How to Create a Stakeholder Requirements Document and a Project Requirements Document for a Business Intelligence Project

If you are a business intelligence (BI) professional, you know how important it is to understand the needs and expectations of your clients and stakeholders, and to plan your project accordingly. In this post, you will learn how to create two essential documents for any BI project: a Stakeholder Requirements Document and a Project Requirements Document.

A Stakeholder Requirements Document is a document that summarizes the information you gather from your client and other stakeholders about the business problem they want to solve, the goals they want to achieve, the data they have available, and the constraints they face. It helps you to define the scope and objectives of your project, identify the key stakeholders and their roles and responsibilities, and ask relevant questions to clarify their requirements and expectations.

A Project Requirements Document is a document that outlines the details of your project, such as its purpose, audience, features, dependencies, success criteria, and risks. It helps you to communicate your project plan to your client and stakeholders, and to guide your development process.

To create these documents, you need to follow these steps:

1. 

Conduct a stakeholder analysis. A stakeholder analysis is a process of identifying and prioritizing the people or groups who have an interest or influence in your project. You need to understand who they are, what they want, what they can offer, and how they can affect your project. You can use tools such as stakeholder maps or matrices to visualize and categorize your stakeholders.

2. 

Conduct a requirements elicitation. A requirements elicitation is a process of gathering information from your client and stakeholders about their needs, expectations, preferences, and constraints. You need to ask open-ended questions, listen actively, and confirm your understanding. You can use tools such as interviews, surveys, workshops, or observation to collect data.

3. 

Create a Stakeholder Requirements Document. A Stakeholder Requirements Document is a document that summarizes the information you collected from your client and stakeholders. It should include the following sections:

•  Business problem: A clear statement of the problem that your client wants to solve with your BI solution.

•  Business goals: A list of measurable and specific goals that your client wants to achieve with your BI solution.

•  Data sources: A description of the data that your client has available or needs to acquire for your BI solution.

•  Data quality: An assessment of the quality, reliability, and completeness of the data that your client has or needs.

•  Constraints: A list of limitations or challenges that your client faces or anticipates for your BI solution, such as budget, time, resources, or regulations.

•  Stakeholders: A list of the key stakeholders involved in or affected by your BI project, along with their roles, responsibilities, interests, expectations, and communication preferences.

•  Questions: A list of questions that you need to answer or clarify with your client or stakeholders before proceeding with your BI project.

1. 

Create a Project Requirements Document. A Project Requirements Document is a document that outlines the details of your BI project. It should include the following sections:

•  Purpose: A brief statement of the purpose of your BI project and how it aligns with the business problem and goals.

•  Audience: A description of the intended users or beneficiaries of your BI solution, along with their characteristics, needs, preferences, and feedback.

•  Features: A list of the key features and functionalities that your BI solution should provide, such as data integration, data transformation, data analysis, data visualization, data security, or data governance.

•  Dependencies: A list of the external factors or conditions that your BI project depends on or affects, such as data availability, data quality, data access, data policies, or other systems or projects.

•  Success criteria: A list of measurable and specific criteria that define the success of your BI project and how it will be evaluated.

•  Risks: A list of potential threats or uncertainties that could jeopardize or delay your BI project and how they will be mitigated or managed.

2. 

Plan your approach. Based on the information you gathered and documented in the previous steps, you need to plan your approach to developing and delivering your BI solution. You need to consider aspects such as:

•  Methodology: The process or framework that you will follow to execute your BI project, such as agile, waterfall, or hybrid.

•  Tools: The software or hardware that you will use to develop and deploy your BI solution, such as SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS), Power BI Desktop (PBID), Power BI Service (PBIS), or Power BI Mobile (PBIM).

•  Timeline: The schedule or timeline that you will follow to complete your BI project, including milestones, deliverables, and deadlines.

•  Resources: The human or material resources that you will need or allocate for your BI project, such as team members, roles, skills, tasks, responsibilities, budget, or equipment.

•  Communication: The methods or channels that you will use to communicate with your client and stakeholders throughout your BI project, such as email, phone, chat, meetings, reports, or dashboards.

By creating a Stakeholder Requirements Document and a Project Requirements Document, you can ensure that you understand the needs and expectations of your client and stakeholders, and that you plan your BI project accordingly. This will enable you to develop an effective and efficient BI solution that solves the business problem and achieves the business goals.

How a Portfolio of Projects Can Boost Your Career as a Business Intelligence Professional

If you are a business intelligence (BI) professional, you know how important it is to have a strong set of skills and experience that can help you deliver data-driven insights and solutions to businesses. But how can you prove your value and stand out from the crowd in this competitive field?

The answer is simple: you need a portfolio of projects.

A portfolio of projects is a collection of work samples that showcase your BI skills and achievements. It can include reports, dashboards, visualizations, code snippets, case studies, blog posts, videos or any other format that demonstrates your BI capabilities.

A portfolio of projects can benefit you in many ways:

•  It can help you land your dream job or project. A portfolio of projects can show your potential employers or clients what you can do for them, how you approach BI problems, what tools and techniques you use, and what results you have achieved. It can also help you highlight your strengths and areas of expertise, as well as your creativity and passion for BI.

•  It can help you improve your skills and learn new ones. A portfolio of projects can be a great way to practice your BI skills and challenge yourself with new and interesting problems. It can also help you learn from your mistakes and feedback, as well as from other BI professionals who share their portfolios online. You can also use your portfolio of projects to experiment with new tools and technologies, and stay updated with the latest trends and best practices in BI.

•  It can help you build your reputation and network. A portfolio of projects can be a powerful tool to showcase your work to the world and attract attention from other BI professionals, influencers, media outlets or potential collaborators. It can also help you connect with other like-minded people who share your interests and goals, and expand your network and opportunities.

Creating a portfolio of projects is not difficult. You just need to follow some simple steps:

•  Choose projects that are relevant to your target audience. Think about who you want to impress or work with, and what kind of problems they face or solutions they need. Choose projects that align with their needs and interests, and that demonstrate how you can add value to them.

