While the term Big data is relatively new, the process of collecting and storing large amounts of structured and unstructured information data for eventual analysis is ages old. The Big data concept gained momentum in the recent past.
Big data is arriving from multiple sources at an alarming velocity, volume and variety (three Vs). The client challenges include data capture, data curation, storage, analysis, search, querying, updating, information privacy to extract meaningful value. Big data need to be analyzed for meaningful insights that lead to better decisions and strategic business values. To extract meaningful value from big data, clients need data science skills, optimal processing/simulation models, big data analytic methods and deep learning algorithms.
Over the past few year “deep learning” gained momentum and thus increased client’s challenges on how to optimally use deep learning on big data to extract meaningful business values. Deep learning is an approach to AI (Artificial Intelligence) and ML (Machine Learning). ML is more often described as sub-discipline of AI. AI and ML are showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries.
Machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. we are using Amazon, Google, Uber, Netflix, Waze, Facebook apps or websites in our daily busy life, they are all internally supported by machine learning algorithms.
Services we offer
We help our clients on implementing Bigdata based business solutions using big data technologies and data mining & management approaches (data capture, data curation, storage, analysis, search, querying, updating, information privacy) to extract meaningful value to support their decisions and product/service innovations. We work closely with our clients, understand and analyze their requirements to define the suitable Bigdata solution approach. We apply data science skills, optimal processing/simulation modeling, advanced big data analytic methods, predictive modeling and deep learning algorithms.
Big-data examples for different verticals
Product Campaign Analytics – When you run online campaign on your product, the customer might enroll into your offering. You need to identify what stage the customer is reached during the enrollment process (like signing-up new account, login to account, filling order form,..etc). You also need to identify what is the influential feature of the product that triggers the customer to sign-up/login/order. Also, you need to compare the signed-up customers, dropped in-middle customers to understand their pattern. With this data, you can decide on improving product offering and better customer engagement during your next campaign.
Product behavior pattern analysis – Identify behavior patterns of products by observing your product’s key parameters (power consumption, data consumption,..etc) and offer valuable suggestions on better utilization of product, suggestion on energy or cost savings, proactive recommendations on serviceability & maintenance and 100% uptime.
Self-learning Model – Build an online self-learning model such as NLP (Natural Language Processing), customer analytics prediction through deep learning algorithms on temporal and contextual data.
Understanding crowd movement – Build extensive simulation model environment to simulate and understand the people movement in side large public or private places like malls, retails, bus stands, airports,..etc.
Customer Sentiment Analysis – Sentiment analysis of customer feedback forms by analyzing the customer feedback sentences and come-up with sentiment analysis score such as good, bad or average.
Employees Engagement Analysis – Identify the engagement of your employees by observing set of key parameters and offer valuable improvement suggestions to your employees and proactive corrective measures.
Bigdata Solution Technologies – The following diagram shows the Bigdata technologies matrix used in real world scenarios with relevant Bigdata technologies to extract meaningful business values.
We cover range of verticals and offer turn-key Bigdata Solutions by closely working with our clients and translate their business requirements into specific Bigdata solutions.