Knowledge Center: Data Integration & Analytics

Data Integration and Analytics

The quantitative analysis of data is data analytics. It is used to identify, explain and disseminate significant data patterns. It typically involves analyzing data through spreadsheets, graphical visualizations, or interactive dashboards.

The descriptive analysis describes what happened during a given period. Why something happened is understood by diagnostic analysis. Predictive analytics is moving toward what is likely to occur soon. Finally, the course of action is governed by prescriptive analysis.

  • Companies that generate data and want to use it to improve the company’s operational and functional metrics should incorporate data analytics. Different approaches to data analysis include tracking what happened (descriptive analytics), why something happened (diagnostic analytics), what will happen (predictive analytics), or what should be done next (prescriptive analytics).
  • A company can increase productivity, improve the user experience, maximize profit or make more strategic decisions with the help of data analysis. Data analysis is significant as it helps in optimizing the performance of businesses. By finding more cost-effective business methods, companies can help reduce costs by incorporating them into their business strategy. In addition, a company can use data analytics to improve business opportunities and monitor consumer preferences and trends to develop fresh, improved goods and services.

Big data describes large sets of diverse data- structured, unstructured, and semi-structured- continuously generated at high speeds and in large volumes. Big data is usually in terabytes or petabytes.

  • Data analytics can be used in almost every industry – from manufacturing, automotive, supply chain, media, retail, operations to healthcare, banking, finance, etc. These industries can collect customer data and identify where the problems are, if any. and how to fix them.
  • Data analysis helps companies find the next product or the next solution that can transform an industry.
  1. Big data analytics follows five steps to analyze any large datasets: 
  • Data collection
    • ETL – Extract Transform Load
    • ELT – Extract Load Transform
  • Data storage
    • Data Lakes
    • Data Warehouses
  • Data processing
    • Centralized processing
    • Batch processing
    • Distributed processing
    • Real-time processing
    • Data cleansing
  • Data analysis
    • Predictive analysis
    • Descriptive analysis
    • Diagnostic analysis
    • Prescripted analysis

Yes, companies can hire external help for data analysis. Management and the executive team can focus on other essential aspects of the company’s core operations by outsourcing data analysis. Dedicated business analytics teams like ours are professionals in their field; they understand the latest data analysis methodologies and are data management specialists. Thanks to this, they can analyze data faster, recognize patterns and correctly predict emerging trends.

  • MS Excel, Google sheets: MS Excel and Google Spreadsheets are today’s best-known spreadsheet applications. They help in quick data analyses using formulas and functions.

Google sheets offer real-time version history and collaboration, allowing multiple users to work on the same sheet simultaneously. 

MS Excel is preferred by many analysts even for larger datasets and complex formulations when no other dedicated tool is available.

  • PowerBI, Tableau, and Qlikview: Business intelligence tools that can help in quick dashboarding, visualizations, and reporting.
  • Python: Python is an open-source programming language. It supports a variety of frameworks for data modeling, data manipulation, and visualization.
  • R: The open-source language R is employed chiefly for statistical analysis. A variety of scripts are offered for data analysis and visualization.
  • Apache Spark: Apache Spark is an open-source data analytics engine that does advanced analytics using SQL queries and machine learning techniques. It analyzes data in real time.
  • SPSS, SAS: These are statistical analysis software that can help you perform analytics, visualize data, write SQL queries, perform statistical analysis, and build machine learning models to make future predictions.
  • Using past data as inputs, forecasting produces accurate predictions of future trends. Businesses use forecasts to decide how to spend their budgets and plan for upcoming costs. This is usually based on the anticipated demand for the goods and services provided. Quantitative forecasting models include time series methods, discounting, leading or lagging indicator analysis, and econometric modeling that can attempt to establish causal relationships.
  • Forecasting’s major drawback is that it includes the future, which is now essentially unknown. Forecasts are, therefore, educated approximations at best. Even with reliable data, forecasting frequently uses past data, which cannot always be relied upon to be accurate in the future because things may and do change over time. Additionally, it is hard to accurately account for unexpected or one-time occurrences like a crisis or tragedy.
  • Visualization is becoming increasingly important for making sense of the billions of rows of data generated daily as the “age of big data” gathers pace. By organizing data into an understandable format and showing patterns and outliers, data visualization helps tell stories. Powerful visualization highlights important information while reducing data noise.
  • But improving the appearance of a chart or adding an “info” component to an infographic is not that simple. A careful balance between form and function is required for effective data display. The most striking visualization can only partially convey the right message or could be telling. The most detailed chart could be too dull to draw attention, or it could be compelling. It takes skill to combine excellent analysis with excellent storytelling successfully, and facts and images must work together.

Dimensional modelling involves using fact tables and dimensions to maintain a record of historical data in data warehouses. Normalized entity-relationship models (ER models) are designed to eliminate data redundancy and quickly insert, update, and delete operations to get data into the database.

The process of building a dimensional model

  • Analyzing business processes.
  • Asserting the granularity.
  • Identifying tables and dimensions.
  • Building the models.

Facts are quantitative measurements of a business process. Dimensions serve as the foundation for comprehending business process events. All non-numerical process data can be considered dimensions.

Objectives of dimensional modelling

  • Improve user-friendly and transparent data architecture.
  • Maximize data efficiency and data processing.
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