The next data governance challenge: Agility
.@philsimon says that data-governance professionals will need to be more agile than ever.
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New features in SAS Enterprise Guide 7.1
SAS Enterprise Guide has come a long way since version 1.0 was released in 1999! Are any of you original users that remember the Help characters, Clippy, Peedy or Merlin? I was working as a statistician for another company that year, and I attended a SAS user group meeting where
Changing the Paradigm for Business Forecasting (Part 10 of 12)
The Aphorisms of the New Defensive Paradigm I want to finish this blog series with a set of 7 aphorisms – concise statements of principle – that characterize the new Defensive paradigm for business forecasting. The first is that: Aphorism 1: Forecasting is a Huge Waste of Management Time This
Analytics: Four key elements for success
Analytics provides better insights into why something happened, or helps provide decision makers with information about what will happen in the future. That allows organizations to act now to improve outcomes instead of reacting to events after they happen. But it takes more than analytics alone. Achieving this level of
How much does it cost to live to 100?
Living to 100 isn't as simple as just paying a certain amount of money for your healthcare. But that is an interesting aspect of longevity, so let's have a look at the data ... In my previous blog post, we analyzed how much people from various countries spend on healthcare.
Changing the paradigm for business forecasting (Part 9 of 12)
Academic Research In an approach akin to FVA analysis, Paul Goodwin and Robert Fildes published a frequently cited study of four supply chain companies and 60,000 actual forecasts.* They found that 75% of the time an analyst adjusted the statistical forecast. They were trying to figure out, like FVA does,
Ahh, that's smooth! Anti-aliasing in SAS statistical graphics
I've written several articles about scatter plot smoothers: nonparametric regression curves that reveal small- and large-scale features of a response variable as a function of an explanatory variable. However, there is another kind of "smoothness" that you might care about, and that is the apparent smoothness of curves and markers
Changing the paradigm for business forecasting (Part 8 of 12)
Typical Business Forecasting Process Let’s look at a typical business forecasting process. Historical data is fed into forecasting software which generates the "statistical" forecast. An analyst can review and override the forecast, which then goes into a more elaborate collaborative or consensus process for further adjustment. Many organizations also have
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
Which SAS course should I choose?
As an instructor for SAS, I receive a wide variety of queries before, during and after delivering my courses. Most frequently, I am asked questions such as: Should I learn SAS programming or a point and click tool instead? I know lots of code, should I go straight to the
How do you measure the value of data governance?
Data governance plays an integral role in many enterprise information initiatives, such as data quality, master data management and analytics. It requires coordinating a complex combination of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data stewardship and change management. With so much overhead involved in
Changing the paradigm for business forecasting (Part 7 of 12)
The Means of the Defensive Paradigm The Defensive paradigm pursues its objective by identifying and eliminating forecasting process waste. (Waste is defined as efforts that are failing to make the forecast more accurate and less biased, or are even making the forecast worse.) In this context, it may seem ridiculous
Analyzing social networks using Python and SAS Viya
The 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
Changing the paradigm for business forecasting (Part 6 of 12)
Why the Attraction for the Offensive Paradigm? In addition to the reasons provided by Green and Armstrong, I'd like to add one more reason for the lure of complexity: You can always add complexity to a model to better fit the history. In fact, you can always create a model
Guest blogger Len Tashman previews Fall 2016 issue of Foresight
Fresh from chairing the Foresight Practitioner Conference on “Worst Practices in Business Forecasting,” hosted two weeks ago at the Institute for Advanced Analytics at North Carolina State University, Foresight editor-in-chief Len Tashman previews the Fall 2016 issue. Preview of the Fall 2016 issue of Foresight In the provocative article, “The
Loess regression in SAS/IML
A previous post discusses how the loess regression algorithm is implemented in SAS. The LOESS procedure in SAS/STAT software provides the data analyst with options to control the loess algorithm and fit nonparametric smoothing curves through points in a scatter plot. Although PROC LOESS satisfies 99.99% of SAS users who
Changing the paradigm for business forecasting (Part 5 of 12)
Implications for the Offensive Paradigm The worldview promulgated by the Offensive paradigm is that if we only had MORE – more data, more computational power, more complex models, more elaborate processes – we could eventually solve the business forecasting problem. But this just doesn’t seem to be the case. Operating
What does IoT mean for utilities?
Utility 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
To stream or not to stream?
Hadoop may have been the buzzword for the last few years, but streaming seems to be what everyone is talking about these days. Hadoop deals primarily with big data in stationary and batch-based analytics. But modern streaming technologies are aimed at the opposite spectrum, dealing with data in motion and
Tip: How to close all data sets in SAS Enterprise Guide
Have you seen this error when running a program in SAS Enterprise Guide? ERROR: You cannot open WORK.YOURDATA.DATA for output access with member-level control because WORK.YOURDATA.DATA is in use by you in resource environment IOM ROOT COMP ENV. Or maybe: ERROR: A lock is not available for LIB.YOURDATA.DATA. NOTE: The
How much does each country spend on healthcare?
With the US Affordable Care Act (ACA) and other health topics in the news lately, I wondered how much people spend on healthcare in various countries. Of course spending varies from person to person, so I decided to take a look at the average per capita spending in each country (it's
Always Open
Open. 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
What's the difference between data governance and data management? (Part 2)
In Part 1 of this series, we defined data governance as a framework – something an organization can implement in small pieces. Data management encompasses the disciplines included in the data governance framework. They include the following: Data quality and data profiling. Metadata (business, technical and operational). Data security. Data movement within the enterprise.
Changing the paradigm for business forecasting (Part 4 of 12)
Is Complexity Bad? It’s necessary to point out that Goodwin’s article is not arguing against complexity per se, and I’m not either. When you have a high value forecast, where it is critical to be as accurate as possible, of course you are going to want to try every technique
What is loess regression?
Loess regression is a nonparametric technique that uses local weighted regression to fit a smooth curve through points in a scatter plot. Loess curves are can reveal trends and cycles in data that might be difficult to model with a parametric curve. Loess regression is one of several algorithms in
List the contents of your ZIP files using SAS
SAS programmers often resort to using the X command to list the contents of file directories and to process the contents of ZIP files. In centralized SAS environments, the X command is unavailable to most programmers. NOXCMD is the default setting for these environments (disallowing shell commands), and SAS admins
Changing the paradigm for business forecasting (Part 3 of 12)
Anomalies: The Beginning of a Crisis While even trained scientists can fail to see things that fall outside what they are looking for, anomalies eventually start to get noticed. But still, for a long time, anomalies within an existing paradigm are seen as mere “violations of expectation.” The response within
Not your father’s IT Department
Today’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
What AirBNB teaches us about traditional data governance
.@philsmion says that even the "best governed" organization today isn't safe from inquiring minds.
Big Data Governance: 5 Tipps für erfolgreiche Big-Data-Initiativen
Das ist der dritte Beitrag zur Blog-Serie Big Data Governance. Bisher sind erschienen Wie Big Data Unternehmen durcheinanderwirbelt und Mit Data Governance gegen den Datensumpf. Zum Abschluss möchte ich 5 Tipps abgeben, die ich selbst von Pionieren in Sachen Big Data übernommen habe. Meine erste Empfehlung lautet, Data Governance bereits zu Beginn der