Vertical Industry BI/Analytics
The timely and comprehensive analysis of data is vital to organizations in a variety of vertical industries. The question though is what exactly are you trying to accomplish:
- Are you trying to answer 3 critical performance questions: How are we doing? Why? What should we be doing?
- Are you trying to: ”Sense and Respond” (BI) or “Anticipate and Shape” (Analytics)?
In every vertical the future belongs to the companies and people that turn data into products / unique services. Let’s look at a few examples.
The communications industry is characterized by intense competition and customer attrition, or “churn.” Targeted marketing opportunities and the rapid response to behavior trends are paramount to the success of communications service providers in retaining existing customers and attracting new customers. Customer relationship management, or CRM, analyses need to be constantly and quickly performed, to enable service providers to market to at-risk customers before they churn, offer new products and services to those most likely to buy, and identify and manage key customer relationships. Other key analytical needs of communications service providers include call data record analysis for revenue assurance, billing and least-cost routing, fraud detection and network management.
Digital Media and E-commerce
For online businesses, the process of collecting, analyzing and reporting data about page visits, otherwise known as click stream analysis, is required for constant monitoring of website performance and customer pattern changes. In addition to needing to address the operational and customer relationship challenges faced by traditional retailers, digital media businesses must also analyze hundreds of millions or even billions of click stream data records to track and respond to customer behavior patterns in real time. Additionally, with online advertising becoming a major revenue generator, many digital media businesses and their advertisers need to understand who is looking at the advertisements and their actions as a result of viewing the advertisements. Fast analysis of online activity can enable better cross-selling of products, prevent customers from abandoning shopping carts or leaving the web site, and mitigate click stream fraud.
With thousands of products and millions of customers, many retailers need sophisticated systems to track, manage and optimize customer and supplier relationships. Targeted marketing programs often require the analysis of millions of customer transactions. To prevent supply shortages large retailers must integrate and analyze customer transaction data, vendor delivery schedules and radio frequency identification supply chain data. Other useful analyses for retail companies include “market basket” analysis of the items customers buy in a given shopping session, customer loyalty programs for frequent buyers, overstock/understock and supply chain optimization.
See this blog posting for KPIs for Retail Industry.
Financial services institutions generate terabytes of data related to millions of client purchases, banking transactions and contacts with marketing, sales and customer service across multiple channels. This data contains crucial business information on client preferences and buying behavior, and can reveal insights that enable stronger customer relationship management and increase the lifetime value of the customer. In addition, risk management and portfolio management applications require analysis of vast amounts of rapidly changing data for fraud prevention and loan analysis. With extensive compliance and regulatory requirements, financial institutions are required to retain an ever-increasing amount of data and need to make this data available for detailed reporting on a periodic basis.
See this blog posting for KPIs for Financial Services Industry.
As some of the largest creators and consumers of data, government agencies around the world need to access, analyze and share vast amounts ofup-to-date data quickly and efficiently. These agencies face a broad range of challenges, including identifying terrorist threats and reducing fraud, waste and abuse. Iterative analysis on many terabytes of data with high performance is crucial for achieving these missions.
Health and Life Sciences
Healthcare providers seek to analyze terabytes of operational and patient care data to measure drug effectiveness and interactions, improve quality of care and streamline operations through more cost-effective services. Pharmaceutical companies rely on data analysis to speed new drug development and increase marketing effectiveness. In the future, these companies plan to incorporate large amounts of genomic data into their analyses in order to tailor drugs for more personalized medicine.
The primary purpose of these companies is providing BI support to enterprises on an outsourced basis. Outsourced analytic providers serving many industries, including retail, telecommunications, healthcare and others, provide clients with domain expertise in database-driven marketing and customer segmentation. Since their clients are looking for faster turn-arounds for more sophisticated reports on continuously increasing amounts of data, these companies require solutions that will scale better with lower cost of ownership to meet their clients’ service-level agreements, while improving their own profitability.
Build information platforms or dataspaces – IT and Storage Industry
The significant growth of enterprise data is fueling a need for additional storage and other information technology infrastructure to maintain and manage it. These technology needs are being further driven by a steady decline in data storage prices, which makes storing large data sets more economical.
As the volume of data continues to grow, enterprises have recognized the value in analyzing such data to significantly improve their operations and competitive position. They have also realized that frequent analysis of data at a more detailed level is more meaningful than periodic analysis of sampled data. In addition, companies are making analytic capabilities more widely available to a broad range of users across the enterprise for both strategic and tactical decision-making.
These factors have driven the demand for next generation data warehouses infrastructure like Hadoop that provide the critical framework for data-driven enterprise decision-making by way of business intelligence.