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iDashboards for Data-Driven Decisions:
Business Intelligence versus Visual Intelligence
An iDashboards Whitepaper
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The Data-Driven Decision Difference
The difference between making good decisions and
making bad decisions is the quality of information used to
make those decisions. In many businesses today, decisions
are being made based on old or incomplete data because
there is no easy way to extrapolate data and communicate
findings in a timely manner. In other businesses, the sheer
volume of data is overwhelming, and important information
is sometimes buried in lengthy reports or overlooked due
to the vast amounts of data available. Both of these
scenarios, and dozens of others, make it difficult to glean
important information, collate it into an understandable
format and report it in a way that allows good decisions
to be made quickly.
What Dashboards Do for Data
Dashboards help aggregate data from various sources,
display that data visually, communicate the message behind
the numbers and encourage action based on the information
presented. The amount of data generated today can be
overwhelming. While business intelligence (BI) is a field that
tries to make sense of the data, there is often a breakdown
when that intelligence cannot be communicated in an
understandable manner or when data is stored in systems
that fail to interact with one another.
How do we get real-time data to stakeholders in an
understandable format? How do we dig through the data to
find outliers that need to be managed? How do we develop
key performance indicators (KPIs) that are common across
the organization and communicated in a way that can be
understood by all — even when data is compartmentalized?
In other words, how do we make visual intelligence (VI) out of
business intelligence? A visual dashboard takes information
out of rows and columns and puts it into charts and graphs
that people can easily understand.
The Importance of Visualizing Data
Anscombe’s Research
Frank Anscombe, the founding chair of the statistics
department at Yale University, illustrated the importance of
visualizing data prior to analysis based on research done in
the 1970s. Using four similar data sets, Anscombe developed
what is commonly known today as Anscombe’s quartet,
showing the effect of outliers within the data set using a
graphical representation of that data. All four charts have the
same linear progression but graph very differently.
Managing Data with Dashboards
A visual dashboard assists viewers in finding and managing
disruptive outliers that can hurt (or sometimes help) a
business. Statistics prove that many managers spend a great
deal of time managing easily sustainable, normal processes
and behaviors within a business and often miss outliers
which fall outside the norm. However, as Anscombe’s quartet
demonstrates, even a few significant outliers can have a
profound impact on a business.
Visual Intelligence Examples
Dashboards allow users to recognize outliers early and deal
with them. For example, one client built a dashboard that
showed overnight shipments and noticed immediately that
the number of red alert shipments consistently rose from one
or two on Monday to dozens on Friday week after week. By
simply asking a few questions, the plant manager determined
that most just in time inventory for their largest client was off
the shelf by Thursday or Friday, and because of service-level
agreements (SLA) with that client, products were being taken
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right from production at the end of the week, then packed
and shipped overnight to avoid penalties. When illustrated
using a visual dashboard, the data on Thursday and Friday for
overnight shipments appeared as a consistent outlier and with
minor changes to production, the client immediately started
saving $10,000 a week in overnight shipments.
Another client was using multiple data sources with no
commonality across the business. Many people reported
using Excel spreadsheets, and each department had its
own database and enterprise resource planning (ERP)
software. The client had no way to measure or monitor their
communal KPIs across the organization. By implementing a
visual dashboard, the client was able to integrate their ERP
databases and spreadsheets into a single visual format. They
now see regular KPI trends and conduct what-if analyses
with those KPIs, ultimately making more informed executive
decisions because the quality of information is higher.
Another client has pulled data from their financial software,
human resources, marketing (Google Analytics), CRM
(Salesforce.com) and other ERP databases, none of which
are tied together. By adding dashboards, they have a
complete picture of their business for the first time and
are updated automatically with real-time data.
Better Information, Better Decisions
Is it possible to communicate these results in a multi-page
Excel report of rows and columns? Of course it is; however, a
visual dashboard provides an intuitive tool that spots trends,
provides analyses and calls for action at-a-glance. Best-in-class
dashboards are real-time, pull from multiple data sources and
provide complete mobility so decisions can be made on the fly.
Explore your BI resources, build a dashboard and discover
for yourself how to turn the BI into VI. By taking information
out of rows and columns and putting it into a visual format,
you will be able to spot trends and make analyses in an easy-
to-use format in real-time. As one client recently said, “Our
dashboards provide information in a format that clearly calls
for action.” By color coding results, defining trends or building
charts that show the outliers, you will make better decisions
based on better information that will result in cost savings,
clear communication and the achievement of standardized
KPIs across the organization. Now that is intelligence.
Learn more at iDashboards.com.
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Business Intelligence vs Visual Intelligence
The difference between making good decisions and making bad decisions is the quality of information used to make those decisions. In many businesses today, decisions are being made based on old or incomplete data because there is no easy way to extrapolate data and communicate findings in a timely manner.