Essential Guide

Get started Bring yourself up to speed with our introductory content.

Enterprise data analytics strategy: A guide for CIOs

Before IT leaders can capitalize on big data, they must first make strategic decisions regarding infrastructure, management approaches, new technologies, Hadoop, NoSQL databases and more. Use the resources in this CIO Essential Guide to help you master enterprise data analytics.


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.


The key to balanced big data analytics project management

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


Cable exec: See the big picture before adopting big data tools

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


Apache Spark in-memory dexterity catches attention of big data analysts

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.


What's next after Apache Hadoop's MapReduce?

MapReduce came first, but what's next? Big data experts gather to discuss the evolution of MapReduce, The Data Mill reports. Continue Reading


Hadoop becomes integral, fades into the background

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


Apache Spark the next big data analytics all-star?

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 provides a new host for analytical ecosystems

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


How to choose the right SQL-on-Hadoop engine to access big data

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


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.


Hopper uses big data to make travel easier

Cambridge, Mass., startup Hopper hopes to disrupt the travel business, with help from big data analytics and an aggressively agile work environment.


IT pro offers his take on Hadoop's challenges, need for the right use cases

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.


Business need should motivate Hadoop project deployment

Consultant Rick Sherman says organizations considering Hadoop projects should ensure they have a real business need for the big data framework before deploying it.

4Strategic considerations-

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.


IBM and Pentaho bank on big data refineries

The concept of a data lake is still up-and-coming, helped out by the latest newcomer -- a data refinery. Is it the magic wand it's been made out to be? The Data Mill reports. Continue Reading


Does the Hortonworks IPO signal big data's arrival?

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


Are APIs the solution to a big data infrastructure problem? Intel thinks so.

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


Are data concierges the next trend in big data projects?

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


Should CIOs build or buy big data architecture?

Build or buy? That's the age-old dilemma that CIOs face in architecting for big data. Add to that new business problems, inadequate vendor solutions and new technologies, and the decision is complicated to say the least. Continue Reading


Relationship between data and devices key to mobile evolution

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.


Big data infrastructure products are transforming the enterprise

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


Big data servers are staying in on-premises data centers … for now

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


How to address big data storage challenges

IT professionals face new big data storage challenges, including the speed of analytical processes, the ability to scale capacity and HDFS integration. Continue Reading


Big data strategies start with solid data center infrastructure

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.


Ten data analytics success stories

How are companies like Burberry, CVS, Coca-Cola and L'Oreal utilizing analytics to realize real-world business gains? Here are 10 data analytics success stories. The Data Mill reports. Continue Reading


Fashion tech startups create virtual dressing rooms using data analytics

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


Analytics helps MGM Resorts serve the digital customer

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


Former City of Chicago CIO uses open data to improve his city

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


How one CTO uses open source big data tools to his advantage

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.


Applied analytics, automation loom large in 2015

The International Institute for Analytics forecasts that automated decision making, ensemble modeling and the "Analytics of Things" will be hot in 2015, The Data Mill reports. Continue Reading


Working with NoSQL systems requires 'a fundamental mindset change

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


Expert: Hadoop and NoSQL are important, but database schemas still needed

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 must mature before it converges

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

Start the conversation

Send me notifications when other members comment.

By submitting you agree to receive email from TechTarget and its partners. If you reside outside of the United States, you consent to having your personal data transferred to and processed in the United States. Privacy

Please create a username to comment.