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Enterprise data analytics strategy: A guide for CIOs

Before IT leaders can capitalize on big data, strategic decisions must be made regarding infrastructure, management approaches, new technologies and more. Use the resources here to 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 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 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 enterprise 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.

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, why more executives are paying attention to data asset management, 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


The new rules of data asset management

As companies use big data to drive business value and decisions, CIOs aren't the only executives paying attention to information asset management. 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 also talking about 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 and ponder next steps.


What's next after Apache Hadoop's MapReduce?

MapReduce came first, but what's next? Read what big data experts have to say about the evolution of MapReduce. 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. 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


Data: The new king of business strategy

In these videos, watch how IT experts are using technologies like data lakes to get the most out of big data and hear from a rising big data startup that's using analytics to change the travel business.


Monsanto IT embraces 'decision science'

In this video, Monsanto CIO Jim Swanson explains how the company is using data to incorporate "decision science" as part of its business strategy.


Cloud intensifies the need for connecting to data

In this video, Sumit Sarkar, chief data evangelist at software company Progress, explains how the rise of cloud computing is making data connectivity imperative for CIOs.


Security, privacy issues challenge data lake implementation

In this video, Sarkar details the challenges associated with data lake implementation and enumerates the security best practices required for it.


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.


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? What are some of the common data science mistakes to avoid? Should IT leaders build or buy big data architecture? This section includes tactical considerations and advice to help you navigate the testy waters of data analytics.


Six 'gotchas' your data science team should avoid

Gideon Mann, head of Bloomberg L.P.'s data science team, enumerates the six big data project "gotchas" that CIOs and data science teams should watch out for. Continue Reading


CIOs, it's time to build a digital data strategy

In this tip, IT practice leader Andrew Horne explains the strategic requirements CIOs must consider to manage data in the digital era. 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. 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 offerings 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

5Building a data foundation-

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 choices 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


The business importance of data lake governance

Experts explain why data lake governance is becoming a critical component in modern data architecture. Continue Reading


The five phases of decision architecture

In this book excerpt, learn about the five phases of decision architecture and how they can help organizations with data monetization. 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 Hadoop Distributed File System integration. 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.


It's time for small businesses to embrace data analytics

Read how two small businesses have realized bottom-line benefits by employing data analytics tools. Continue Reading


UsTrendy harnesses the value of technology

Read how UsTrendy, an online fashion marketplace started in 2008, is using technologies like data analytics, cloud and social media to achieve better business outcomes. Continue Reading


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. 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.


The future of big data and analytics

IT consultants and vendors offer predictions about business trends related to business intelligence, cloud analytics, data management and big data. 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 flexible-schema 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

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