How mature is your decision-making strategy?

In recent healthcare blogs I’ve looked at the need to drive more value from the UK’s National Health Service (NHS) and how this relies upon the ability to make decisions based on robust, data-driven insights. But what value will these decisions have if they're not founded on a mature data strategy?healthcare

The answer is obvious. So, the more interesting questions are these: What makes some organisations pioneers when it comes to data management and analytics, and what steps are they taking to mature their strategy in this area?

SAS recently polled some 600 senior-level decision-makers across all sectors and gained some interesting answers that will be very simple for NHS leaders and clinicians to deploy.

But before I go on, let’s just clarify: In this situation, data maturity means having a consistent and single view of all data sources and integrating the core components, such as collection, cleansing, analysis and its application. Read More »

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Analyzing social networks using Python and SAS Viya

viya_drug_network_community_betweenessThe study of social networks has gained importance over the years within social and behavioral research on HIV and AIDS. Social network research can show routes of potential viral transfer, and be used to understand the influence of peer norms and practices on the risk behaviors of individuals.

This example analyzes the results of a study of high-risk drug use for HIV prevention in Hartford, Connecticut, using Python and SAS. This social network has 194 nodes and 273 edges, which represent drug users and the connections between those users.


SAS support for network analysis has been around for a while. In fact, I have shown related techniques using SAS Visual Analytics in my previous post. If you are new to social network analysis you may want to review the blog first as it provides a great introduction into the world of networks.

This post is written for the application developer or data scientist who has programming experience and seeks self-service access to comprehensive analytics. I will highlight how to gain access to SAS® ViyaTM using REST API in Python as well as demonstrate how to drive a simple analytical pipeline to analyse a social network.

The recent release of SAS Viya provides a full set of innovative algorithms and proven analytical methods for exploring experimental questions but it's also built based on an open architecture. This means you can integrate SAS Viya seamlessly into your application infrastructure as well as drive analytical models using any programming language. This blog post highlights one example of how this openness can be used to access powerful SAS analytics.

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What does IoT mean for utilities?

utilitiesUtility leaders are struggling with a world that's quickly changing and barely recognizable from the one they knew growing up. Many of the old assumptions are gone, and the business model upon which careers have been built is on the verge of disappearing. So what does the internet of things (IoT) have to do with any of this? A lot.

Over the last 25 years, I've been intimately involved in many of the events that have lead our industry to this point -- and having the graying temples to prove it, I feel qualified to make a few observations as to why IoT matters.

First of all, let's agree on a simple IoT definition: the internet of things is the networking of physical devices (typically referred to as "connected devices" or "smart devices") that are embedded with electronics, software, sensors and network connectivity that enable these objects to collect and exchange data.

Sounds a lot like the smart grid, doesn't it? In fact, being the utility guy in a large analytics software company that serves many industries, this is one time where I can raise my hand and say, "We have one of those!" Read More »

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Always Open

Open.Open sign

The very word evokes a sense of happiness and possibility.

When you’re hungry at an odd hour and everything around you seems to be closed, that lone neon sign glowing in a restaurant window is a most welcome relief.

When a shop or service you’ve longed for finally builds a branch in your neighborhood, you count the days until it opens.

And when you’re solving a tough analytics problem, “open” is what you want and need your software to be.

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Not your father’s IT Department

serversToday’s IT department isn’t your grandfather’s IT department. It’s not even your father’s IT department. When people talk about Information Technology Departments of the past, it's usually broken into three distinct periods: The Mainframe; PCs; the Internet/post PC.

The IT department was seen as the hardware support arm of an organization -- the data center in the basement, out of sight and out of mind until something broke. But with the advent of networked computers and the invention of the web, the next logical phase of IT was managing those environments and the incredible volumes of data that came with them.

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The analytics of things ... and sports

Who cares about sports and data? Not just athletes, coaches and fans. It turns out that many companies outside of sporting organisations are also associated with the sports industry.  For example, financial services organisations are actively involved in sports sponsorships. Retailers sell fan merchandise. Telcos build social engagement strategies around sporting events. And more recently, sporting apps and devices are a large part of the internet of things (IoT) data deluge. At a recent analytics in sports conference, almost every delegate at the conference talked about wearables, video imaging and smart building devices to stream instant data.

