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.