•  Choose projects that showcase a variety of skills and tools. Don't limit yourself to one type of project or one tool. Show that you are versatile and adaptable, and that you can use different methods and technologies to solve different BI problems. For example, you can include projects that use SQL, Python, R, Power BI, Tableau, Excel or any other tool that you are proficient in.

•  Choose projects that have clear goals and outcomes. Make sure that your projects have a clear purpose and objective, and that they show the impact of your work. For example, you can include projects that have improved business performance, increased customer satisfaction, reduced costs or risks, or generated new insights or opportunities.

•  Choose projects that are well-documented and presented. Make sure that your projects are easy to understand and follow, and that they have a clear structure and flow. Include documentation that explains the problem statement, the data sources, the methodology, the results and the conclusions. Use visual aids such as charts, graphs, tables or screenshots to make your projects more appealing and engaging.

•  Choose projects that are original and creative. Don't copy or plagiarize other people's work. Show that you have your own style and voice, and that you can come up with innovative and unique solutions to BI problems. You can also include projects that are fun or personal, such as analyzing your favorite movie or sport.

You can host your portfolio of projects online on platforms such as GitHub, Medium, WordPress, YouTube or LinkedIn. Choose the platform that suits your preferences and goals, and that allows you to showcase your work in the best possible way.

You can also promote your portfolio of projects online on platforms such as Twitter, Facebook, Instagram, Reddit, Quora, Stack Overflow or LinkedIn Groups. Share your portfolio with your friends, followers or communities, and join discussions related to your portfolio topics.

A portfolio of projects is a must-have for any BI professional who wants to advance their career and showcase their skills. It can help you land your dream job or project, improve your skills and learn new ones, build your reputation and network, and have fun with data.

So what are you waiting for? Start creating your portfolio of projects today!

How to Improve Your Business Processes with Gap Analysis: A BI Professional's Guide

Summary: Gap analysis is a method for examining and evaluating the current state of a process in order to identify opportunities for improvement in the future. In this post, you will learn what gap analysis is, how to do it, and how it can help you optimize your business intelligence (BI) systems and tools.

What is gap analysis?

As a business intelligence (BI) professional, you know how important it is to choose the right metrics to measure the success of your projects. But how do you measure the success of your entire business or team over time? That's where gap analysis comes in.

Gap analysis is a process of comparing the current state of a process with the desired state of a process. It helps you understand where you are now compared to where you want to be so that you can bridge the gap. BI uses gap analysis to do all kinds of things, such as improve data delivery systems or create dashboard reports.

For example, suppose a sales team uses a dashboard to track sales pipeline progress that has a six-hour data lag. They use this dashboard to gather the most up-to-date information as they prepare for important meetings. The six-hour lag is preventing them from accessing and sharing near-real-time insights in stakeholder meetings. Ideally, the delay should be one hour or less.

How to do gap analysis?

The first step in bridging the gap is to work with stakeholders to determine the right direction for this BI project. You need to establish stakeholder needs and understand how users are interacting with the data. What needs do stakeholders have that aren't being met or could be addressed more efficiently? What data is necessary for their decision-making processes? Working closely with stakeholders is necessary to understand what they actually need their BI tools to do.

The BI professionals collect information and learn that, as the company grew, it opened offices across the country. So, the sales teams are now more dispersed. Currently, if a team member from one office updates information about a prospective client, team members from other offices won't get this update until the workday is almost over. So, their goal is to reduce the data delay to enable better cross-team coordination.

In addition to identifying stakeholder needs, it’s also important for the BI professional to understand the context of the data they interact with and present. Context is the condition in which something exists or happens; it turns raw data into meaningful information by providing the data perspective. This involves defining who collected it or funded its collection; the motivation behind that action; where the data came from; when; the method used to collect it; and what the data could have an impact on. BI professionals also need to consider context when creating tools for users to ensure that stakeholders are able to interpret findings correctly and act on them.

It’s also critical that BI professionals ensure the quality and integrity of the data stakeholders are accessing. If the data is incorrect, the reporting tools won’t be accurate, and stakeholders won’t be able to make appropriate decisions — no matter how much context they have been given.

Now, the sales team's BI professional needs to identify data sources and the update frequency for each source. They discover that most of the key data sources update every 15 minutes. There are a few nonessential data sources that rarely get updated, but the team doesn’t actually have to wait until those data sources are updated to use the pipeline. They’re also able to confirm that the data warehouse team will verify these data sources as being clean and containing no duplicates or null fields that might cause issues.

A large part of a BI professional’s job is building structures and systems. This means designing database storage systems, organizing the data, and working with database governance specialists to maintain those systems. It also involves creating pipeline tools that move and transform data automatically throughout the system to get data where it needs to go to be useful.

These structures and systems can keep data organized, accessible, and useful for stakeholders during their decision-making process. An ideal system should be organized and structured to do just that. To address the sales team’s needs, the BI analyst in this case designs a new workflow through which data sources can be processed simultaneously, cutting down processing time from 6 hours to less than an hour.

If you have some experience in data analysis, you may already be familiar with

the share stage of the data analysis process

. This is when a data analyst creates data visualizations and reports and presents them to stakeholders. BI professionals also need to share findings, but there are some key differences in how they do so. As you have been learning, creating ways for users to access and explore data when they need it is a key part of an ideal BI system. A BI professional creates automated systems to deliver findings to stakeholders or dashboards that monitor incoming data and provide current updates that users can navigate on their own.

In the sales team dashboard example, the final output is a dashboard that sales teams across the country use to track progress in near-real time. In order to make sure the teams are aware of the updates, the team’s BI analyst shares information about these backend improvements, encouraging all sales teams to check the data at the top of the hour before each meeting.

BI focuses on automating processes and information channels in order to transform relevant data into actionable insights that are easily available to decision-makers. These insights guide business decisions and development. But the BI process doesn’t stop there: BI professionals continue to measure those results, monitor data, and make adjustments to the system in order to account for changes or new requests from stakeholders.

After implementing the backend improvements, the sales team also creates system alerts to automatically notify them when data processes lag behind so they're prepared for a data delay. That way, they could know exactly how well the system is working and if it needs to be updated again in the future.

