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".

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

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!

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

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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.

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.

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.

​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.

​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.

​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. ​