Start here - Introduction

You need analytics.

In today’s business world, everyone needs analytics. Analytics powers and informs most decision making in organizations, from sales, marketing, partnership to product and engineering. Running a business without analytics is like driving in a foreign country without GPS.

Yet most companies fumble when starting to build their analytics stack. Many of them either spend too much time building a system that’s unnecessarily complicated, or spend too little time building a system that doesn’t work well.

This is understandable. When starting out, most companies don’t have an experienced data engineer or architect to help them build things the right way. So they attempt to do it themselves; when they do their research online, they get lost in a sea of ‘Big Data’ fads, marketing jargon, and worse.

The questions:

• How can I set up a simple, scalable analytics stack that serves my business?
• How can I start small but still follow best practices that help me scale the system up easily later?

sound simple, but are actually difficult to answer if you’re looking in the wrong places.

Our hope is that this book will help you answer the above questions.

This book aims to:

• Give you a high-level framework and understanding of a proper modern analytics setup, and how each component interacts with each other.
• Go into enough practical detail on each of the components. Explain the best practices, and help you understand the role of each component in the entire pipeline for data delivery: that is, consolidating, transforming, modeling, and using data.
• Show readers how to get started quickly on an analytics setup, yet remain able to scale it as time passes.

This book is not about what metrics to track for your industry. It is about how can you build an adequate system for your business to produce those metrics in a timely manner.

First, who are you and why should I trust you?

We are Holistics. We’ve been making data analytics tools for over four years, and helped more than a hundred companies build their business intelligence capabilities, sometimes from scratch.

A huge chunk of our time is spent educating and assisting companies as they migrate to this new world of cloud-based business intelligence tools. For the first time ever, we’re putting that experience up for the world to read.

Who is this book written for?

This book is written for people who need a map to the world of data analytics.

The field of business intelligence has been around for about 60 years. It is incredibly confusing. There are many vendors, fads, trends, technologies and buzzwords in the market — and it’s been this way for most of those six decades. It is impossible to expect new data professionals to be familiar with all that has come before, or to identify new developments as trends that will repeat in the future.

This book will give you the bare minimum you need to orient yourself in the contemporary BI environment. It assumes some technical knowledge, but won’t get mired in technical detail.

Our goal is to give you ‘just enough so you no longer feel lost’.

Who might find this useful? We can think of a few personas:

• A junior data analyst (or product manager) with knowledge of SQL. You have basic facility with data analytics, but do not yet have a full picture of your company’s data stack. You find it difficult to talk to data engineers when you need them to help with a particular pipeline problem.
• A software engineer who is assigned to set up a data stack from scratch. You think you know what tools you need, but you’re not sure if the stack you’ve chosen is the best for your company going forward. This book will give you a lay-of-the-land overview of the entire data analytics world, so you’ll be able to pick the right components for your company.
• An experienced data professional who wants a framework to understand the latest cloud-oriented developments. You are experienced with business intelligence best practices, and cut your teeth during the heydays of Cognos dominance. You want to know what’s up in this crazy new world of cloud data warehouses. You will skim this book where you are familiar, and you intend to slow down when you spot differences between what you know and what we present as the contemporary approach to analytics. You will find Chapters 2 and 3 the most interesting.

Who is this book NOT for?

This book is not written for non-technical business practitioners.

If you are a CEO, a project manager or a business team leader initiating a data analytics project for your company, it is best that you have a technical team member to help you go through the content presented in this book.

This book is also not written for experienced data engineers who manage large-scale analytics systems and want deeper knowledge about one particular problem. If you are familiar with cloud-first environments, you probably already know most, if not all of the content that is covered in this book. That said, you might still find some parts of the book useful as a refresher.

What you won’t get from this book

There are a lot of things we won’t be covering in the book.

As much as we’d like to, there’s an entire topic on the human and organizational aspects of business intelligence that we won’t cover in this book, which include questions like:

• How should I engage different stakeholders in the analytics process?
• How should I structure my data team?
• When is the right time to hire a head of analytics? What should I be looking for when I hire?
• How do I hire a data analyst?

These are questions that we hope to cover in another, dedicated book.

Additionally, we also won’t cover:

• Industry-specific data knowledge (e.g. what is the standard metrics and best data practices for eCommerce industry?)
• Language-specific knowledge like Python or SQL (how to optimize queries, how to use different Python visualization packages …)
• Data analysis techniques (how do you identify causal relationships, what different ways are there to verify a hypothesis?)

Let’s start

Are you ready to read the book? If so, let’s begin.