Category Archives: Insights

Ever thought about how big data could improve airports?

Most people are aware of the fact that large, international airports generate millions of dollars in revenue for their respective regions. But the fact is, even smaller airports play a huge role in the economics of their hometowns. For example, in 2010, the State of Washington calculated that a single-runway airport in the small town of Moses Lake had an overall financial impact on the region of $770,000. Imagine how the growth of that income might produce more jobs and even better economics – and big data can play a key role in making that dream a reality.

The aviation industry as a whole is undergoing massive changes. Today, airlines need to work with airports and airport managers to understand passenger flow, reduce costs and enhance revenues. It’s an industry ripe for data-driven marketing, particularly if an IT solution can provide insights from both structured and unstructured data in real time so that there is a cohesive understanding of passengers and their behaviors.

For this reason, the aviation industry, and in particular, aviation facilities, represents a new and exciting area of big data business consulting for Alacer.

The data generated within this environment is so complex that it is often difficult to slice and dice using traditional tools. Usually, the structured data is managed through customized databases, but unstructured data is held in separate, functionally specific warehouses or embedded within software.

We recently had the opportunity to address this problem as we worked with an aviation management company to help it better examine its vast amounts of available data. Specifically, we needed to find a way to determine whether or not the management company’s Passenger Facility Charge rebate was achieving its goal of boosting revenue/traffic on selected routes.

An aviation facility’s ecosystem stretches from its ground handlers and supply chain to retailers, security and air traffic control. Technology is the sole enabler that can help this diverse set of customer touch points work together to maximize per-passenger revenues.

For this particular client, we designed algorithms and data analytics tools that would allow airport managers to track each passenger on incentivized routes, award a rebate as appropriate and have immediately updated access to information on the cost and success of the program. They could also calculate the amount of revenue the rebated passengers generated in the airport’s retail outlets, such as its Duty Free store. As a result, the managers could holistically see whether or not substantial rebates were of value to the airport authority and on which specific routes and periods they assisted in market growth.

As a side note, the airport authority chose to share its big data insights with the airport’s partner airlines and retailers, creating deeper relationships.

by Ed Sarausad

We’ve talked a lot about how big data is being used within industries such as banking and healthcare – but we’re equally fascinated to see how big data is collected and analyzed by other industries. Energy-related companies are creating and deploying big data based projects that are accomplishing everything from the creation of a more intuitive smart grid to helping homeowners be more energy efficient.

The energy sector’s data management issues are the same as those experienced by other industries. Utilities are looking for measurement and analytics systems that enable them to tap real-time information and to use it to take appropriate actions to avoid shortages. Here’s one example: 10 years ago, a real-time measurement system that could provide data from multiple points on the energy grid to avoid a blackout was a pipedream. That’s why, in 2003, the U.S. experienced a massive blackout affecting more than 50 million people in the Northeast and Midwest and even into Canada.

Fast forward to 2013. Today, we have phasor measurement units (PMUs) in place that measure electrical waves on a grid. PMUs are essential components of a smart grid monitoring system, and more than 150 are already installed and running. The first deployment was right in our area at the Bonneville Power Administration, which provides 35 percent of the power in the Pacific Northwest. It established a Wide Area Management System in 2000 to rapidly transfer and digest data collected from PMUs in real-time, enabling it to improve power flow over the grid and minimize power disruptions to consumers.

It’s easy to see how big data algorithms are helping utilities crunch the data from PMUs to learn from the smart grid; however, a massive amount of data is now becoming available to utility companies via homes with smart meters. In a survey conducted by Oracle:

• smart meters were delivering information on outages (78 percent), voltage usage (73 percent), tampering (63 percent) and diagnostics (56 percent).
• responding utility companies see a need to improve their ability to take this information and turn it into actionable intelligence; 64 percent of utilities said it was one of their top three priorities

On the downside, the survey also pinpointed that utilities are still struggling to deal with the mountain of information now at their fingertips; they only rate themselves as 6.7 on a scale of 10 in preparedness, and 50 percent say they have missed opportunities to deliver useful information to customers.

As progress is made in this area, generated data will play a crucial role in tightening smart grid operations. One day in the very near future, real-time data crunching will make the grid self-healing, needing far less hands-on maintenance, reducing disruptions due to weather and safeguarding resources for the future.

The Alacer Group is currently working on a project dealing with big data and energy systems. Watch for more information in the coming months.

by Ed Sarausad

 

On Saturday, Richard Paxton and I will be guest lecturers at the University of Washington – Bothell to talk about how process analytics can be better utilized in the healthcare setting, particularly when it comes to redesigning processes based on Lean principles.

