Perspective 6: More About Data (Part 2)

Hello everyone,

Hope you are well, healthy, and staying safe! ☺️

In my last blog post we started discussing the topic of data. We discussed how to identify data, and one of the tools (Business Glossary) you can leverage to ensure everyone is speaking the same language.

In this blog post I will take you through the last 3 techniques you can leverage to document and analyze data. To be clear, these are not the only techniques, just the last 3 of the 4 I wanted to discuss. We will also talk briefly about data analytics.

Let's jump in shall we?


The purpose of the data dictionary is to identify data elements, define the elements, the data type of the elements, the length of the elements, if the elements are required, and if there are default values per element. There may be other attributes that may be captured in regards to the data elements based on the organizational need, but these are a greater started.

  • Data Element – a specific value that has a specific meaning.

  • Description – the defined meaning of the element.

  • Data Type – the category of data defined by the value(s) it can take.

  • Length – Specific requirements on how long or short an element name can be.

  • Required – Defines if the element is required or not

  • Default Values – Defines if the data element needs to have a default value or not. The default value is a preexisting value.

So building upon our example from Part 1 in regards to interacting with a financial institution you may have data elements defined as below that relate back to data that would be captured.

Figure 1


An entity relationship diagram describes entity types and specifies the relationships that can exist between entities.

Again, building upon our example from Part 1 in regards to interacting with a financial institution, 3 entities that may exist in the financial data structures are "Account", "Customer", and "Product". Within the "Account" entity you may have data elements such as "Account ID", "Account Name", "Account Description" and "Account Open Date". Within the "Customer" entity you may have data elements such as "ID", "Name", "Address", "Phone", "Email", "Account Number" and Type". Now the relation between the "Account" entity and the "Customer" entity for this example is as follows:

  • An Account could have 1 to many Customers

  • A Customer could have 1 to many Accounts

In order to be considered a customer you must have an account with the financial institution. For this example and account and product are different. The account is either a checking or savings account. A product is a credit card, or personal loan as an example.

Figure 2


A system context diagram demonstrates the external components that may interact with the system. I like to define a core system and then show the other systems, applications, or other external components that interact with the core system. The below diagrams shows the core system name the "System of Record" and the other systems or application that interact with that core system, as well as, the data that is passed between those systems.

Figure 3

Now that we have completed reviewing the last 3 techniques I wanted to focus on let's move into how to analyze data and wrap up with an introduction to data analytics.

How to Analyze Data

There are many different ways to slice and dice data, and it can be fun doing so. I like to pursue analyzing data in 6 steps:

  1. Step 1: Determine what type of data you need to answer specific questions to solve problems. Make sure you level set on the purpose of obtaining the data and the desired outcome. When diving into ambiguous and unstructured data, you should define hypotheses to validate though the process.

  2. Step 2: Collect data depending on the requirements you defined in step 1. This data can come from a wide array of sources like we discussed. You can get it through reports, databases, data warehouses, processes, customer satisfaction surveys and so much more. You can also conduct your own focus groups, interviews and even observe how individuals do their work.

  3. Step 3: Scrub the data. With an initial data set, you may find missing, incomplete, or repetitive data, which can bias the results. You will want to check for outliers and ensure metrics, like the mean, median, mode, and range, make sense given the context. Sometimes you also need to convert data into a format that is readable by data analytics tools.

  4. Step 4: Analyze the data through leveraging tools in your organization. Make sure your analysis goes back to the objective you want to achieve conducting the data analytics. Look for patterns, significant variances, and data that looks out a place and doesn’t fit with the data set. Start asking questions for items that don’t make sense to you.

  5. Step 5: Compile the results. Once the data is collected and analyzed, compile it and organize it.

  6. Step 6: Present - Present it in an easy-to-understand format. Many companies have internal dashboards that track Key Performance Indicators (KPIs) through graphs and charts. But if not, know your audience and know the story you want to tell with the data. For example, if you are using the data to help drive a business case for a project that will render huge transformational change for the organization, you want to make sure the data aligns to that objective.

Finally, I would like to focus on how to leverage the different types of data analytics to analyze data.

Data Analytics

What is data analytics?

Data analytics is a method, or science, to analyze raw data.

Types of Data Analysis

  • Descriptive (What) – analysis that seeks to explain what happened with variables.

  • Diagnostic (Why & How) – analysis that seeks to explain “why” and “how” between a particular data set.

  • Predictive (Predicts) – analysis that seeks to predict the future and what actions to take based on how variables are likely to behave.

  • Prescriptive (Improve) – determines which action to take to improve a situation or solve a problem.

Examples of Each of the Types:

Descriptive - What happened with exercise equipment sales in the month of June?

  • Explanation: In this example, you are describing the data in which you want to analyze. This could be a result of reporting that shows a unusual decline in sales from previous months and there is a desire to understand why. This could also be a result of year over year trending where sales decline in June and you want to understand why.

  • Focus: You are looking at the data to describe what the data is telling you.

Diagnostic – Why did exercise equipment sales increase in some retail stores, but not in others in the month of June?

  • Explanation: In this example, you are trying to diagnose a specific situation, which for this example is, why sales increased in some areas and not in others.

  • Focus: You are looking at the data to determine the root cause.

Predictive – Can we leverage the same type of strategy across all stores that increased sales for those stores that are underperforming?

  • Explanation: In this scenario you are trying to predict what options/recommendations/solutions you could implement to get the desired result.

  • Focus: You are looking at the data to make predictions on what you can do

Prescriptive – We are finding that the same sales strategy doesn’t work across all of our markets, what other options can we try to drive sales growth in those areas that are not seeing growth.

  • Explanation: In this scenario you are using the data to determine what options are available to get the desired result you desire.

  • Focus: You are prescribing a solution to the problem leveraging data.

You can leverage data analytics to help with forecasting, continuous improvement, competitive advantage and/or price optimization, as well as, adhere to fraud protection and regulatory compliance. There are also many tools you can leverage to perform data analytics, such as SAS, Tableau, SQL, Python, and more.

As you can see identifying, analyzing and understanding data can:

  • Bring clarity

  • Identify patterns and trends

  • Build stronger solutions to enhance or totally transform organizations

  • Can easily tell you what is going well and what may not be going as well

  • Gives your knowledge on questions you can ask

Though data may be complex it’s extremely powerful. Don’t run away from data. Gradually approach it or jump in, but take opportunities to understand the data which your organization works with.

In my next post I will be sharing our last Videocast of the year with a special guest who will discuss data in more detail. So, be on the look out for that.

Until next time, signing off,

The BA Martial Artist 🥋


P.S. Sign-in and leave a comment, I would love to hear your comments.



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