Chapter 1

Exploring Definitions and Roles

IN THIS CHAPTER

Understanding the different types of data

Managing large datasets with business intelligence tools

Recognizing the importance of data analytics

Appreciating the role of data management

Presenting data analytics visually

Data is everywhere - literally. From the moment you awaken until the time you sleep, some system somewhere collects data on your behalf. Even as you sleep, data is being generated that correlates to some aspect of your life. What is done with this data is often the proverbial 64-million-dollar question. Does the data make sense? Does it have any sort of structure? Is the dataset so voluminous that finding what you're looking for is like finding a needle in a haystack? Or is it more like you can't even find what you need unless you have a special tool to help you navigate?

The answer to that last question is an emphatic yes, and that's where data analytics and business intelligence join the party. And let's be honest: The party can be overwhelming if data is consistently generating something on your behalf.

This chapter discusses the different types of data you may encounter when you begin working with data. It introduces the key terminology you should become familiar with upfront. You learn a few key concepts to give you a head start working with business intelligence, and you get the "what's what" of business intelligence tools and techniques.

What Is Data, Really?

Ask a hundred people in a room what the definition of data is and you may receive one hundred different answers. Why is that? Because, in the world of business, data means a lot of different things to a lot of different people. So, let's try to get a streamlined response. Data contains facts. Sometimes, the facts make sense; sometimes, they're meaningless unless you add a bit of context.

The facts can sometimes be quantities, characters, symbols, or a combination of sorts that come together when collecting information. The information allows people - and more importantly, businesses - to make sense of the facts that, unless brought together, make absolutely no sense whatsoever.

When you have an information system full of business data, you also must have a set of unique data identifiers you can use so that, when searched, it's easy to make sense of the data in the form of a transaction. Examples of transactions might include the number of jobs completed, inquiries processed, income received, and expenses incurred.

The list can go on and on. To gain insight into business interactions and conduct analyses, your information system must have relevant and timely data that is of the highest quality.

Data isn't the same as information. Data is the raw facts. That means you should think of data in terms of the individual fields or columns of data you may find in a relational database or perhaps the loose document (tagged with some descriptors called metadata) stored in a document repository. On their own, these items are unlikely to make much sense to you or a business. And that's perfectly okay - sometimes. Information is the collective body of all those data parts that result in the factoids making logical sense.

Working with structured data

Have you ever opened a database or spreadsheet and noticed that data is bound to specific columns or rows? For example, would you ever find a United States zip code containing letters of the alphabet? Or, perhaps when you think of a first name, middle initial, and last name, you notice that you always find letters in those specific fields. Another example is when you're limited to the number of characters you can input into a field. Think of Y as Yes; N is for No. Anything else is irrelevant.

This type of data is called structured data. When you evaluate structured data, you notice that it conforms to a tabular format, meaning that each column and row must maintain an interrelationship. Because each column has a representative name that adheres to a predefined data model, your ability to analyze the data should be straightforward.

If you're using Power BI (covered in Book 2) or Tableau (covered in Book 3), you notice that structured data conform to a formal specification of tables with rows and columns, commonly referred to as a data schema. In Figure 1-1, you find an example of structured data as it appears in a Microsoft Excel spreadsheet.

FIGURE 1-1: An example of structured data.

Looking at unstructured data

Unstructured data is ambiguous, having no rhyme, reason, or consistency whatsoever. Pretend that you're looking at a batch of photos or videos. Are there explicit data points that one can associate with a video or photo? Perhaps, because the file itself may consist of a structure and be made of some metadata. However, the byproduct itself - the represented depiction - is unique. The data isn't replicable; therefore, it's unstructured. That's why any video, audio, photo, or text file is considered unstructured data. Products such as Power BI and Tableau offer limited support for unstructured data.

Adding semi-structured data to the mix

Semi-structured data does have some formality, but it isn't stored in a relational system and it has no set format. Fields containing the data are by no means neatly organized into strategically placed tables, rows, or columns. Instead, semi-structured data contains tags that make the data easier to organize in some form of hierarchy. Nonrelational data systems or NoSQL databases are best associated with semi-structured data, where the programmatic code, often serialized, is driven by the technical requirements. There is no hard-and-fast coding practice.

For the business intelligence developer utilizing semi-structured languages, serialized programming practices can assist in writing sophisticated code. Whether the goal is to write data to a file, send a data snippet to another system, or parse the data to be translatable for structured consumption, semi-structured data does have the potential for business intelligence systems. A semi-structured dataset has great potential if the serialized language can communicate and speak the same language.

Discovering Business Intelligence

Many IT vendors define business intelligence differently. They put their spin on the term by injecting their tool lingo into the definition. For example, if you were to go to a Microsoft website, you'd be sure to find a page or two that would have a pure definition of business intelligence, but you'd also find a gazillion pages detailing how you can apply Power BI or Excel-based solutions to every conceivable business problem.

So, let's avoid the vendor websites and stick with a no-frills definition of business intelligence: Simply put, business intelligence (BI) is what businesses use in order to be in a position where they can analyze current as well as historical data. Throughout the process of data analysis, the hope is that an organization will be able to uncover the insights needed to make the right decisions for the business's future. By using a combination of available tools, an organization can process large datasets across multiple data sources in order to come up with findings that can then be presented to upper management. Using the enterprise BI tool, for example, interested parties can produce visualizations via reports, dashboards, and KPIs as a way to ground their growth strategies in the world of facts.

Not so very long ago, businesses had to do many tasks manually. BI tools now save the day by reducing the effort to complete mundane tasks. You can take four actions right now to transform raw data into readily accessible data:

• Collect and transform your data: When using multiple data sources, BI tools allow you to extract, transform, and load (ETL) data from structured and unstructured sources. When that process is complete, you can then store the data in a central repository so that an application can analyze and query the data.
• Analyze data to discover trends: The term data analysis can mean many things, from data discovery to data mining. The business objective, however, is all the same: It all boils down to the size of the dataset, the automation process, and the objective for pattern analysis. BI often provides users with a variety of modeling and analytics tools. Some come equipped with visualization options, and others have data modeling and analytics solutions for exploratory, descriptive, predictive, statistical, and even cognitive evaluation analysis. All these tools help users explore data - past, present, and future.
• Use visualization options in order to provide data clarity: You may have lots of data stored in one or more repositories. Querying the data to be understood and shared among users and groups is the actual value of business intelligence tools. Visualization options often include reporting, dashboards, charts, graphics, mapping, key performance indicators, and - yes - datasets.
• Taking action and making decisions: The process culminates with all the data at your fingertips to make actionable decisions. Companies act by taking insights across a dataset. They parse through data in...