Recently, you’ve been learning about the importance of context in business intelligence. You discovered that, when you contextualize, you put something into perspective by considering its origin and other relevant background information; the motivation behind it; the larger setting in which it exists, such as a particular time period; and what it might have an impact on. Contextualization also supports fairness and reduces the chance of bias when your users seek to gain useful insights from the data you’re presenting.

Likewise, as a BI professional, you have a responsibility to treat data ethically. Data ethics refers to well-founded standards of right and wrong that dictate how data is collected, shared, and used. Throughout your career you will work with a lot of data. This sometimes includes PII, or personally identifiable information, which can be used by itself or with other data to track down a person’s identity. One element of treating that data ethically is ensuring that the privacy and security of that data is maintained throughout its lifetime. In this reading, you will learn more about the importance of data privacy and some strategies for protecting the privacy of data subjects.

Privacy matters

Data privacy means preserving 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. For the people whose data is being collected, this means they have the right to:

• Protection from unauthorized access to their private data
• Freedom from inappropriate use of their data
• The right to inspect, update, or correct their data
• Ability to give consent to data collection
• Legal right to access the data

In order to maintain these rights, businesses and organizations have to put privacy measures in place to protect individuals’ data. This is also a matter of trust. The public’s ability to trust companies with personal data is important. It’s what makes people want to use a company’s product, share their information, and more. Trust is a really big responsibility that can’t be taken lightly.

Protecting privacy with data anonymization 

Organizations use a lot of different measures to protect the privacy of their data subjects, like incorporating access permissions to ensure that only the people who are supposed to access that information can do so. Another key strategy to maintaining 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 just about every industry. As a BI professional, 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 it. This data might include: 

• Telephone numbers
• Names
• License plates and license numbers
• Social security numbers
• IP addresses
• Medical records
• Email addresses
• Photographs
• Account numbers

Imagine a world where we all had access to each other’s addresses, account numbers, and other identifiable information. That would invade a lot of people’s privacy and make the world less safe. Data anonymization is one of the ways we can keep data private and secure!

Key takeaways

For any professional working with data about actual people, it’s important to consider the safety and privacy of those individuals. That’s why understanding the importance of data privacy and how data that contains PII can be made secure for analysis is so important. We have a responsibility to protect people’s data and the personal information that data might contain.

If you’re interested in learning more about data privacy and ethics, you can check out the Google Data Analytics Certificate program’s section on bias, credibility, privacy, ethics, and access.

We live in a world where data is constantly being generated. There is so much information out there to learn from. But we also live in a world that is constantly changing, and often the data that we encounter has certain limitations we need to consider as we analyze data and draw insights from it.

Factors of data availability

Previously, you learned about the importance of data availability, which is the degree or extent to which timely and relevant information is readily accessible and able to be put to use. The factors that influence data availability are:

• Data integrity: The accuracy, completeness, consistency, and trustworthiness of data throughout its life cycle.
• Data visibility: The degree or extent to which information can be identified, monitored, and integrated from disparate internal and external sources.
• Update frequency: How often disparate data sources are being refreshed with new information.
• Change: The process of altering data, either through internal processes or external influence.

Next, you are going to consider the limitations of data that might change the availability and how you can anticipate those limitations as a BI professional.

Missing data

If you have incomplete or nonexistent data, you might not have enough data to reach a conclusion. Or, you might even be exploring data about a totally different business problem! Understanding what data is available, identifying potential other sources, and filling in the gaps is an important part of the BI process.

Misaligned data

As a BI professional, you will often use data from different sources. Some of these might be internal sources to the business you’re working with, but they might also include external sources. These sources might define and measure things in completely different ways. In cases like these, establishing how to measure things early on standardizes the data across the board for greater reliability and accuracy. This will make sure comparisons between sources are meaningful and insightful.

Dirty data

Dirty data refers to data that contains errors. Dirty data can cause errors in your system, inaccurate reports, and poor decision-making. Implementing processes for cleaning data by fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset is one way you can prepare for this limitation.

