BRIDGEi2i Analytics Solutions announced its inclusion in Gartner's Market Guide for Artificial Intelligence Service Providers, 2021. The Gartner report compiles a list of service providers that represent the breadth of offerings in the AI market. Organizations of varied size, complexity, type and location are listed here. This list is based on client interactions and publicly available information. BRIDGEi2i CEO and Co-Founder - Prithvijit Roy remarked, "At BRIDGEi2i, we are excited to leverage AI to build the digital enterprise. Our pre-built, ready-to-deploy algorithmic engines, the AI Accelerators, enable enterprises to transform their businesses and achieve powerful outcomes using AI. Being featured in the Gartner's Market Guide for Artificial Intelligence Service Providers, 2021, takes us one step further on the journey of making AI real for enterprises. I'm grateful to all our employees and clients who are partnered with us in reimagining businesses using AI, and also for the Gartner analyst team's support."

The data industry is a rapidly evolving space with the development of new technologies, methodologies, and platforms almost every other day. So, keeping pace with this industry can be challenging indeed. I have, therefore, created a list of 15 tech and data science influencers who are not just a source of inspiration to data science professionals and aspirants alike but also ensure that you keep abreast of all the new developments. I have not taken social media influence and related metrics into account. I have created this list based on how much I personally enjoy the content these individuals share on their social media.

According to MarketsandMarkets, the global retail analytics market will likely more than double in size during 2015-2020, totaling about $5.1 billion at the end of the forecast period. The adoption of analytics solutions is increasing as more enterprises worldwide are realizing significant returns from using BI and analytics platforms and services. Of late, many retailers seem to be jumping on the AI bandwagon to improve their marketing efforts. Sailthru, a marketing technology company, published a study that included a survey with more than 200 retail marketers. Over the course of 2017, these retailers plan to leverage AI to expand their mobile and social media marketing strategies aside from improving customer journeys.

A good sales rep is differentiated by his ability to identify the right customers and "know" them better. This ability helps the rep tailor perfect solutions, thereby improving the likelihood of sales success. According to The TAS Group, only 46% of reps feel their sales pipeline is accurate, and 40% of all salespeople can't understand the problems of their customers. Clearly, realizing "sales success" is no walk in the park. So, how does one identify the right customers?

Not far from now, you will have hard time in figuring out whether this blog is written by an machine or a human! Machine learning, the recent buzz word in the tech industry have started closing the intelligence gap between humans and machine. And its making inroads in many sectors of industries, which are solely dominated by humans. Deep learning, the newcomer in machine learning have recently even outperformed humans in terms of recognizing complex objects in images. What differentiates machine learning is that it is not the usual programming that we are taught in school, where we instruct the machine how to respond by a specific set of rules(algorithm) for every instance of input it gets from the environment, but rather it tries to replicate the human intelligence which extracts the useful patterns or signals from the huge amount of data scattered with noise.

Not far from now, you will have hard time in figuring out whether this blog is written by an machine or a human! Machine learning, the recent buzz word in the tech industry have started closing the intelligence gap between humans and machine. And its making inroads in many sectors of industries, which are solely dominated by humans. Deep learning, the newcomer in machine learning have recently even outperformed humans in terms of recognizing complex objects in images. What differentiates machine learning is that it is not the usual programming that we are taught in school, where we instruct the machine how to respond by a specific set of rules(algorithm) for every instance of input it gets from the environment, but rather it tries to replicate the human intelligence which extracts the useful patterns or signals from the huge amount of data scattered with noise.

In the immortal words of Steve Jobs: "A lot of times, people don't know what they want until you show it to them." You might believe that customers will love your movie, your product, your job opening- but they may not even know it exists. The job of a Recommendation Engine is to open the customer/user up to a whole new world of products and possibilities, which they would not even think of directly searching for themselves. A Recommendation Engine plays a vital role in increasing the chances of a user buying a product. In today's world, with tons of data from searched products available (thanks to the digital data explosion), it is very easy to find what people are likely to buy – just by looking at their'intent' data. Needless to say, right analysis of such BIG data is absolutely necessary.

"We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances." "When you have two competing theories that make exactly the same predictions, the simpler one is the better." One famous example of Occam's Razor in action is found in conspiracy theories surrounding the NASA moon landings. Many conspiracy theorists believe that the first Moon Landing was staged and filmed in a studio, part of an elaborate hoax. Their justification relies upon many twisted and convoluted theories, whereas the NASA argument is fairly straightforward.