Conclusion

A large part of a BI professional's work revolves around identifying how current systems and processes operate, evaluating potential improvements, and implementing them so that the current system is closer to the ideal system state. Throughout this course, you’ll learn how to do that by collaborating with stakeholders, understanding context, maintaining data quality, sharing findings, and acting on insights

The Ultimate Metric for BI Professionals: How to Find and Use Your North Star Metric

Summary: A north star metric is a crucial metric that reflects the core value of a business and guides its long-term growth. In this post, you will learn what a north star metric is, why it is important, how to choose one, and some examples from different industries.

What is a north star metric?

As a business intelligence (BI) professional, you know how important it is to choose the right metrics to measure the success of your projects. But how do you measure the success of your whole business or team over time? That's where a north star metric comes in.

A north star metric is a single metric that reflects the core measurable value of your business's product or service. It is intended to represent your business's mission and vision, and to drive your business forward. That's why it's called a north star metric– like the north star can be used to navigate the wilderness, this metric can be used to navigate your business decisions and lead you to growth.

Why do you need a north star metric?

Having a north star metric as the guiding light for your whole business is useful in three primary ways:

•  Cross-team alignment: Different teams have different specialties and focuses that help your business function. They aren't always working on the same projects or with the same metrics, which can make it difficult to align across the whole business. A north star metric allows all of the teams to have a consistent goal to focus on, even as they work on different things.

•  Tracking growth: It can be difficult to understand and track the growth of your whole organization over time without understanding the driving metrics that determine growth. A north star metric provides a long-term measurable data point that stakeholders can focus on when discussing overall performance and growth in your business.

•  Focusing values: A north star metric is primarily a guiding principle for your business– it determines what is important to you and your stakeholders. This means that choosing the right metric to guide your business can help keep your values in check– whether that's customer loyalty, number of users completing a core action, or user engagement.

How do you choose a north star metric?

Because north star metrics are so key to your business's ongoing success, choosing the right metric is a foundational part of your BI strategy. The north star metric has to measure the most essential part or mission of your business. And because every business is different, every business's north star metric is going to be unique. In order to determine what the most useful north star metric might be, there are a few questions you can ask:

•  What is essential to this business's processes?

•  What are the most important KPIs being measured?

•  Out of those KPIs, what captures all of the necessary information about this business?

•  How can the other metrics be structured around that primary metric?

What are some examples of north star metrics?

Because more businesses have begun using north star metrics to guide their BI strategies, there are a lot of examples of north star metrics in different industries:

•  Travel:

•  Number of trips booked

•  Number of referrals

•  Average trip duration

•  Entertainment:

•  Number of monthly active users

•  Number of songs played

•  Average listening time per session

•  Health and fitness:

•  Number of workouts completed

•  Number of calories burned

•  Workout satisfaction score

•  Banking:

•  Number of accounts opened

•  Total deposits made

•  Customer retention rate

These are just a few examples– there are a lot of potential north star metrics for businesses to choose from across a variety of industries, from education to gaming!

Key takeaways

As a BI professional, one of your responsibilities will be to empower stakeholders to make BI decisions that will promote growth and success over the long term. North star metrics are a great way to measure and guide your business into the future because they allow you to actually measure the success of your whole business, align teams with a single goal, and keep your business's values at the forefront of their strategy.

How to Choose the Right Metrics for Your BI Dashboard

If you are a BI professional, you know how important it is to choose the right metrics for your dashboard. Metrics are the indicators that help you measure the success of your project and guide business decisions. But how do you choose the best metrics among the many available? In this post, we will give you five tips to select the most effective and relevant metrics for your BI dashboard.

1. Limit the number of metrics

More information is not always better. If you fill your dashboard with too many metrics, you might confuse your stakeholders and distract them from the ones that are really crucial for the project's success. Key metrics are those that are relevant and actionable, meaning that they tell you if you are reaching your goals and what you need to do to improve. For example, if metric X goes down, is that a good or bad thing? What action would you take if it went down that would be different if it went up instead? Your goal is not to cover every single use case, but 90% of the most common ones.

2. Align the metrics with the business objectives

To choose the best metrics, you need to understand what are the business objectives that you want to support and measure. For example, if the business objective is to increase customer retention, include churn rate in your dashboard. You will most likely not want to include a metric such as website traffic because that is not directly related to the business objective of increasing customer retention.

3. Check the necessary technologies and processes

Before choosing a metric, make sure that you have the necessary technologies and processes in place to obtain and analyze the data related to that metric. If you can't access the data or if they are not reliable, that metric won't be very useful.

4. Consider the cadence of data

You have to take into account how often the data are available and updated. If many metrics have a different cadence and frequency, it becomes hard to plan a periodic review.

5. Use SMART methodology

SMART methodology is a useful tool for creating effective questions to ask stakeholders. It can also be used to identify and refine key metrics by ensuring that they are specific, measurable, action-oriented, relevant, and time-bound. This can help you avoid vague or too high-level metrics that are not useful to stakeholders, and instead create metrics that are precise and informative.

An integrated view

In the BI world, data requires a dynamic and thoughtful approach to detect and respond to events as they happen. An integrated view of the whole business is required. In some cases, metrics can be straightforward. For example, revenue is fairly clear: Revenue goes up, and things are going well! But other metrics are a bit more complicated.

Let's take an example of a team of online tutors who want to measure their ability to effectively answer students' questions. Every time a student asks for help, a help request is created. These requests are handled by the first response team of tutors. Sometimes the first response team needs help answering more complex requests. They then reach out to the second response team. This is marked as a referral on the help request.

Imagine that the BI professionals working with this team now are trying to decide which metrics are useful in a dashboard designed to increase students' satisfaction ratings for help requests. Perhaps their stakeholders are interested in monitoring referrals to ensure that students are getting the help they need in a timely manner. So the BI team considers adding referral rate, which is the rate at which tutors are asking for help from internal experts, as a metric in their dashboard.