Conclusion

As a BI professional, you’ll need to understand that sometimes the data you work with will have limitations. This could mean that it doesn’t fit within a certain time range, or it only applies to specific situations, or there are challenges identifying the data you need. Being able to anticipate those issues and consider them when you build tools and systems for your business will allow you to ensure that those limitations don’t stop your stakeholders from getting the data they need to make great decisions and ensure project success!

Contextualization

This situation involves contextualization. Contextualization gives something greater meaning and helps people understand it more completely.

Data availability

Data integrity, data visibility, update frequency, and change are all aspects of data availability. Data availability is the degree or extent to which timely and relevant information is readily accessible and able to be put to use.

Change

This situation describes the data limitation of change. Data availability may be affected because of a change to internal procedures, such as a system update or a new record-keeping process. It may change externally because of a user interface upgrade or an adjustment to a particular algorithm.

• BI professionals choose metrics to include in their dashboards based on five key factors: the number of metrics, alignment with business objectives, necessary technologies and processes, cadence of data, and use of SMART methodology.
• The number of metrics should be limited to the ones that are most important to the project’s success.
• The metrics should be aligned with the business objectives and be measurable, action-oriented, relevant, and time-bound.
• The necessary technologies and processes must be in place to collect and analyze the data for the metrics.
• The cadence of data should be considered so that the metrics can be reviewed on a regular basis.
• An integrated view of the entire business is important to ensure that the metrics are relevant and informative.

The passage also discusses an example of a BI professional choosing metrics for a dashboard designed to increase customer satisfaction ratings for support tickets. The BI professional considers the consult rate, which is the rate at which customer support agents are asking for help from internal experts. However, this metric is not a good indicator of success or failure because it could be due to either good or bad factors. Instead, the BI professional chooses to include a metric that tracks when a support agent experiences missing support documentation. This metric is more meaningful because it indicates a specific problem that can be addressed to improve customer satisfaction.

The passage concludes by emphasizing the importance of choosing metrics that inform decision-making and support project success. BI professionals should carefully consider all of the factors involved in choosing metrics to ensure that they are effective.

Here are some additional key takeaways from the passage:

• The ability to choose the right metrics is a critical skill for BI professionals.
• The right metrics can help BI professionals to track progress, identify areas for improvement, and make informed decisions.
• The wrong metrics can lead to inaccurate results and poor decision-making.
• It is important to consider all of the factors involved in choosing metrics, such as the business objectives, the data availability, and the desired outcomes.
• BI professionals should regularly review their metrics to ensure that they are still relevant and effective.

Choosing your metrics

In a previous video, you learned how business intelligence professionals determine which metrics to include in their dashboards to deliver relevant and actionable data to their stakeholders. In this reading, you’re going to consider how choosing the right metrics can determine the success of a project. You’ll do this by exploring an example of a BI professional identifying key metrics for their project.

There are five key points BI professionals take into account when choosing metrics:

1. The number of metrics: More information is not always better. BI professionals limit the number of metrics on dashboards to focus specifically on the ones that are key to a project’s success. Key metrics are relevant and actionable. For instance, if metric X drops, is this good or bad? What action would a user take if it dropped that would be different if it rose instead? Too many metrics that aren’t relevant to the project can be confusing and make your dashboard less effective. The goal isn’t to overload the dashboard to account for every single use case, but 80% of the common use cases.
2. Alignment with business objectives: Understanding the business objectives can help you narrow down which metrics will support those goals and measure their success. For example, if the business objective is to increase sales, include revenue in your dashboard. You will most likely not want to include a metric such as customer satisfaction because that is not directly related to the business objective of increasing sales.
3. The necessary technologies and processes: It’s important to confirm that the necessary technologies and processes are in place for the metrics you’re choosing. If you can’t obtain and analyze the necessary data, then those metrics aren’t going to be very useful.
4. The cadence of data: You have to consider how frequently the data becomes available. If a lot of metrics are delivered at a different cadence and frequency, it becomes difficult to schedule a review.
5. Use SMART methodology: If you earned your Google Data Analytics Certificate, you know the 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 super-high-level metrics that aren’t 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 unambiguous: Revenue goes up, and things are going well! But other metrics are a little more complicated.

