Today, with the growing volumes and types of data, organizations are bound to use this data to produce valuable business insights to stay at the competitive edge. The process of Data Mining at ITCrats reveals unmatched business insights and runs with absolute mitigation of the following three cardinal sins of Data Management and Analytics:
- Overlooking default fields in data mining tools
- Algorithms can assume data distribution
- Algorithms cannot figure out patterns on their own
Our statistical wizards bring together the advanced analytics capabilities using predictive modelling, data mining, text analytics, entity analytics, optimization, real-time scoring, machine learning, data streaming and much more to help data driven organizations realize their data related goals. Our specialization in data stream analytics helps enterprises decipher and examine patterns of interest as data is being created via the Internet of Things.
Our Cloud Computing and Advanced Analytics services include
Analytic Roadmap and Ecosystem
We make multiple environments act like one by seamless capture, deploy, support, manage and access of data from multiple platforms. Our well supported and unified data architecture supports multiple platforms within an analytical environment that helps in seamless data integration and easy management of multiple analytic systems.
Big Data Technologies
We offer expert implementation services for open-source big data solutions ranging from Strategy & Architecture, Analytic Solutions, Data Science, Data Lake and Managed Services. We identify and prioritize key areas where big data can drive value to your business and accordingly perform gap analysis, readiness assessments and ecosystem blueprints to find key solutions that include organizational structure, governance models and resource needs. Our team of experts can help your organization to turn your big data challenges into solutions and tangible business outcomes, faster and at low risk by reducing IT and operational costs, improving insight into customer behavioral data, increasing ROI, and detecting threats and risks.
Agile Development Methodology
We produce agile solutions for analytics to create business value on a frequent basis. Based on the business, we build POC (working prototypes for the analytic solution) and conduct a workshop for the business users to get their feedback. Once the POC is approved, we build solutions based on real data with improved requirements from business users that serve the following benefits:
- Our agile methodology supports concurrent development work for varied areas, thus increasing the development speed and delivering faster values
- Increased collaboration among different organizations within a business to address issues and adapt to changes earlier
- Development teams can easily adapt to stakeholders' feedbacks and minimize the redundancy of expensive and time-consuming changes at the end of a development life cycle
With the techniques like data mining, statistical algorithms, modelling, machine learning and artificial intelligence, we identify the likelihood of future outcomes basis the historical and existing data lying in your data warehouse. We help you understand Beyond What Has Already Happened and provide the best assessment of what will happen in the future. We build and deploy predictive modelling directly into your business processes using our multi-faceted predictive analytics capabilities in a single solution. We amplify your analytics without losing control with R, Python and more.
Our key Predictive Modelling techniques include
Easily understand your consumer’s decision path with Decision trees that look like a tree with branches representing choices and leaves representing classifications/ decisions. This technique handles missing values well and is useful for preliminary variable selection.
Neural networks are used to handle nonlinear complex relationships in data where prediction is more
important than explanation and confirms findings from regression and decision trees.
When performing a Bayesian analysis, you begin with a prior belief regarding the probability
distribution of an unknown parameter. After learning information from data you have, you change or
update your belief about the unknown parameter.
Regression(linear and logistic)
Regression analysis is used to predict a number, called the response or Y variable and can be classified into Linear, Multiple and Logistic Regression.
Time series data mining
Also, known as Prediction algorithm this technique combines traditional data mining and forecasting and applies to data collected over time with the goal of improving predictions.
Ensemble models are produced by training several similar models and combining their results to improve accuracy, reduce bias, reduce variance and identify the best model to use with new data.
Like decision trees, Gradient Boosting makes no assumptions about the distribution of the data and is less prone to overfitting the data than a single decision tree, and if a decision tree fits the data well, then boosting often improves the fit.
Clustering algorithms group cases into clusters that are like one another, and as different from other clusters as possible. It is often used to segment the customers into smaller, homogenous groups for customized promotions.
Association or Affinity Grouping
Association looks for correlation among the items in a group of sets. E-commerce systems are big users of association models to increase sales using market basket analysis.
Specific industries use Predictive Analytics to reduce risks, optimize operations and marketing to increase revenue
Banking and Financial Sector
Detect and reduce fraud, measure credit risk, maximize cross-sell/ up-sell opportunities and retain valuable customers.
Oil, Gas and Utilities Sector
Predict equipment failures, future resource needs, mitigate safety and reliability risks and improve overall performance.
