Tag Archives: Big Data Analytics

Big Data Utilization

17 Jul

If big data is such a popular technology, why haven’t more enterprises adopted it? What benefits can BI professionals expect, and what best practices can make your big data project a success? Where will big data be in five years?

Business value of big data

Beyond providing the traditional data from the transactional systems, big data is capable of providing business insights that offer valuable perspectives from a B2C model and insights into B2B model. Therefore one can perform deeper contextual analytics by integrating big data and data warehouse (DWH) analytics into one platform, which was not possible earlier.

Utilization of big data analytics

Big data enables BI professionals to leverage expanded analytics and create round the clock view by providing access to consumable data. Let’s see how big data analytics are being utilized.

For CRM systems, you can create powerful bird eye view of customer opinions, wish lists, and customer response data from campaigns to evaluate true campaign potentiality. You can model and forecast customer behaviors by integrating data across call centers, blogs, forums, and social media platforms into deeper analytics. You will have the ability to position better call-center metrics for customer management or even produce an efficient micro-targeting and micro-segmentation model for new customer acquisition that can provide better response rates of acceptance.

If your organization deals with products and/or services, with big data analytics you can create powerful models for trends, behaviors, and markets, and you can solve research and intellection issues by leveraging tasks to a distributed group of people and embedding analytical results from your work. If you work in the utility industry, you can create predictive models of consumer markets by implementing technologies such as a smart grid. This would help organizations to create more revenue opportunities in advisory services and render better models for rate management.

Healthcare is another popular industry where utilization of big data analytics is exponentially high. For example, service providers can leverage big data to deploy Body Area Networks (an application of wearable computing devices that enable wireless communication between several miniaturized body sensor units (BSU) and a single body central unit (BCU) worn at the human body), helping lower patient costs while providing “patient-centric” services. Lowering costs and enabling efficiencies are critical goals for hospitals, nursing homes, and clinics. Another application of big data is to optimize clinical trials to prevent errors, reduce costs, and ensure compliance and ensure that regulatory requirements are met consistently. Although these analytics are partially fulfilled today, their expansion will enable proactive approaches rather than reactive ones.

With new technologies being evolved into the burgeoning BI market it’s possible to integrate any information into traditional platforms. These data points can be represented in analytics and visualization that can help any organization in any industry to improve their quality of services.

Difference between big data and a traditional analytics

With respect to knowing why more organizations are adopting big data analytics, we need to know the difference between traditional and big data analytics. Traditional analytics are based on structured data providing only the insights of an issue but often fall short in predictive and indicative analytics. Therefore, lack of near-real-time information and expanded information beyond structured data is unavailable. This is where big data analytics comes into existence enabling better analytical insights by integrating more voluminous data of varying complexity and timeliness into one structured output.

After integrating text, voice, streaming data, and unstructured data analytics into one model, we will be able to tackle the different aspects of related information into analytical models rendering potential, multi-dimensional metrics that can be leveraged with traditional analytics.

However, adoption of big data is less amusing than expected in traditional enterprises because current business models and goals don’t demand big data integration. Moreover, there is no perceived additional value offered by big data as to the organization. Hence, there is no clear business case articulated, and thus no business value calculated.

There are other suppressing factors. Lack of understanding of big data by the executives, which also brings processing complexities that create additional stress on IT teams (in terms of maintenance) and business teams (in terms of adoption and usage). In these times of financial constrains, IT teams obviate to implement yet another new system or technology.

Legitimately, there is going to be definitely a vulnerable change in the infrastructure for executing the current technologies in the future. It might take a long and complex period, but with guided navigation from concept to adoption, big data will continue the authority into the predictable future.

About the Author

Shaughn is an industry analyst for business intelligence. For over ten years, he has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Shaughn is the channel expert for BI for the small and Mid-Market segments at ZSL and conducts research of leading technologies, products and vendors in business intelligence, marketing performance management, master data management, and unstructured data. He can be reached at shaughnk@zslinc.com. And please visit Shaughn’s blog atzslbiservices.wordpress.com

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.