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6 Design Tips for Better Dashboards

6/12/2016

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In his article "Choosing the Right Chart for your Data," Brian Petersen (VP of Professional Services at Jet Reports) writes: "Data is the foundation of effective business.  ... Being able to quickly read and analyze your data enables you ... to understand how a particular set or group of facts contributes to your overall success and steer your decisions proactively."
He then goes on to describe several common charts including:
  • Area Charts
  • Bar Charts
  • Scatter Charts and
  • Stacked Column Charts
Read the full article here:

For more seasoned analysts, the challenge is less about finding the right chart so much as laying out a dashboard that effectively communicates a broad scale of both summarized as well as detailed information. As we delve into optimizing dashboards, we move away from technical or domain expertise and transition toward questions of User Experience and User Interface.

For these projects, I'll leverage knowledge gleaned from Stephen Few's guide to dashboarding -- "Information Dashboard Design" which was heavily influenced by Edward Tufte's seminal work "The Visual Display of Quantitative Information".

Charts Jump off the Page with these 6 Tips

1) "Brevity is the soul of wit" -- Do not exceed 1 page.
Any CxO will describe the perfect dashboard as an interactive report where they can see all the important information on one page. Translation: Edit. Edit. Then edit again. Examine how much excessive detail or decoration you can pare away without supplying inadequate context.

​Your final product shouldn't require scrolling, changing tabs, or (ideally) a legend.

2) How good is good? -- Provide enrichment and context through comparison.
  • Same measure at a point in the past (Last Year or Last Month)
  • Target (Budget or Forecast)
  • Relationship to future target or prior prediction (% of annual budget or forecasted budget)
  • Benchmark for norm (company average or industry standard)
  • Separate but related measure
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3) Consider providing non-quantitative data
If you frame dashboards around improving a process or keeping 'two fingers on the pulse of the company'; in addition to measuring activity, it may make sense to provide non-numeric data.
  • Top & bottom performers
  • Prioritized issues to investigate
  • Upcoming due dates.

4) Emphasize the important things.
By understand how the eye travels across the page, designers can highlight, prioritize and de-epmphasize.  This is particularly important when planning the placement of auxiliary elements including filters, slicers, legends and labels.
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5) Maximize the "Data-Ink Ratio"
  • If 'data ink' is any line or pixel that communicates quantitative data (a line, point or bar in a chart) and 'total ink' includes all the lines used to create said chart (axes, tick marks, borders), then the data-ink ratio measures the distribution of ink used for communication versus formatting and decoration.
  • Strive to enhance data ink while reducing and deemphasizing non- data ink.
    1. Can you remove unnecessary tick marks or grid lines?
    2. Do variations in color provide additional meaning, or is the message equally clear with just one or two muted colors?
    3. Do you really need that 3-D effect or color gradients?
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6) Organize information to support interpretation and application
  • Organize groups according to departments, entities or use
  • Co-locate and subtly delineate related data
  • Support meaningful comparison while minimizing meaningless comparisons

My favorite feature of Few's book was his analysis of sample dashboards, wherein he described, not only the flaws in various dashboards but also modeled various alternative ways of presenting the data.  For dashboard developers this analysis would prove invaluable for sharpening our critical eye, but also provide inspiration for what dashboards can (or shouldn't) look like!
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Bridge the Gap between Concept and Execution

For those of you using Pivot Tables or PowerBI to access data from a Jet Enterprise cube, it can be difficult pursue the optimum dashboard layout or chart because you're constrained by the limits of the pivot table or data model.
In a previous post: Better Dashboarding with Cube Functions, Onyx Reporting presents a tutorial for converting Pivot Tables into infinitely more manipulable Cube functions.

One Book to Rule them All

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Content and images from this blog post were taken from Stephen Few's Information Dashboard Design (buy it on Amazon).

Check out our blog at www.onyxreporting.com/blog or sign up for our weekly newsletter: "The Data Dump".

If you need support knocking out a batch of reports or want to customize your Jet Reports cubes to include some new comparative measures, our services team is amoung the best in the business.
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Best Practices:  Working with Multiple Environments

29/11/2016

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What exactly are "multiple environments?" It's an infrastructure that allows your business to separate BI development endeavors from the live environment the organization uses for reporting and analytics.

"Why would I need it?" Development is not always a quick 5 or even 15-minute fix. Larger projects can take days, weeks, even months to complete, so you need a sandbox for the developers to 'play' in while the rest of the organization continues forward with 100% uptime on their live reporting environment. Some organizations may even choose to separate the Dev sandbox from QA (Quality Assurance) efforts, so a third Test environment may be needed!
But "do I need multiple environments?" As with any business question, 'it depends'. If your development is limited to just adding a field here or a new measure there during the initial implementation of your business intelligence project, you may be able to wiggle by without separated environments.

It may make sense to separate development from the live environment if:
  • your organization has many customizations in the ERP (which won't be reflected in an off-the-shelf implementation)
  • you're tackling questions unanswerable in the off-the-shelf BI environment you purchased
  • your organization is actively using and reliant on the reporting environment
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It may make sense to implement a QA environment if your organization has
  • formal processes for auditing data quality
  • legal obligations to report 100% accurate data to external bodies (ex. financial auditors, government bodies)
  • low tolerance for seeing 'weird data' in their live environment

Ready to get started today?
  • Review your current implementation with our  4hr - Scoping & Assessment Workshop
  • Put your BI development team through our DWA Master's Course

This presentation is preparation for a training series Onyx Reporting will be conducting in 2017! Join our mailing list to keep up with special offers, training opportunities and our blog series.
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What is Data Warehouse Automation (and why should your CXO care)?

