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Eight Best Practices in Dashboard Design


Radha R would like to thank Derick Jose, chief architect with the business intelligence practice at MindTree Consulting, for contributing this month’s column.


In today’s world where people are inundated with more data than they can process, dashboard design plays a very important role as it is the central conduit through which information flows to the decision-maker.


If we do not present the right metrics in the right form at the right time, the business users can end up making the wrong decisions, impacting the business process.


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Let’s consider some of the questions faced while designing a dashboard:


  • Am I measuring the right key performance indicators (KPIs)? What are the domain specific metrics that are best practices to measure the health of a business process?
  • How actionable is the information I am presenting on the dashboard?
  • Where did I draw the data for the dashboard from? How hygienic is the quality of the dashboard data?
  • Does your dashboard show context to the information? Is the source of information tagged to the dashboard? Can the end user find out when the dashboard information was last refreshed and when the next cycle of refresh is going to be (daily, weekly, monthly)?
  • Are the right analytical constructs used to synthesize insights?
  • Are the insights disseminated to points of action, empowering people to act on them? Do they know all the action options?
  • How actionable are these insights that are disseminated? Are they synthesized?
  • Does your dashboard “action enable” the consumer of the information? For example, can they invoke the mail utility from within the portal dashboard to send a mail request or trigger a transaction to reset the reorder point in a supply chain scenario?
  • Does your dashboard give the provision of adding end user comments to the information presented?
  • Can your dashboard guide a new user through a prescribed path to do analysis in addition to exposing them to cubes to explore data at will?

All of these are critical elements to be addressed as a part of the dashboard strategy.


Benchmark Key Performance Indicators with Industry Standards


The following section distills some of the dashboard related findings I have experienced while executing customer projects.


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Many customers, at some point in time, want to know if the metrics they are measuring are the right metrics to monitor. Many at times customers have found that the metrics they are tracking are not the right ones to track. Doing a gap assessment with industry benchmarks aligns you with industry best practices.


For example, a leading glass manufacturer wanted to audit the metrics, which they currently use to monitor the health of their sales, finance, manufacturing and sourcing process. An “as is” map of all the KPIs and the business processes they monitor across sales, manufacturing, finance and sourcing was done. These metrics were then benchmarked against the SCOR supply chain metrics framework. SCOR is a business process reference model created by an industry consortium. The resulting gaps in KPIs were isolated and a strategy was outlined to capture and source those metrics. The SCOR metrics framework broadly covers three areas:


  • KPIs related to business process in sourcing of raw materials,
  • KPIs related to manufacturing and
  • KPIs related to distribution processes.

Wrap the Dashboard Metrics with Contextual Metadata


Often when a report or a visual dashboard/scorecard is presented to the business users, many questions remain unanswered. The following are some examples:


  • Where did you source this data from?
  • While loading the data warehouse what percentage of the data got rejected/encountered data quality problems?
  • Is the dashboard presenting “fresh” information or “stale” information?
  • When was the data warehouse last refreshed?
  • When is it going to be refreshed next?
  • Were any high value transactions that would skew the overall trends rejected as a part of the loading process?

As a part of MindTree’s reusable business intelligence (BI)components (RUBIC) framework for quality enhanced and accelerated BI implementations, there are multiple good practice contextual metadata elements to consider:


  • Which are the sources of the data? This question helps understand if it is the authoritative source.
  • Who owns this data? The answer helps determine data quality.
  • When was it last refreshed? This question highlights actionability of the information.
  • When is it going to be refreshed next? This helps understand the latency of information.
  • What percentage of data got rejected? This helps understand if there are “does not feel right” data skews.

{{eval var=$ImageLine assign="Image3"}}{{capture assign=zk_imgcap_caption_3}}Figure 3: Example of RUBIC Information{{/capture}}{{$Image3|replace:'#Name#':"040908_radha_fig3_N.gif"|replace:'#Width#':"auto"|replace:'#Height#':"auto"|replace:'#Orientation#':"null"|replace:'#CaptionLine#':""|replace:'#/CaptionLine#':""|replace:'#Caption#':$zk_imgcap_caption_3|replace:'#CaptionWidth#':""|replace:'#CaptionHeight#':""|replace:'#CaptionOrientation#':""|replace:'#ImageCredit#':""|replace:'#AltText#':""}}


Validate the Dashboard Design by a Usability Specialist


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