Today’s consumer are demanding devices that can handle more and more content, requiring integrated digital, audio, voice, and data that is always on, always connected. Larger, more complex designs with more software and tighter power budgets require new verification solutions that target the associated technological challenges. The questions are:
Why traditional digital simulation and hardware prototypes fall short when it comes to verifying IoT, Why using emulation is critical for a total verification solution.
Five characteristics of IoT and network designs have the most impact on the challenges companies face in verifying them:
More Protocols per chip
Higher software Content
Increasing Network Switch and Router Activity
Emulation is the only way to provide the capacity, performance, and cycles to verify the large, complex, software rich designs that make up the IoT. However, a new emulation solution is required that is tailored to the IoT.
Simulation allows pre staging/validation services and real time support when the architecture is deployed. With Emulation/Simulation Software, vendors deliver a whole new generation of verification solutions for IoT and network providers. This solution is more flexible, provides more visibility, and scales with the increasing capacity and complexity of IoT and network system designs. It supports higher productivity and increases design quality, while delivering much of the traditional capabilities, without all the additional cables and hardware units.
In a recent Gartner study showed that by 2018, half of all business ethics violations will happen as a result of improper big data analytics usage. For that reason, companies need to be very careful that as they embrace the idea of using analytics to make business decisions, they don’t put themselves or their customers in jeopardy.
Using data, especially the personal data of consumers, for analytics and making business decisions can quickly become unethical or immoral if proper care isn’t taken regarding the types of information gathered and used. If a business gets too personal with customer information, it may be viewed as an invasion of privacy and could result in the loss of a customer. And even though the U.S. doesn’t have the strongest laws around privacy, the European Union and other countries “have more stringent privacy laws,” says Michael Walker, co-founder and president of the Data Science Association. This means, depending on where you’re practicing analytics, that it can be easy to shift from unethical to illegal.
There are countless examples for data, in general, being used in unethical ways from “judges allowing junk science into the courtroom that can skew a lot of legal cases” to quantitative analysts (aka quants) on Wall Street building flawed predictive analytic models that led to the housing market crash and economic crisis in 2008, Walker says. However, some of these examples cross the line from unethical into illegal territory, which is a distinction businesses need to learn to identify and take into consideration.
Illegal and/or Unethical
The problem with thinking in legal terms with data analytics is that because the U.S. doesn’t have stringent privacy laws, simply following the law isn’t enough to prevent the unethical use of information.
There are obvious situations where gathering data is illegal, such as hacking into devices and stealing information directly from them, but when it comes to business analytics and big data, it’s better to not only rely on the law, but also come up with your own reasons for why you should or shouldn’t use data in a certain way.
Big Data comes with a wide number of sources: Mobile devices, Social media, and IoT (Internet Of Things) through Sensors and Actuators will play a major role. This is data explosion and the term Big Data refers to the massive data sets all these sources are and will generate with cloud
All stakeholders (Government agencies, Businesses) want to capture and use them. The goal of the latest Big Data software is to discover, extract and present the data that matters. The key words in Big Data are:
Volume: identify how large is the volume of data
Variety: indicates the numerous of data and sources involved
Velocity: is the extremely quick rate at which data is generated
Hadoop: is an open source technology framework, it helps to simplify storage, management a,d analysis of various data types.
Most of the time Big Data means “unstructured”: Photo, videos, Social Media updates, ect …
A big Data analytics refers to the process of analyzing the different type of datas within a specific data set in order to identify unseen patterns.
What types of Businesses use Big Data analytics?
Insurance, retailers, Government agencies use Big Data analytics. Not only Large organizations can benefits from Big Data analytics but small businesses as well. Some analytics Service Providers offer analytics and Big Data services.
Bruno Dambrun Specialist in management and business development of services in new technologies since 25 years. Worked for major vendors as Senior RSD and CEO. Sales leadership in complex, high-end and fast-paced environment, direct and indirect business development and strategy in a multinational and multicultural business environment (EMEA, LATAM). Managed international sales teams.
Track record exceeding quotas and negotiating worldwide OEM and resellers agreement at “C” level (Alcatel-Lucent, Orange) selling 8 figures deals, with excellent customer and business relationships.
Wind River (Intel Affiliate) – Internet of Things and Virtualization software
SRMVision CEO – SaaS solution for shared processes
ContentMind CEO – Network CDN and security french reseller