Five Technologies to Watch in 2013. Another year has come and gone, and it’s time to look back on 2012 at the most intriguing gadgets and doohickeys of the year.
In particular, here are five technologies that stand out, looking back–largely because the rest of their stories remain unwritten. Analytics Reporting for 2012: The year of the enterprise tool suite for analytics. This special report provides insights from a our reporting over the last 12 months, including summaries of our Weekly Analytical Tools Newsletter.
Be sure to sign up for this report by visiting As a writing team we have close ties to the analytical missions of the national security space (our publisher, Bob Gourley, was an operational intelligence officer and analyst in the USN in his first career, and most of our other writers also have national security policy or technology experience). So we were very happy to see more open reporting on the analytical mission needs of the national security sector in reports like the one provided by the Intelligence and National Security Alliance on “Expectations of Intelligence from the Intelligence Community in the Information Age” This paper should be of high interest to anyone in the intelligence community or in policy-making positions in the national security community. Using technology to create high efficiency solutions. To read the latest issue of Manufacturing Digital, click here Using the latest technologies can help reduce production costs and increase productivity, but it can also raise one or two new problems that in turn require new technological developments to solve them.
Take, for example, high efficiency diesel engines. Contamination from the ingress of water is often more problematic in diesel engines than in petrol engines. The subsequent corrosion can cause serious damage to the injection pump and injectors. Diesel engines require fine fuel filters and water traps to alert operators when there is too much water in the trap, so that it can be drained before the engine is damaged. The good thing about problems is that they generate solutions that can be applied not only to the problem in question but beyond. De la statistique aux big data : ce qui change dans notre compréhension du monde.
Par Hubert Guillaud le 19/12/12 | 9 commentaires | 4,776 lectures | Impression Lors de la dernière édition des Entretiens du Nouveau Monde industriel qui avaient lieu les 17 et 18 décembre 2012 au Centre Pompidou, le sociologue Dominique Cardon a proposé une intéressante matrice pour comprendre à la fois les limites de la réutilisation des données ouvertes et celle des “Big Data“. Nous sommes désormais confrontés à un incroyable déluge de données produit par le numérique, reconnaît Dominique Cardon. C’est ce qu’on appelle le Big Data. Les données semblent innombrables, proliférantes… Et vont demain nous en apprendre toujours plus sur nous-mêmes… Mais sont-elles nécessaires ? Depuis quelques années, un ensemble de discours (relayé à la fois par des militants, des citoyens, des entreprises, des pouvoirs publics) se met en place autour “du nécessaire partage des données”.
Le modèle statistique des données ouvertes consiste, lui, à porter sur la place publique de la donnée “brute”. Top Five Machine-to-machine predictions for 2013. Written by Jürgen Hase, Vice President of M2M Competence Center at Deutsche Telekom To read the latest issue of Manufacturing Digital, click here At present, over 100 million vending machines, vehicles, smoke alarms, and other devices are sharing information automatically.
IBM offers a real sense of future technology. To read the latest issue of Manufacturing Digital, click here Computer giant IBM has made its annual predictions as to how technology innovations are set to transform people’s daily lives in the way they live, work and play.
Big Data Defined for 2013: A definition that can help in your interaction with the IT community. We have previously written about the importance of discipline in terms of art like Big Data.
There are plenty of indications that more discipline and rigor is required on how we use the term. To date, our key message has been that it is the enterprise CTO who is responsible for defining how the term should be used. We still believe that. We have also always supported using the community-edited site Wikipedia’s entry on Big Data as a starting point for a Big Data definition. The definition I put there was morphed and edited by the community pretty significantly, but that is just the nature of the beast. But another source now offers a Big Data definition that I like even better than Wikipedia’s.
So, as you consider how you will be using this term in your organization, I suggest you use this TechAmerica report as a starting point. Can We Grow American Manufacturing? Michael Collins, President, MPC Consulting America has done a good job of solving internal manufacturing problems, improving quality, and reducing waste and costs. These internal programs have kept American manufacturing in the game. But there is a problem. Manufacturing isn't growing in terms of factories, % of GDP, employees, or sales revenue. Staying with current marketing and sales strategies is not going to increase sales. If you examine the customer list of most manufacturers you will find the 80/20 rule applies. Needless to say, the profitability of these customers becomes a very serious issue.
Solutions.