Designing an Effective Dashboard
July 20, 2009 | Leave a Comment
Here’s a nice overview presentation of effective dashboard design. The presentation does a good job of going over the do’s and don’ts and also provides a few good resources. The author follows a fairly minimalist approach to dashboard design, but with an edge of practicality.
Some key points:
- Highlight exceptions only
- Go easy on the eye candy
- It’s okay to deviate from a best practice, but only in rare instances
- Packaged dashboard solutions rarely meet all of your needs.
Campus Technology: BI Project Success
February 10, 2009 | Leave a Comment
If you thought the implementation of your institution’s student information system was challenging, try deploying an institution wide business intelligence platform and data warehouse. While it doesn’t have to be hard, most institutions tend to approach business intelligence as a technology and not a strategy. This is a fundamental mistake that almost always ends in failure. Yet, if you are willing to think differently about business intelligence (the strategy) then business intelligence (the project) can go smoothly and most definitely will provide a greater return on investment than most any other enterprise project.
In the February 2009 issue of Campus Technology Magazine, Dr. James Riha from Oklahoma City Community College, and I wrote about the keys to BI Project Succes. The article focuses on the organizational aspects of managing a business intelligence implementation project. In the article we discuss the importance of:
- Developing a clear institutional vision for what business intelligence means to decision makers.
- Fostering the development of self-organizing teams.
- Rapid prototyping.
- End-user involvement, early and often.
- Planning to be ‘nimble.’
So give it a read and let us know what you think in the comments.
Build an informative dashboard without dashboard software
January 28, 2009 | Leave a Comment
Let’s face it. Dashboard development tools are still in their infancy. While promising, today’s tools are too rigid in their configuration options, do not conform to a standard implementation approach, and are often heavy on garish ‘bling.’ Though the predominant voices in visualization and dashboard design (Few, Tufte, etc.) espouse a best practice approach that can be summed up as ‘less is more,’ there still seems to be a fairly large gap between tool capability and best practice approach.
While we have had success developing dashboards for clients with dashboard development tools, the good news for those that don’t yet own such tools is that you can still have a well designed custom dashboard with the technology you probably already own.
Recently, I’ve come across quite a few ‘best practice’ dashboards emerge in the field, including those developed by our own consultants. The best thing about them is that they have been developed using tools that many organizations have already adopted including, Excel and SAS/Graph.
The Dashboard Spy points us to this Few inspired manufacturing dashboard done with SAS/Graph. Not only are the visualizations minimalist in nature while displaying maximum information, but they are incredibly ‘lightweight’ from a performance perspective. In addition, a tremendous amount of information is represented in a relatively small footprint.
In this example, our consultants put together a dashboard in Excel based on OLAP cubes developed with SQL Server Analysis Services. Not only is Excel used as a front end for a dashboard, but it also serves to integrate the key performance indicators for additional ad hoc reporting and analysis.
So you see, not only is it possible to have a dashboard without additional investment in tools, but in some cases it may even be more desirable.
Gartner’s Magic Quadrant for BI Platforms
January 26, 2009 | Leave a Comment
2009 Gartner Magic Quadrant for Business Intelligence (BI) Platforms
Gartner is out with it’s annual Magic Quadrant ratings of Business Intelligence Platforms. I must say that there are no real revelations here – all of the big names are bunched fairly closely together in the leaders quadrant. 2008 was clearly a year of regrouping for the major players, as IBM, Oracle, and SAP ‘digested’ their acquisition of major BI vendors from the year prior.
I am a little surprised that Microsoft doesn’t rank more highly on the visionary axis as they do seem to be moving quite aggressively toward offering a platform for ubiquitous BI. Perhaps, 2009 will be their breakout year?
My takeaway – The platform is the least of your worries. It is important from the standpoint of fit within the organization’s IT strategy. But don’t forget that the platform is only an enabler. When deploying a BI platform, your focus should be on understanding the organizations goals, identifying the right views of data, and providing greater access to actionable information.
