Book Image

Efficiency Best Practices for Microsoft 365

By : Dr. Nitin Paranjape
Book Image

Efficiency Best Practices for Microsoft 365

By: Dr. Nitin Paranjape

Overview of this book

Efficiency Best Practices for Microsoft 365 covers the entire range of over 25 desktop and mobile applications on the Microsoft 365 platform. This book will provide simple, immediately usable, and authoritative guidance to help you save at least 20 minutes every day, advance in your career, and achieve business growth. You'll start by covering components and tasks such as creating and storing files and then move on to data management and data analysis. As you progress through the chapters, you'll learn how to manage, monitor, and execute your tasks efficiently, focusing on creating a master task list, linking notes to meetings, and more. The book also guides you through handling projects involving many people and external contractors/agencies; you'll explore effective email communication, meeting management, and open collaboration across the organization. You'll also learn how to automate different repetitive tasks quickly and easily, even if you’re not a programmer, transforming the way you import, clean, and analyze data. By the end of this Microsoft 365 book, you'll have gained the skills you need to improve efficiency with the help of expert tips and techniques for using M365 apps.
Table of Contents (15 chapters)
1
Section 1: Efficient Content Creation
7
Section 2: Efficient Collaboration
10
Section 3: Integration

Data analysis in three steps

There are three steps involved:

  1. Get the data.
  2. Clean it up.
  3. Analyze it (and then act on it).

Obviously, analysis (and action) is the crucial step.

Unfortunately, we spend too much time cleaning up the data, as mentioned in step 2.

Why does the data need cleaning? Because we do not understand how exactly to get data in the first step! It is a bad, vicious cycle.

Figure 4.1 – Data analysis process

Let's solve the problem in a simple, logical manner.

When you buy products, or log in to an app and use it, or get a medical test done, or just travel to a place – all these activities are generating data. Someone is typing it somewhere in an app. In other cases, data can be generated automatically, such as a history of which videos you have seen. All this is input data.

Input data is usually lengthy as it has an ever-growing number of rows. By looking at and scrolling the input data...