The amount of data produced and collected in the world daily is growing dramatically. As of a 2017 study (https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/), the best estimates are that 2.5 quintillion bytes of data are generated each day, but that number is only expected to have grown since that study is a number of years old now and, more importantly, with the popularity of Internet of Things (IoT) devices. With such staggering numbers, it produces major problems for organizations trying to ensure their workforce has a high level of data literacy.

Not unlike learning a foreign language, data literacy is the concept of reading, understanding, and communicating with data. In its simplest form, someone with high data literacy skills would know how to take raw data provided to them and convert it into something they can use to drive business decisions. This is a skill that takes time to learn but once an individual masters it, they can become incredibly valuable to an organization. Without high levels of data literacy, organizations can seemingly make decisions on gut feelings without supporting data influencing business plans.

With the amount of data collected, one would assume that every organization treats the data they collect as an incredibly prized resource. However, that’s far from true. Many organizations are struggling to understand the meaning behind key business metrics and how those metrics should serve as indicators for driving timely business decisions.

Many organizations lack the skills required to properly show the value behind their data. Other companies take the approach of having only a select few that specialize in understanding and utilizing their data. While this strategy is better than complete data ignorance, it’s still as if every organization has amassed a collection of the world’s most important books for gaining knowledge but only a small percentage of employees actually know how to read.

More forward-thinking companies realize that data in the hands of just a few experts creates a bottleneck, and the optimal strategy is to democratize data to the masses. As organizations grow, it’s easy to become overwhelmed with these problems, but if companies don’t put an emphasis on treating data as an asset, they will quickly fall behind competitors who put a priority on data literacy.

So how should organizations facing the challenge of staff with low data literacy respond? The first thing leadership must focus on is developing a data strategy. Data itself has no intrinsic value without a strategy for using it properly. The goal of a data strategy is to provide an organization-wide plan on how data should be collected, stored, protected, and analyzed. Without such a plan, an organization is susceptible to issues like data loss, violating international personal data collection laws, and even data breaches, which you hear about often in the news.

It is important to realize that every organization is different, and each has its own unique set of challenges to working with data. So, you shouldn’t stress out about trying to find the definitive data strategy guide because it doesn’t exist! There is no one-size-fits-all data strategy approach for all organizations. For example, a data strategy for a university would look very different from a data strategy for a Fortune 500 company. The former is focused on the success of their students while the latter is likely focused on overall profitability. During the planning of a data strategy, an evaluation of each segment of data must be completed to determine how the data will be processed, stored, and shared. This process will often uncover that not all organizational data should be treated the same. For example, timecard entry data from two years ago is far less important than financial statements from the same timeframe. The idea is that a data strategy should be more granular and not make large declarations about all data. Some data is more valuable than others and the time and resources spent should not be the same for all data. An organization’s data strategy should be centered around its unique needs, but the point of this chapter is not to give you a step-by-step guide on developing a data strategy. More than anything, this is to stress the importance of simply having one!

The second thing organization leadership should focus on to drive higher data literacy is building a data culture. An organization with a healthy data culture is inclusive, meaning it puts data in the hands of everyone, leaving no one left out. This can be challenging when there is such a skill gap between the typical business user and a professional data analyst or data scientist. So, what do you do to overcome that skill gap?

Anyone can improve their data literacy skills, but the question is: what is your organization doing to foster an environment that encourages engaging with and thinking about data? Many organizations are embracing a positive data culture by promoting data enablement programs, which include ways for individuals to improve their skills with both training and mentoring. An enablement program is far more than sending a group of eager data enthusiasts to a class and hoping they learn enough to be productive. A thoughtful data enablement program is an ongoing exercise over the course of weeks and sometimes months to groom your team into becoming citizen developers.

Gartner defines a citizen developer as:

”An employee who creates applications capabilities for consumption by themselves or others, using tools that are not actively forbidden by IT or business units. A citizen developer is a persona, not a title or targeted role. They report to a business unit or function other than IT.”

Growing a data culture full of citizen developers doesn’t happen all at once. Many organizations often start by building a group of data champions. This group would consist of individuals from multiple departments that are eager to learn and ready to make a commitment to improving their data literacy skills. The primary goal is to grow experts in each department so that way, as new citizen developers emerge, they have a known resource within their department who can help them learn. As you might expect, it’s always easier to bounce ideas off someone who knows the kind of data you work with rather than someone who is unfamiliar with your data

Education in data literacy can vary from broad topics that apply to any data analytics tool or more specific tutorials geared toward a particular technology you want your citizen developers leveraging. An example of tool-agnostic learning would be how to spot trends in your data, how to determine outliers in data, or even how to choose the best visualization for the data you are working with. Any of these topics can apply to every data analytics tool on the market. While there are many great data analytics tools on the market, this book spotlights Power BI.

