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.
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.
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
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.
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. 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
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.
In this video, Monsanto CIO Jim Swanson explains how the company is using data to incorporate "decision science" as part of its business strategy.
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.
In this video, Sarkar details the challenges associated with data lake implementation and enumerates the security best practices required for it.
Cambridge, Mass., startup Hopper hopes to disrupt the travel business, with help from big data analytics and an aggressively agile work environment.
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? 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.
Hortonworks files for an initial public offering, but big data enthusiasts -- and CIOs -- shouldn't celebrate just yet, according to analysts. 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
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.
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
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.
Read how two small businesses have realized bottom-line benefits by employing data analytics tools. 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 flexible-schema 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