2 We’re all data professionals now

The business world has been transformed by data. With more information available than ever before, the ability to generate, understand, and make connections with data is now an essential skill for everyone regardless of industry, function, or job level.

Saying I’m not a data person is no longer a viable option. This is because we are all increasingly asked to quantify the complex world around us to make more evidence-based decisions.

The goal of this program is to increase your confidence in understanding, manipulating, and communicating data. It is designed explicitly for those looking to rediscover the fundamentals of data analysis to better support organizational decision-making.

What is data?

We often hear that data is all around us. But how do actions or behaviors turn into data points for which we can analyze? Data is generated by asking questions about objects of interest, such as:

How many?	Where?
How often?	What kind?
How long?	Did an event occur?

Think about your work week. You can probably come up with a list of questions that would generate fresh data.

How many monitors do you have?
How often do you take breaks during the day?
How long are they?
Where are your meetings held?
What kind of computer and web browser do you use?
Did your boss ask for anything new?

All of these revolve around you. Answering them yields a specific set of results. Image what happens when we turn a data lens on the wider world to people, countries, or business operations. Asking questions about these groups creates an endless list of potential data points to track and analyze.

And this might feel overwhelming.

The modern world of data

Several years ago, it was estimated that 2.5 quintillion bytes of data were created every day. A quintillion is a million raised to the power of five. Therefore, 2.5 quintillion bytes equals 2.5 megabytes, which equals 2.5 million terabytes or 200 billion gigabytes.

A byte encodes a single character of text on a computer — in a single day, ten trillion copies of Shakespeare’s Romeo and Juliet are created. This is the same as ten million Blu-ray discs. If stacked, they would reach the height of four Eiffel towers.

Although these figures would be even larger if representing information generated in the year 2021, the actual amount is inconsequential. The point is that more raw data is being created, stored, and analyzed than ever before. This is due to increasingly connected technology, a growing number of devices and services operating in digital ecosystems, and the ability of cloud computing to make it easier for organizations to capture data and run large-scale statistical models.

So, whether it is Tesla working on autonomous vehicles in the auto industry, Google leveraging people analytics to help their employees grow and stay engaged, or FedEx using data from real-time traffic conditions and fuel prices to find optimal distribution paths, data is shaping how organizations tackle problems and attempt to build solutions.

One of the main strategic roles of today’s data-centric managers and product leaders is to ask:

What information do we already have?
What information could we find or collect?
How can existing and potential data points help us do something better?

The challenge

Although the promise of data is there, getting from data to a given measure of success isn’t always so clear. Many organizational leaders fail to comprehend the black box often sitting between these two points.

In this black box there are buzzwords like big data, predictive analytics, machine learning, and artificial intelligence. It is not that these techniques can’t result in game-changing business outcomes, just that there are many factors blocking organizations from reaching their lofty data-driven aspirations.

Five organizational blockers in becoming data-driven

Siloed data strategies: Data projects that are distributed across different divisions with little to no coordination between the groups.
Investment indecision: Questions on how much should be invested in data tools and what should ultimately be done in-house versus through third-party providers.
Recruitment confusion: Lack of clarity in hiring in terms of key responsibilities and organizational placement for various data roles.
Macro uncertainty: A changing legal and ethical environment for data collection, analysis, storage, and use.
Lack of data literacy: Fear of data and a lack of shared understanding for what data is and how it can be used to support operations.

This final blocker is an education problem, and we believe that it can be solved by encouraging those at all skill levels to become just a little more data fluent.

Where data helps

Beyond achieving organizational objectives, data skills also empower you, the individual, to gain confidence and stand apart from your peers.

A data-driven approach can help you develop into an expert in an authentic way — especially if you are early in your career and lack industry insight.

Bringing data to the table in the form of real insight will increase your influence, help you build compelling presentations and reports, and, most importantly, enable you to see commercial opportunities and challenges in ways that non-data people won’t.

Being someone who can speak data will also get you involved in more high-impact projects, hopefully increasing your engagement and satisfaction with work along the way.

The role you might play

There are plenty of data roles to play in a modern organization.

Data-driven professional: Anyone within an organization who actively seeks out data in order to make strategic and tactical decisions.

Business analyst: Guide organizations in improving processes, services, and products. They utilize data and strategy analysis to deliver data-driven recommendations to executives.

Data analyst: Help businesses make data-driven decisions. They create reports and dashboards from raw data sources that track key performance indicators to guide decision makers within an organization.

Data scientist: Use business acumen and advanced programming skills to build data products that are often powered by machine learning. They create predictive models, recommender systems, image recognition software, and other products that improve business processes and better server customers.

Data engineer: Create data pipelines that take raw data and transform it into a format that is clean, consistent, and actionable by analysts and data scientists. They play a critical role in maintaining the data assets of an organization.

Data architect: Design, build, optimize, and maintain data management systems which support the data needs of an organization. They ensure that enterprise data systems will scale with increased demand.

Chief data officer (CDO): A senior executive who reports to the CEO and is responsible for preparing and implementing company-wide data strategies to align data system development with business goals.

In reality, there is fluidity across these functions in terms of desired skills, job responsibilities, and team placement.

Regardless of which role or roles you may play, having a general understanding of all the functions will help you contribute more effectively to whatever data ecosystem currently exists in your company.

Goals for this program

We will cover the basics of data understanding and manipulation before pivoting to techniques for communicating data findings to wider audiences in more effective ways. The goal is simply to incrementally increase your skills and confidence working with data.

If you currently refer to yourself as not a data person, this means that you’ll start to see some patterns and ask more pointed questions when data is put in front of you.
If you are already comfortable with the basics, this material should help you identify strategies for conducting new analysis and more clearly explaining findings to colleagues.

For everyone, we hope it means surfacing techniques and tools to build stronger data fundamentals that ultimately make data-driven decisions a more natural part of your workflow.

Let’s get started.