You might have gravitated toward this book for a number of reasons. Maybe you are completely new to Power BI, and you needed a way to kickstart your learning. Perhaps you have been learning about Power BI for some time now but you’re completely self-taught, so you are hoping to fill in the gaps of things you just haven’t seen yet. This would be the phase of learning where many would say, “I don’t know what I don’t know.” Whatever the case may be for you, the authors of this book hope to give you the essentials necessary for achieving high data literacy within Power BI.

By grabbing this book, there’s a bit of an assumption that you have already made the decision that Power BI is the tool you or your organization has chosen. If for some reason you are still on the fence, or perhaps Power BI is one of many business intelligence tools your organization uses, then it’s helpful to have an understanding of why so many have already made Power BI their data analytics tool of choice.

Let’s start with collaboration. One of Power BI’s central goals is to get data in the hands of decision-makers. So even though Power BI does come with a central cloud-based portal that users can visit, it may not make sense to give users a new web page to bookmark in their browser. Collaboration really means bringing Power BI to where your users are rather than forcing them to go somewhere new. The way Power BI does this is with integration into many of the tools you know and love from Microsoft like SharePoint, Excel, PowerPoint, Dynamics, Teams, and even your mobile phone. Within each of these tools, Power BI allows collaboration and discussion to occur around the data visualized on reports. This idea of bringing data to where your users already are is one of the significant ways Power BI helps grow a data culture.

Another reason many are drawn to Power BI is because of its ease of use. As you work your way through this book, you will find that Power BI has a very intuitive interface. It allows you to quickly connect to data, build data cleansing transformations, create relationships between data sources, and visualize your data in minutes. More complex problems can take longer of course, but Power BI tends to follow this 80/20 rule: 80 percent of the problems you encounter in Power BI can be solved with a 20 percent level of Power BI knowledge. The deeper knowledge is important of course, but the times you will actually need it are far rarer.

One of the major considerations when picking a business intelligence tool is price. Fortunately, when comparing many of the other top tools on the market, Power BI wins on cost hands down. The competitor with the closest feature parity is seven times more expensive than Power BI for basic report development.

It is important to note that licensing costs can vary depending on your specific needs so this chapter won’t go into more detail on it here. However, we would recommend reviewing licensing details here: https://powerbi.microsoft.com/en-us/pricing/.

If these reasons aren’t enough, look at the unbiased annual survey performed by Gartner for analytics and BI platforms. Gartner Inc. is a well-recognized technology research firm that conducts research on technologies, which it shares with the public. Power BI continually rates as the highest tool on the market in the categories of “Completeness of Vision” and “Ability to Execute.”

Microsoft Excel is the number one most popular computer program in the world. While Excel is an amazing tool, the millions of users using it to analyze their data are thirsty for more. Fortunately, Power BI was designed with the Excel fanatic in mind. Many of the skills collected over time while designing Excel solutions still apply in Power BI. Concepts like modeling data, writing Excel formulas, and building PivotTables, all have comparable features in Power BI.

Excel was the first self-service business intelligence tool provided by Microsoft. Starting in Excel 2010, features known as Power Pivot and later Power Query were added to enable more advanced data analytics problem solving that traditional Excel could not handle. These two features would later become the core building blocks for what Power BI is today. So much so that even today, any Excel solution developed using Power Pivot and Power Query can be migrated into Power BI via a simple migration wizard.

Having an understanding of these additional Excel features can give someone an incredible head start when learning Power BI. If you are reading this book and feel confident in your Excel skills, pay close attention throughout this book to each tutorial and consider how you would have solved the various use cases in Excel. You will likely find that Power BI is an incredible time saver over how you would have previously solved these problems in Excel.

While low levels of data literacy continue to be a massive challenge for organizations, there remains hope for improvement! Technologies like Power BI are one, but not the only, ingredient for a successful data literacy recipe. A well-thought-out data strategy cannot be overlooked. Without a proper plan for your organization’s data, you are treating one of your biggest assets too nonchalantly. Commitment to a data strategy doesn’t happen without buy-in on a culture change within your organization. A strong data culture leads to enablement that scales throughout your organization.

In the next chapter, you will get your first look at the capabilities of Power BI as you learn about the data connectivity options that are available.

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