Foundations of Business Intelligence

Table of Contents

The term business intelligence dates back to 1865, when it appeared in the encyclopedia of commercial and business anecdotes. Business intelligence (BI) involves automating processes and information channels in order to transform relevant data into actionable insights that are easily available to decision-makers. By showing decision-makers what is currently happening, organizations become more intelligent and successful.

Careers in BI

BI analysts gather requirements from stakeholders, partners, and team members. They use their understanding of large datasets to retrieve, organize, and interpret data. Then they create visualizations, dashboards, and reports to use when presenting and communicating insights to others. The intelligence they share might be used to make decisions, develop a new process, create a business strategy and apply to even deeper analysis

BI engineers manages tools and processes, which enables a BI analyst to put those tools and processes to work. They are responsible for designing, configuring, implementing, and improving data tools and processes. They evaluate and streamline various devices, infrastructures, and information channels called pipelines. Engineers are excellent troubleshooters and help find solutions to technical challenges.

Brainstorming and building together, pulling knowledge, and working out issues is essential to the BI process. When you’re working in business intelligence, you might collaborate with:

API professionals in order to create the interface that you need for a particular project, especially if some of your data is coming from a third party platform. APIs bring that data into the internal company database in order to build reporting tools and dashboards. API professionals code in a number of different computing languages.

Data warehousing specialists who develop processes and procedures to effectively store and organize data. These people also help ensure BI professionals can easily access the data they need.
Data governance professionals, who are responsible for the formal management of an organization’s data assets. This may involve managing the availability, integrity, and security of data based on internal standards and policies. This is very important for making sure data is trustworthy and doesn’t get misused or corrupted.
Data analysts are key partners who collect, transform and organize data.
Information technology professionals are people who test, install, repair, upgrade and maintain hardware and software solutions.
Project managers are the people who handle the day-to-day project steps, scope, schedule, budget, and resources.

BI Professionals vs Data Analysts

Data is useless if it can not be put to work. Putting data to work requires skilled professionals who can apply techniques and technologies to achieve high levels of maturity. Data maturity is the extent to which an organization is able to effectively use its data in order to extract actionable insights.

Both Business Intelligence and Data Analyst professionals are a key part of their company’s data maturity, enabling data-driven decision-making in their organizations. However the two roles are quite different:

BI professionals	Aim to achieve higher levels of data maturity by building data reporting tools such as dashboards. Involved with: 1. Establishing repeatable methods for monitoring data 2. Working on large-scale projects that are helpful to multiple stakeholders 3. Building data-reporting tools and dashboards
Data analysts	Apply tools created by BI professionals in order to answer a question or solve a problem by examining the data through a specific topic or lens.

Stakeholders

A stakeholder is someone who invests time and resources into a project, and is interested in its outcome. There are all sorts of stakeholders in the BI process, we’re going to focus on the four most common ones:

Project sponsor	Project sponsors have overall accountability for a project and establishes the criteria for its success. They are representatives of the business side, which typically means they’re involved when a project is being envisioned and they advocate for its undertaking. It’s important for BI professionals to always keep the project sponsor informed.
System analyst	System analysts identify ways to design, implement and advance information systems, in order to ensure that they help make it possible to achieve business goals.
Developer	Developers (also called computer programmers, coders, or software engineers) use programming languages to create, execute, test, and troubleshoot software applications.
Business stakeholder	Business stakeholders invest time and resources into a project and are interested in its outcome.

There are some important communication strategies that BI professionals use when collaborating with these people. These strategies involve:

Knowing how to ask the right questions
Define project deliverables
- A deliverable is any product, service or outcome that must be achieved in order to complete a project
- In BI the most common deliverables are the dashboards and reports that provide insights to users
Effectively share the business intelligence you discover
- Bias: a conscious or subconcious preference in favor of or against a person, group of people, or things.
- Fairness: ensure the work does not create or reinforce bias.

BI Stages

Recall that the data life cycle is a sequence of stages that data experiences, which include:

Plan ⟹ Capture ⟹ Manage ⟹ Analyze ⟹ Archive ⟹ Destroy

The data analysis process occurs in six phases:

Ask ⟹ Prepare ⟹ Process ⟹ Analyze ⟹ Share ⟹ Act

Business Intelligence also has a sequence of three stages that determines the value of BI, as well as an organization’s data maturity level:

Capture ⟹ Analyze ⟹ Monitor

As you advance through each one, the process requires a deeper level of exploration and investigation and adds significant business impact, so each becomes more complex.

Capture	What happened? Produces a record of static, backward-looking data.
Analyze	Why did it happen? Connects data elements and defines their relationships. Draw conclusions, make predictions, and drive informed decision-making.
Monitor	What is happening now? Identifies current opportunities and issues so they can be acted upon right away.

Monitoring

BI is all about near-real-time rapid monitoring, BI insights are most effective when they make an impact right now. A big part of BI is creating dashboards that provide users with clear snapshots of the current state. These tools must be impactful and easy to interpret. Even for non-technical people.

A metric is a single quantifiable data point that is used to evaluate performance. A KPI (Key performance indicator) is a quantifiable value, closely linked to business strategy, which is used to track progress towards a goal. Metrics support KPIs, which in turn support overall business objectives.

