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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Monthly Archives: February 2013


5 Areas Where Predictive Analytics Can Help Insurance Firms

The insurance industry’s past success has been due, in part, to its cautious and risk-averse nature. But to succeed in the future, insurance companies have to adopt new technologies like predictive analytics, according to a blog post by Joe McKendrick in Insurance Networking News.

image topper insurance 5 Areas Where Predictive Analytics Can Help Insurance FirmsIn the post, McKendrick points to a paper written by Deloitte’s Howard Mills that makes a strong business case for advanced or predictive analytics within insurance organizations.

Mills’ opinion is that predictive analytics will help insurance companies better understand future threats and opportunities. And Mills believes that insurance companies will be more successful by embedding analytics into their business processes, McKendrick explains.

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Why Big Data Analytics Could Spell Big Energy Savings

Coming in July – the ban on incandescent light bulbs of yore. This law, which aims to kill off the light bulb made famous by Thomas Edison, is designed to increase the reliance on more energy-efficient incandescent bulbs and increase the use of compact fluorescent bulbs (CFL).

save power Why Big Data Analytics Could Spell Big Energy Savings  The CFL bulbs are projected to save about $600 million in energy costs per year (if every American replaces one incandescent with a CFL) and last up to seven years, according to the federal government’s ENERGY STAR program.

While the move to more energy-efficient lighting is a big cost cutter, it’s looking at the energy productivity of states, municipalities, homeowners and businesses that will really pay off, notes Tyler Hamilton (@Go2CleanBreak) at The Energy Collective.

The Alliance Commission on National Energy Efficiency Policy recommends “doubling energy productivity by 2020.” And the place to start is with big data analytics.

Hamilton says the first step toward increasing energy productivity is monitoring energy use, combined with “tighter building codes, stricter vehicle emission standards, serious attempts to recycle waste heat at industrial facilities and better tax breaks for companies that install more energy-efficient equipment.”

With data on usage, companies can save money and energy by retrofitting or optimizing the operation of their buildings. And data analytics can provide “information about how buildings function on a day-to-day, even minute-by-minute basis,” says Dan Seto, founder and president of CircuitMeter, a company that monitors and reports on energy usage.

In addition to monitoring for cost savings and energy productivity, companies are just beginning to break ground with big data on “predicting future generation” needs, notes Ben Holland (@BenInBoulder), program manager for the Rocky Mountain Institute’s Get Ready Project, an initiative aimed at integrating electric cars in cities across the US.

Holland predicts energy and big data will become synonymous, as we’re able to track “nearly everything that we do that uses energy.” He says, “We will see the data we’re capturing from electric utilities be used in a variety of ways including predicting future generation needs, balancing renewable energy loads or suggesting measures to consumers for reducing energy use.”

One area of promise in this laundry list of energy-saving uses for big data is monitoring “plug loads,” Michael Bendewald (@MikeBendewald) a consultant with RMI, tells Holland. He suggests measuring plug loads – what tenants and occupants plug into the wall – could give us guidelines for reducing energy use.

But Holland points out that this must be handled with care – “people still want hot showers and cold beer.” However, “entrepreneurs and startups can harness the power of big data analytics to provide those services in a cleaner, more efficient manner,” he adds.

Next Steps:

  • Get answers to questions about energy consumption like: “How has world energy consumption grown and changed over the last 45 years? or “What are projections for world energy markets to 2030?” in our World Energy Survey Analysis demo.
  • Subscribe to our blog to stay up to date on big data analytics and energy productivity.

Amanda Brandon
Spotfire Blogging Team

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Data Analysis to Rule a New Manufacturing Era – Part 2

Developing countries have been the go-to options for US manufacturers for many years because of low labor costs. But a new global consuming class will have emerged by 2025 and the majority of consumption will take place in developing economies, according to a new report from McKinsey Global Institute.

digitalmanufacturing Data Analysis to Rule a New Manufacturing Era   Part 2This will provide a wealth of new opportunities for manufacturers in these emerging markets, especially given the recent decline in US manufacturing during the recession, according to the report.

But these opportunities are developing in a volatile new landscape with dramatic swings in the cost and availability of things like labor and natural resources – a landscape that combines with rising complexity, uncertainty and risk to create an environment that is far more uncertain than it was before the Great Recession, according to McKinsey.