We’ve talked and written on this topic in the past, most recently in Richard’s blog post. Hospitals and healthcare facilities typically have a lot of structured and unstructured data available that can be used to build a process flow model. The beauty of a model is that it allows operational changes to be tested, observed and fully optimized before they are actually instituted within the facility. This is an important benefit, since model testing minimizes the impact change has on patients and medical personnel.

We’ll be discussing this and other related tools, such as Value Stream Mapping IGrafx.

We’re also talking for the first time about a new project we’re initiating with UW School of Business grad students that involves using a mobile application to collect and validate data that impacts customer satisfaction within the healthcare setting.

The program is being held Saturday, October 12 at 10:00 a.m. at the North Creek Events Center, University of Washington – Bothell campus.

 Ed Sarausad

In today’s economic world, financial organizations are struggling to remain profitable when escalating costs are the norm.  Design for Six Sigma (DFSS) is one way to bridge the gap between profitability and cumbersome processes, often legacy ones, that no longer operate at peak efficiency.

Lean Six Sigma has been used within the financial community for more than a decade, particularly in the area of continuous process improvement.  It is still a popular and effective tool to improve financial services operational efficiency; however, many organizations are finding that identifying and reducing incremental defect variability does not fully maximize the full spectrum of improvement opportunities. To achieve a larger return on investment, many organizations are discovering how DFSS can help them re-examine, radically recreate and often build entirely new processes.

DFSS is a separate and emerging business process management methodology (process generation rather than process improvement) that helps organizations determine its customers’ needs and to use that information to create a solution.  These techniques include tools and processes to predict, model and simulate the product delivery system (the processes/tools, personnel and organization, training, facilities, and logistics to produce the product/service) as well as the analysis of the developing system life cycle itself.

Financial organizations rely on a complex network of interrelated processes and systems to deliver quality products and services for their customers.  New levels of performance and quality can be achieved using DFSS tools to take an end-to-end view and create a new design that crosses existing functional boundaries.

Case in point: we worked with a national bank with thousands of branches across the U.S. to solve its increasing Suspicious Activity Report (SAR) workload.  While SARs play a significant role in reducing the country’s vulnerability to terrorist threats by identifying potentially fraudulent activities, the process was causing an increased financial burden on the financial organization.  Specifically, the bank was experiencing an unanticipated related cost of $40 million in vendor and contracting expenses.

Alacer partnered with existing bank vendors to develop a multiphase approach that focused on expense controls, end-to-end process reviews and the identification of high variability areas.  Using DFSS principles, Alacer developed new processing and staffing models to address work backlog and to mitigate operational impact.  As a result, the bank realized $36 million in savings over three years, and $16 million in savings through vendor contract reductions.  The result outperformed regulatory expectations and was designated “Best in Class” by government agencies.

Going forward, DFSS principles can help future-proof banking systems.  While sales, fulfillment and service functions will remain the same, what will differentiate a financial institution will be the way it delivers the products and services customers want, in the way they want it and at a level of efficiency that makes sense for the business. When used in conventional and unconventional ways, DFSS can help organizations deliver this value to customers and, simultaneously, increase the bottom line.

For more information, check out our new white paper, “Design for Six Sigma Drives Increased Revenue for Financial Services Firms.”

 by Richard Paxton

The explosion of online games has resulted in the creation of a new industry: in-game currency, currently valued to be $1 billion in the U.S. alone.  But game developers are losing significant revenue as savvy social game players figure out how to “game” the system by stealing currency rather than buying it.  Normally, network security flaws involving game-play such as this are identified and solved on the back end, often by using thousands of servers at high capital and operational expense.   IT security personnel will play back every transaction and analyze it in order to determine who the cheaters are and how they are manipulating the game.

Online game developers have vast amounts of rich, unstructured data at their fingertips that could be used to help achieve revenue stability. The data delivers an understanding of each game player, and what he or she is doing while in the game.  With this in mind, what if that data could be manipulated to identify and stop fraudulent game activities in real time?

The Alacer Group tested this premise when it was hired by one of the world’s largest casual online gaming companies to halt revenue leakage caused by cheating players.  Alacer used analytics to examine and model the game to determine common player navigational paths. It was then possible to design mathematical algorithms that could place parameters around the average player and predict the average way he or she would progress in the game.  This information could then be used to determine the threshold range for what would be acceptable play.  Any players that fell outside of the threshold range would be flagged as being potentially fraudulent. The game developer could then choose to immediately freeze accounts for those advancing too quickly—thereby plugging the revenue hole.