Note that an increasing referral rate could be good or bad. It might mean that tutors are being more student-centric and trying to ensure each student gets the best answer. But it could also mean that tutors are being overwhelmed with questions and having to pass them on to internal experts in order to keep up. Therefore, referral rate is a metric that doesn't have a clear direction; nor does it have an obvious influence on the decision-making process on its own. So, it's not a useful metric for this dashboard. Instead, the BI professionals select metrics that indicate success or failure in a more meaningful way. For instance, they might decide to include a metric that tracks when a tutor experiences missing help documentation. This will help leaders decide whether to create more documentation for tutors to reference. Notice how this metric has a clear line of action that we can take based on how high or low it is!

Conclusion

The ability to choose metrics that inform decision-making and support project success is a key skill for your career as a BI professional. Remember to consider the number of metrics, how they align with your business objectives, the technologies and processes necessary to measure them, and how they adhere to SMART methodology. It's also important to maintain an integrated view of the entire business and how the information your metrics deliver is used to guide stakeholder action.

Data Ethics, Privacy, and Availability: What BI Professionals Need to Know

 As a business intelligence (BI) professional, you use data to create solutions that provide insights and help organizations make better decisions. But to do that effectively, you need to handle data ethically, privately, and reliably. In this post, you will learn what these concepts mean, why they are important, and how to overcome the challenges and limitations related to them.

Data Ethics: Respect the Rights and Interests of Data Subjects

Data ethics is the application of well-founded standards of right and wrong to how data is collected, shared, and used. You have a responsibility to treat data ethically, especially when it involves personally identifiable information (PII), which can reveal a person's identity.

Treating data ethically means respecting the rights and interests of the data subjects, such as:

•  Protecting their data from unauthorized access or inappropriate use

•  Allowing them to inspect, update, or correct their data

•  Obtaining their consent for data collection

•  Giving them legal access to the data

It also means avoiding bias in data collection, analysis, and interpretation. Bias is any systematic error or deviation from the truth that affects the validity or reliability of data. Bias can lead to misleading or inaccurate results and unfair or harmful outcomes for individuals or groups.

Some of the common types of bias that you may encounter are:

•  Confirmation bias: The tendency to seek or interpret data in a way that confirms your preexisting beliefs or expectations

•  Selection bias: The distortion of data caused by using a sample that is not representative of the whole population

•  Historical bias: The reflection of socio-cultural prejudices and beliefs in data collection or processing systems

•  Outlier bias: The distortion of data caused by ignoring or hiding anomalies or extreme values that deviate from the norm

To avoid bias in data, you need to follow some best practices, such as:

•  Recording your prior beliefs and assumptions before starting the analysis

•  Using highly randomized and large datasets that are representative of the population

•  Gathering more data and doing more research about the opposite side of your hypothesis

•  Being cognizant of outliers and using appropriate measures of central tendency and dispersion

Data Privacy: Protect the Privacy and Security of Personal and Sensitive Data

Data privacy is the preservation of a data subject's information and activity any time a data transaction occurs. This is also called information privacy or data protection. Data privacy is concerned with the access, use, and collection of personal data. Data privacy is important because it protects the rights and interests of individuals, as well as their trust and confidence in organizations that handle their data.

One of the key strategies to maintain data privacy is data anonymization. Data anonymization is the process of protecting people's private or sensitive data by eliminating PII. Typically, data anonymization involves blanking, hashing, or masking personal information, often by using fixed-length codes to represent data columns, or hiding data with altered values.

Data anonymization is used in almost every industry. You probably won't personally be performing anonymization, but it's useful to understand what kinds of data are often anonymized before you start working with them. This data might include:

•  Phone numbers

•  Names

•  License plates and license numbers

•  Social security numbers

•  IP addresses

•  Medical records

•  Email addresses

•  Photographs

•  Account numbers

Data anonymization helps keep data private and secure for analysis!

Data Availability: Ensure that Data is Accessible and Usable

Data availability is the degree or extent to which timely and relevant information is readily accessible and able to be put to use. Data availability is essential for creating reliable and impactful BI solutions that deliver value for organizations. However, there are many factors that can affect data availability and compromise the quality of BI solutions. Some of these factors are:

•  Integrity: The accuracy, completeness, consistency, and trustworthiness of data throughout its life cycle

•  Visibility: The degree or extent to which information can be identified, monitored, and integrated from disparate internal and external sources

•  Update frequency: How often disparate data sources are being refreshed with new information

•  Change: The process of altering data, either through internal processes or external influence

Each factor poses different challenges and limitations for you. Here are some examples:

•  Integrity: Data integrity issues include duplicates, missing values, inconsistent formats, or not following business rules. These issues can lead to inaccurate or incomplete results and damage the credibility of BI solutions. To ensure data integrity, you need to perform data quality checks, such as validating, cleaning, standardizing, deduplicating, and enriching data. You also need to document the data sources, processes, and rules that you use for your analysis.

•  Visibility: Data visibility issues include lack of awareness or access to data stored in different departments or external sources. These issues can lead to missed opportunities or incomplete insights. To achieve data visibility, you need to work with your colleagues to create a list of data repositories for stakeholders. You can request a short interview with the data owners or ask them to complete a quick online survey about the data they collect and use. You also need to explore external data sources, such as free public datasets, that can contribute to your BI project.

•  Update frequency: Data update frequency issues include mismatched or outdated data from different sources. These issues can lead to erroneous or misleading results and poor decision-making. To address data update frequency issues, you need to understand how the update frequency of different data sources can affect insights. You also need to align your data sources with your analysis goals and time frames, or use appropriate methods to account for the differences.

•  Change: Data change issues include modifications or disruptions to data due to internal or external factors. These issues can lead to inconsistent or invalid results and reduced trust and confidence in BI solutions. To address data change issues, you need to have a plan for how you will keep stakeholders up-to-date on changes that might affect the project. You also need to encourage team members to think about what tools or methods they are using now, what could change, and how it may influence the data being tracked and how to fill any potential gaps.

Conclusion

Data ethics, privacy, and availability are important concepts that you need to consider as a BI professional. They help you respect the rights and interests of the people whose data you are working with, protect the privacy and security of personal and sensitive data, avoid bias in data collection, analysis, and interpretation, confirm that you are using the right data for the right stakeholders, ensure that your data is in the correct format and can be effectively used and shared, make sense of your results and explain them clearly, enhance your understanding and decision-making, and achieve better business outcomes.

