Digital IX

Data Analytics

The growth of data is being measured in the same scale as population census, study of celestial bodies and behavioral science. They are huge in size (Volume), abstract and include numerous factors (Variety and Complexity), and are growing instantaneously (Velocity) as we speak.

Anblicks helps you to manage this sheer scale of data with Big Data Analytics. Our solutions are powered by the right mix of Big Data and Business Intelligence components allowing the enterprise to leverage the maximum out of a particular use case.

Big Data Strategy

  • Every new technology requires a powerful strategy and Big Data is not an exception.
  • As Big Data services are in trend, its adoption is also rapid. Though there are endless success stories, it is still essential to have an effective adoption strategy for your business.
  • We help determine the use cases for Big Data services & further craft a good strategy for your organization.
  • The entire strategy is built in close collaboration with the key stakeholders of your company. This ensures that their requirements are fulfilled & are aligned with big data and analytic solutions.

Big Data Architecture

  • Once a clear strategy & use of big data services are defined, this is the time to develop a comprehensive architecture that works well for the primary set of applications.
  • Big data analytic services can also be expanded easily for the future applications.
  • Our consultants possess immense experience as big data service provider. They work closely with customers and provide them integrated big data solutions, which encompasses unstructured & structured data and various other transactional data sources.
  • We also suggest big data and analytic solutions that will work the best as per your needs.

Proof Of Concept

  • Once the design and architecture are received, it is good to develop a POC (proof of concept) or POT (proof of technology) application.
  • The proof of concept is a prototype, which shows that the proposed big data solution and technology stack will serve your requirements. In such phase, the application’s small scale version or a specific module is tested and implemented.
  • We set goals, measure, implement and as well as evaluate the outcomes of POC.
  • Dashboards and KPIs: A Dashboard is rich in the Visual representation of the key performance indicators (KPIs) that decision makes use to manage, collaborate with other functions and efficiently run their functions. We help implement appealing, crisp dashboards that helps decision makes in planning and improving business processes.
  • Multi-dimensional analyses: We help in developing multi-dimensional OLAP data sources that will enable Roll-ups and drill-downs as well as Slice and dice of various measures in an enterprise.
  • Reporting: We implement self-service, end-to-end bi reporting. We help turn your business into a data-driven enterprise with interactive reporting with filters, dynamic visualizations, self-service BI that has the quick turn-around.
  • BI Rationalization: With time, static reports get proliferated, some reports get dated and some data assets lose their relevance. We identify the non-performing assets and retire them from the eco-system so that the most relevant information is available at any point in time.
Data warehousing allows for answering questions across enterprise functions. We perform Evaluation, Assessment and Diagnostics of the current DW landscape, provide Solution Architecture and Design, conduct performance assessments and create improvement roadmap, assess the Technology Architecture (Loosely coupled systems, Data Appliance or Cloud).

Data Acquisition

  • Data Sourcing: Managing data sourcing has become challenging due to the explosion in the varieties of Data Sources. We have the ability to handle Streaming or Log Data, Machine or Sensor data, Harvest Web content utilizing variety of scrapping/crawling and search and develop/utilize Data APIs to subscribe and insert from other data service providers
  • Data Integration and Transformation: We develop not only highly performant ETL, data virtualization, data wrangling and Munging involving desperate source systems to provide a unified and consistent view of enterprise data assets.
  • Data Standardization: We provide the services for cleansing, normalizing, standardizing and enriching a messy data. This includes Data Profiling, Cleansing, Deduplication, and Enrichment. In addition, we provide data wrangling services like merging, transform Data Structures/types, Parse, missing value imputation, cast and melt, aggregation.
  • Data Migration: We can strategize, build and migrate either storage and data migration with minimally invasive cut-overs. This includes Datamart and system rationalizations.

Data Warehousing

  • Data Warehousing: Build and deploy high performant, high-quality data structures that could yield decisions by integrating data from many Internal/External transactional systems. In the emerging architecture, we could integrate/enrich data from public domains.
  • Master Data Management: Reference data proliferation within an enterprise result in inconsistent performance measurements. We have the capability to create data hubs for master entities and processes to synchronize changes to the master data.
  • Data Management: Our approach to Data management comes with a realization that Data is the new oil. We have experts who help with managing and maintaining large databases and ensure their security, integrity, and performance.
  • Data Migration: We evolved best practices around Data Governance, tools and techniques for ensuring Data quality. We ensure Accuracy, Completeness, recency, relevance, Consistency across data sources, Reliability and accessibility of Data

Tools and Technologies

We leverage the following tools and technologies related to BI and Big Data in implementing the solutions.

Featured Case Studies

Artificial Intelligence for Lending

How a global lending firm reduced operating costs & enterprise risk by using a single Artificial Intelligence based platform for various types of lending

Predictive Marketing Analytics

How a US based large education provider increased lead acquisition and conversion rate using Artificial Intelligence

Financial Fraud Analytics

How a global financial firm builds a strong fraud detection strategy to safeguard customers and reduce financial risk using financial fraud analytics

IoT Analytics

How a US based heavy equipment rentals agency improved equipment utilization and saved costs with predictive maintenance

Sales Forecasting

How a global E-commerce store achieved optimized pricing and identified candidates for various forecasting techniques using sales forecasting

Service Center Analytics

How a global BPO Improved customer retention by analyzing the customer interactions using machine learning and natural language processing techniques

Legacy Data Integration

How a U.S. based insurance company achieved 18% savings in cost and 1% improvement in sales by integrating legacy and complex business applications

Insurance Process Automation

How a large insurance company improved work efficiencies and turnaround time leveraging business process automation