In an earlier reading, you discovered the importance of context for the CloudIsCool Support team when measuring their ability to effectively answer customer support questions. As a refresher, a customer support ticket was created every time a customer reached out for support. These tickets were addressed by the first response team at CloudIsCool. Sometimes the first response team needed help answering more complex tickets. They would then reach out to the second response team. This was marked as a consult on the support ticket.

Imagine that the BI professionals working with this team now are trying to decide which metrics are useful in a dashboard designed to increase customer satisfaction ratings for support tickets. Perhaps their stakeholders are interested in monitoring consults to ensure that customers are getting the help they need in a timely manner. So the BI team considers adding consult rate, which is the rate at which customer support agents are asking for help from internal experts, as a metric in their dashboard.

Note that an increasing consult rate could be good or bad. It might mean that customer support agents are being more customer-centric and trying to ensure each customer gets the best answer. But it could also mean that agents are being overwhelmed with complaints and having to offload them onto internal experts in order to keep up. Therefore, consult 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 support agent experiences missing support documentation. This will help leaders decide whether to create more documentation for agents 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.

• A north star metric is a metric that measures the long-term success of a business or team.
• It is a guiding light that drives the business forward and helps it achieve its goals.
• North star metrics are useful for cross-team alignment, tracking growth, and focusing values.
• When choosing a north star metric, it is important to consider the business’s essential processes, most important KPIs, and how other metrics can be structured around it.
• Some examples of north star metrics include:
  • Weekly number of customers completing the sales cycle
  • Value of daily purchases
  • Number of daily active users
  • Messages sent per day
  • Number of new sign-ups
  • Total reading time
  • Total watching time
  • Monthly subscription revenue
  • Number of nights booked
  • Number of repeat customers

Here are some additional key takeaways from the passage:

• North star metrics are a valuable tool for BI professionals because they can help to:
  • Measure the success of the entire business
  • Align teams with a single goal
  • Keep the business’s values at the forefront of its strategy
• North star metrics should be reviewed regularly to ensure that they are still relevant and effective.
• The choice of a north star metric will vary depending on the specific business and its goals.

So far, you have been learning about how BI professionals choose the right metrics to measure the success of their projects. BI professionals also use another specific kind of metric to measure the long-term success of the entire business or team; this metric is often referred to as a north star metric. In this reading, you will learn more about north star metrics, how BI professionals choose them, and how they can help a business’s growth over time.

The guiding star

A company’s north star metric goes beyond short-term goals– it’s intended to capture the core measurable value of a business’s product or services over its entire lifetime. These metrics are a guiding light that drive a business forward. That’s why it’s called a north star metric– like the north star can be used to navigate the wilderness, these metrics can be used to navigate business decisions and lead a business to growth.

Having this metric as the guiding light for the entire business is useful in three primary ways:

1. Cross-team alignment: Different teams have different specialties and focuses that help a 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 entire 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.
2. Tracking growth: It can be difficult to understand and track the growth of an entire 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 a business.
3. Focusing values: A north star metric is primarily a guiding principle for a business– it determines what is important to the organization and stakeholders. This means that choosing the right metric to guide a business can help keep the values in check– whether that’s customer satisfaction, number of customers completing the sales cycle, or customer retention.

Choosing a north star metric

Because north star metrics are so key to a business’s ongoing success, choosing the right metric is a foundational part of a business intelligence strategy. The north star metric has to measure the most essential part or mission of the 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?

Real north star metrics

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

• E-commerce:
  • Weekly number of customers completing the sales cycle
  • Value of daily purchases
• Social media:
  • Number of daily active users
  • Messages sent per day
• Streaming and media services:
  • Number of new sign-ups
  • Total reading time
  • Total watching time
  • Monthly subscription revenue
• Hospitality:
  • Number of nights booked
  • Number of repeat customers

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 tech to finance!