By considering the ethical, privacy, and availability aspects of data, you can create more reliable and impactful BI solutions that deliver value for your organization.

Data Availability Challenges and Solutions for BI Professionals

Business intelligence (BI) professionals use various tools to create data-driven solutions, such as data models, pipelines, visualizations, and dashboards. These solutions can provide valuable insights and help organizations make better decisions. But to do that effectively, they need to have data availability. Data availability refers to the degree or extent to which timely and relevant information is readily accessible and able to be put to use.

However, there are many factors that can affect data availability and compromise the quality of BI solutions. In this post, we will discuss some of these challenges and how to overcome them.

What are the Data Availability Challenges?

Some of the most common data availability challenges are:

•  Integrity

•  Visibility

•  Update frequency

•  Change

Let's look at each challenge in more detail.

Integrity

Data integrity involves the accuracy, completeness, consistency, and trustworthiness of data throughout its entire life cycle. Typical issues related to data integrity include duplicates, missing values, inconsistent formats, or not following business rules. These issues can lead to inaccurate or incomplete results and damage the credibility of BI solutions.

To ensure data integrity, BI professionals need to perform data quality checks, such as validating, cleaning, standardizing, deduplicating, and enriching data. They also need to document the data sources, processes, and rules that they use for their analysis.

Visibility

Data visibility is the degree or extent to which information can be identified, monitored, and integrated from disparate internal and external sources. For example, employees working in a company's marketing department might not know what data is stored in the finance department. Or someone working in the customer service unit might have data files that contain useful information, but no one else is aware of them.

On the other hand, when there is clear data visibility, it is possible to achieve accurate and timely insights and improve the organization's responsiveness and agility. To achieve this goal, BI professionals need to work with their colleagues to create a list of data repositories for stakeholders. They can request a short interview with the data owners or ask them to complete a quick online survey about the data they collect and use. This is a simple but effective exercise to discover the kind of data that is available.

Also, keep in mind that data visibility challenges do not only exist within an organization. Sometimes BI professionals are unaware of useful external data. As you may know, there are many free public datasets, such as government statistics, social media trends, industry reports, and more. All of these can contribute to a successful BI project.

Update Frequency

Often times, BI projects will involve multiple data sources. It is very common for different sources to refresh at different times, such as daily versus weekly. For example, a BI professional works for a clothing retailer based in France and analyzes product sales volume by region. If a supplier changes its prices in the middle of August, all of that month's sales would reflect the old prices simply because the supplier's data has not been updated yet in the BI system. Either the supplier's data needs to refresh sooner to match sales data or the retailer should look at all data on a weekly basis.

This is why it is important for BI professionals to understand how the update frequency of different data sources can affect insights. Even if individual data sources are perfect, the integration aspect can be challenging.

Change

Change is inevitable in almost every aspect of our lives and data is no different. Data availability may be affected by a change in internal procedures such as a system upgrade or a new record-keeping process. It may also change externally because of a user interface change or an adjustment to a particular algorithm.

To address this issue, BI professionals need to have a plan for how they will keep stakeholders up-to-date on changes that might affect the project. They should encourage team members to think about what tools or methods they are using now, what could change, and how it may influence the data being tracked and how to fill any potential gaps.

Why Data Availability Matters in BI?

Data availability matters in BI because it helps you:

•  Confirm that you are using the right data for the right stakeholders

•  Ensure that your data is in the correct format and can be effectively used and shared

•  Make sense of your results and explain them clearly

•  Enhance your understanding and decision-making

•  Achieve better business outcomes

By addressing data availability challenges, you can create more reliable and impactful BI solutions that deliver value for your organization.

How to Contextualize Data and Dashboards for Better BI Outcomes

Data analytics is the process of transforming raw data into meaningful information. But to do that effectively, you need to contextualize your data. This means putting it into perspective by considering its origin, background, motivation, setting, and impact. Contextualization gives your data more meaning and helps you and your users understand it more completely. It also supports fairness and reduces bias when you want to gain useful insights from your data.

In this post, we will explore how to contextualize data and dashboards in a business intelligence (BI) setting. We will also share some best practices and tips on how to create contextualized BI solutions that align with your business objectives and user needs.

What is Context in BI?

Context in BI is not only about the data itself, but also about the tools you create for your users to interact with the data. One key practice that promotes context is to put the data in a central location, such as a well-designed dashboard. A dashboard is a visual representation of your data that shows key metrics, trends, and insights at a glance.

The second step is to ensure that there is a common method for everyone to interact with the dashboard. You want your users to be able to easily understand, access, and use the dashboard without having to switch contexts or go elsewhere to find the information they need. This way, you empower your users to be more effective in their work.

How to Contextualize Data and Dashboards?

To contextualize data and dashboards, you need to consider the following aspects:

•  The source and quality of the data

•  The purpose and goal of the analysis

•  The audience and stakeholders of the dashboard

•  The format and design of the dashboard

•  The interaction and collaboration among users

Here are some tips on how to contextualize each aspect:

Source and Quality of Data

You need to ensure that the data you use for your analysis is reliable, accurate, and relevant. You also need to document where the data comes from, how it was collected, processed, and cleaned, and what assumptions or limitations it has. This will help you establish trust and credibility with your users and avoid misleading or inaccurate results.

Purpose and Goal of Analysis

You need to define what question or problem you are trying to answer or solve with your analysis. You also need to specify what metrics or indicators you are using to measure your performance or progress. This will help you focus on the most important and relevant information and avoid unnecessary or distracting details.

Audience and Stakeholders of Dashboard

You need to understand who will use your dashboard, what their roles and responsibilities are, what their expectations and preferences are, and how they will use the dashboard. This will help you tailor your dashboard to their needs and interests, as well as communicate effectively with them.

Format and Design of Dashboard

You need to choose the best format and design for your dashboard based on the type and amount of data you have, the message or story you want to convey, and the user experience you want to create. You also need to follow some basic principles of visual design, such as clarity, simplicity, consistency, contrast, alignment, hierarchy, balance, and color.