Health Insurance Sector
Detect fraud insurance claims, identify patients with maximum risk of chronic diseases and interventions needed and to identify those not adhering to prescribed treatments.
Use predictive analytics to determine which products to stock and to measure the effectiveness of promotional events and offers.
Governments and the Public Sector
Understand population trends, improve service and performance, detect and prevent fraud, better understand consumer behavior and enhance cyber security.
Identify factors leading to reduced quality and production failures and to optimize parts, service resources and distribution systems.
Sentimental Analysis, also known as Opinion Mining, helps you analyse the sentiments, opinions, feedback of your audience. A rightly done Sentimental Analysis lets you know if there has been a change in public opinion towards any aspect of your business. A regular review of public opinion towards your business empowers you to take more proactive efforts to the changing dynamics in the market place.
Our algorithm-based sentiment analysis tools can handle huge volumes of customer feedback consistently and accurately. We also pair text analytics with sentiment analysis to reveal customer's opinion about topics ranging from your products and services to your location, advertisements, or even your competitors. We configure our Sentiment analysis tools over a range of data sources like social media, website, call center agents, etc to uncover the complete feelings of a customer. The objective is to make results more clear, interpretable and actionable. In the meantime, we also ensure to make the sentimental analysis as accurate and easy to understand as possible. We evaluate written or spoken language to determine if the expression is favourable, unfavourable or neutral and to what degree and score it on a 10-point scale. Scores are given after a detailed analysis of grammar, context, industry, and source. The peaks and valleys in sentiment scores will put you in a position where you can make decision about product improvements, train sales or customer care agents, or to create new marketing campaigns.
Behavioral Analytics is important for finding why people behave the way they do when using e-commerce platforms, social media sites, online games, and any other web application. We help you get a clearer picture of user behaviour using Splunk, a key User Behaviour Analytics platform that helps understand the interactions and dynamics between processes, machines and equipment which may yield new deductions about operational risks and opportunities. Organisations benefit from Splunk UBA as it determines a baseline of normal activities specific to the organization and its individual users along with the deviations from normal using big data and machine learning algorithms to assess these deviations in near-real time.
Our analytics experts use various open source platforms that give powerful machine-learning framework, customization ability, and breadth of use cases to help organizations automate the detection of known, unknown, and hidden threats. Our experts also address the entire lifecycle of an attack including insider threats and external attacks and provides customers with the ability to detect, respond and contain threats using Splunk Enterprise Security.
Key platforms used for User Behavior Analytics are
The key industries benefitting from Behavioral Analytics
Banking & Financial Sector
Financial services organizations leverage behavioral analytics to identify suspicious and anomalous behavioral patterns as a means to strengthen anti-fraud capabilities; they also link demographic data and traffic patterns to customer profiles to figure out where to locate branches and ATMs
Communications providers enrich customer information with external sources and network usage patterns to gain better views of subscriber behavior – one European telco saw 40% cost savings from 360-degree views
Retailers closely track customer pathing across channels – when, where, how frequently and for which transaction types do customers use various channels (including bricks-and-mortars stores); how they respond to email campaigns, mobile couponing or even television ads
eCommerce players specifically test scenarios and track every click to understand why customers abandon shopping carts or otherwise leave "the funnel" before completing check out.
A picture is worth a thousand or even millions of variables! Especially when you're trying to find relationships or identify new patterns in your data. We facilitate interactive data visualization, so that you can interact with the data you see . Regardless of industry or size, all types of businesses are using data visualization to help make sense of their data and identify areas that need attention or improvement.
Here are the key features and benefits of ITCrats Data Visualization solutions
- Find which factors influence customer behavior the most
- Identify outliers that affect product quality or customer churn, and address issues before they become bigger problems
- Share business insights discovered via data visualization in an engaging and faster manner using charts, graphs or other visually impactful representations of data
- See large amounts of data in clear, cohesive ways and draw conclusions
- Faster analysis and comprehension of data helps businesses address problems or answer questions in a timelier manner
- Predict sales volumes
ITCrats Data Visualization platform services include
Microsoft Power BI
Our data experts use Power BI, a suite of business analytics tools by Microsoft to analyze data and draw insights. Power BI dashboards offer customizable views for business users for all important metrics in real-time and can be accessed from any device.
Datazen is a Windows 8 app that enables dashboard creation and publishing on Datazen Server,
dashboards and KPIs accessible on any device via it's native app or web browser.
We make use of Tableau to help you see, understand and interpret your data. Tableau is compatible with desktop, server, and cloud versions.
