Search Results: api (2286)

John Farrelly 0
Why we’re creating 150 new jobs in Ireland

In today’s information economy, the ability to engage and develop meaningful digital relationships is fundamental to any business. A growing number of organisations, including small-to-medium sized enterprises, are investing in easy-to-use analytical software and services to extract insights from data about their business. As a result, we're now experiencing the

Charlie Chase 0
You can no longer hide behind MAPE!

There are four key areas that require continuous investment in order to become demand-driven: people, process, analytics, and technology. However the intent of your demand forecasting process along with business interdependencies need to be horizontally aligned in order to  gain sustainable adoption.  Adoption alone doesn't necessarily mean it will be sustainable.       As

Data Visualization
Ian Jones 0
Time for VirtualOil 2.0?

Since our last VirtualOil update in May, oil prices have continued to take a beating. As the chart of the rolling five-year portfolio shows, much of our strip of options is now out-of-the-money and the average value per barrel of that optionality has sunk below $7. No surprise then that

Sustainability: Where’s it heading?

From the public debate over Amazon’s workplace culture to the Security and Exchange Commission’s approval of the CEO-to-worker rule, it’s been an interesting few weeks for those of us who care about sustainable organizations. Clearly people have strong feelings about what companies owe society and how front-line workers should be

Leo Sadovy 0
Big Model: The necessary complement to big data

With all the hype over big data we often overlook the importance of modeling as its necessary counterpart. There are two independent limiting factors when it comes to decision support: the quality of the data, and the quality of the model. Most of the big data hype assumes that the data

Data Visualization
Leo Sadovy 0
Visualization – Worth a thousand words

Why visualization? Several reasons, actually, the most compelling being that sometimes visualization literally solves the problem for you. I remember an exercise in eighth grade English class where we were asked to describe, in words only, an object set in front of us with sufficient clarity such that our classmates,

Data Management
David Pope 0
Oil and gas data management overview

In 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

Analytics | Data Visualization
Leo Sadovy 0
Why build models?

We are all modelers.  Whenever you plan, you are building a model.  Whenever you imagine, you are building a model. When you create, write, paint or speak, you first build in your head a model of what you want to accomplish, and then fill in the details with words, movements

Kathryn McLawhorn 0
Customizing output from PROC MEANS

Customizing the output data set created using the OUTPUT statement When you request statistics on the PROC MEANS statement, the default printed output creates a nice table with the analysis variable names in the left-most column and the statistics forming the additional columns.  Even if you create an output data

Analytics | Customer Intelligence | Data Management
SAS Colombia 0
6 maneras de repensar su estrategia de gestión de datos y evitar los peores escenarios

“Aquellos que no conocen su pasado están condenados a repetirlo” La retrospección es un proceso lento. Así como en los seres humanos aún persisten en el tiempo comportamientos que no funcionan, en las organizaciones perduran procesos de información que se rompen y pueden causar grandes crisis ¿cómo evitarlo? En la

Analytics | Data Management
Mark Torr 0
What’s the future of analytics within the enterprise architecture?

What does the future of analytics look like in your organizations enterprise architecture? Does it include thinking about a two speed approach to analytics which includes both: An agile rapidly changing analytics platform for innovation (a lab) seperated from operations and broad enterprise audience usage A slowly moving systematic enterprise analytics platform (a factory)

Marco Heidelberger 0
Enterprise Stresstesting: Die Sandbox als erste Etappe auf dem Weg zum Ziel

Enterprise Stresstesting bedeutet die Abbildung zahlreicher Disziplinen: Datenmanagement, Modellierung, Szenariomanagment, Risiko Rechenkerne, Analyse- und Reportingumgebung. Jedes dieser Themen birgt in sich bereits eine gewisse Komplexität. Um sie zu integrieren, bedarf es einer übergeordneten Prozesssteuerung, die alle Fäden beisammen hält, die einzelnen Schritte koordiniert, dokumentiert und wiederholbar macht.

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