Road to Rio: the countdown begins!

The 2016 Rio Olympics are less than a year away, and British sports fans are hoping for a performance that beats Team GB’s record medal tally at London 2012. That’s never been achieved by a country directly after hosting the Games. And the pressure’s on us in the GB Rowing Team given that our sport is one of the nation’s strongest Olympic teams and kicked off the incredible “Super Saturday” at the 2012 Games.

Fans will be pleased to know we’re working hard to improve our performance on the road to Rio. In fact, I wrote this recently from high up in the Swiss Austrian Alps, where we’re in high altitude training for the World Rowing Championships. The altitude puts our bodies through different physiological effects and makes training even more strenuous than usual! But coupled with more and better use of data and analytics, we’re confident of making those crucial marginal gains that could be the difference between gold and silver, or even winning a medal at all.

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Identifying sustainable organizations: How does it work?

Sustainability is an idea whose time has come. Individuals, organizations and governments are increasingly recognizing that it doesn’t make sense to compromise the future to meet the needs of the present. To that end, the UN recently replaced the Millennium Development Goals (MDGs) with the Sustainable Development Goals (SDGs). It’s a good start.

In my last post, I discussed the idea that organizations aren’t keeping pace with technology, instead remaining mired in authoritative and competitive mindsets that breed inequality and do little to improve our future. I introduced our work towards a new business model that can instill sustainability and further general happiness, an idea that’s covered in my book, The Sustainable Organisation: a paradigm for a fairer society. Now I want to present the reasoning behind the sustainability metric put forth in that book, and how to use it.

As people, we can make the changes that the world needs to become sustainable, but we cannot make much of a dent on an individual basis. Change can only happen if people organize and work together, meaning that organizations are the pillars of sustainability. How can we measure progress?

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An introduction to machine learning

Machine learning is moving into the mainstream. Once the sole purview of academic researchers and advanced technology firms, machine learning is now being is used by many companies in more traditional industry verticals.

Machine learning uses mathematical (not necessarily statistical) models to learn about data. In this context, learning about data basically means curve-fitting or hyper-plane fitting, without the traditional statistical concerns about underlying distributions of the data or possible differences between the current sample of data and other samples of data. Machine learning techniques handle these concerns in different ways.

Perhaps surprisingly, such machine learning models can be quite adept at making accurate predictions about new samples of data, even when the phenomenon they are trying to predict is very rare or when it exhibits nonlinear behavior.  Read More »

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Appliance malfunctions and predictive modeling?

It’s been very hot here in Northern Italy: electricity provision has struggled to keep up and we’ve had frequent power outages in the area, even within our apartment building. A bit inconvenient? Don’t get me started. I feel like my home appliances have turned against me, taking me back to the Stone Age.

This begs the question – why do we continue to have this problem? What’s the cause?  Is it the grid? Is it excessive appliance usage? Could this have been prevented? From my perspective as a data analyst, I would wholeheartedly say – “absolutely, a resounding yes! This can be prevented.”

Let me explain.

First: based on similar failures in the past, create predictive models that assess the likelihood of appliance malfunction (… and I hope this doesn’t jinx things!), such as dishwashers, electric curtains, washing machines, refrigerator, dryers, and – of course, air conditioning.  What would these models do? Well, they would consider operating characteristics of the different machines, isolate key critical factors indicative of past failures. Then these factors can be monitored to predict the likelihood of future failures - ahead of time - and generate alerts for repairs, usage adjustments, and the like. But that means a lot of individual model, so an automated method to create such predictive models, like SAS® Factory Miner would be the ideal solution, building a “factory of models”.

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Financial intelligence: What it is and why it matters

In my previous post I talked to John Cassara about the growing threat of mobile payments and how mobile phones can be used to launder illicit funds globally.  I spoke with him again recently on the topic of financial intelligence.  Here are the highlights from our discussion.

So what is financial intelligence?

John:  Financial intelligence originated during the “War on Drugs” to help criminal investigators follow the money trail. It started about 1970 with the passage of the Bank Secrecy Act or BSA. Bank secrecy is actually a misnomer. It equates to financial transparency. That’s why today “financial intelligence” is also sometimes known as “BSA data” or “financial transparency reporting requirements.”

Interesting.  But what does it do? Read More »

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What do crime shows and data science have in common?

crime scene tape over laptopI enjoy watching TV crime series like Law and Order, Crime Series Investigation (CSI), CriminalMinds, Numb3rs, Person of Interest, as well as real-life mystery stories on shows like 20/20 and others. Obviously, the popularity of these types of shows means I'm not the only one who enjoys this type of entertainment.

Here at SAS, we often distinguish between two major uses of analytics:

  1. Reporting on historical data to discover patterns and trends based on past events. Often referred to as business intelligence, this type of analysis can identify root causes of events.
  2.  Advanced analytics that can be used to make predictions or optimize complex processes based on multiple data sources. This type of analysis can help you identify relevant factors that occur prior to an event you are investigating, so you can monitor and detect similar situations in the future. This allows you to take proactive migration actions that can lessen the impact or potentially avoid a future situation all together.

