Get ahead of your competition with Machine Learning

Companies are integrating AI and Ml solutions to their business to stay ahead in the competition. Read the points mentioned to elucidate your thoughts on the subject.

There is a lot of uproar going on about the influential technologies that are dominating the business Intelligence market for almost every sector. Big data, artificial intelligence, and machine learning are being discovered and analyzed and with passing time people are coming with new applications for a better world and an improved future. While there are some who are still not aware of the potential and capabilities of Machine learning and the value it can add to their business. Machine learning draws insights from raw data and provides solutions to complex business problems based on analysis. Data processing in AI and ml algorithms help computers find hidden insights without any programming or human assistance. It is a technology of the future that ensures heightened efficiency, enhanced customer service relationship, and boosting sales for any business. With big companies like Amazon, Google, and Microsoft bringing in Cloud machine learning platforms, the credibility of artificial intelligence and machine learning just springs up.

There are now several applications of the insights that machine learning draws from raw data. These are used to solve complex business problems, increase efficiency, improve customer relationships, and hike up sales.

NATURAL LANGUAGE PROCESSING

The tech industry just like any other industry faces several challenges from time to time and recently one of them has been the capability for machines to analyze, manipulate, and understand human language. And NLP, a field of machine learning, helps with just that. It gives computers the ability to understand human language by building systems that are able to make sense of text so that they could translate, recommend text, and check grammar. However, certainly there are NLP equipped tools that are used to derive insights that can be used to automate tasks. Algorithms are used to build intelligent systems like voice activated personal assistants – siri, google assistant, alexa, and AI-powered chatbots that are capable of solving issues by translating the language when and where required for machines and humans.

Now when we come to the mechanism on how this works, we will first have to understand the two techniques that go into helping machines understand text. Syntactic analysis that assesses text using basic grammar rules to understand the structuring of sentences. Semantic analysis emphasises on understanding the meaning of the text, first the meaning of individual words and then the meaning when those words are combined. 

Business enterprises are getting better at understanding online conversations and how their customers perceive them and their products. They are also able to automate tedious tasks and make them more efficient. Language translation, sentiment analysis, text extraction, topic classification, and chatbots are some of the important applications of NLP known in the technical world. Read More- Get ahead of your competition with Machine Learning

Why is Big Data Analytics the Key to Stay Ahead of Competition?

No matter what type your business is, data analysis always plays a vital role in providing you a competitive edge and bring innovation in the way you do your business. Collating data to discover new business opportunities and meet customer expectations is not something very new, dating back to the decade of 70s. However, what has significantly changed since then is the amount of data growing everyday, its diversity and analytics techniques to make sense out of it.

The term Big Data was coined considering the abundance of data that exists today in structured, semi-structured and unstructured form. According to a new report from IBM Marketing Cloud, 90% of data that exists today was created in the last two years alone. The report further adds that nearly 2.5 quintillion bytes of data is created every day from numerous sources, like social media sites, business apps, public web, sensors connected to Internet of Things, etc…Read More

Apache Hadoop vs Apache Spark: Two Popular Big Data Frameworks Compared

The term Big Data has created a lot of hype already in the business world. Chief managers know that their marketing strategies are most likely to yield successful results when planned around big data analytics. For simple reasons, use of big data analytics helps improve business intelligence, boost lead generation efforts, provide personalized experiences to customers and turn them into loyal ones. However, it’s a challenging task to make sense of vast amounts of data that exists in multi-structured formats like images, videos, weblogs, sensor data, etc.

In order to store, process and analyze terabytes and even petabytes of such information, one needs to put into use big data frameworks. In this blog, I am offering an insight and analogy between two such very popular big data technologies – Apache Hadoop and Apache Spark.

Let’s First Understand What Hadoop and Spark are?

Hadoop: Hadoop, an Apache.org. Project, was the first big data framework to become popular in the open source community. Being both a software library and a big data framework, Hadoop paves the way for distributed storage and processing of large datasets across computer clusters using simple programming models. Hadoop is a framework composed of modules that allow automated handling of common hardware failure occurrences…read full blog here- Apache Hadoop vs Apache Spark: Two Popular Big Data Frameworks Compared

How Will Big Data Impact Effective Lead Generation for better Conversions?

People are looking for information (read products and services) all across the web. You, as a salesperson might have an offering but the problem is – what are the chances that the person interested in it will find you and reach across to you? Frankly, the chances are quite less. So what do you do to increase your chances to make a sale? Well, obviously the best thing you can do is to find and reach across to that person before he decides to give his money to someone else. But how to do that? Traditional lead generation methods are only so effective as to give you an excuse of an alternate to shooting in the dark. The generated lead data is limited, the windows are short, the targets are big, the work is harder and the results are uncertain. The conversion rates can well be compared to the conversion rate of a toiling army of bees for one drop of honey.

A decade ago, most salespeople would  agree that the traditional methods only  took them so far in terms of conversion  rates. The data was too limited or  redundant and took too long to  accumulate but the silver lining, if we can  call it that, was that because it was too little, it was easy to process. You got 30 leads, you go and do your salesperson thing with 12 based on some quick prospecting/scoring and depending on how good or lucky you are, you score a couple.

Then five years ago to until recently, salespeople were agreeing that the contemporary methods with the power of web and social media, brought improved capabilities in data acquisition and reach but still something was keeping them from milking that cow. You’d think with all that talk about shrinking degrees of connection, businesses increasing their online presence and all, you’d be better off than mere 3% growth.

Yes, something was definitely missing from the picture. And that something was to do with this – “Having access to a lot of data means nothing if you don’t have a way to utilise it…to its full potential.”.. Know more about- How Will Big Data Impact Effective Lead Generation for better Conversions?