I recently attended the Analytics in Sports conference in my hometown of Melbourne, Australia along with more than 750 attendees from all types of industries. It was fascinating to engage with the sporting buffs who are also crazy about data.  Who would have thought, right?

Attendees from every industry were scratching their heads over HOW to use the collected sports and performance data for commercial value.

ballsEnter the analytics of things (AoT) - the ONLY application that is going to bring meaning to the emergent IoT creation.  Here are some facts as to why you should start considering the AoT.

  • Fact 1: Cisco - 50 billion devices will be connected to the internet by 2020 – that’s only one Olympics Games away!
  • Fact 2: Gartner - Economic value of the IoT, across a number of industries, will reach $1.9 trillion worldwide by 2020.

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ESSA – accountability, indicators and analytics to drive informed decision making

Last December, The Every Student Succeeds Act (ESSA) was signed into law to ensure opportunity for all students in the United States. As part of this federal legislation, states now have the flexibility to design their own accountability systems following certain parameters outlined in ESSA. These accountability systems include academic and non-academic indicators. By using analytics and dashboards to monitor these indicators, states can discern how well students, schools and districts are performing, and help to identify where changes need to be implemented, allowing them to course-correct ineffective programs.

To understand more about the the new accountability systems and what SAS is doing to help education agencies and states with the analysis of their selected indicators, I interviewed Emily Baranello, Vice President SAS Education Practice, Susan Gates, SAS Special Advisor on Education and Nadja Young, Senior Education Manager, SAS State and Local Government Practice.

As part of ESSA, states must include more indicators in their accountability systems than were required under No Child Left Behind or U.S. Department of Education regulations. Can you explain?

Susan Gates: Each state must include at least three “academic” indicators, such as test scores, English language proficiency, graduation rates, and student growth, and at least one “non-academic” indicator, such as access to AP courses, teacher qualifications, and school climate. These new systems can be directly correlated with each state’s ultimate education goals for their students, such as ensuring college- and career-readiness, allowing interventions as early as possible to keep students progressing. Read More »

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Building clinical data and insight visually

84751061Have you ever been involved in executing an exploratory analysis based on an integrated clinical trial database? If so, you've probably experienced firsthand how elaborate the initial phase of data access and data processing can be.

Market analysts estimate the ratio for preparing the data, compared to actually analyzing the information, is often 80:20. So under normal circumstances, the vast majority of your time is spent with data preparation activities instead of gaining meaningful insights.

Historically, data preparation has been a classic IT task, requiring time consuming interactions between the business departments and IT. The good news is that agile BI has ushered in a new way of gaining access to your information. Modern analytical tools give statistical programmers and project statisticians the ability to prepare data on the fly, providing data insight quickly and without the complex process of engaging external departments, such as IT.

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Machine learning changes the way we forecast in retail and CPG

machine-learning2Machine learning is taking a significant role in many big data initiatives today. Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement and create more accurate demand forecasts as they expand into new sales channels like the omni-channel. With machine learning, supercomputers learn from mining masses of big data without human intervention to provide unprecedented consumer demand insights.

Predictive analytics and advanced algorithms, such as neural networks, have emerged as the hottest (and sometimes controversial) topic among senior management teams. Neural network algorithms are self-correcting and powerful, but are difficult to replicate and explain using traditional multiple regression models.

For years, neural network models have been discarded due to the lack of storage and processing capabilities required to implement them. Now with cloud computing using supercomputers' neural network algorithms, along with ARIMAX, dynamic regression and unobserved components, models are becoming the catalyst for "machine learning-based forecasting." Read More »

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Achieving analytics ROI – the path to success

Are you happy with the ROI on your analytics investments? Recently we’ve seen an upswing in organisations investing in analytical platform capabilities. One can assume the goal of these investments is to turn transactional data into a strategic asset. However, analytics alone will not do it. Unless your organisation is ready and able to invest significant resources in both acquiring and exploiting insights, you’ve potentially bought a vepic1ry expensive analytical toolkit. It’s not until you use those insights for determining the next best step, identifying the next analysis question to ask, or making better decisions faster, that your analytics data can be considered a strategic asset.

Does this sound familiar?

We’ve got the money to build the model, but not to … actually get it into production”
- Quote from a 
University of Wollongong research study.

research team at the University of Wollongong in Australia, recently conducted an external study to determine whether or not those who have enhanced their analytical capabilities are executing on the insights discovered.

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