---
title: Data Analytics
description: Data Analytics turns raw data into valuable insights and helps businesses make better decisions through advanced techniques and strategies.
---

[Dictionary | Cegal ](https://www.cegal.com/en/dictionary)

# [Data Analytics](https://www.cegal.com/en/dictionary/data-analytics)

 Written by [Editorial staff](https://www.cegal.com/en/dictionary/author/redaksjonen) | Aug 12, 2025 1:34:30 PM

## What is Data Analytics?

Data Analytics is the process of collecting raw data and converting it into useful insights and activities. It involves various techniques, including statistical analysis, machine learning and [data mining](https://www.cegal.com/en/dictionary/datamining), to transform large amounts of data into valuable information. By shifting the focus beyond data, towards insight and activity, organizations can create better decision-making, experiences and productivity.

Data analytics is used in many fields, from marketing and finance to healthcare and industrial production, to improve efficiency, optimize processes and create competitive advantage.

Data Analytics is usually divided into four types:

1. Descriptive analysis - answers the question "what happened"

2. diagnostic analysis - answers the question "why did it happen"

3. predictive analytics - answers the question "how likely is it to happen in the future"

4. Prescriptive analysis - answers the question "how should I act"

The difference between Data Analytics and [Business Intelligence (BI)](https://www.cegal.com/en/dictionary/business-intelligence-bi)

Although Data Analytics and Business Intelligence are often used synonymously, there are important differences:

| **Aspect** | **Data Analytics** | **Business Intelligence (BI)** |
| --- | --- | --- |
| **Purpose** | Discovering new insights and predicting future trends | Reporting and analyzing historical data |
| **Focus** | Explanatory and predictive analysis | Descriptive analysis |
| **Methodology** | Uses advanced techniques such as machine learning and [AI](https://www.cegal.com/en/dictionary/ai) | Mainly uses dashboards and reports |
| **Time frame** | Future-oriented | Historical and contemporary data |

 

Simply put, BI helps organizations understand what has happened and why, while Data Analytics focuses on what will happen and how to optimize future decisions.

A successful Data Analytics implementation requires both technical expertise and a strategic understanding of business needs. By combining this, organizations can create a powerful foundation for data-driven decisions and competitive advantage.

##### **Cegal and**** Data analytics**

At Cegal, we have a large number of consultants who work daily with this type of delivery and projects with many large and small clients. We have experience from a number of industries. We often work with technologies from [Oracle](https://www.cegal.com/en/dictionary/oracle) and [Microsoft](https://www.cegal.com/en/dictionary/microsoft).

Our consultants work with the entire chain from data collection to data analysis and thus give our customers the opportunity to choose areas that they need support in. The goal of many customers today is to become more data-driven. To manage this, data needs to be collected, structured and stored on the right platforms, either traditional structured relational data warehouses or big data solutions.

The entire architecture can be placed in [cloud](https://www.cegal.com/en/dictionary/cloud-computing) and our consultants have extensive experience in both building and moving solutions from [on-prem](https://www.cegal.com/en/dictionary/onpremises) to the cloud.

We have also developed Data Analytics as a Service, where we have created a proactive and scalable service to monitor and manage changing demands on your solution.

[View full post](https://www.cegal.com/en/dictionary/data-analytics)

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