Interaction and Collaboration among Users

You need to enable your users to interact with your dashboard in a way that suits their needs and goals. You also need to facilitate collaboration among users by allowing them to share feedback, comments, questions, insights, or actions based on the dashboard. This will help you create a single source of truth that fosters dialogue and learning.

Why Context Matters in BI?

Context matters in BI because it helps you:

•  Confirm that you are using the right data for the right stakeholders

•  Ensure that your data is in the correct format and can be effectively used and shared

•  Make sense of your results and explain them clearly

•  Enhance your understanding and decision-making

•  Achieve better business outcomes

By contextualizing your data and dashboards, you can create more meaningful and impactful BI solutions that deliver value for your organization.

How to Master the Art of Asking SMART and Fair Questions as a Data Analyst: Practical Examples and Tips

 In this post, you will learn how to ask effective questions as a data analyst using the SMART framework and fairness. You will also understand why stakeholder expectations are important for your work and see some examples of stakeholder needs in a project.

•  Stakeholders are the people who have invested time, interest and resources in the projects you do as a data analyst. You need to understand their needs and communicate with them effectively.

•  To ask effective questions, you need to follow the SMART framework, which means that the questions must be specific, measurable, action-oriented, relevant and time-bound.

•  To ask specific questions, you need to focus on a single topic or a few related ideas. For example, instead of asking "Do children get enough physical activity today?", ask "What percentage of children achieve the recommended 30 minutes of physical activity at least three days a week?".

•  To ask measurable questions, you need to be able to quantify and evaluate the answers. For example, instead of asking "Why did our recent video go viral?", ask "How many views did our video get on YouTube the first week it was posted?".

•  To ask action-oriented questions, you need to encourage change. For example, instead of asking "How can we get customers to buy our products more often?", ask "What marketing strategies will increase our customer loyalty?".

•  To ask relevant questions, you need to focus on what matters and has meaning for the problem you are trying to solve. For example, instead of asking "Why does it matter that the polar bear started disappearing?", ask "What human activities affected the Arctic habitat of the polar bear between 2010 and 2020?".

•  To ask time-bound questions, you need to specify the period to be studied. For example, if you want to study the period between 2010 and 2020, limit your questions to that time span.

•  To ask fair questions, you need to avoid bias, which is the preference in favor or against a person, group or thing. Bias can influence data results in a systematic way. To avoid bias and ensure fairness, you need to follow these guidelines:

•  Don't use biased questions that influence answers in a certain way. For example, instead of asking "These are the best sandwiches ever, right?", ask "How do you rate these sandwiches on a scale of 1 to 5?".

•  Don't use closed-ended questions that can be answered with a yes or no. Use instead open-ended questions that allow for more detailed and nuanced responses. For example, instead of asking "Did you enjoy growing up in Canada?", ask "What was your experience growing up in Canada?".

•  Don't use vague questions that lack context or clarity. Use instead specific and precise questions that make sense to everyone. For example, instead of asking "Do you prefer chocolate or vanilla?", ask "What is your favorite flavor of cake?".

•  Don't use questions that make assumptions or exclude certain groups of people. Use instead questions that are inclusive and respectful of diversity. For example, instead of asking "What do you love most about our exhibits?", ask "How accessible are our exhibits for people with disabilities?".

Asking effective questions is a crucial skill for any data analyst. It helps you understand the goal of a project, communicate more effectively across your team, and build trust in your work. By using the SMART framework and ensuring fairness, you can craft questions that lead to valuable insights and solutions.

To make a schematic post, you can use bullet points, headings, subheadings, tables, charts, graphs, or other visual elements to organize your information in a clear and concise way. 

How to Boost Your E-commerce Sales by Reducing Cart Abandonment

Have you ever checked out an online store and added something to your cart, but then backed out of buying it? Maybe you were shopping for a new camera, a fitness tracker, or a gift for a loved one. But then you changed your mind because you found a better deal elsewhere, or you realized that you didn't really need it, or you got distracted by something else. When that happens, the online store has what's called an abandoned cart. According to e-commerce platform Shopify, online merchants lose 20 billion dollars a year in sales revenue because of cart abandonment. This is a huge challenge, but it's one that business intelligence professionals are very good at tackling. In this post, we'll show you exactly how they do that.

How BI Professionals Use Data to Understand Customer Behavior

BI professionals can use data to track where a customer came from, whether it was a Google search, an email link, or a social media post. Then they can visualize the journey the shopper took when visiting the website. They're even able to pinpoint exactly where that customer left and try to figure out why. For example, a BI professional might create a tool to monitor how attractive and relevant the website's product images are. If the team finds that they are too low-quality or outdated, the company can update them to better showcase the features and benefits of their products and persuade the customer to buy. The attractiveness and relevance of the website's product images is an example of a metric. A metric is a single quantifiable data point that is used to evaluate performance.

How BI Professionals Use KPIs to Track Progress Towards Goals

In BI, some of the most important metrics are KPIs, which are quantifiable values closely linked to business strategy that track progress towards a goal. Many people confuse KPIs and metrics, but they are different things. The basic point to keep in mind is that metrics support KPIs and in turn, KPIs support overall business objectives. It's also helpful to understand that KPIs are strategic, whereas metrics are tactical. Going back to our abandoned cart example, strong KPIs might be the number of visitors who complete a purchase, customer retention, or average order value. Think of it this way: A strategy is a plan for achieving a goal or arriving at a desired future state. It involves making and carrying out plans to reach what you're trying to accomplish. A tactic is how you get there. It's a method used to enable an accomplishment, including actions, events, and activities. Tactics take place along the way as part of your strategy to reach your final objective. Like stepping stones between each milestone. Reach enough milestones and you'll reach your goal.

How BI Professionals Use Monitoring Tools to Enable Data-Driven Decisions

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. Let's say our e-commerce merchant sets a goal to decrease cart abandonment by 10% in six months. The BI professional would create a tool that monitors product images in order to help achieve that KPI. Rapid monitoring means that the people using BI tools are receiving live or close to live data. In this way, key decision makers know right away if there's a sudden drop in the number of visitors who complete a purchase, or if they run out of stock on a popular item, or if customers are leaving negative reviews or feedback. Knowing right away means that the company can fix whatever the problem may be as quickly as possible. This is one of the main ways in which BI professionals add real value to their organizations.

