Trends and Outliers
TIBCO Spotfire's Business Intelligence Blog
2013
Making the Business Case for Mobile BI
As the BYOD (bring your own device) movement continues to gain momentum across industries, IT and business leaders are pushing to find ways to leverage the use of employees’ mobile devices to improve productivity.
Popular enterprise mobility apps include mobile sales force applications, field service, and work/share apps.
But there’s also growing interest in the deployment of mobile business intelligence (BI) tools that employees can use on their smartphones or tablets to improve decision making among business leaders and employees, regardless of where they may be located at a given time.
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2013
Using Analytics to Pinpoint Natural Gas Reserves and Production
As energy companies continue to search for new areas to drill for and extract natural gas, energy producers can use analytics to identify potential locations for natural gas deposits as well as to better forecast their anticipated production rates.
Analytics have already proven to help oil companies such as BP identify opportunities to increase crude oil output.
For example, BP is using predictive analytics, visualization tools, and deep-sea drilling technologies to access previously unattainable oil reserves and to dramatically increase crude oil production, according to a blog post in the Wall Street Journal.
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Filed under: Big Data, Big Data Analytics, Energy, Predictive Analytics
2013
Making Better Decisions with Big Data
Big data may be all the rage. But if the quality and integrity of the data that’s collected and used from structured and unstructured data sources is suspect, then business leaders won’t be properly armed with the insights they need to make well-informed decisions.
When business leaders can access company performance data faster, react more nimbly to business events, and be more accurate in their decision making than their competitors, “then your company can begin to distinguish itself in the marketplace,” notes Aberdeen Group researcher Nathaniel Rowe in a recent report about gaining accurate information from big data.
According to Aberdeen’s survey of 125 organizations, 56% of best-in-class organizations, or the top 20% of aggregate performance scorers, report they’re using faster, more complex analytics to gain a competitive advantage over their peers.
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2013
How BI and Data Analytics Pros Used Twitter – Greenie Edition
It’s been a fun month on Twitter in the BI and data analytics space. And with lots going on in the world of energy including climate change talks and making the world greener with big data analytics, we thought it would be fun to take a look at what’s been happening from the “greenie” perspective.
As we mention in a recent blog post, a green strategy is a hot-button issue for the upcoming year in the Obama administration. A key follow for this initiative is Amy Harder (@Amy_NJ), the energy and environment correspondent for the National Journal.
A good news source we’ve discovered on Twitter for green energy and data analytics looks to be the Green Computing Report (@GreenCompReport). This wire service of sorts releases regular news, features and research on the greening of IT.
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2013
Data Analysis to Rule a New Manufacturing Era – Part 3
In his recent State of the Union address, President Barack Obama outlines a new public-private initiative to create 15 manufacturing hubs in the US. The plan calls for the private sector to work with the federal government to offset some of the domestic manufacturing jobs that have been lost for years to outsourcing.
Indeed, manufacturing is entering a new era, one with a competitive landscape that’s been forever changed by the recession that has stifled demand for many goods and services.
Yet, at the same time, manufacturers will soon encounter a new paradigm shift where emerging economies like China and India, once seen only as sources of cheap labor, will be home to the vast majority of the world’s consumer class.
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Filed under: Big Data, Big Data Analytics, Manufacturing analytics
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.
In 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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Filed under: Advanced Analytics, Insurance, Predictive Analytics
2013
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).
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:
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Amanda Brandon
Spotfire Blogging Team
2013
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.
This 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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Filed under: Big Data Analytics, Manufacturing analytics
2013
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.
The 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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Filed under: Big Data, Big Data Analytics, Manufacturing analytics
2013
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.
These 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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Filed under: Big Data, Big Data Analytics, Business Intelligence, Enterprise
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