Data represents the starting point for taking actions that directly impact business and an entire organization. Data analysis is necessary for the growth of organizations. Consequently, we’ll define the concepts related to this area and introduce the leading solutions that are helping companies worldwide make data-driven decisions introducing improvements in their organization.

The effectiveness of data-driven business decision-making depends on the people, followed by the infrastructure where the business strategy is built. Choosing both wisely is a crucial step. This union will allow you to analyze and describe the current situation, its progress and, depending on the organization’s maturity, predict specific business scenarios. Let’s go through the primary set of attributes that affects this process.

First, it’s necessary to define what steps are likely to lead an organization to make decisions in a healthy, data-driven way:

1. Define business goals.
2. Identify the data to be used and verify that it’s aligned with the business goals.
3. Select data sources. Clean and organize it.
4. Establish a data management strategy.
5. Set what’s to be measured and how to achieve it.
6. Select which tools will manage, process, and visualize the data and how to connect them.
7. Build visual reports and transform them into information.
8. Make business decisions based on data.

A couple of points stand out in this list: a differentiation between data and information and the importance of software tool selection for practical business decision-making based on data.
Let’s establish the importance of some of these points. Let’s explain why:

Data vs Information

Data and information are often referred to interchangeably; although they share certain similarities, they’re slightly different terms in this context. The definitions offered by the Oxford English Dictionary are as follows:

1. Data: “Facts, especially when examined and used to find out things or to make decisions.”
   
2. Information: “Data that is processed, stored or sent by a computer.“

To summarize, data refers to a specific representation of one or a set of attributes; these can be qualitative and quantitative and represent a variety of facts, while information is the set of processed data that communicates what it means. This transformation process is referred to as Business Intelligence (BI).

Therefore, we can state that the expected goal of data analysis is to transform data into information and communicate it in the best possible way. This goal is achieved through the creation of data visualization.

Apart from the data and information terms, there are also Business Intelligence and Data Analytics concepts. What’s the purpose of each of these disciplines, and at what point do they collide?

Business Intelligence or Business Analytics?

It’s important to differentiate between both concepts in this context, as Business Intelligence is a consequence of the previous Analysis:

1. Business Intelligence: A practice that enables an organization to transform information into tactical and strategic business decisions. More popularly, this term refers to software tools and services facilitating data transformation.
   
   Business Intelligence offers the opportunity to answer concrete business questions directly. I.e., “Where is there excess inventory, or Why are sales increasing in X region?”
   
2. Business Analytics: It’s a set of disciplines and technologies that process and analyze large amounts of accumulated data to visualize trends, patterns, and causes that allow predictions.
   
   Business data analytics focuses on solving problems by exploring and studying quantitative data models and methods to support data-driven decisions based on business intelligence.

Software tools to make data-driven business decisions

Using software tools to support BI and analysis processes centralizes data from all organization departments (sales, marketing, finance, people, etc.), establishing each area’s performance and how it affects others, and vice versa.

There are many ways to achieve this successfully. The people who analyze and process the data and the decision-makers in the organization are responsible for this. And, of course, supported by the software tool of choice that centralizes the final information.