So go ahead, pick a BI platform, any platform. All of the leading vendors provide excellent tools. Just don’t forget that once the technology is in place it’s people and process that make it useful.
Overwhelmed with BI Jargon
December 31, 2008 | Leave a Comment
Ever feel like you are swimming in business intelligence jargon? Never fear, even the experts are overwhelmed with terminology, concepts, and acronyms that all attempt to describe or label things having to do with BI. One look at this graphic from the Data Doghouse and its no wonder that many people put their head in the sand when it comes to reporting and analytics.
My own personal hypothesis is that it is not only the terminology that overwhelms us, but the use of the terms to describe both concepts and methodologies as well as tools and technologies (a subject for another post).
7 Recommendations for BI Success
September 9, 2008 | Leave a Comment
One of the biggest reasons a BI initiative fails is lack of ownership by the ‘business’ units of an organization. A recent article in DM Review provides seven recommendations in an attempt to remedy the situation. The article provides a few new and interesting ways to think about this problem, that I like, however, the authors lay it out a little more verbosely than I would like. I’ll summarize the main points below:
The authors do a good job articulating the causes of user satisfaction or dissatisfaction with BI:
- The pace at which users receive answers to questions.
- The degree of relevance between the answer and the question asked. (An answer with little or no relevance to the user’s question is as good as no information at all.)
- The (perception of) reliability of the answer. (Do I trust the answer enough to use it?)
- The cost of information compared to its value. (Users are not interested in answers where the cost exceeds the value of the information.)
The article continues with seven recommendations to resolve these problems, however, I’m not so sure that – in the end – the recommendations squarely address the causes articulated above. Still, the recommendations in their own right are worthy of consideration. For purposes of clarity I will paraphrase the recommendations in my own words:
- Create an organizational unit that is responsible for the design, development, and support of your business intelligence initiative.
- Funding for the BI foundation should come from IT or another central budget source, while funding for individual subject area reporting and analysis should come from the business unit.
- Develop a federated enterprise data warehouse – make sure it is flexible and can grow as the business unit’s needs grow.
- Use technology that helps you manage the BI foundation when data structure changes are needed.
- Use an iterative approach to BI application development – seek frequent user feedback and deploy in days and weeks, not months and years.
- BI tools provided by your enterprise software provider (ERP) are useful for creating an Operational Data Store (ODS), not an Enterprise Data Warehouse (EDW).
- Create a data governance group that is led by your BI Organizational Unit.
I’d be interested to hear what others think about some of these recommendations and the counter arguments they may have.
Forrester Releases Comprehensive, Yet Overwhelming Overview of the World of BI
August 28, 2008 | Leave a Comment
Business Intelligence Architectural Stack from Forrester Research
Business Intelligence (BI) is a no brainer, right? Every organization needs it. Yet there are so few organizations that get it right.
This diagram from Forrester Research (via The Dashboard Spy) serves up a clue as to why.
While, it’s purpose is to provide an overview of the BI landscape, it certainly highlights the complexities in the field. Not only that, but it doesn’t even get into the non-technical side of BI – changes in business process, organizational politics, and transparency – which, are the real challenges of BI.
Still, one should not be daunted by the BI challenges ahead. Sure, that’s easy for us to say, we’ve been doing this for years, but I’ll let you in on a few of the secrets to our success:
- Start small, do not try to do it all – It is better to find one or two key subject areas from which to build, then to develop an enterprise data warehouse from the ground up right from the start.
- Use the technology that you already own first – So many organizations already own incredibly useful BI tools. There is no need to make a major technology investment in tools and technologies as a way to kick start your BI initiative. Start with the tools that came with your enterprise software. Your ERP or CRM system may have provided a means for you to extract data. Your database probably has reporting tools built into it. Use these tools first, and grow into others as your use and knowledge grows.
