Penning the Use of Big Data Analytic in your Organization

23 Feb

Companies have been stacking and analyzing huge volumes of data since the inception of the data warehousing drift in the early 1990s. From terabytes the rate of growth in data volumes has reached to petabytes and continues to escalate as organizations seek to stack and analyze greater levels of transaction details, as well as Web- and machine-generated data, to gain a better understanding of customer behavior and drivers.

Why Big Data?

There has been a lot of talk about “big data” in the past year, what is big data? Why are we are talking about big data today?

Big data is nothing but the term that describes the voluminous amount of unstructured and semi-structured data that are created by an Organization i.e., data that would take too much time and cost too much money to load into a relational database for analysis.

There are several factors influencing to talk about Big Data

Varying data types: Until few years ago it was easy for organizations to capture the data that was transactional in nature and numeric to fit easily into rows and columns of a relational database. But today, the growth in data has predominantly increased from websites and social media contents that has made organizations find it difficult to structure the data

Advanced Hardware Technologies:  The advancement in price performance and technology of the hardware materials has finally made it easy to store and analyze the huge volumes of data at affordable prices. Vendors exploit the technology advancement by developing high speed analytical platforms to accelerate large volumes of data, while the open source community has developed Hadoop, a distributed file management system designed to capture, store and analyze large volumes of Web log data, among other things.

Outsourcing Big Data: Because of the complexity and cost of storing and analyzing Web traffic data, most organizations traditionally outsourced these functions to third-party service bureaus like Zylog. As the size and importance of data analysis increased, many are now eager to outsource this data to gain greater insights about customers. At the same time, virtualization technology is beginning to make it attractive for organizations to consider moving large-scale data processing outside their data center walls to private hosted networks or public clouds.

New and Exciting:. The biggest reason for the popularity of the term big data is that Web and application developers have discovered the value of building new data-intensive applications. To application developers, big data is new and exciting.

Use of Big Data Analytic

Now that we understand the business context for analytical platforms, an analytical platform is a data management system optimized for query processing and analytic that provides superior price-performance and availability compared with general purpose database management systems.

According to a survey, 72% of the organizations had purchased or implemented an analytical database whereas 46% have no plans to do so, 42% are exploring the idea and just 12% are currently evaluating vendors. On the whole, about 75% of the organization will have an analytical platform in the near future.

Implement a new BI architecture

The BI architecture of the future integrates traditional data warehousing technologies to handle detailed transactional data and file-based and non-relational systems to manage unstructured and semi-structured data. The key is to incorporate these systems into an amalgamated architecture that enables casual and power users to query report and analyze any type of data in a comparatively unseamed manner. This integrated information access is the hallmark of the next generation BI architecture. More immediately, companies are using Hadoop to preprocess unstructured data so that it can be loaded and integrated with other corporate data for reporting and analysis. This allows BI and ETL users to use familiar tools to query and analyze data.

Implement analytical platforms that meet business and technical requirements

Today, organizations implement analytical platforms for various reasons. For example, analytical appliances are fast to deploy and easy to maintain and make good replacements for Microsoft SQL Server or Oracle data warehouses that have run out of gas and are ideal as freestanding data marts that offload complex queries from large, maxed-out data warehousing hubs. Analytical databases, as software-only solutions, run on a variety of hardware platforms and are good for organizations that want to tune database performance for specific workloads or run the RDBMS software on a virtualized private cloud. Analytical services are great for development, test and prototyping applications as well as for organizations that don’t have an IT department or want to outsource data center operations or get up and running very quickly. File-based analytical systems and non-relational databases are ideal for processing large volumes of Web traffic and other log-based or machine-generated data. Organizations need to carefully evaluate the type and capabilities of the analytical platform they need before purchasing and deploying a system.

Choosing a Vendor

When an organization is implementing or purchasing an Analytical database from a vendor they need to keep in mind the 4 important criteria before choosing them

  • Whether the selected vendor will meet your requirement?
  • How successful the vendors are in implementing an analytical platform?
  • Whether the vendor is liked and trusted by other organizations?
  • How efficient is their support and services?

Interestingly the pricing, customer references and vendor incumbency should be considered as minimum qualifying criteria.

A vendor should have the ability to meet more of a customer’s requirement. This is because many analytical platforms implemented by the startup vendors are new to the market and heavily priced and customers might not know the exact value of the product. So Organizations should make sure that the product they are planning to implement meets their current and future requirement of the DW architecture. Also, Organization should look for the quality and responsiveness of the vendor to meet their needs.

How can your organization choose the right vendor? How can you get benefited by Big Data Analytic?

Email me your interests Shaughn Knight, AVP – Business Development and Inside Sales Operations.


One Response to “Penning the Use of Big Data Analytic in your Organization”

  1. genghiskhan February 24, 2012 at 8:21 #

    This is an absolute golden words that you have posted in this article.this article is very nice which can impress anyone and tempt to post a comment.

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