Hip-hop, housewives and hot startups: A guide to Atlanta’s startup scene

Last month Paul Judge (@pauljudge), cofounder of Tech Square Labs and Chief Research Officer at Barracuda Networks, put together an excellent guide explaining the current status of the vibrant startup movement in Atlanta. Predikto is proud to be part of this hot startup scene (hint: look at the page 21 of the guide!). BTW, we are hiring technical talent! Check out our career page.

The original post is here and the guide is posted below.

The Guide to Atlanta’s Start Up Scene


Using Predictive Analytics in Maintenance

predictive analytics for maintenence
Predictive Maintenance is the best type of maintenance a company can undertake, but not all assets classes and applications justify Predictive Maintenance. That is why the best type of maintenance is the type that works for each client. The latest technology in Predictive Maintenance is the use of Predictive Analytics. In some cases Predictive Analytics is reaching accuracies above 95% to predict an asset failure. These results are much higher when compared to traditional predictive maintenance techniques like Lubrication Analysis, Infrared, and Vibration. These are all excellent techniques and companies should continue using them if they are seeing success reducing downtimes, extending the lifetime of equipment, and subsequently saving money.

In the MAPCITE blog, Eric Spiegel, CEO of Siemens U.S.A., consider that “while analytics were implemented widely in industries such as banking and communications initially, we view capital-goods organizations as a huge untapped opportunity, driven primarily by the “Internet of things” and the significant potential to optimize product development, supply chain and asset related services. One example is predictive maintenance – if we were able to better predict when critical and expensive equipment is most likely to fail, we could reduce downtimes, extend the lifetime of the equipment, and realize significant savings”. Read the entire story HERE.

3 Steps to improve your Decision-Making process

3 Steps to improve your Decision-Making process

The ability to make high quality decision is central to any organization. The expansion of the volume, variety, and velocity of information make this ability increasingly difficult. With the state of current data explosion, decision-makers have to respond more quickly and with greater precision than ever.

To improve your decision-making process you should be considering the following:

  1. Do not focus exclusively on historical experience Traditionally, decisions have been made on the basis of anecdotal experience of domain experts. These decisions are subjective and often inconsistent, thereby limiting their value for the organizations.
  2. Business rule tools become obsolete quickly Having the need to standardize key decisions and make them more consistent and reliable, many organizations have moved toward automated decision-making by using business rules. Although this automation provides a degree of efficiency and objective consistency, and improves the collective quality of decisions, static rules quickly quickly become obsolete in ever-changing situations and conditions, and the limits of this approach become apparent.
  3. Predict for specific conditions rather than for generalizations: Predictive decision-making, based on analysis of historical patterns and current conditions, is the basis for the highest quality means of making decisions. The reasons are because the models consider all available data, and also continuously adapt to new information, becoming smarter over time. With predictive analytics, the right decision for the given conditions can be made at the point of impact at the time when the decision needs to be made. Decisions are now customized for each unique case, rather than using generalizations for the aggregate.

Predictive Analytics tools can generate extremely useful insights and actionable predictions that will improve the decision-making process by doing it less anecdotical, less obsolete, and more relevant to the problem to solve.

Transit Asset Management System 49 U.S.C. 5326 as part of the MAP21

Transit provides more than 10 billion passenger trips each year, which represents more trips each month than all of the Nation’s airlines combined will make in a year. When transit assets are not in a state of good repair (SGR), the consequences often include increased safety risks, decreased reliability, higher maintenance costs, and an overall lower quality of service to customers.

The Moving Ahead for Progress in the 21st Century Act, MAP-21, states in its section 5326 that the Federal Transit Administration (FTA) will implement a national transit asset management system, including “a strategic and systematic process of operation, maintaining, and improving public transportation capital assets effectively throughout the life cycle of such assets.”

The Transit Asset Management System will include at minimum:

  1. The definition of the ‘state of good repair’, that should include standards for measuring the condition of equipment, infrastructure, rolling stock, and facilities of the capital assets of the public transportation systems that receive FTA funding.
  2. Federal financial assistance to develop a transit asset management plan.
  3. Reports of the conditions of the systems, and any changes to it.
  4. An analytical process or decision support tool that allows for the estimation of capital investment needs, and the prioritization of the public transportation systems.
  5. Technical assistance to the FTA funding recipients.

Predictive Analytics tools for Asset Management can help to meet the requirements of the Transit Asset Management systems, 49 U.S.C. 5326, especially those points related to the implementation of a national transit asset management system, that include an analytical process or decision support tool for use by public transportation systems.

M2M Analytics Will Generate $14 Billion by 2018

Senior analyst, Aapo Markkanen, from ABI Research forecasts that the M2M analytics and big data industry will grow 53.1% over the next 5 years from US $1.9 billion in 2013 to US $14.3 billion in 2018.

The forecast includes revenue segmentation for the five components that together enable analytics to be used in M2M services:
1) Data integration
2) Data storage
3) Core analytics
4) Data presentation
5) Associated professional services

Predikto is uniquely positioned to make an impact in this transformational trend. Predikto is not a consulting company. Our solutions harness the power of Predictive Analytics to address operations challenges in asset intensive industries. We use our clients data and turn it into actionable insights to reduce asset failures, predict production yields, and improve operational performance. Our solutions enable clients to perform an action.