How to Communicate Effectively with Stakeholders and Avoid Bias in Business Intelligence

 Business intelligence (BI) is not just about building BI tools; it’s about making those tools accessible to users to empower them with the data they need to make decisions. As a BI professional, you need to master the art of communication to ensure that your stakeholders and project partners understand and use the BI systems you create. In this post, you will learn some key communication strategies and best practices that will help you in your BI career. You will also discover the importance of fairness and avoiding bias in BI, and how to promote ethical and inclusive data analysis.

Make BI Accessible to Stakeholders

Communication is a vital skill for any BI professional. You need to be able to simplify technical processes and present data insights in a clear and concise way to a variety of users who might not have the same level of knowledge or expertise as you. You also need to be able to ask the right questions, define project deliverables, and share business intelligence effectively.

To communicate with stakeholders and project partners, you need to consider four main aspects:

•  Who is your audience? Different stakeholders have different goals, needs, and expectations. You need to tailor your communication style and content to suit your audience. For example, the sales or marketing team might be more interested in the business impact and the user experience of the BI tools, while the data science team might be more interested in the technical details and the data quality.

•  What do they already know? Before communicating with your audience, you need to assess their level of knowledge and expertise on the topic. This will help you avoid over-explaining or skipping over important information. You can use surveys, interviews, or feedback forms to gather this information.

•  What do they need to know? Depending on your audience and your purpose, you need to decide what information is relevant and useful for them. You don’t want to overwhelm them with too much data or too many details, but you also don’t want to leave out anything essential. You can use SMART (Specific, Measurable, Achievable, Relevant, Time-bound) criteria to define your communication objectives and scope.

•  How can you best communicate what they need to know? After you have identified your audience, their knowledge, and their needs, you need to choose the best way to communicate with them. This might be an email report, a small meeting, or a cross-team presentation with a Q&A section. You also need to choose the best format and medium for your communication, such as text, charts, graphs, dashboards, or videos.

In addition to these aspects, there are some other best practices for communicating with stakeholders:

•  Create realistic deadlines. Before you start a project, make a list of dependencies and potential roadblocks so you can estimate how much time you need for each task. Then communicate your timelines and expectations clearly with your stakeholders and update them regularly on your progress.

•  Know your project. When you have a good understanding of why you are building a new BI tool, it can help you connect your work with larger initiatives and add meaning to the project. Keep track of your discussions about the project over email or meeting notes, and be ready to answer questions about how certain aspects are important for your organization.

•  Communicate often. Your stakeholders will appreciate regular updates on your project status, achievements, challenges, and changes. Use a document or a tool that allows you to share your reports easily and transparently with your stakeholders. You can also use a changelog to provide a chronological list of modifications.

Prioritize Fairness and Avoid Biased Insights

Providing stakeholders with the data and tools they need to make informed, intelligent business decisions is what BI is all about. Part of that is making sure you are helping them make fair and inclusive decisions. Fairness in data analytics means that the analysis doesn’t create or reinforce bias (a conscious or subconscious preference in favor of or against a person, group of people, or thing). In other words, you want to help create systems that are fair and inclusive for everyone.

As a BI professional, it’s your responsibility to remain as objective as possible and try to recognize the many sides of an argument before drawing conclusions. The best thing you can do for the fairness and accuracy of your data is to make sure you start with data that has been collected in the most appropriate and objective way. Then you’ll have facts that you can pass on to your team.

A big part of your job will be putting data into context. Context is the condition in which something exists or happens; basically, this is who, what, where, when, how, and why of the data. When presenting data, you’ll want to make sure that you’re providing information that answers these questions:

•  Who collected the data? Knowing the source and the motivation of the data collection can help you assess the reliability and the validity of the data. For example, if the data was collected by a third-party vendor, you might want to check their credentials and reputation.

•  What is it about? Knowing what the data represents and how it relates to other data can help you understand the meaning and the value of the data. For example, if the data is about customer satisfaction, you might want to know what factors influence customer satisfaction and how they are measured.

•  When was the data collected? Knowing when the data was collected can help you determine the relevance and the timeliness of the data. For example, if the data is about market trends, you might want to know how recent and how frequent the data is.

•  Where did the data come from? Knowing where the data came from can help you identify the scope and the limitations of the data. For example, if the data is about user behavior, you might want to know what platforms, devices, or locations the data covers.

•  How was it collected? Knowing how the data was collected can help you evaluate the quality and the accuracy of the data. For example, if the data was collected by a survey, you might want to know how the survey was designed, administered, and analyzed.

•  Why was this data collected? Knowing why the data was collected can help you align your analysis with the business goals and expectations. For example, if the data was collected to improve customer retention, you might want to focus on finding patterns and insights that can help achieve that objective.

One way to provide context for your data is by clarifying that any findings you share pertain to a specific dataset. This can help prevent unfair or inaccurate generalizations stakeholders might want to make based on your insights. For example, imagine you are analyzing a dataset of people’s favorite activities from a particular city in Canada. The dataset was collected via phone surveys made to house phone numbers during daytime business hours. Immediately there is a bias here. Not everyone has a home phone, and not everyone is home during the day. Therefore, insights from this dataset cannot be generalized to represent the opinion of the entire population of that city.More research should be done to determine the demographic make-up of these individuals.

You also have to ensure that the way you present your data—whether in the form of visualizations, dashboards, or reports—promotes fair interpretations by stakeholders. For instance, you’ve learned about using color schemes that are accessible to individuals who are colorblind. Otherwise, your insights may be difficult to understand for these stakeholders.

Key Takeaways

Being able to provide stakeholders with tools that will empower them to access data whenever they need it and the knowledge they need to use those tools is important for a BI professional. Your primary goal should always be to give stakeholders fair, contextualized insights about business processes and trends. Communicating effectively is how you can make sure that happens.