Roambi Analytics transforms your data into a simple, engaging, and intuitive experience by surrounding data with context and story behind the numbers.
Mobility is the core of digital transformation. We help you leverage contextual, real-time reports and dashboards anytime, anywhere, On The Go!
CASE STUDY: REAL TIME ANALYTICS
REAL TIME DATA ANALYTICS USING AZURE
About the Company
A decades old water technologies company reinvented itself for the digital age with Microsoft Azure advanced analytics and transformed field-equipment data into valuable insights for customers. The company utilizes this same data intelligence throughout its organization; in particular, its field service team uses data-driven insights in combination with Microsoft Dynamics 365 to recommend new services while onsite with customers by providing water purification and management solutions—filtrations, separation, disinfection, technologies, and service.
- Instead of taking readings on customer water flow once a day, the company wanted to take them continuously and surface that data to customers in a real-time dashboard.
- Use data analytics—as a competitive differentiator in an increasingly commoditized business.
- Microsoft Azure infrastructure and analytics services, Power BI for data visualization, Dynamics 365 for managing customer and field data, and Office 365 for productivity.
- A technology foundation for gathering real-time data from the equipment installed at customer sites was built which resulted in thousands of data points pouring into the database every minute from equipment's all over the globe.
- In brief, field device data comes into Azure, where it’s added to a central data repository and analyzed in real time by Azure HDInsight. From there the data is placed in Azure SQL Data Warehouse, where it’s available for business intelligence queries and graphical insight delivery using Power BI Embedded.
- The customer portal mentioned earlier (built using Azure Web Apps and Power BI) provided customers with a wider view of their relationship with the company.
- Within a year, the company had all 3,000 employees using Office 365 for communications and collaboration, and it had a good start on its field data infrastructure
- The company had set up key line-of-business applications in Azure and had created a customer portal in Azure
- By pulling data from its entire water landscape, they were able to gain insights on the performance of all its equipment, compare each customer’s equipment performance to the larger groups, and pass insights and recommendations onto customers.
- Better service to customers, minimizing disruption to their water flow, and building a long-term future with them.
- Automated service tech dispatching, improved product designs, and lower manufacturing costs will yield savings across the board
CASE STUDY: PREDICTIVE ANALYTICS SOLUTION
- Lack of Slicing and Dicing of Risk Management complex KPI’s across various dimensions
- Need to make BI self Service across the organization
- Need to create client centric reporting framework.
Scope of Work
- Develop a Data Warehouse to accommodate data from different OLTP systems.
- create reports using Microsoft Power Pivot to cater Accounting and Risk management users.
- Create Tableau visual analytical reports to assist Executives , Risk Management team and external clients based on each Clients business model to better understand current state and expected future state.
- Create a BI portal interface to view SSRS and Tableau Reports.
- Used MSBI tool SSIS for ETL
- Created cubes using SSAS
- Used Microsoft power pivot and SSRS for traditional BI and operational reports
- Used Tableau to create Analytical reports from both Data mart and Cubes
- Implemented Client Level security for Canned and Adhoc reporting.
- Created one stop BI portal for internal and external users that serve as self service tool for BI
- Created Tableau Dashboard for executive and external Client users to view on Desktop , Laptop , tablet and mobile.
- Created Tableau framework for Adhoc reporting to both internal and external users.
CASE STUDY: DATA VISUALIZATION SOLUTION
What were the Drivers?
- Displaying capability in providing development services using QlikView tool
- Solution and architectural ability in QlikView
- Showcase technically qualified resources for support activities
- Showcasing competency to provide ongoing help for projects
Scope of Work:
- Showcasing AR Module reports with different available features in QlikView
- Architect the solution to demonstrate capabilities
- Create data model in QlikView
- Demonstrate some technical features in reports like
- Use of .qvd files
- Data level security
- Dashboard level security
- Set Analysis / What if Analysis / Trending & use of time series functions
- Create a consolidated report.
- Used sample data from AR module of Oracle apps
- This was imported in MS access database which was used to imitate the source system
- Using this data .qvd files were created and used for reports
- Consolidated report was based on data from qvd files and it demonstrated all the features identified as scope
- Report development with most of the features of QlikView
- Showcased security using MS access database
- Implementation of ETL using scripts
- Understanding and usage of QlikView strengths (in addition to
- Scripting (ETL) feature
- Leveraging AQL
- Qlikview in an enterprise architecture