What is Data Analytics? -Introduction to Data Analytics Tools

Turning Data into Opportunity

The Digital data world is growing 40% year on year into the next decade. It is expanding to include not only the increasing number of people and enterprises doing everything online. But also all the smart devices connected to the Internet, unleashing a new wave of opportunities for businesses. This data explosion has created opportunities for enterprises to use data in new ways to learn about customers, expedite business cycles or transform themselves.

However, most organizations struggle to use data effectively. The complexity, quality, or capability in analyzing this data is preventing organizations from leveraging this wealth of information.

What is Data Analytics?

Data Analytics is a process of finding meaningful information from raw data using specialized computer systems. These systems transform, organize and model the data to draw conclusions & identify patterns. Data Analytics tools are used based on the types of data analytics you want to focus upon.

Types of Data Analytics

1. Descriptive

• Summarizing raw data from multiple sources.
• Extract meaningful insights & drawing interpretations from past events.

2. Diagnostic

• Drilling down the data to identify the business problems.
• Examine the data to know the cause behind the problem.

3. Predictive

• Future trends & possibilities in market-based on current events.
• Optimizing business plans for the future.

4. Prescriptive

• Steps to remove the future problems & predict outcomes for optimizing business.
• Demands the usage of Artificial Intelligence & Big data.

Data Analytics Lifecycle

Business Data Understanding

• Understanding the purpose & requirements of the project from a business viewpoint.
• It involves collecting the data & analyzing the data to extract meaningful insights.

Data Preparation

• Construction of datasets that will be incorporated into the model
• This phase involves the transformation & cleaning of datasets.

Data Modelling

• It involves the selection of various modeling techniques, applying them and their parameters are marked with a scale of readings to optimal values.

Evaluation

• Evaluate the model built rigorously before the final deployment of the model.
• It involves reviewing the steps carried out for constructing the model.

Deployment

• Publish the model you have built for use.

Data Analytics Tools – Solutions and Offerings

1. Audit Analytics

• Analytics as a precursor to Audit to identify risk profile and prioritize Audit activities.
• Sample large and complex populations through modeling and sampling algorithms.
• Maturity and Readiness Assessment, Feasibility Study, Gap Analysis.
• Tools Selection, Solution Blueprint, Analytics Scope Definition.
• Automate data analytics for specific processes through a library of business rules, covering 100% of the population.
• Dashboards for exception reporting, Audit Committee Reporting Transformation.
• Classroom Training and skill augmentation programs.

Click Here to know more in detail – Audit Analytics.

2. Business Intelligence

• Build interactive Reporting and Data Visualization solutions on top of the existing enterprise data store for decision-makers.
• Live and On-the-Go Dashboards on your Mobile Devices refreshed in near real-time.
• Address reporting/coverage gaps in existing BI / DW solutions through custom Dashboard and Reporting Solutions.
• ‘What-If’ and ‘Predictive Analytics’ through interactive Dashboard applications.

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3. Business Analytics

• Just-in-time inventory to optimize your supply chain costs.
• Increase loyalty and share of wallet. Offer personalized and differentiated customer experience.
• Identify missing revenue in complex value chains.
• Provide visibility into Procurement and Supplier performance and anomalies.
• Analyze complex relationships and Spheres of Influence.
• Logistics Optimization and increase in operational efficiency.
• What-If analysis of cost and revenue variables to determine optimal margins.

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4. Data Management

• Custom Data Processing, Enrichment, and Loading Services.
• Assessing Master Data and Gaps as compared to standards.
• Define governance processes and the guidelines to drive data governance.
• Identify assess, measure, and remediate data quality of key data elements.
• Customized statistical model development based on training data-set.
• Architecture Review, Query Optimization, Gap Analysis.
• Setting up the Data Governance Council and the Data Stewards.

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