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AML Models: Moving from rules to statistical based models to better measure customer risk

Cover page for AML Customer Risk Rating white paper

Learn more about customer risk rating models in this whitepaper.

Financial institutions have been managing their AML models to meet regulatory expectations for some time. But what about customer risk rating models? We’re seeing a trend where firms are re-evaluating whether their heuristic, rules-based customer risk rating models can withstand regulatory expectations.

Rules-based models follow simple analytical formulas, such as, “If a customer is a resident of a certain country then assign them a certain number of risk points towards their overall score.” Statistical models, on the other hand, profile the data take the most relevant risk factors and apply them to them model.

Already, early adopters are finding that statistically-based customer risk rating models, especially ordinal logistic regression, offer the following benefits:

  • More effectively assigns risk.
  • More justifiable to the regulators.
  • Easier to update, validate and maintain.

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Oil and gas data management overview

Black background with shiny squaresIn the oil and gas industry, analytics are used to improve both upstream and downstream operations, from optimizing exploration and forecasting production to reducing commodity trading risk and understanding customer's energy needs.

If you plan to derive value from the digital oil field, big data, and analytics, one of the first things you'll need is a proper data management strategy.

Your oil and gas data management strategy should consider data from existing systems as well as new sources, including all the sensors being added to equipment used in the upstream as well as the downstream areas of the oil and gas industry.

With data flowing in from exploration, drilling, production and usage meters, the rapid growth of data sources presents a data management challenge for the industry.

Consider this excerpt from Chapter 2 of Keith Holdaway's book, Harness Oil and Gas Big Data with Analytics:

Oil and gas operators are faced with a daunting challenge as they strive to
collate the raw data that serve as the very foundation of their business success, transforming that raw data into actionable knowledge. However, with the exponential growth in data volumes and the breadth of siloed, disparate data sources increasing at ever-faster rates, the industry is realizing that data management is fundamental to their success.

Even as data volumes grow, it's important to remember that data management isn't just about storing larger and larger amounts of data but figuring out what data is most relevant for the problems being explored, and then processing this data in a timely manner. Depending on your analytics needs, that processing might involve simple queries or more intensive resources like analytic workloads with more iterations to produce more informative results.

Data management involves handling streaming data, near real-time data, data stored on disk, and data stored in archive systems and being able to integrate all or some of these together in the best formats for analytics and reporting.

It's important to incorporate predictive analytics in some or all of the data gathering or data reporting points.

For example, event stream processing is part of most modern data management strategies because it  allows you to apply if-then type rules to data in stream. It also applies analytics to your sensor data in stream, and take actions with that data as a result of the activity in the data.

Data quality capabilities should also be applied to clean up messy or missing data so decision makers receive quality information that helps them make better decisions in running your organization.

For more details on these topics, please read our whitepaper, Analytic Innovations Address New Challenges in the Oil and Gas Industry.

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Measuring the success of sustainable organizations

Book cover for The Sustainable OrganisationLooking at the top 100 organizations in the world, have you ever wondered which ones we’d really miss if they were to disappear? Give it a try. I bet you don’t choose the ones with the highest profits. You probably make your picks based on meaning. You probably chose the organizations that give us something we truly need, or add to our health and happiness, or take special care to protect resources for future generations.

I started asking these types of questions when I became a family man. Thinking of the future awaiting my children and grandchildren, I grew alarmed by the increasing levels of instability and insecurity in the world today. I wondered why we’re so willing to shortchange the future for the sake of immediate gain, and I set about looking for a way to assess who the good guys are. Who’s doing things differently, and what impact are they having?

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Education meets big data: implement, improve and expand your SLDS

In my previous blog post, I discussed the benefits of a Statewide Longitudinal Data System (SLDS) and shared a SAS book on the subject: Implement, Improve and Expand Your Statewide Longitudinal Data System by Armistead W Sapp III and Jamie McQuiggan.

Implement, Improve and Expand your Statewide Longitudinal Data System

Today, I'm sharing a conversation I had with one of the book’s authors, Armistead Sapp. In it, we discuss state funding, big data and overcoming challenges with patience and persistence.

What was the initial impetus for writing the book and how has it been received?

Armistead Sapp: We wrote the book to address the questions that states might have with implementing, improving and expanding their SLDSs. When I first starting write this, I envisioned we would be talking to the state education leaders and the folks in charge of the SLDS in each state.

As the federal grant program expanded, we realized there was an abundance of funding but there was also a lack of direction. Even after several rounds of funding, states were still struggling. Some states were doing things well and some not so well.

When I started working with the State of North Carolina, on their SLDS at SAS, I realized that there were questions that every state needed to answer. So, I thought it would be important to write a book to share the best practices we were seeing by working with the states on these projects.

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