, Mass., startup Hopper hopes to disrupt the travel business, with help from big data analytics and an aggressively agile work environment.
In a video interview, Ryan Fenner, an enterprise data solutions architect at MUFG Union Bank, gives his take on whether Hadoop has become a must-have technology for organizations.
Consultant Rick Sherman says organizations considering Hadoop projects should ensure they have a real business need for the big data framework before deploying it.
Make note of these data analytics approaches
As with any area of IT, the strategic considerations involved in enterprise data analytics are abundant and often complicated to address. What technology or approach is best for your organization? Should IT leaders build or buy big data architecture? What innovations could be the next big thing in analytics versus a passing fad? This section includes tactical considerations and advice to help you navigate the testy waters of data analytics.
Hortonworks files for an initial public offering, but big data enthusiasts -- and CIOs -- shouldn't celebrate just yet, according to analysts. The Data Mill reports. Continue Reading
Relationships are a big data problem, according to Michael Alton of Intel. One solution: build a big data infrastructure on the back end to allow data analysts to easily interact with the technology and explore its connection to the front end. Continue Reading
Big data analysts need to ask better questions; local data concierges might be the next information governance trend; and the downside of big data. The Data Mill reports. Continue Reading
As enterprise mobile computing evolves and data proliferates, companies need to put the two together to compete effectively in today's market. Continue Reading
5What infrastructure is best?-
How the right infrastructure can make or break your big data projects
Of course, any good data analytics strategy needs a solid foundation to build upon. IT professionals are consistently challenged with new options on hardware, storage and other aspects of data center infrastructure. In this section, explore these choices and get tips on how to overcome infrastructure hurdles.
Enterprise big data workloads demand a lot from storage and network bandwidth. Storage, cloud and as-a-service vendors are developing the infrastructure to support big data workloads, making it easier to store and move customer information, real-time streams and more. Continue Reading
A significant portion of big data workloads won't stray to the cloud yet as security, cost, management and processing power keep analytics in the data center. Continue Reading
An effective big data analytics and reporting strategy starts from the ground up with these five data center infrastructure changes. Continue Reading
6Data analytics in action-
Learn how organizations are using analytics to their advantage
How are other IT leaders harnessing the power of data analytics? In this section, see enterprise data analytics strategies in action and find the approach that best fits your organization's IT and business goals.
Startups Virtusize, Clothes Horse and LoveThatFit are using data science to try to solve a burning question for online shoppers: Will it fit? Continue Reading
Find out how MGM Resorts uses customer analytics to dismiss a long-standing myth on how to successfully deliver customer service to the digital customer. Continue Reading
While serving under Chicago Mayor Rahm Emanuel, Brett Goldstein, the city's former CIO, launched an app that integrated data from multiple sources to improve situational awareness. Now, he's seeking to take that project to the next level. Continue Reading
Manny Puentes, CTO of online ad platform developer Altitude Digital, is riding the open source big data wave and leading the use of Hadoop, Spark and other technologies at his company. He's learned some lessons along the way. Continue Reading
Data analytics terminology
8On the horizon-
Pondering the future of data analytics
How will enterprise data analytics technologies impact your organization's big-picture business models going forward? Stay attuned to the up-and-coming analytics trends and practices in this section.
From Enterprise Data World 2015: As NoSQL databases continue to flourish, designing data models for the systems is a looming challenge for data architects and developers. Continue Reading
Hadoop and NoSQL haven't eliminated the need for database schemas, according to one expert. Instead, consider them flexible schemas waiting to be integrated. Continue Reading
The big data market is in flux, but there are signs convergence is imminent. Plus, Spark takes on MapReduce: The Data Mill reports from the MIT Sloan CIO Symposium. Continue Reading