It should be pointed out that not every game developer chooses to ban a fraudulent player when he or she is identified. Sometimes, after running a basic cost-benefit analysis, the cost of the fix is higher than the loss of revenue attributed to cheating players. However, taking a broader view of the analytics, it may be possible for the fraud analysis to more than pay for itself.

For example, rather than using the extracted data merely to fix security breaches and stop cheating players, a developer might also use the data to provide marketing and sales insights. Slower game players and one-time users, quickly identified through the data analysis, might be pinpointed and targeted for promotions to entice faster and more frequent play—increasing company revenues. Other monetization strategies might also be developed through a closer examination of how players navigate the game.

For those interested in how data analytics is used specifically within the gaming genre, a good primer is now available on Amazon: Game Analytics: Maximizing the Value of Player Data by Magy Seif El-Nasr, Anders Crachen and Alessandro Canossa.

Author: Ed Sarausad

Most hospitals and clinics don’t have the luxury of experimenting with operational changes that might affect the quality of patient care. So how can these facilities enact continuous improvement solutions without incurring the wrath of healthcare providers and recipients?

Process simulation, or designing a model that can mimic the real-world care delivery process, has stepped forward as an option. Since hospitals typically have a lot of structured and unstructured data available, ranging from volume and time data to detailed patient records, that data can be used to build a process flow model.

Within the model, operational changes can be tested, observed and fully optimized before they are actually instituted within the facility, minimizing impact on patients and medical personnel.

The Alacer team recently created a simulation model of a clogged waiting room within a major Midwestern hospital’s outpatient pediatrics clinic. Over half of the “well baby” appointments took longer than their allotted time, resulting in needed follow-up visits to complete tests and vaccinations – amounting to over $400,000 in lost revenue annually. It was quickly apparent that the right resources were not in the right places at the right times to support patient surges and deliver maximum throughput. Adding to the complexity was the fact that it was a teaching hospital, where residents needed the advice of the attending physician and to have scheduled hands-on experience with patients.

To identify pain points and areas of opportunity, the Alacer team conducted a detailed assessment of the site, gathered data and documented potential improvement opportunities. Some of these deliverables included an examination of staffing schedules, time/motion studies, an inventory review (process/aging/ordering), flow maps (patient/physician/staff) and department specific performance data, such as wait time.

Once data acquisition was complete, the existing environment was digitally replicated. We then created multiple simulation modeling “what if?” scenarios that changed the number of patients arriving at any given time, the number of nurses available, the length of time a resident worked with a physician, etc. On the seventh try, we found the formula that would deliver the best operational results for the clinic — and it resulted in a 98.8% reduction in cycle time and a whopping 188.1% reduction in patient wait times.

There probably isn’t a single panacea for curing healthcare operational woes. But with this one example, it’s clear to us that simulation modeling can be successfully used to develop new and more profitable ways of managing healthcare delivery processes and optimizing capacity.

 by Richard Paxton

It is more costly to find and add a new customer than to maintain and grow an existing one.  In fact, Gartner recently reported that marketing budgets for customer retention will increase by more than 50 percent by 2015.  This is especially true in mobile telecommunications, where intense competition means consumers are bombarded with images, slogans and low-cost enrollments to entice them to jump from one carrier to the next.

By mining unstructured data to understand and track the variables that cause a customer to leave a service provider, we can predict the level and likelihood of customer churn – and give a carrier a roadmap for countering churn to help maintain profitability.

Alacer recently worked with a U.S.-based telecommunications firm to help it develop a plan to proactively reach out to vulnerable subscribers before they terminated their contracts.   To do this, we took a profit-centric approach to the task by developing a Profit-Churn score for each customer.  Over 70,000 subscribers with 75 relevant data points were examined, of which more than a fourth had already churned.

By considering the profitability of each customer, we could then prioritize which customers should receive immediate attention, and identify the high cost/low return customers where churn might actually be desirable.

And this is where it got interesting: we learned that 35 percent of the company’s subscribers were more profitable than the average customer, and that they generated a whopping 61 percent of the company’s cumulative expected profit per month (see chart).

Once the most profitable customers were identified, the company could then concentrate its retention efforts on those with the highest likelihood of termination.  It effectively moved the company from a reactive stance to a proactive one – and it’s just one example of how Alacer utilizes big data to provide its clients with a useful framework for developing a marketing plan of action.

 by Ed Sarausad