Tag Archives: big data utilization

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