Introduction

If you're a decision maker at a growing company, you're faced with a decision today that will impact just about every area of your business:

“How should my organization go about making decisions with data?”

This decision—figuring out an organization's data strategy—has become increasingly important over the past ten years as data technologies have become more sophisticated and have been able to provide a greater edge to companies that use them well.

Large enterprises have had data strategies for a long time. Ten years ago, if you worked for a Fortune 1000 business you had some IT folks, some consultants, and some big software vendors that did back-room magic and presented you with the reports you asked for—or at least something close. Getting a new report built involved a lot of work and cost, and took a while to turn around. You could typically get what you needed, but you'd better have the budget (and the patience) for it.

This was the “enterprise” data strategy.

If you didn't work at a large company, your data strategy consisted of whatever data and whatever tools you could get your hands on. Often, this meant emailing your engineers to get data dumps, then spending days massaging the data in Excel. Expecting the pieces to fit together well—connecting marketing data to sales data for example—was just not realistic, and having time to research anything but the most basic answers was out of reach.

This was the “startup” data strategy.

Neither of these strategies was particularly good. The enterprise strategy typically got the job done, but it was slow and clunky. It caused enterprises to be unresponsive to change. The startup strategy relegated smaller but faster-growing companies to the land of gut instinct—they simply couldn't financially afford to get the data they needed to make good decisions.

But the entire data landscape has changed over the past ten years. Data volumes have grown, data is now distributed throughout the cloud, and data tools have gotten far more sophisticated. Today, decision makers at businesses of all sizes have to make real choices when defining their data strategies.

Unfortunately, most decision makers aren't data engineers, and answering the question “How should my organization go about making decisions with data?” can send you down a rabbit hole trying to make sense of the entire big data ecosystem. This gets overwhelming, fast.

But...do I really need a data strategy?

Not necessarily. After all, businesses have been grown from scratch throughout history without having a data strategy, and you could follow in their footsteps. We wish you the best with that.

The problem is the world has changed. Innovative companies today are using data to understand their customers' preferences and behaviors, their supply chains, their acquisition channels, and every other aspect of their businesses. These companies are your competitors. And they will out-execute you along every single competitive dimension if they're making more effective use of data than you are.

Data is the critical resource of our age. Data drives decisions, and better decisions lead to growth. Good decisions are the currency of business.

So, while you definitely don't need a data strategy, your business will certainly grow a lot faster if you have one.

What's in this guide

This guide is not fluffy “big data” speak. It's not packed with buzzwords. It definitely won't leave you with that soul-destroying sense of emptiness you felt the last time you read a piece on “the data revolution” in the popular business press. Instead, we're going to get right down to nuts and bolts.

Business leaders need to understand the data ecosystem because their ability to do so represents a key competitive advantage for their business. So we're going to dive right in and address what we believe to be the big four questions you need to answer when setting your organization's data strategy:

• How should we consolidate our disparate data sources?
• What technology should we use to store and analyze our data?
• How should we facilitate data exploration?
• How do we collect the data we will need to analyze?

We'll examine each question and evaluate the choices available to you. By the end, you'll be ready to roll up your sleeves and make some decisions.

We've ordered these chapters in the sequence that we believe you should be tackling them. We advocate for an agile, iterative approach: data projects are too complicated to realistically foresee everything during a planning phase. Instead, we recommend you get to work with the data you have, start learning, and build out your processes, technology, and organization over time.

The first question you need to tackle is how to consolidate your data into a single location so that you can actually work with it.