Depending on the vertical and the phase of business growth, the needs of Analytics priorities could be myriad. Typically, large enterprises could benefit from increasing the cost or process efficiencies; SMEs from customer acquisitions, revenue increases or profitable growth. The analytical asymmetry will be accentuated in the future as businesses become more analytically aware. The divide will increase between laggards and innovators, so are their profits and market share. Analytics is a “business capability” that can deliver high performance and increase competitiveness. For those IT leaders, who have traditionally used to managing IT as a cost function, there is an immediate need for corrective action and do a catch up by means of establishing CoEs and thus “leap frogging’ their journey in analytics. They have a unique opportunity to the benefit of adopting state of the art architectures rather then re-architecting archaic BI/Analytical architectures that existed. For those who have lagged behind can take hear from the fact that we no longer have to experiment with “what works” or what doesn’t work; there is enough body of knowledge available that gives the direction on the best practices and methods from thousands of Big Data Analytics implementations across the globe. We will have to learn from them.
Foundational:"Struggling to meet the business information needs"
Competitive:"Creating value from information assets"
Pioneering:"Managing information as a service"
There are various technologies that address the challenges of Big Data.
Anomaly detection or outlier detection is the identification of objects, events or observations which do not conform to an expected pattern or expected business rules. These techniques can detect fraud, a structural defect or medical problems etc.
Our team has a repertoire of various recommenders and ranking/scoring algorithms. Most Data products such as ?Whom you may know?, ?Who also bought?, ?Followers?, ?trending topics? and ?Relevant Information? Consolidators.
Socail Network Analysis:
Social Network Analysis and Community Detection these techniques are used to identify various networks to uncover terrorists or communities (entities have a shared feature sets), affinities between various entities. These are important in establishing linkages in various fields such as crime detection, influence identification and fraud detection.
Recommender Systems providing suggestions for items to be of use to a user. These are essentially reducing the vast choices/alternatives generally available to for a user purpose and filter those that are most relevant. These are generally powerful ways to create most relevant, personalized content to a user or a purpose.
Entity Extraction and resolution:
Entity identification and extraction results in identifying things such as persons, places and organizations. Entity extraction can add a wealth of semantic knowledge to the content to help quickly understand the subject of the text. This is useful in network analysis and Master Data Management.
Sentiment analysis determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document by the use of natural language processing, text analysis and computational linguistics.
Data Science is about making sense of large amounts of messy data, using it to model and solve complex problems, and presenting the compelling solutions by "telling a story with data." It draws up skills and knowledge from different disciplines - Data engineering, math and statistics, modeling and programming, visualization - aided by deep domain expertise. To expect a single person to have this expertise is unrealistic. Generally, this taklent is pooled as data science teams. Generally, the team composition could beProgrammers use statistical software and other contemporary tools to build analytical models and present the results.Data Preparers spend most of their time acquiring data and selecting, combining, and organizing it for use, plus doing some analytics on it.Generalists do a bit of everything, including working with business people to frame problems and discuss analyses, and advising or pitching in on data preparation and programming.Managers provide direction and resources, participate in analytics design and interpretation, and do most of the presentation of results. Our team has several with different level of skill sets, experience and knowledge to form Data Science teams. We have ability to put together a full-stack Data Science Team.
Enterprise are deluged with data irrespective of the industry sector-from health care and financial services to retail and manufacturing. In this voluminous data, lies business insights, opportunities and potential success drivers. Leveraging this Data is a daunting task. We understand challenge part is combining everything and making sense out of it. We also are cognizant of the competitive advantage this data can yield. Our competencies lie in Relational Databases, NewSQL systems, Non-relational systems like NoSQL databases and large scale processing systems like Hadoop. We are conversant with varieties of Data Structures, Data latencies and volumes of data for acquisition, integration, assimilation and processing.