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Data Analysis to Rule a New Manufacturing Era – Part 1

As the world slowly recovers from the Great Recession, the competitive landscape for at least one sector – manufacturing – is entering a new era, ripe with opportunities but also fraught with challenges, according to a new report from McKinsey Global Institute.

developpement capteurs optiques sensor development Data Analysis to Rule a New Manufacturing Era – Part 1The winners in the global manufacturing arena will be those companies that can adeptly harness big data with manufacturing analytics to uncover customer insight, identify new markets, monitor sensors and collect after sales data.

This is the first article in a series that delves into how manufacturers can effectively compete domestically and globally in the new post-recession era by wielding big data as a weapon to drive innovation and growth.

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Making Visual Data Pop for Top Brass

Chief among the critical areas of functionality for a business intelligence (BI) platform is its ability to offer end users rich visualization capabilities to enable and strengthen data discovery and data exploration.

bi Making Visual Data Pop for Top BrassThese features are vital because they enable executives and other employees to access, analyze, and grasp large volumes of big data sets quickly and effectively, according to the Gartner BI Magic Quadrant.

Data visualization also lets executives and other end users view information in a variety of different formats and find previously unnoticed trends and insights that can help lead to business or operational breakthroughs.

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How to Deliver More Value with Your BI Solution

Does your BI solution deliver real value to your organization?

Most BI solutions under deliver value to organizations, Steve Dine (@steve_dine), founder of Datasource Consulting, opines over at Information Management.

feat img9834 How to Deliver More Value with Your BI SolutionDine makes a very good point about why organizations continue to struggle with the “value” factor – it stems from three areas: “lack of business involvement, long delivery cycles and poor data quality.”

Using some of Dine’s recommendations for value-driven business intelligence, we’re going to look at three ways to find real value in your BI program and solution.

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Data Analysis Powers Paradigm Shift for Insurers

Consumers are becoming increasingly demanding about the way they purchase products and services – a shift that is affecting every industry from consumer product goods to health care.

risk1 Data Analysis Powers Paradigm Shift for InsurersThis new competitive landscape – where consumers expect the seamless choice and accessibility that online retailers offer – is also challenging the insurance industry, according to a study from PricewaterhouseCoopers.

The research report, “Coming to Grips with Market Transformation,” summarizes key findings from interviews with 92 insurance CEOs in 39 countries.

The report notes that the insurance industry is poised to evolve from a “reactive claims payer to preventative risk manager,” a move that’s powered in large part by data analysis and data discovery.

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Data Analysis to Unearth Banking ‘Silk Roads’

It likely will not be as Herculean an effort as it was for China’s Han dynasty to expand the Silk Road trade routes, but banks are turning to big data to help uncover their own Silk Roads, according to a recent Wall Street Journal article.

silk road Data Analysis to Unearth Banking Silk RoadsFour of the nation’s largest banks are beginning to exploit data analysis to successfully tap into the gold mine of information often hidden in big data, unstructured data like social media posts and call center notes that don’t easily fit into conventional databases.

For example, JPMorgan Chase & Co is combining its credit card and transactional data with publicly available information from the US government to unearth consumer trends, which the bank is then offering to its clients.

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Strengthening Customer Loyalty via Data Discovery

One of the strengths of analytics tools is their ability to reveal previously hidden insights about customers to help companies strengthen their relationships with those customers.

customer loyualty Strengthening Customer Loyalty via Data DiscoveryThis includes mining information that customers share in social media channels and contact center interactions that can uncover the types of things that matter most to customers (e.g., timely service, consistent experiences, private shopping opportunities).

Strategies for attracting and retaining customers are much simpler when efforts to drive repeat business are largely centered around segmenting customers and creating the right incentives and rewards to earn loyalty, as a recent article in Information Management highlights.

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Applying Analytics to Manufacturing Capacity Planning

As manufacturers continue to face intensified global competition and increased pressure on profit margins, they can use analytics to identify ways to improve efficiencies and run their operations more effectively.

200x200 0027 Toolbox Applying Analytics to Manufacturing Capacity Planning“Virtually every facet of the manufacturing operation – product design, customer relations, finance, risk, supplier and partner management, and sales and marketing – is now on a quest for more accurate and accessible information,” according to a Deloitte post in The Wall St. Journal.

Analytics can help manufacturers with a number of opportunities including the ability to identify, quantify, and prioritize margin improvement opportunities by delivering thorough information that provides insights into SKU velocity, price band and segment performance, etc.

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