The Business Analytics Loop and Big Data Applications

Business analytics is all about transforming irrelevant and meaningless data into valuable insights. For analytical practices and approaches to reach their full and optimum potential, business analytics tools and platforms are required to make it easy for customers to take action based on their new insights. They must analyze, feed and monitor the final results of actions taken back into the system in an automated and systematic way to improve and inform the predictive models that led to the insights at the very first place. One can actually consider this process as of closing the big data analytical loop. For an example – A business consumer at a big vendor uses self business intelligence tools to decide whether to offer or not a new promotional campaign. In between user analyzes that the three hour online sale will likely result in significant revenue generation based on the intelligence yielded by the underlying analytics engine. The user has to implement the campaign, keep a track of its performance and makes necessary adjustments to future ones as assured and guaranteed.

In existing environments, the foremost step needs the user to exit the business intelligence application and log into a different campaign management system to raise the sales and its related promotional efforts. The user afterwards waits for a day or two to get the canned and static report with metrics and finally suggest to a central analytics team to adjust the analytical models based on the positive or negative results. If the user is lucky, those suggestions might make their way into the analytic process and loop will never be closed. Customers continue to make decisions entirely based on the analytics and intelligence essentially frozen and numb in time till someone lifts the chain to redo the entire process. Again the whole process gets repeated.

Closing the business analytical loop needs essential anticipation into how data and application infrastructures are crafted and defined. Big data visualization strategies, analytical engines and transactional systems should be capable to bi directionally interact in real time situations. The transactional and BI applications should be tightly blended with the functionality built into BI applications. In an ideal manner, non relational databases should play a role to board multi structured data including social and machine generated data. Cultural issues should be overcome and no longer be an excuse acceptable from IT pros, analysts and business user’s side for clinging on inefficient, outdated and hoaxed analytic processes. So the next time the user conduct a similar kind of analysis, the big data recommendation engine suggestions generated will be informed by the relative success or failure of real world campaigns and the entire process will get repeated. Moreover more iteration in the recommendations and analytics improvises and increases the revenue generation.

Along with this, big data pioneers and supporters maintain that the sheer volume of data and information diminishes the effects of occasional poor quality of data. If you are browsing for peta bytes of data to determine historical trends, a few data input issues will barely list as a glitch on a dashboard or reports. It does not mean that the data quality is not vital to big data. It is true in real time transactional situations. Likewise big data applications that recommend doses and medicines for critically ill patients are better be entrusting over good data. Same goes for big data operational applications that affirm commercial aviation, industrial internet use cases and the power grid. It is mandatory for professionals to acknowledge the source and structure of the data along with the data quality prerequisites for apt big data use cases including recommendation engine in order to evaluate the type and quality of tools and measures to apply. And, the biggest key players dealing in big data and its real time use cases are IBM, Oracle, Microsoft, CSC, 

IB Technology , Informatica and Infosys.

Real World Big Data Use Cases

What actually makes big data technologies including Hadoop and others so compelling and imperative are that they let enterprises and firms to figure out the answers of the questions they didn’t even know to ask for. It can result in deeper insights that drive to new product ideas and assist in identifying ways to improvise operational efficiencies. There is a wide range available in the number of already identified big data real world use cases for leading web giants like Google, LinkedIn, Facebook etc. Some of them are mentioned below in brief.

1.       Recommendation Engine – Online retailers and other web properties make use of Hadoop to suggest customer’s products and services based on the analysis of users profile and their behavioral data. There has been a vast usage of big data recommendation engine used by umpteen applications like LinkedIn empower its visitors by offering them “People you may know” feature and Amazon by suggesting related products for buy to online users.

2.       Sentiment Analysis – Mainly used in combination with Hadoop, and advanced text analytics tools determine the unstructured text of social networking and social media posts including tweets to evaluate the user sentiment detailed to specific companies, products and brands. The analysis starts from macro level sentiment down to the individual’s consumer sentiment.