How to Build a Successful Software Project Team

Software projects are complex and challenging endeavors that require the collaboration of different roles and skills. A software project team consists of people who have specific responsibilities and tasks to ensure the project's quality, functionality, and delivery. In this post, we will explore some of the key roles in a software project team and how they work together to achieve the project's goals.

Project Sponsor

A project sponsor is a person who provides support and resources for a project and is accountable for enabling its success. The project sponsor is usually a senior manager or executive who has a stake in the project's outcome and can influence the organization's strategy and direction. The project sponsor's main responsibilities are:

•  To define the project's vision, scope, objectives, and benefits

•  To secure the necessary funding, resources, and approvals for the project

•  To monitor the project's progress, risks, and issues and provide guidance and feedback

•  To communicate the project's status, achievements, and challenges to the relevant stakeholders

•  To champion the project and ensure its alignment with the organization's goals and values

A project sponsor is essential for a software project because they can help overcome obstacles, resolve conflicts, and motivate the team. A good project sponsor should have strong leadership, communication, and decision-making skills, as well as a clear understanding of the business needs and expectations.

Developer

A developer is a person who uses programming languages to create, execute, test, and troubleshoot software applications. This includes application software developers and systems software developers. Application software developers create software that performs specific tasks for users, such as games, web browsers, or mobile apps. Systems software developers create software that runs the computer systems and networks, such as operating systems, databases, or security software.

A developer's main responsibilities are:

•  To analyze the user requirements and design specifications for the software

•  To write, debug, and optimize the code for the software using various tools and frameworks

•  To test the software for functionality, performance, reliability, and security

•  To document the software development process and code

•  To collaborate with other developers and stakeholders to ensure the software meets the quality standards and expectations

A developer is vital for a software project because they are responsible for creating the core product that delivers value to the users. A good developer should have strong technical, analytical, and problem-solving skills, as well as a passion for learning new technologies and best practices.

Systems Analyst

A systems analyst is a person who identifies ways to design, implement, and advance information systems in order to ensure that they help make it possible to achieve business goals. A systems analyst acts as a bridge between the business needs and the technical solutions. A systems analyst's main responsibilities are:

•  To gather and analyze the business requirements and processes for the information system

•  To design and model the system architecture, components, data flow, and interfaces

•  To evaluate and recommend the best technologies, tools, and methods for the system development

•  To coordinate and oversee the system development, testing, deployment, and maintenance

•  To provide training and support to the system users and stakeholders

A systems analyst is crucial for a software project because they can help define the scope, functionality, and feasibility of the system. A good systems analyst should have strong communication, collaboration, and critical thinking skills, as well as a broad knowledge of business domains and technical domains.

Business Stakeholders

Business stakeholders are groups of people who have an interest or influence in the software project's outcome. Business stakeholders can include one or more of the following groups of people:

•  The executive team: The executive team provides strategic and operational leadership to the company. They set goals, develop strategy, and make sure that strategy is executed effectively. The executive team might include vice presidents, the chief marketing officer (CMO), chief financial officer (CFO), chief operating officer (COO), chief technology officer (CTO), chief information officer (CIO), chief data officer (CDO), chief innovation officer (CINO), chief digital officer (CDO), chief customer officer (CCO), chief experience officer (CXO), chief human resources officer (CHRO), chief diversity officer (CDO), chief sustainability officer (CSO), chief ethics officer (CEO), chief legal officer (CLO), chief compliance officer (CCO), chief risk officer (CRO), chief security officer (CSO), chief privacy officer (CPO), or senior-level professionals who help plan and direct the company's work.

•  The customer-facing team: The customer-facing team includes anyone in an organization who has some level of interaction with customers and potential customers. Typically they compile information,set expectations, and communicate customer feedback to other parts of the internal organization. The customer-facing team might include sales representatives,account managers, customer service representatives, technical support specialists, marketing specialists, social media managers, community managers,or user experience designers.

•  The data science team: The data science team explores the data that’s already out there and finds patterns and insights that data scientists can use to uncover future trends with machine learning. This includes data analysts, data scientists, and data engineers.

Business stakeholders' main responsibilities are:

•  To provide input, feedback, and approval for the software project's scope, objectives, and deliverables

•  To participate in the software project's testing, validation, and evaluation

•  To use the software product and provide ongoing feedback and suggestions for improvement

•  To advocate for the software product and promote its adoption and usage

Business stakeholders are important for a software project because they are the ones who will benefit from the software product and ensure its alignment with the business goals and values. A good business stakeholder should have strong communication, collaboration, and negotiation skills, as well as a clear vision of the desired outcomes and benefits.

Conclusion

A software project team is a group of people who work together to deliver a software product that meets the user needs and business goals. A software project team typically consists of a project sponsor, a developer, a systems analyst, and business stakeholders. Each role has specific responsibilities and tasks that contribute to the project's success. By understanding the roles and skills of each team member, you can build a successful software project team that delivers value and quality.

What is Business Intelligence...

Our world is constantly changing and evolving. Businesses across the globe are working to create the next big thing, and consumers expect fast and thrilling product launches and packages that magically arrive at their homes. With all of this amazing change, the value of speed has become priceless. It's truly one of the most crucial factors of business success today. After all, the ability to spot a challenge or an opportunity is great, but real value is realized when you spot that challenge before it becomes a major problem, or seize that opportunity before the competition. These days, there's more data on markets, organizations, customers, competitors, and employees than ever before, but we need more than just data to make smarter decisions and deliver quicker results. And that's where intelligence comes in... business intelligence. Business intelligence, or BI, involves automating processes and information channels in order to turn relevant data into useful insights that are easily accessible to decision-makers. In other words, by showing decision-makers what is going on right now, organizations become more savvy and successful. A national chain of coffee shops might analyze millions of customer orders to optimize its beverage supply and reduce costs, or a local clinic could integrate various different data sources to evaluate feedback and outcomes in order to improve the patient experience, or perhaps a global e-commerce company uses supply chain data from around the world to make more accurate demand predictions and ensure sufficient inventory levels. The BI applications are limitless, and so are the BI career opportunities. The field involves such diverse organizations and industries, that wherever your interest may be, there's a way for you...