Death to the “Perfect Report”

When designing your Power BI solution, are you designing your solution for maximum long term effectiveness? One of the biggest mind shifts that my Power BI students have to make when beginning their Power BI journey is to stop thinking in terms of reports when approaching their Business Intelligence (BI) needs. Rather, it’s more effective to approach those BI needs from a holistic data model perspective.

Why the “perfect report?”

In years past, designers were focused on writing the “perfect report” as the amount of time and effort needed to create a report was substantial. This led to reports that were used for multiple purposes. This multi-use design also created complexity in the presentation and increased maintenance costs. Each additional report also creates a long tail of maintenance effort to ensure that the report stays relevant over time.

With tools like Power BI, the focus is on rapid development of new reports and dashboards. The “perfect report” approach is no longer necessary as it is very easy to create new single focus reports and dashboards. The value of a single report is then diminished and the importance of the data model design increases.

Think data models, not reports

The rationale for this design approach is that, on average, 50% of your annual reporting needs are ad hoc in nature. Therefore, you can’t design a report for every possible contingency. Having a 200 reports that don’t meet your immediate need creates noise rather than benefit to the organization. They simply slow down the ad hoc reporting process as the data consumer wastes time evaluating the existing content first. Flexibility offered from a well-designed data model is the expedient path to fulfilling your ad hoc needs effectively for the long term.

Power BI design for the business conversation

The purpose of business intelligence is to support business conversations with relevant data to enable decision making. Therefore, your data model should be designed to support a specific business scenario. The scenario provides the necessary context for scoping the type of data required to meet the most likely questions. It also provides the necessary context to the data consumer to identify the data model as the correct one for use in a given business conversation.

Conversation-centric design

The path to outlining the necessary data is using an approach we at Tumble Road call “Conversation-Centric Design.”

The first step is to identify the conversation to be supported. Standing meetings are usually a good starting point for the identification process. Meetings provide a lot of information for the design process as the timing, audience and typical agenda are known. The agenda can be used to identify the key questions and needed answers. From the answers, we can extract the core data elements for the data model. Each answer becomes a visualization in the eventual dashboard or report.

For example, there’s a weekly Vice President status meeting every Wednesday. Project status of the overall portfolio is reviewed during the meeting. The key questions for the portfolio review are:

• Which projects have implementation milestones due this month
• Which projects have resource constraint issues
• Which projects have escalated issues to the meeting

Each of these questions can be broken down into the data elements necessary to provide the needed answers. This forms the core data for the data model. The designer then adds the most likely secondary data, like custom fields and other key data to anticipate future needs.

Designing for the long term

There are three aspects to consider to support your long term Business Intelligence needs.

Reports are still needed but are not the focal point

First, you should still provide reports over the data, understanding that they provide an immediate starting point for the data consumer to use the data. The report represents the designer’s view of the data and is generally designed for a specific conversation need.

Ultimately, the data consumer is in control

Second, a well-designed data model supports the data consumer needs for ad hoc information. The data consumer can create ad hoc constructs of data using two methods within Power BI.

The data consumer can create personal dashboards, pinning visualizations from multiple reports to create a personalized view of the data to meet their specific needs. This pinning process supports a visualization being pinned to multiple personal dashboards so the data consumer can narrowly define their dashboards.

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The data consumer can also create new reports in Powerbi.com via the browser.  The primary difference between the report and the dashboard is intent. Dashboards provide a view of the data; reports provide a way to explore the data. Data consumers will create reports when the need to slice and dice the data is the primary need. The effectiveness of their report development is dependent on the underlying data model adequately supporting the business scenario.

Business scenario focus means smaller, easier to use data models

Lastly, there is value in the less is more approach. Data models shouldn’t be monolithic in design. A well-designed data model provides only the data frames and data elements necessary to support the business scenario.

For example, one of the challenges of Project reporting is the sheer magnitude of available data. Overwhelming the data consumer with too many data frames (aka data tables) to search through for the requisite data elements, slows down the ad hoc reporting process and creates user frustration. In our Marquee™ designs, for example, we separate project reporting from timesheet reporting as separate business scenarios. This in turn reduces data consumer confusion.

A business scenario focused data model also reduces the risk of the data consumer inadvertently creating cross-join situations. When a cross-join arises, typically the data consumer has combined two data elements together in a visualization that don’t have a direct data relationship. The result is the data consumer seeing every potential combination of the two data elements. This may then lead to you receiving a support call as to why the tool is doing what it is doing.

Finally, keeping individual data models business scenario focused also enables you to better maintain the data model as the business scenario changes over time.

The wrap up

My hope is that you now understand why a data model approach is better than a report centric approach is superior when designing Power BI. Data model centric design approaches yield better long term support of ad hoc reporting. Focusing the data model on the business conversation also yields many benefits from a data consumer experience, from ability to correctly select the right model to improved speed of creating new reports.

How are you approaching your Power BI design today? What questions do you have? Please post your feedback in the comments below. I look forward to hearing about your design approaches.

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