2/9/2016

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In the last decade, software developers addressed the barriers to comprehensive data programs by developing robust data warehouse automation tools that automated code generation for recurring tasks in data projects. Although the solution had obvious benefits to the developers hacking out code in the basement, the value proposition was unclear for business executives. "You want me to spend 100k on what?"
​
Assumption:
 To remain competitive organizations must leverage data to augment their product or services offering and/or use analytics to reduce costs or innovate value-added business processes.

Why do I need a governed data warehouse? Can't I stick with my self-service solution?
No, self-service business intelligence (BI) will not replace a data warehouse. Your organization needs:
  • A single version of the truth -- Self-service solutions frequently require users to perform their own 'data-munging' (extraction and cleansing) which translates into inconsistencies between reports and departments. In comparison, pre-defined metrics in a data warehouse enforce consistency across the enterprise.
  • The ability to keep history -- Operational and transactional systems overwrite and lose critical data history.
  • Improved end-user productivity -- In creating structured datastores, organizations democratize data access and facilitate data-driven innovation (with cleansed & consistent data).

We still need self-service tools. Data-driven innovation frequently stems from exploration by individuals or small teams before transitioning into enterprise-wide solutions. The cutting edge of data innovation lies at the intersection of self-service flexibility and agile datastore implementations serviced by an automation tool.

--Update 4/10/2016 --
Onyx Reporting uses and recommends data warehouse automation tools developed by TimeXtender; because while the application does auto-generate code, all parts of the business intelligence development process (extract, transform and load) are accessible and customizable using traditional tools in the Microsoft BI stack.

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BI for Execs:  Plan your Data Strategy pt. 1

25/8/2016

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​This two-part article describes a 6-step framework, Vision, Mission, Strategy, Goals, Initiatives, Actions (abbreviated VMSGIA) executives can leverage for refocusing business innovation around strategic goals.

Onyx Reporting uses this methodology during the needs assessment phase of larger business intelligence (BI) and data strategy projects to prioritize, frame, and deliver high-value analytics solutions.  Over the next two posts, we will:
  • Examine a common way organizations derail new BI initiatives
  • Explore examples of the successful alignment of data initiatives with corporate strategy.

By Jae Myong Wilson - keep abreast with BI strategy from the comfort of your inbox.

A Data Project Sunk in Dry-dock

​Piecemeal design produces hamstrung data strategy teams.
As organizations evaluate new analytics tools, the question "Do you have any sample dashboards?" invariably arises. Though it seems a reasonable request, in most cases, it derails the data project team; because the process rapidly devolves into piecemeal design and never recovers. "I don't like the layout of this report" "How much would it cost to change this feature?" "How many hours would it take to add a new calculation?"  
 
Instead of proactively designing a comprehensive solution, the data consultancy is relegated to reactively implementing fixes.

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BI for Executives:  Applying Business Intelligence

27/7/2016

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​In this article, we'll use Onyx Reporting's workshop, "The Strategist's Journey" as a framework for addressing the question "What can BI do for me?"

Last week I received an email from an IT director and senior financial controller asking, "How can I convince the executive suite to invest in a BI project?"  The email went on to hint that there were limited resources available to allocate to new initiatives. 


Ironically, when analysts describe why BI projects fail, they never mention the not-so-insignificant challenge of convincing management to green-light new BI initiatives.  If my experiences are any indicator, the challenge stems from technologists using technologists' language to champion projects to business people who evaluate based on business merit. ​

Overcome the Language Barrier

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When the executives ask  "What value can business intelligence bring to my company?" technological benefits or features--applications, infrastructure, and tools--seldom (read never) translate well.

​
We Want You to Save Money Too.
The subtext of the email from the IT director implied the opportunity for a critical paradigm shift in the executive suite's understanding of business intelligence.

1) Business Intelligence is not an IT/IS project.
Business Intelligence includes reporting and analytics that support measuring and controlling progress toward achieving strategic initiatives.

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Business Intelligence for Executives:  Assemble the Right Team

25/7/2016

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In our 3-part series, we'll examine action items executives and project managers must understand and integrate into their BI implementation staffing strategy:
  • Part I: Achieve buy-in and improved user adoption by collecting input from horizontal and vertical cross-sections of the organization. Remember BI is a continually evolving process, not a product or technology for IT to implement and maintain.
  • Part II: Make sure your project leader matches skills and roles for each phase of the project, by understanding the spectrum of skills including business analytics, development, and infrastructure that BI professionals specialize across. Although BI professionals do cross-train, rates and expectations are set according to the role you're filling.
  • Part III: Have better interactions with the project team by understanding Kimball's Lifecycle Methodology as a framework for understanding BI project phases (starting with defining business goals and data analysis, followed by development, and concluded by handover) and match the required BI skills.
If we view implementing BI as a series of conversations between organizational resources (executives, managers, analysts, end users), we'll understand the importance of staffing 'the right' BI professionals to each phase of the project.

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jae@OnyxReporting.com
+44 747.426.1224
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