Data analytics for my company
The objective of this guide is to introduce the organization to the value of incorporating data analytics tool in the daily use of its work teams. It is focused on managerial profiles, has a low level of complexity and does not require prior knowledge.
The guide explains how to identify if the company has an adequate data analytics tool (this term will be used hereinafter, as it is more common) or should consider adding some for the exploration of its data. In addition, she will explain how to select the appropriate tool and how to take the first steps to use it.
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Data analytics for my company
People who analyze and make decisions based on data in organizations often depend on information technology teams to obtain it. The introduction of data analytics tools such as Power BI, Tableau, among others, to the market, made it possible for the tasks of data extraction, cleaning and visualization to be carried out in a much simpler way and without the need for long years of technical training. Thanks to these tools, nowadays, in a few steps you can make a connection to the databases of customers / human resources / sales / operations and begin to analyze them based on the direct gaze of those who make decisions in companies, empowering those people in the implementation of data analytics solutions.
This paradigm shift in knowledge management within companies presents a unique opportunity to make more and better decisions based on information. In particular, adding a look from the gender perspective to these data is key so that decision-makers have in an accessible way the possibility of exploring various crossings of information and visualizations to identify and evaluate the gender gaps that may exist, relying on explanatory variables (after differentiating the population by sex, exploring the age or some other demographic characteristic of clients, work teams, etc.), which allows the implementation of targeted and evidence-based policies.
For example, the work teams of the companies can analyze by their own means if there are gender gaps in positions and areas of the company, in their clientele, in the design of products and / or services, or if communication actions and sales are being unintentionally directed toward one sex. Specifically, the opportunity for decision-making teams to analyze company data is also an opportunity to analyze information with new and varied approaches.
What are data analytics tools?
Tools that allow us to model, manipulate and/or visualize company data
means that they allow us to prepare the data and make it ready for analysis. For example, sometimes it is necessary to combine data from various sources (various Excel files, from the web, from a system database). Data analytics tools allow you to unify the various data sources and automate that process. In terms of the gender approach, it is key in this regard to ensure that the information is collected and stored with adequate criteria for subsequent analysis. For example, ensure that the variable gender (male / female) and the age of the people are stored as a minimum, as well as other variables of interest when possible.
means that, from an analysis of our data in the past, we can predict how some variables of the organization will perform in the future. For example, from an analysis of the historical sales of a company based on investment in advertising, we can create a model that allows us to estimate what sales will be like in the future for different levels of investment in advertising. In terms of the gender approach, it is key to think about what techniques can be used to visualize whether the design of products and / or services have unwanted gender biases.
means that we can create graphs that are easy and simple to interpret and that are useful for decision making. In terms of the gender approach, it is important to know how to choose which visualizations are the ones that best show the existing gender gaps.
When are they used?
Data analytics tools can be used in all areas of the company to answer various questions based on data. And for each of these questions within each area of the company it is worth understanding whether there are gender gaps and how they can be eliminated.
Questions that can be answered with a data analytics process with a gender perspective:
- Analysis of a specific event. What happened? Are there gender gaps?
- Automated reporting. What’s going on? What is the trend of gender gaps over time?
- Strategic analysis. What is happening and why? What variables explain the existing gender gaps?
- Predictive analysis. What’s going to happen? What will be the future trend of gender gaps?
- Prescriptive analysis. How can we make something happen? What can we do to close gender gaps?
Examples of data analytics tools
Below are examples of the use of data analytics tools in different areas of a company with a gender focus.
Sales evolution report per month in the last 3 years. What is the ratio of men to women in my clientele?
What's going on?
Report with automatic update of daily sales. What is the trend among my clients according to gender?
What is happening and why?
Analysis of which variables explain the growth in sales. What variables explain the differentiated behavior of sales according to the sex of my clients?
What's going to happen?
Estimation of how sales will evolve based on variables external to the company. What is the sales projection taking into account the gender distribution of my clientele?
How can we make it happen?
Analyze what changes should be made to increase sales without presenting unwanted gender gaps.
Steps to select the right tool for my company
From the experience of using the two main data analytics tools, it is possible to identify that the choice of which one to use is a function of which other tools should be integrated with them. For example, if all my company information is hosted on Microsoft Azure servers (cloud environment), it is recommended to use Power BI; On the other hand, if the data management of my organization is based on solutions in the cloud of the Google environment, it is possible that the integration with Tableau is simpler.
The following flowchart presents some of the questions (not all) that we can ask ourselves to reflect on which tool to choose.
How to start a data analysis with Power BI?
To start using Power BI Desktop, you need to download the application for free from the Microsoft site, at this link: https://powerbi.microsoft.com/en-us/downloads/
Once downloaded, the installation must be carried out. Then the application should be opened and connected to a data source.
Choose the type of data source to which we are going to connect. In this case we are going to connect to an Excel file.
Choose the data sheet within the Excel file with which we want to work. We are going to use a database available in Power BI. Power BI.
Select the data sheet in the Power BI file to be able to see all the fields that the Excel file where we connect to has.
Select the report panel and see the types of visualizations that we have available in Power BI to perform.
Select the graph we want to make and the data we want to show in it.
Tool application example
In order to show how to connect to a database and start making the first charts in Power BI, we are going to use the results of a company’s customer satisfaction survey. We are going to make three simple graphs in a first board with data filters.
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