Is Big Data analytics right for your organization and is it the right time? The first and foremost decision that organizations must make is if the business is ready to adopt the benefits from this analysis. Big Data analytics is effective only if your data scientists ask the right business questions, understand the business data set, and identify their relationships and patterns that Big Data analysis may illuminate.


Success Through Insight

Success in the Big Data era is about more than size. It’s about getting insight from these huge data sets more quickly, and the cloud enables Big Data processing with cloud computing capabilities for organizations of all sizes.

pramanick-graphic Big Data

The IDC states that cloud spending will also rise by 25% to $100 billion. “Data-optimized” Platform-as-a-Service (PaaS) products will become increasingly popular, the IDC predicts, with Amazon Web Services taking a lead in providing various specialist solutions for businesses. With the Big Data cloud platform, you get a production-ready and performance-tested cluster, supported by a broad ecosystem of partners. A recent IDG Enterprise survey of enterprises and small and medium-sized businesses found that 60% and 46% of these firms said Big Data is a top priority, respectively.

The study discovered that a total of 70% of participants believe their cloud infrastructure investments will grow during the next three years. Another 52% of organizations anticipate similar expenditures to grow regarding data analytics, while 42% said the same for data mining and 36% for data visualization.

Cloud computing has become a viable, mainstream solution for data processing, storage, and distribution, but Big Data analytics and cloud computing don’t come with a silver bullet to fix bad data, dysfunctional organizational cultures, or automatically integrate with legacy applications. Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. The cloud model promotes availability and is composed of five essential characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service; three service models: cloud Software as a Service (SaaS), cloud Platform as a Service (PaaS) and cloud Infrastructure as a Service (IaaS); and four deployment models: private cloud, community cloud, public cloud, and hybrid cloud.

The cloud computing model offers the promise of massive cost savings combined with increased IT agility. In addition, cloud computing democratizes Big Data — any enterprise can now work with unstructured data at a huge scale. In theory, managing cloud-based Big Data is cost-effective, scalable, and fast to build. Sticking data in Windows Azure Tables, Amazon SimpleDB, or MongoDB is just the start of the data science required to make the most of Big Data.

A survey by GigaSpaces found that 80% of IT executives who consider Big Data processing important are considering moving their Big Data analytics into the cloud. The two types of deployment models for cloud computing are public and private. These are offered for general purpose computing needs as opposed to specific types of cloud delivery models.

The public cloud is a set of hardware, networking, storage, services, applications, and interfaces owned and operated by a third party for use by other companies and individuals. Public clouds are viable because they typically manage relatively repetitive or straightforward workloads

A private cloud is a set of hardware, networking, storage, services, application, and interfaces owned and operated by an organization for the use of its employees, partners, and customers. The private cloud is a highly controlled environment not open for public consumption. Thus, the private cloud sits behind a firewall. The private cloud is highly automated with a focus on governance, security, and compliance.

Big Data is an inherent feature of the cloud and provides unprecedented opportunities to use both traditional, structured database information and business analytics with social networking, sensor network data, and far less structured multimedia. Big data applications require data-centric compute architecture, and many solutions include cloud-based application program interfaces (APIs) to interface with advanced columnar searches, machine learning algorithms, and advanced analytics such as computer vision, video analytics, and visualization tools. If organizations are managing a Big Data project that demands processing massive amounts of data, the private cloud might be the best choice in terms of latency and security.


The Top 10

Here’s the list of Top 10 Big Data Analytics platform vendors:

  1. AWS
  2. Cloudera
  3. HP Vertica
  4. Hortonworks
  5. IBM
  6. InfiniDB
  7. Kognitio
  8. MapR
  9. Pivotal
  10. Teradata

Big Data technology and cloud computing demand will push global IT spending beyond $2 trillions in 2014, according to the IDC. Spending on Big Data technologies should see a 30% rise, with $14 billions expected to be shelled out on the analysis of huge pools of data such as customer behavior and business performance. High-volume Big Data cloud platforms will be in demand as firms look to gain significant business insight without the initial overhead costs of buying in physical infrastructure to handle the task. Gartner lists Amazon, AT&T, Google, HP, IBM, and Microsoft among what it says are the top 10 cloud storage providers.


Here’s The Big Data Institute’s (TBDI) Top 10 Big Data Trends in 2014:

  • Big Data as a Service and Big Data analytics will go mainstream

  • More companies will implement predictive analytics, and machine learning

  • Data science and Big Data analytics will be embedded in business intelligence (BI) for actionable insights into operational reports and executive dashboards

  • Cloud computing and Big Data will be tightly integrated with BI solutions

  • Enterprise will be using Big Data techniques to secure IT infrastructure

  • Hadoop will be used for operational system and transactional application

  • Hadoop will be implemented as extensions to part of enterprise information management solutions

  • The Big Data and data scientists skills shortage will grow as companies start ramping up hiring for Big Data and data science projects

  • A rise in mergers and acquisitions (M&A) in the Big Data space with legacy BI companies acquiring niche Big Data vendors

  • Companies will start new roles defined as chief data scientists, chief data officers, and chief analytic officers

The cloud computing model is a perfect match for Big Data since cloud computing provides unlimited resources on demand. Both cloud computing and Big Data appear to be at the top of the list of many IT executives. According to the IDC, the market for Big Data will reach $16.1 billion in 2014 growing six times faster than the overall IT industry. A Sierra Ventures survey of Fortune 500 CIOs and CTOs conducted in 2013 found that 32% cited mobile devices and Big Data as the top tech innovations influencing their companies, while 24%  said the same regarding the use of the cloud. Are you prepared to deploy your Big Data in cloud? If you would like to get started with Big Data analytics on the cloud, take the course “Hadoop and the IBM SmartCloud Enterprise” available at no cost from Big Data University.