Three steps to ensuring data you can trust

A data governance framework organizes people, processes and technologies together to create a paradigm of how data is managed, secured, and distributed. But the path to a data governance framework often seems difficult to embark upon. Here are three steps to help you get started:

Step 1: Discover and cleanse your data

Your challenge is to overcome these obstacles by bringing clarity, transparency, and accessibility to your data assets. You have to do this wherever this data warehouse resides: within enterprise apps like, Microsoft Dynamics, or SAP; a traditional data warehouse; or in a cloud data lake. You need to establish proper data screening so you can make sure you have the entire view of data sources and data streams coming into and out of your organization.

Step 2: Organize data you can trust and empower people

While step 1 helped to ensure that the incoming data assets are identified, documented and trusted, now it is time to organize the assets for massive consumption by an extended network of data users who will use it within the organization.

Step 3: Automate your data pipelines and enable data access

Now that your data is fully under control, it is time to extract all its value by delivering it at scale to a wide audience of authorized humans and machines. In the digital era, scaling is a lot about automation. In the second step of this approach, we saw how important it was to have people engaged in the data governance process, but the risk is that they become the bottleneck. That’s why you need to augment your employees’ skills, free them from repetitive tasks, and make sure that the policies that they have defined can be applied on a systematic basis across data flows.

The final task: Enforcement and communication

All of the topics above cover key focus areas to be considered when building a data governance framework. Building the framework is important — but enforcing it is key. A data governance process must be created on the heels of the framework to ensure success. Some organizations create a data governance council — a single department that controls everything. Smaller organizations may appoint a data steward to manage the data governance processes. Once your data governance framework is in place and is being rolled out, it needs to be communicated to all areas of the business. Make it resonate with employees by demonstrating how this framework will help carry out your company’s vision for the future. As with any new policy or standard, employee awareness and buy-in is important to success; and this holds true with the data governance framework.

There is certainly no shortage of data, and building a data governance framework is a challenge many enterprises face. With any Cloud base systems, you can build a systematic path to get to the data you can trust. A path that is repeatable and scalable to handle the seemingly unending flow of data to make it your most trusted asset.


Spam Detection in Deep Learning

What is the so-called Spam?

E- messages are a crucial means of communication between many people worldwide. But several people and corporations misuse this facility to distribute unsolicited bulk messages that are commonly called Spam SMS. Spam SMS may include advertisements of medicine, software, adult content, insurance, or other fraudulent advertisements. Various Spam filters are wont to provide a protective mechanism that will design a system to acknowledge spam.

Spam Detection

After submitting your personal details like mobile number or email address on any platform, they started the advertisement of their unusual products by constantly pinging you. They try to advertise by sending constant emails and with the help of your contact details they keep sending you messages as well they are doing WhatsApp more nowadays. Hence, the output is nothing but a lot of spam alerts and notifications popping up in your inbox. This is often where the task of spam detection comes in.

Spam detection means detecting spam messages or emails by understanding text content in order that you’ll only receive notifications regarding your messages or emails that are crucial to you. If spam messages are found, they’re automatically transferred to a spam folder and you’re never notified of such alerts. This helps to enhance the user experience, as many spam alerts can bother many users. Read more…

The Future of Deep Learning Is Photonic

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

While machine learning has been around a long time, deep learning has taken on a life of its own lately. The reason for that has mostly to do with the increasing amounts of computing power that have become widely available—along with the burgeoning quantities of data that can be easily harvested and used to train neural networks.

The amount of computing power at people’s fingertips started growing in leaps and bounds at the turn of the millennium, when graphical processing units (GPUs) began to be harnessed for nongraphical calculations, a trend that has become increasingly pervasive over the past decade. But the computing demands of deep learning have been rising even faster. This dynamic has spurred engineers to develop electronic hardware accelerators specifically targeted to deep learning, Google’s Tensor Processing Unit (TPU) being a prime example.

Here, I will describe a very different approach to this problem—using optical processors to carry out neural-network calculations with photons instead of electrons. To understand how optics can serve here, you need to know a little bit about how computers currently carry out neural-network calculations. So bear with me as I outline what goes on under the hood.

Almost invariably, artificial neurons are constructed using special software running on digital electronic computers of some sort. That software provides a given neuron with multiple inputs and one output. The state of each neuron depends on the weighted sum of its inputs, to which a nonlinear function, called an activation function, is applied. The result, the output of this neuron, then becomes an input for various other neurons. Read more

DataStage Developer

Title: -Datastage Developer
Location:-    Charlotte, NC
Duration: Full Time Permanent Position
Job Description:

*          At least 3 years of experience in software development life cycle
*          At least 3 years of experience in Project life cycle activities on development and maintenance projects
*          At least 1 year of experience in Relational Modeling, Dimensional Modeling and Modeling of Unstructured Data
*          Good experience in end-to-end implementation of DW BI projects, especially in data warehouse and mart developments
*          Good understanding of Data integration, Data Quality and data architecture
*          Experience to Big data technologies is preferred.
*          Good expertise in impact analysis due to changes or issues
*          Experience in preparing test scripts and test cases to validate data and maintaining data quality
*          Strong understanding and hands-on programming/scripting experience skills – UNIX shell, Perl, and JavaScript
*          Experience with design and implementation of ETL/ELT framework for complex warehouses/marts. Knowledge of large data sets and experience with performance tuning and troubleshooting