- Focus on outcomes – If you have not been able to gain traction on your BI initiative, it is almost never the fault of your tools. Usually the fault lies in the lack of alignment between the organization’s strategies and the people and processes in place to execute on those strategies. The tone of your BI endeavor should be outcome based, stress transparency, and serve to align the organization tightly around its mission and values.
In fairness, the Forrester document does not purport to highlight all that is involved in BI. It is clearly intended to be more of a technical overview. Nevertheless, I can’t help but feel that there are a whole segment of leaders that might view a document of this nature and do one of two things:
- Go out and spend several hundred thousands (or even millions) of dollars on a new BI toolset.
- Nothing.
Let neither of these options be acceptable and if you follow these three simple principles, you will be better off in the long run.
Data Visualizations: Will the big BI vendors catch up to the niche players?
June 5, 2008 | Leave a Comment
The New York Times has really been stepping up its infographic department in recent years. Take a look at this data visualization showing how different demographics voted in the recent democratic primary contest.
All politics aside, I’m most interested in the fact that although this was more than likely developed by an information or graphic designer, it looks like it could have been produced by a BI tool. Of course a tool is just a tool without the data organized in such a way that makes the tool useful (but that is a subject for another post).
What fascinates me is how far the major BI tool providers are from producing these types of visualizations. It seems to me that they are so focused on the integration issues with all of the consolidation in the industry that there isn’t enough time to add the features that decision makers want – new and exciting ways to communicate information that helps them take action.
If you’re interested in data visualization, I highlighted a few interesting developments in the field in my recent column in Campus Technology Magazine.
It seems to me that that the quality gap between the data visualizations produced by tools and those produced by designers is getting smaller, but it is the niche players in the business that are making this happen. I’d love to be proven wrong though.
Thoughts?
Write a book supported by econometric models in minutes
April 15, 2008 | Leave a Comment
This one goes in the category of ‘it has to be seen to be believed.’ Phillip M. Parker of Websters-Online-Dictionary.org has been experimenting with the use of computers to automate content development. This video shows how he has been able to produce books and reports fully backed by data and econometric models.
Imagine, being able to write your next report, business case, or economic study in less time than it takes for one to read it. Watch the video to see how it is done.
Moving Beyond BI Shadow Systems
February 16, 2008 | Leave a Comment
A recent post on The Data Doghouse, a Business Intelligence blog, explains that data shadow systems are still extremely prevalent across industries and will continue to be so for the foreseeable future. Data shadow systems are groups of spreadsheets and local, customized databases – often built in Microsoft Access or Excel – created by business groups to provide data for their users. An excerpt from the article shows just how prevalent these shadow systems are:
In a survey we conducted in the fall of 2007 we found that the medium number of data shadow systems at an enterprise was 30. According to the survey, data shadow systems are prevalent in all industries, companies of all sizes and throughout various business functions in the enterprise.
The presence of shadow systems is a trend we’ve seen with our own clients in education, government, banking, and insurance. For many of our clients, different shadow systems are providing business analysts with the data they need to answer important business questions, recognize trends, and make informed decisions. Obviously, these shadow systems are providing a valuable service. However, despite these benefits there is a downside to operating shadow systems. Let’s take a look at some of the negative aspects of shadow systems.
First, it is often time consuming to implement and maintain data shadow systems. Data shadow systems are typically cobbled together by business users. Let’s face it, designing these systems is not the strength of business users (this is something that is bettered handled by the IT department). This leads to a design framework that requires lots of labor-intensive maintenance activities and a lack of felxibility and extensibility.
Second and most importantly, these shadow systems do not enforce rigorous data management techniques and principles. For example, consistent business rules may not be used across the organization to clean and codify the same data. The result, data quality and consistency suffer and it is difficult to know how confident decision-makers can be in the numbers being reported.
So the question is, what can be done to move beyond the use of shadow systems in your organization. The best approach is to engage the IT department and use the organization’s existing Business Intelligence (BI) tools to tackle this problem. BI tools should be used to centralize data, implement consistent and documented business rules to clean data, and develop reports that meet the needs of business users and stakeholders.