Big Data Solutions

Analytics and Implications - Data Science Innovations

Data matters! We provide rich and actionable insights from all data touch points, for smart business growth. Our ability to perform data enrichment by filling gaps in existing data and combining data from other available sources with the base datasets makes us unique.

we believe that Business + Data + Math = Actionable Insights.

Data science is the art and science of acquiring knowledge through data. Data science is all about how we take data, use it to acquire knowledge, and then use that knowledge to make decisions, predict the future, understand the past/present, and create new products & opportunities. Data science is about using data in order to gain new insights that you would otherwise have missed. With the sheer volume of data that been collected in various forms and from different sources that often comes in very unorganized format, it is impossible for a human or an excel tool to parse and find insights.

Our expertise in data science & machine learning algorithms across multiple languages such as R, Python and Spark ML allows us to provide specific solutions to address the unique challenges that businesses face. We have helped clients in performing some of the machine learning analysis as listed below:

Regression Analysis:

These kind of analysis helps to understand how the typical value of the dependent variable changes when any one or more of the independent variables is varied, while the other independent variables are held fixed. This includes algorithms such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, decision tree regression, random forest regression, and evaluating different regression model for better performance

Clustering Analysis:

These kinds of analysis involve grouping set of objects that are more similar to each other than those in other groups (clusters). Cluster analysis can be achieved by various algorithms such as K-means clustering, Hierarchial clustering, Fuzzy K-means clustering, Model-based clustering, and Topic modeling using LDA.

Classification Analysis:

Classification is a process of using specific information (input) to choose a single selection (output) from a short list of predetermined potential responses. Classification algorithms are at the heart of what is called predictive analytics. The goal of predictive analytics is to build automated systems that can make decisions to replicate human judgment. This includes algorithms such as Logistic Regression, K-NN, Fisher’s linear discriminant analysis, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest Classification

Recommendation Systems

These systems produce recommendations using collaborative filtering, content-based filtering and a combination of both called the hybrid approach. Collaborative filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. Content-based filtering methods are based on a description of the item and a profile of the user’s preference. Hybrid approaches can be implemented by making content-based and collaborative-based predictions separately and then combining them or by adding content-based capabilities to a collaborative-based approach or by unifying the approaches into one model

Association Rule Learning

The use of predictive analytics as a mining tool also seeks to discover hidden relationships among items in your data. These hidden relationships are called mining association rules. Some well-known algorithms are Apriori, Eclat and FP-Growth that only does half the job, since they are algorithms for mining frequent itemsets. Another step needs to be done after to generate rules from frequent itemsets found in a database

Graph Analysis

While graph analysis is most commonly used to identify clusters of friends, uncover group influencers or advocates, and make friend recommendations on social media networks, graph analysis has other use cases such as graph-based search, master data management, identity & access management, entity identification & linkage, identify hidden patterns and insights, explore causes and effects, etc

Trend Analysis

One of the most basic yet very powerful exploratory analytics, trend analysis can quickly uncover trends and events that tend to happen together or happen at some period of interval. It is a fundamental visualization technique to spot patterns, trends, relationships, and outliers across a time series of data which yields mathematical models for each of the trend lines that can be flagged for further investigation.

Geo Spatial Analysis

This includes techniques for analyzing geographical activities and conditions using a business entity’s topological, geometric, or geographic properties. Anything that is capable of associating latitude and longitude will enable interesting use cases such as whitespace analysis, market and sales penetration, geographical reach, competition analysis, saturation analysis, etc. Geographical analysis can be combined with trend analysis and external sources such as BLS and Census to identify changes in market patterns across the organization’s key markets

Text Analytics & Natural Language Processing

There is a huge potential in unstructured data as it is accounted for 80% of enterprise data. Text analytics & NLP is a powerful technique to mine text data and glean insights from the wealth of internal customer, product, social, and operational data. Typical text mining techniques and algorithms include text categorization, text clustering, concept/entity extraction, taxonomies production, sentiment analysis, document summarization, and entity relation modeling

Entity identification, linkage & disambiguation

This is one of the most challenging tasks while normalizing the entities that comes from multiple data silos to construct a 360 view as different systems refer the same thing in different formats such as a customer or member. It becomes even more challenging when this information is hidden in unstructured format such as text. It needs complex machine learning techniques to identify, disambiguate and link the entities during normalization process