KPIs	Strategic A plan for achieving a goal or arriving at a desired future state.
Metrics	Tactical A method used to enable an accomplishment.

Understanding business objectives and what is needed in order to achieve them is the first step in BI monitoring. BI monitoring involves building and using hardware and software tools to easily and rapidly analyze data and enable stakeholders to make impactful business decisions.

Vanity metrics are data points that are intended to impress others but are not indicative of actual performance and therefore cannot reveal any meaningful business insights. It’s critical to ensure each metric you monitor is productive, informative, and effective.

Metrics best practices:

Limit the number of metrics
Make sure metrics are aligned with business objectives
Confirm that the necessary technologies and processes are in place
Use the SMART methodology to identify the key metrics for the particular issue at hand
- Help determine a question’s effectiveness and refine metrics based on whether they are Specific, Measurable, Action-oriented, Relevant, and Time-bound

BI Strategy

You may recall learning about data strategy, which is the management of the people, processes, and tools using data analysis. Creating a winning strategy is also a big part of BI. Similarly, business intelligence strategy is the management of the people, processes, and tools used in the business intelligence process.

BI governance	involves defining and implementing BI systems and frameworks within an organization.
Data governance	A process for ensuring the formal management of a company’s data assets.

To build a BI strategy, there are a few elements to consider:

People. What to do is simply communicate with all of the team members and stakeholders who are involved from all levels of the organization so you will get many diverse perspectives. Be sure to ask about
- What is the vision for the BI process?
- How does that vision align with current business strategy?
Process. You’ve established who will be responsible for the rules and policies that govern BI processes. Ask these people:
- What solutions are we using and how?
- Which of them bring value?
- What types of solutions do we plan to implement?
- How will we deliver them?
- How will we support them?
Tools. Choose each tool with the user in mind. Consider which dashboards, reports and other solutions will be most effective. Establish key performance indicators (KPIs) for each particular business need. In order for KPIs to do their job, it’s important that the tools you select align with the KPIs established for each particular project. Ask:
- Do different users, teams and departments require different technologies?
- Which technologies do we have access to?
- Can we gain access to others if needed?
- How will we measure success?
Documents. The last step in this process is documenting everything you’ve learned:
- Stakeholder Requirements Document
  1. Business problem
  2. Stakeholders
  3. Stakeholder usage details
  4. Primary requirements
- Project Requirements Document
  1. Purpose
  2. Key dependency
  3. Stakeholder requirements
  4. Success criteria
  5. User journeys
  6. Assumptions
  7. Compliance and privacy
  8. Accessibility
  9. Roll-out plan
- Strategy Documents
  1. A collaborative place to align with stakeholders about project deliverables
  2. Establish information about dashboard functionality and associated metrics and charts

BI Toolbox

The first tool in BI toolbox is data model, which organize data elements and how they relate to one another. They:

Explain to users how the data is organized
Help keep data consistent across systems
Give BI professionals clear directions when navigating a database

The second tool in the toolbox is data pipeline, which is a series of processes that transports data from different sources to their final destination for storage and analysis. Along the way, it’s up to BI professionals to transform that data so that by the time it pulls into the database, it’s ready to be put into use.

ETL, or extract, transform, and load is a type of data pipeline that enables data to be gathered from source systems, converted into a useful format, and brought into a data warehouse or other unified destination system.

The third tool is data visualization, which is graphical representation of data. People who don’t have a lot of experience with data can easily access and interpret the information they need. BI professionals use data visualizations within dashboards.

The last tool is dashboards, which is an interactive visualization tool that monitors live incoming data.

No matter which BI tool you’re using, a very important concept in our field is iteration. BI professionals always want to find new solutions and innovative ways to advance our processes. Iteration involves repeating a procedure over and over again, in order to keep getting closer to the desired result.

Importance of Context

Context is the condition in which something exists or happens, it helps eliminate the risk of misinterpretation, and turn raw data into meaningful information. It’s very important for BI professionals to contextualize our data. This gives it an important perspective and reduces the chances of it being biased or unfair.

In BI, there’s another aspect of context that professionals care about a lot and that’s contextualizing the tools we create for our users.

Data Availability

BI professional’s toolbox, including data models, pipelines, and dashboards, is powerful only if they have relevant, timely, consistent, and bias-free data to work with. This is known as data availability.

Data availability describes the degree or extent to which timely and relevant information is readily accessible and able to be put to use. Unfortunately, there are various factors that can affect data availability and therefore can compromise the integrity of BI solutions.

Some of the most common data availability issues involve:

Data integrity	The accuracy, completeness, consistency, and trustworthiness of data throughout its entire life cycle.
Data visibility	The degree or extent to which information can be identified, monitored, and integrated from disparate internal and external sources.
Update frequency	BI projects will involve multiple data sources. It’s very common for disparate sources to refresh at different times.
Change	Change is a constant in pretty much every aspect of our lives and data is no different.

There are other things that can affect the availability of data. Therefore, it’s important to be realistic about the level of quality you’re aiming for. Be sure to acknowledge the limitations and constraints. For many projects, good enough is sufficient.

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