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Friday, 07 June 2024 09:37

Mastering Data Analytics: Best Practices for Reporting Systems, Dashboards, and Visualisations

By Andrew Hudson, Data Consultant at V2 Digital
Andrew Hudson, Data Consultant at V2 Digital Andrew Hudson, Data Consultant at V2 Digital

 Guest Opinion:  Best Practices for Reporting Systems, Dashboards, and Visualisations

Best Practices for Optimising Reporting Systems

Data and its insights form the foundation of decision-making, making Business Intelligence (BI) a critical part of any organisation. Reporting and analytics should provide a clear view of relationships, trends, statistics, and insights to support decision-makers across the business.

The platform’s purpose is to increase the availability, cadence, and quality of data and insights. To get the most enablement out of any BI platform, it should focus on providing correct and complete data to users in the form of meaningful, digestible visualisations.

Fundamentally, the BI platform should serve as a presentation layer for prepared data and ideally act as a one-stop-shop for all of a data consumer’s queries and questions. Analysts should be able to quickly create reusable dashboards or stand-alone visualisations that can be refreshed on-demand — removing tedious manual data-wrangling tasks.

A BI platform and an analyst’s use of it should aim for:

  • Accessibility. Users should be able to easily find the answers to their questions and have rapid access to the data that forms them.
  • Readability. The platform should have dashboards that cater to a wide audience, with varied levels of understanding. Subject matter experts should have access to detail, and high-level summaries should exist for broader scopes of use.
  • Quality. Data should be of high quality, and the standard of data should be analysed in its own right in the platform, to convey any amount of error propagation or issues in the data.
  • A single source of truth. With standardised measures and calculations, common definitions and uses of metrics should exist across the platform for everyone to abide by for consistency and reliability.
  • Rapid delivery. In a fast-paced reporting environment, your BI platform should be able to readily deliver updated and fresh insights to consumers within the business.
Dashboards - The Heart of Analytics

Dashboards are the bread and butter of analytics and Business Intelligence. They present a medium for answering business questions in a simple, readable and consumable manner.

Best Practices and Key Considerations for Dashboards:

The five main considerations when constructing a dashboard are Purpose, Audience, Interaction, Optics and Sharing, easily recalled under the acronym PAIOS.

Do note that the design principles listed below should apply not only to the detailed level of the dashboards but to your entire platform as well.


Beginning with the ‘why’ of the dashboard, everything must centre on the requirements it fulfils; the question or questions it seeks to answer. The content and purpose should sit at the very heart of the dashboard and be the first aspect tended to.

Ideally, dashboards should tell a story and take the audience on a journey, telling them what they need to know along the way. Using key sets of underlying data, targeted queries should pull out the information, either raw, calculated, or aggregated, and surface them in visuals that answer the key questions.

To elevate your dashboard even further, consider adding auxiliary visuals that answer additional purpose-aligned questions that the audience might think to ask, but might not have explicitly asked in the foundational requirements.

At V2, we define these questions that business users may not have initially considered by working closely with both technical and business stakeholders to conduct in-depth discovery, employing both a top-down and bottom-up approach. These activities include collaborating with users to create personas and understanding their needs, requirements, and pain points. In addition to understanding user personas, we investigate the data itself to identify fields or measures within the base data that could bring potential value to the platform, even if not explicitly requested. By conducting prudent early discovery, we unlock additional value in BI platforms, enabling the delivery of specific and bespoke dashboards alongside a holistic data model that fosters self-guided platform use.

Getting to the heart of the ‘why’ can help deliver more insights than what a user might have initially had in mind, where appropriate.


  • Collect the questions or problem statements you’ve been given and spend time disassembling them. Find the directive at the core of the requirements and work around this as the central focus.
  • Examine the available dimensions and metrics in the dataset and platform and ensure you have the information needed.
  • Inspect the quality of the data you will be using, and raise or resolve data quality issues wherever possible.
  • Collate the required information, and only the required, into visualisations on your dashboard, and ensure the content covers the scope of the purpose.
  • When you find something not asked for, but aligned with the purpose, see if it can be added for completeness (if appropriate).


Consider your audience: the key users or consumers of the dashboard. Reflect on what they may ask of the data and the types of questions or considerations they might have. This should guide the content you provide in the dashboard. Ensure whatever level of detail they require from the core dataset, is readily accessible. Finally, make sure their key questions are promptly addressed by core visualisations positioned at the top of the dashboard.

Understanding the direct audience will also guide the appropriate considerations for the remaining points about interaction, optics, and scheduling. You must know how much your users will want to interact, the type of visuals that will resonate, and the cadence with which they will want to check or receive a copy of the dashboard.

In addition to the above, an important consideration is the data maturity of the audience. Key questions you should find the answers to would include:

  • How high is the data literacy of my audience?
  • What is their understanding of and appreciation for data quality?
  • What are the overall capabilities of the company with respect to data and analytics?
  • Do end users have the ability and capability to self-serve?

At V2, we uplift data maturity within our clients with a tight focus on data quality. This includes prioritising data quality assurance and KPIs, assigning and educating data stewards to maintain accountability and training end users on platform usage. We allow them to get the most out of a BI platform by self-serving insights rather than pushing dashboard creation to backend data teams.

In addition to your direct audience, you must also consider your wider audience, namely who else might see the dashboard, and how they will be able to interpret and interact with it. Adding additional tools or notes to the dashboard to help with knowledge sharing is strongly recommended.


