Business Intelligence & Analytics

RealSoft has focused on Augmented & advanced data analytics, data integration and data presentation since 1999


RealSoft offers a comprehensive set of Business Intelligence & Analytics services to help clients harness structured data to improve decision-making, statistical analysis and customer services. RealSoft solutions have powered many regional clients with comprehensive functionality, such as Query and reporting, on-line analytical processing (OLAP), Dashboards, BI Platforms. In addition to GIS analytics, performance measurement technology (balanced scorecards, KPIs, analytics) using the most advanced technologies and best implementation practices.
Data analytics is the process of turning raw data into meaningful, actionable insights and presenting them in visualizations, such as graphs and charts, so that stakeholders can understand and act upon them.

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Descriptive Analytics

What happened? What is happening now?

Descriptive Analytics parses historical data to better understand the changes that have occurred in a business. Using a range of historic data and benchmarking enables decision-makers to obtain a holistic view of performance and trends on which to base business strategy.
Aims to answer the question, “What happened?” (or What is happening?), characterized by traditional business intelligence (BI) and visualizations such as reports, pivot tables, and visualizations like histograms, line graphs, pie charts, and box and whisker plots.
It involves parsing (or breaking down) data and summarizing its main features and characteristics. In this way, descriptive analytics presents what has happened in the past without exploring why or how.

Diagnostic Analytics

Why did it happen, Why is it happening, What are the trends, and What patterns are there?

Diagnostic Analytics is the process of using data to determine the causes of trends and correlations between variables. It it a logical next step after using descriptive analytics to identify trends.
It helps businesses delve into data to understand what’s driving trends and anomalies, such as an unexpected drop in revenue, a shift in customer behavior, or an increase in expenses.
Examines causes of trends to help companies better understand performance and customer behavior variations. It also employs data drilling, data mining, and correlation analysis.
Diagnostic analytics uses various techniques to provide insights into the causes of trends. These include:
– Data drilling: Drilling down into a dataset can reveal more detailed information about which aspects of the data are driving the observed trends. For example, analysts may drill down into national sales data to determine whether specific regions, customers, or retail channels are responsible for increased sales growth.

– Data mining hunts through large volumes of data to find patterns and associations within the data. For example, data mining might reveal the most common factors associated with a rise in insurance claims. Data mining can be conducted manually or automatically with machine learning technology.

– Correlation analysis examines how strongly different variables are linked to each other. For example, ice cream and refrigerated soda sales may soar on hot days.

Predictive Analytics

What will happen, How will people react, and Is this a fraud?

Looks at how trends might unfold in the future and their potential impact so that business leaders can take a more proactive, data-driven approach to decision-making
Predictive analytics is a form of technology that predicts certain unknowns in the future and uses statistics and modeling techniques to determine future performance.
The most common predictive models include decision trees, regressions (linear and logistic), and neural networks, an emerging field of deep learning methods and technologies.

Prescriptive Analytics

What should I do, and How can I make it happen?

Uses the results of descriptive, diagnostic, and predictive analytics to suggest actions to respond to those future trends and improve business outcomes
Prescriptive analytics specifically factors information about possible situations or scenarios, available resources, and past and current performance and suggests a course of action or strategy.
It uses machine learning to help businesses decide a course of action based on a computer program’s predictions.