Key takeaways

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

• Business intelligence (BI) professionals use gap analysis to identify opportunities for improvement in business processes.
• Gap analysis involves understanding the current state of a process and the ideal state, and then identifying the steps needed to bridge the gap.
• In the case of the sales team, the current state is that there is a six-hour data lag in the dashboard they use to track sales pipeline progress. The ideal state is to have a one-hour or less data lag.
• To bridge the gap, the BI professional needs to work with stakeholders to understand their needs and how they are interacting with the data. They also need to understand the context of the data and ensure its quality.
• Once the BI professional has a good understanding of the current state, they can design a solution to bridge the gap. In this case, the BI professional designs a new workflow that allows data sources to be processed simultaneously, cutting down processing time from 6 hours to less than an hour.
• The BI professional also shares the findings of their analysis with stakeholders and creates a dashboard that sales teams across the country can use to track progress in near-real time.
• Finally, the BI professional monitors the system to make sure it is working as expected and makes adjustments as needed.

Here are some key takeaways from the passage:

• Gap analysis is a valuable tool for improving business processes.
• By understanding the current state and the ideal state, BI professionals can identify the steps needed to bridge the gap.
• Working with stakeholders is essential to successful gap analysis.
• The context of the data and its quality are also important factors to consider.
• BI professionals can use a variety of tools and techniques to bridge the gap, such as designing new workflows, creating dashboards, and sharing findings with stakeholders.
• Monitoring the system and making adjustments as needed is essential to ensure that the improvements are sustainable.

Bridge the gap

Business intelligence professionals continually monitor processes and systems to determine if it’s necessary to make updates for greater efficiency and optimization. These professionals explore ways to bring the current state closer to the ideal state. They do this through a process called gap analysis, which is a method for examining and evaluating the current state of a process in order to identify opportunities for improvement in the future.

Gap analysis involves understanding where you currently are 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, perhaps 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.

Setting direction with stakeholders

The first step in bridging the gap is to work with stakeholders to determine the right direction for this BI project. Establishing stakeholder needs and understanding how users are interacting with the data are important for assessing what the ideal state of a system actually is. 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.

Context and data quality

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. As you know, 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.

Building structures and systems

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. This empowers users to access the data they need when they need it — 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.

Sharing findings

If you are coming to this course from the Google Data Analytics Certificate, you may already be familiar with the share stage of the data analysis process. This is the point at which 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.

Acting on insights

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.

• USDM is a company that helps life science companies with their database systems.
• One of their clients is a company that researches and develops antibody treatments for cancer patients.
• The client wanted to better understand how effective their product is and forecast demand.
• USDM worked with the client to identify the key metrics that were important to them, such as sales performance, product performance, insurance claims, physician information, and facility data.
• USDM then architected a data storage system that consolidated all of this data from across a variety of sources.
• The system also included reporting dashboards that allowed the client to easily track their key metrics.
• As a result, the client was able to gain insights into their product’s effectiveness and forecast sales demand more accurately.

Here are some key takeaways from the case study:

• It is important to collaborate with users and stakeholders to select metrics early on in a project.
• This can help to ensure that the metrics are relevant and useful to the stakeholders.
• It can also help to ensure that the BI tools are designed to best address the specific needs of the stakeholders.
• By consolidating data from a variety of sources, BI tools can provide faster insights and save time and improve efficiency.
• Reporting dashboards can help stakeholders to easily track their key metrics and make informed decisions.

In this part of the course, you have been focusing on how business intelligence professionals identify effective metrics for a project. A key part of this process is working with stakeholders to understand their data needs and how those interests can be measured and represented with the data. In this case study, you will have the opportunity to explore an example of how the BI team at USDM worked with stakeholders to develop metrics.

Company background

USDM, headquartered in Santa Barbara, California, collaborates with life science companies across a variety of industries, including biotechnology, pharmaceutical, medical device technology, and clinical. USDM helps its customers, from large-scale companies to small businesses, ensure that their database systems are compliant with industry standards and regulations, and work effectively to meet their needs. USDM’s vision is to bring life sciences and healthcare solutions to the world better and faster—starting with its own company values: customer delight, accountability, integrity, respect, collaboration, and innovation.

