BI and Analytics

Business Analytics

Business analytics plays a significant role today in driving business success. It uses explanatory and predictive modeling based on extensive use of statistical analysis to drive decision making. Business analytics helps organizations in answering critical questions such as, Who are my most valuable customers? What are my most important products? What are my most successful campaigns? Why is this happening? What if these trends continue? What will happen next (that is, predict)? What is the best that can happen (that is, optimize)?

Our team of data scientists and analysts with their domain expertise across Retail, Telecom, BFSI, Healthcare & Pharma, and Hospitality, help our clients in drawing meaningful and actionable insights from the ocean of available data.

AnalyticsWorx, adopts a flexible approach to match the specific needs of each business, which can be any one or a combination of the following mentioned offerrings:

Turnkey Analytics Solutions

Managing Analytics project end to end right from defining the business objective to analysing the data and all the way through to prescribing the actionables

Analytics CoE (Centre of excellence)

Establishing and maintaining a team of Data Scientists and analyst dedicated for the client to take are of enterprise analytics needs

Capacity Augmentation of specific skills (SAS, R, Tableau, PowerBI etc.)

Working as an extension of client's Analytics team and assisting in managing the overflow of work or any gap in the client teams skill sets. The exposure of our team of Data scientists spans across several industry verticals (Telecom, Retail, BFSI, Hospitality & Healthcare), latest Analytics Tools (SAS, R, Python, Tableau, Qlikview & PowerBI) and ever evolving statistical techniques (Predictive modeling , Machine Learning etc.)

BI and Analytics

BI And Visualization

To make highly informed decisions quickly, organizational leaders need to be able to access and interpret data in real-time. Information, and the ability to decipher and act on it swiftly, has become a competitive differentiator. To identify new business opportunities ahead of the market, business leaders require the ability to access, evaluate, comprehend, and act on data faster and more effectively than ever before.

Our expertise in the Business Intelligence and Visualization practice enables us to tap data from disparate sources and multiple formats to provide you with actionable insights, right business metrics and visualization for informed decision making. We crunch the data at the backend and present a meaningful information for decision making on the front-end.

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Smart Dashboards
If you are looking for a flexible business intelligence system that's powerful enough to crunch complex data, then AnalyticWorxs "SMART DASHBOARDS" are the right choice for you. Businesses today need BI tools to measure their industry-specific metrics and KPIs, and provide on-time, ask-any-question data analysis, for department heads, high-level executives and any business user to make the best decisions possible.

With AnalyticsWorxs "SMART DASHBOARDS":

  • See interactive BI reports in action
  • Get stunning custom dashboards built to your specs
  • Slice and dice data fast with dynamic controls
  • See performance of different brands, profile buyer segments and understand the sales funnel
  • Identify the most profitable sub-regions and important buyer demographics
  • Prioritize best selling brands and work out the impact of product condition on brand sales
  • View the sales funnel and potential bottlenecks to improve conversions

Big Data Visualization
One of the most valuable means through which to make sense of big data, and thus make it more approachable to most people, is through data visualization. Data visualization is way finding, both literally, like the street signs that direct you to a highway, and figuratively, where colors, size, or position of abstract elements convey information. In either sense, the visual, when correctly aligned, can offer a shorter route to help guide decision making and become a tool to convey information critical in all data analysis.

Let our BI Experts show you the way:

  • Get the right amount of interactivity to derive actionables from your Big Data
  • Navigate through dashboards with ease, with the user friendly designs
  • See analytics presented visually in our professionally designed dashboards
  • Grasp difficult concepts with ease or identify new patterns in data
BI and Analytics

Data Integration

Data integration involves combining data from several disparate sources, which are stored using various technologies and provide a unified view of the data. Data integration becomes increasingly important in cases of merging systems of two companies or consolidating applications within one company to provide a unified view of the company's data assets. The later initiative is often called a data warehouse.

Probably the most well known implementation of data integration is building an enterprise's data warehouse. The benefit of a data warehouse enables a business to perform analyses based on the data in the data warehouse. This would not be possible to do on the data available only in the source system. The reason is that the source systems may not contain corresponding data, even though the data are identically named, they may refer to different entities.

ETL, or Extract, Transform and Load, eases the combination of heterogeneous sources into a unified central repository. Usually this repository is a data warehouse or mart which will support enterprise business intelligence. A unified view of your data is imperative. Without this, every link in the big data chain-from machine learning to artificial intelligence to information gathered from the Internet of Things-becomes less useful.

ETL combines three important functions (extract, transform, load) required to get data from one big data environment and putting it into another data environment. Traditionally, ETL has been used with batch processing in data warehouse environments. Data warehouses provide business users with a way to consolidate information to analyze and report on data relevant to their business focus. ETL tools are used to transform data into the format required by data warehouses.

ETL provides the underlying infrastructure for integration by performing three important functions:

  • Extract: Read data from the source database
  • Transform: Convert the format of the extracted data so that it conforms to the requirements of the target database. Transformation is done by using rules or merging data with other data
  • Load: Write data to the target database

Master data management (MDM) is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file that provides a common point of reference.

  • Streamline Data Sharing among personnel & departments
  • Link all critical data to one master file
  • Facilitate computing in multiple system architectures, platforms, and applications
  • Achieve timely delivery of projects and build cost-effective processes
  • Improve quality of information by complying with company data practices
  • Formulate effective data governance policies and procedures

Data quality management is a crucial part of any data integration process. It may be considered the first step to the integration process, as quality data is the key to achieving profitable insights. The data integration analysis will not be successful until good data quality processes are in place.

  • Make data quality management part of the data lifecycle
  • Improve the quality of data
  • Improve data flow in the organization
  • Build efficient data quality procedures

Data harmonization is the improvement of data quality and utilization through the use of machine learning capabilities. Data harmonization interprets existing characteristics of data and action taken on data and uses that information to transform or suggest subsequent data quality improvements.Reduce information requirements by eliminating redundancies and duplications, thus making the submission easier,Improve the quality of the data and therefore reduce errors,Facilitate receiving, processing and checking of information, and Facilitate exchange of data and improve automation as this ensures inter-operability.

Common Data Modeling is defining the unifying the structure used in allowing heterogeneous business environments to interoperate. A Common Data Model is very critical to a business organization.
Especially with today's business environment where it is common to have multiple applications, a Common Data Model seamless integrates seemingly unrelated data into useful information to give a company a competitive advantage over its competitors. Data Warehouses make intensive use data models to make companies have a real update on how the business is faring.