3.       Risk Modeling – Banks, financial companies and others make use of next generation data warehouses and Hadoop to determine big volumes of transactional data to conclude risk and wide exposure of financial assets, prepare for targeted “what-if” scenarios completely based on counterfeit market behavior and score potential customers for risk.

4.       Fraud Detection – It is recommended to make use of real time use cases of big data techniques to blend historical and transactional data, customer behavior to detect any fraudulent activity. For example – credit card companies use big data technologies in order to identify transactional behavior that represents high likelihood especially in case of stolen cards.

5.       Marketing Campaign Analysis – Big data technologies allow marketing teams of companies to incorporate larger volumes of increasingly crude data such as call detail records and click stream data to raise the efficacy and preciseness of analysis.

6.       Customer churn analysis – Companies use big data technologies and Hadoop to determine the user behavior data to analyze the patterns that symbolize which customers are more likely to leave for a competent service or vendor. Actions would be taken further to prevent to retain the most profitable customers.

7.       Social Graph Analysis – In alliance with next generation data warehousing, social networking and Hadoop, data is mined to evaluate the customers that have the most likely influence over others inside social networks. The process helps firms to determine who the most important customers are, and those not purchase the products and spend much and more related to the same.

8.       Network Monitoring – Big data technologies are employed to analyze and showcase data collected from storage devices, servers and other IT hardware to let administrators to counsel network activity and identify bottlenecks.

9.       User Experience Analytics – Many enterprises use big data techniques to incorporate data from different user interaction channels like online chat, twitter, call centers etc. to gain a complete view of the user experience enabling companies to acknowledge the impact of one channel on another and optimize the entire user lifecycle experience.

10.   Research and Development – Companies including pharmaceutical sector make use of Hadoop and big data to go through huge volumes of text based research and historical data to help in the development of new products.

Above mentioned use cases are the samples and most compelling ones of big data. Though, many more have yet to be discovered and this is the promise of big data. Adding to it, the key players offering big data solutions are IB technology , Amazon, Datameer, Attivio, Zaponet and Microsoft and Caserta Concepts.

Market Research – Big Data

Recently in a market research conducted for big data solutions, it provided a detailed information and analysis on the concept. In big data, basically the data and information is studied, collected and construed by data specialists and experts. With the passing time, the accumulated data also increases. In fact, data used to double and triple each year. As technology soaks into becoming a significant and vital part of our day to day lives, researchers and scientists have created text analytics. It is a tech intricately designed to look for answers to very particular inquiries held in multiple databases all over the internet platform. Of course, as the information increases in amount, it demands a better and more efficient tool to handle and manage its trillions of gigabytes of data. And, this new technology that is applied for the same purpose is known as ‘Big Data’. The technology has tools and techniques that let the data agent to browse for solutions to queries in a vast collection of databases that are humongous in figures as well as too complex to determine in the traditional ways. Big data solutions can not only help in mining and drilling down the big chunks and bytes of data and analyze invaluable answers related to the same but also could examine the data written in multiple programming languages in different formats and posted in different sites.

Questions related to the big data mines and the sentiments of the users represented in varied forms including keywords that are entered in the leading search engines, social networks, applications used across the web irrespective of the fact from where it was, whether from a personal system or a tablet or a smart phone, it can actually delved into all letting the sentiment analysis to be done and completed. Remarkably, big data is booming and growing. It makes sense and adds beneficently to even unstructured data that is much easier and widely used to extract useful information. Most of the companies demand this and prepares other companies for the future and just because of this new tool; many other new opportunities have flourished and boomed. Big data firms demand people and users to know logics of data sciences, mathematics and even programming and manage the huge tech and make sure to keep themselves up to date according to the tasks. The new trends are the opportunities figured and examined by the analysis of the patterns found though big data and many other entrepreneurs are able to find which of the business they should venture into.

The leading firms dealing and handling bigger data sets are IB Technology , Wipro, Cognizant, R Systems, NetApp, Oracle and McKinsey. Last but not least, big data solutions make use of modern analytics software including customized data and predictive analytics to meet the purpose from which big data is created.