The challenge

In this case study, you’re going to explore an example of USDM’s work with one of their clients. The client for this project researches and develops antibody treatments for cancer patients. The client needs analytics that measure the effectiveness and efficiency of their products. However, with the client’s existing database, to get the types of reports they need, they have to access many systems, including facility data, licensing information, and sales and marketing data. All of this data exists in various places, and as a result, developing analysis reports creates issues for the client’s stakeholders. Also, it makes it harder to compare key metrics because so many KPIs needed to be brought together in one place. 

To help better understand how effective their product is and forecast demand, the client asked USDM to help architect a data storage system that could address their specific needs. They needed a system that could bring the data their team needs together, follow industry regulations, and allow them to easily create reports based on key metrics that can be used to measure product effectiveness and market trends. A significant part of this initiative started with the basics: what were the actual key metrics for the client’s team and what data systems did they come from?

The approach

To identify which metrics were most important for the client’s business needs, the USDM team needed to get input from a variety of different people from across the organization. For example, they needed to know what charts the sales and marketing teams who used this data for their reports needed, what their existing processes were, and how to address these needs in the new system. But, they also needed to know what data the product development team used in order to measure efficacy.

USDM worked closely with different teams to determine what charts they needed for reports, how they were accessing and using the database system currently, and what they were hoping to achieve with the new system. As a result, the team was able to determine a selection of key metrics that represented their client’s business needs. These metrics included:

• Sales performance
• Product performance
• Insurance claims
• Physician information
• Facility data

To enact a business intelligence solution there must be both the business interaction with stakeholders and the technical interaction with the architects of other team’s systems. Once these metrics were identified by the client, the USDM team collaborated with other members of the client’s team to begin building a new solution that could capture these measurements. 

But, almost every project comes with unexpected challenges; the database tool the team was using to develop the new system didn’t have all of the features the team needed to capture their must-have metrics. In this case, the USDM team collaborated with leadership to develop a list of requests from the tool vendor, who was able to address their team’s unique needs.

The results 

By the end of the project, the USDM BI team architected a data storage system that consolidated all of the data their team needed from across a variety of sources. The system captured the key metrics the client needed to understand their product’s effectiveness, forecast sales demand, and evaluate marketing strategies. The reporting dashboards created with this data storage system included everything the stakeholders needed. By consolidating all of the KPIs in one place, the system could provide faster insights and save the client time and improve efficiency without having to run reports from every individual system. The solution was more automated and efficient—and importantly, designed specifically with their team’s most useful metrics in mind.

Conclusion 

Collaborating with users and stakeholders to select metrics early on can help determine the long-term direction of a project, the specific needs stakeholders have, and how to design BI tools to best address unique business needs. As a BI professional, a key part of your role will be considering key metrics and how to tailor the tools and systems you create to capture those measurements efficiently for reporting use.

Eliminate vanity metrics

They are eliminating vanity metrics from the dashboard. Vanity metrics are data points that are intended to impress others, but are not indicative of actual performance and, therefore, cannot reveal any meaningful business insights.

Data availability

This situation describes data availability. Data availability is the degree or extent to which timely and relevant information is readily accessible and able to be put to use.

• Display the most important metric at the top.
• Display supporting metrics below the top metric, as they reinforce it.

Data availability: The degree or extent to which timely and relevant information is readily accessible and able to be put to use

Data integrity: The accuracy, completeness, consistency, and trustworthiness of data throughout its life cycle

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

Vanity metric: Data points that are intended to impress others, but are not indicative of actual performance and, therefore, cannot reveal any meaningful business insights

• Considering how business intelligence aligns with the teams’ overall business objectives
• Focusing on collaboration by creating an easily accessible dashboard
• Understanding how each particular stakeholder will use the dashboard

integrity

The degree or extent to which timely and relevant data is accessible and can be put to use

Data visibility

diverse dashboard users

They are not indicative of actual performance

• Avoid vague or very high-level metrics.
• Make sure metrics are aligned with business objectives.
• Confirm that necessary technologies and processes are in place.

contextualizing

• The dashboard includes both big-picture insights and supporting information
• The data is in the right format
• Users provide details about how they will use the dashboard

Data availability

Update frequency

Processes, Technologies