  • Understand the personas of your users. How are they intending to consume and use the data?
  • Know the level of detail they will require or understand. For example, does the main user want a high-level view, with some eye-catching graphics, or do they want a lot of interactivity that allows them to drill down into the nitty gritty?
  • Will users need to actively work with and engage with the board and its filters for customisation, or will they just want to do a quick scan? Noting that these are also not mutually exclusive.
  • Estimate usage frequency and timing. Will they be checking it every day or week, or just towards the end of a month?
  • Consider other users, and what information they may be seeking or find useful.
  • Add tiles with commentary or instructions to ensure the dashboard is tailored to your audience and their level of understanding.


Most BI platforms heavily focus on interactivity in the presentation layer. Users can hover, click, and adjust filters on dashboards, which can be very advantageous.

Having modular dashboards with pre-set filters that users can toggle or change when they look at the data is a great way to leverage the benefits of interactivity. Not only can this save time during the build process, but it can also help reduce clutter on the dashboard, as long as the differing filter results don’t need to be side-by-side.

For dashboards spanning a relative period of time, such as examining data within the last three months, you may consider a filter that sets the dashboard within this relative time frame. Instead of having to rebuild the analysis or re-edit the dashboard regularly, analysts can build the board once, and move on to the next.

A well-built dashboard will encourage analytical interactivity in its consumers, and minimise maintenance interactivity for those who build it.


  • Keep relevant filters such as ‘Department’ handy for when users may want to context switch
  • Set filters to point at any and all of the required visualisations it should apply to. High level figures may not need to be updated on a filter change.
  • Set filters to relevant default values and enable the functionality for users to change during a view of the dashboard if they wish.
  • Build modular visualisations that can be used with the different sets of filters, so a user can easily generate an extract of the dashboard.
  • Depending on the question, best practice is to add filters as opposed to extra tiles.


How does the dashboard look? Remove clutter or excess colours and use whitespace to prevent users from being overwhelmed with information. Is it presented in an easy-to-digest format so users can easily get the answers they seek? This question applies to the whole dashboard as well as individual visualisations.

Whilst optics is secondary to content and correctness, it is a vital consideration for the presentation of your data for legibility. In fast-paced analytics, users and stakeholders will want their questions answered quickly, understandably and in a visually appealing manner.


  • High-level or vital information should be at the top, with the most important infographic at the top left.
  • Detailed graphs or drill downs should be further below.
  • Group the tiles in a logical manner, such as type, or the data presented.
  • If you’re starting to add too many tiles, it’s time for a new dashboard. The ideal number of tiles is 10 to 15.
  • Have a standard for eye-catching colour coding, such as KPIs under target in red. Ensure this standard is consistently applied within and between dashboards for improved legibility.


Consider the sharing of data and information. Tying back to your immediate or large audience, first answer the question of “who” the dashboard should be available to. If the presentation answers a set of questions repeatedly, consider when the information should be updated and redistributed.

Most BI platforms and tools can connect to your underlying data in a manner that enables scheduled refreshes of the visualisations and notifications indicating that a dashboard has been updated. The ease of sharing within a platform should encourage users to create assets and derive insights from the platform without having to leave it or download data.

Automated data sharing with end users or stakeholders should be seen as an opportunity to drive engagement with the platform, not simply to receive regular reports. The more an end user can self-serve the information they are looking for, the quicker insights can be shared through the business, and the more time can be allocated to analysts to produce the assets that yield and communicate them.

Some tips for this:

  • Cater to your audience’s desired cadence. Ensure they are notified when data is updated and ready for consumption, or if their data is expected to arrive late.
  • Ensure the tiles are set to update when, and only when, they need to be.
  • Ensure permissions are set appropriately so the dashboard can be shared with a wider audience. Try to provide links, not copies.
  • Consider embedding dashboards or tiles into external presentations rather than taking screenshots, where possible.
  • While most BI tools have the capacity to email end users copies of dashboards, try to save this as a last resort.
Visualisations - The Building Blocks of Your Analytics Dashboards

The principles of designing a dashboard should carry from initial structure through to your visualisation design and choices. This section contains some tips and best practices for constructing visualisations:

Your visualisations should:

Answer a targeted question.

Ideally, this should be a single, direct question. For example, “How many bookings were sold across each month this fiscal year?” or “What are the components that add up to the total revenue, compared to one another?”

Select the appropriate type.

The mantra should be ‘content before formatting’, so the type of visual you choose should convey the key information in your query in a clear, concise, digestible manner. For example, pie charts are only for proportions, columns and bars can show the scale of differences for comparison and line charts across dates are best for time series.

Be simple.

Trying to do too much in a single visual can lead to clutter, which will affect not only how the user reads the visual, but the whole dashboard. Try to choose a chart type and a restricted set of information in the underlying query that answers the question. If you are putting so much into a visual that it becomes illegible, it’s time to split it into a few or more for better readability.


 Final Thoughts

Stafford Beer coined the phrase: “The purpose of a system is what it does.” This sentiment should be at the forefront of your mind when designing, building, and maintaining your Reporting System.

To recap PAIOS:

  • Purpose: The ‘why’ that drives your reporting, the data required, and the objectives met.
  • Audience: The ‘who’ with respect to the users and stakeholders, their needs and data maturity.
  • Interaction: Motivate your users to engage with the platform with ease of use and wealth of information.
  • Optics: Create eye-catching, digestible dashboards and visual assets that communicate information clearly and concisely.
  • Sharing: Ensure maximal user access to reports and data by scheduling updates and engaging notifications to drive users to engage with the dashboard.

Applying the above principles to both the high-level platform design and the detail of data assets such as dashboards and reports, will ensure the success of a mature self-service reporting and analytics platform.

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