BI and Dashboarding Best Practices

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BI and Dashboarding Best Practices
Dorien Gardner, Solution Engineer

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Abstract
 This session focuses on Business Intelligence Best Practices with an
emphasis on dashboard design and performance techniques. Learn about
the different types of users and consumers of BI and how they impact your
development strategy.
©2015 Rocket Software, Inc. All Rights Reserved.

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Agenda
Types of Business Intelligence
General best practices for BI
Dashboards
Performance techniques
Reporting
CorVu NG with MV best practices
CorVu NG vs. Discover

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Types of Business Intelligence
 Reporting
 Analysis
• Spreadsheet analysis
• Ad-hoc
• Visualization tools
• Exploration
 Monitoring
• Dashboards
• Key Performance Indicators (KPIs)
• Business performance management
 Predictive
• Data mining
• Predictive modeling
© 2015 Rocket Software, Inc. All Rights Reserved.

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Types of Business Intelligence Users
 Executive team
• Wants quick pulse of key business drivers
• Dashboards, score cards, EPM, BPM
 Data analyst
• Explores and finds new insights
• Creates insight for people within the organization
• Dashboards are not typically their preferred consumption
 Business user
• They make decisions
• Don't want to build from scratch
• They want the critical information to make them successful
• They want the insights to be actionable

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Business Intelligence Best Practices
 Insure data is clean and accurate
• Obviously garbage in = garbage out
• Data cleansing is imperative (particularly for any predictive analysis)
• You cannot make accurate business decisions based on inaccurate data
 Work with customers to determine best KPIs/Metrics
 Dictionary descriptions should be user friendly and meaningful
• Use clear naming conventions for descriptions that have obvious meaning
• Centralize the metadata definitions for consistency
• Centralize calculations as much as possible (one version of the truth)
• Create and use alternate dictionaries

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 Leverage the secondary (Hot Backup) for BI dashboards
• Create aggregate tables for the various hierarchical levels
• Initial KPI view should be highest possible level
• Enough information to determine next course of analysis
 Provide a guided path analysis
• Allow user to drill to each next level of detail based on the information
• Provide comparative analysis to determine trend vs. anomaly
• Basis for root-cause analysis
• Critical for a performant dashboard

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 Leverage visual alerts
• Provide focus on the things that are most impacting business
• Manage by exception (best/worst based on a trend)
 High and low latency data requirements
• Acceptable age of data needed to make timely decisions
• What is real time?
• Historical trend analysis
 View BI from both top down AND bottom up
 Utilize professional services for Business Intelligence consulting

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Why are they so valuable?
Easier than opening dozens of reports
Readily provides key information
Improves productivity and decision making
Focuses the attention on the target and goals
Long lasting

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Types of Dashboards
Strategic
• Executive
• Measure high level performance
• More KPIs across functional areas
• Focused on trend analysis and predictive
• Data latency is not critical
• Long decision horizon

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Types of Dashboards
Analytical
• Help understand the who, what, how, why
• Highly interactive
• Drill to root cause
• Data latency requirements are mixed
• Leverage alerts to help drive analytic navigation
• Mid-decision horizon

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Types of Dashboards
Operational
• For monitoring and lower level decision making
• High use of alerts
• Data latency is low (regular live updates)
• Short decision horizon

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Planning your Dashboard
 Identify your consumer type(s)
 What data do they need?
• KPI, metrics, trend, target, variance
• Is it available and from where?
• Trust of the data (one version of the truth)
 What security needs to be applied?

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 Who are you building the dashboard for?
• Sales
• Marketing
• Supply Chain
• Finance…
 Draw out and plan the general layout, what and where
 Show draft and get feedback BEFORE building

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 Design the dashboard or TAB for a specific Business Purpose
 Typically one subject area per tab (except scorecard)
 Do not overwhelm the users or boil the ocean

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Layout Considerations
 How do people read? (left to right; top to bottom)
 Allocate the size of objects based on their importance
 Leverage report links for related reports
• Too many objects on a dashboard will have a performance impact
 Elements need to be well labeled
• There should be no question what a particular chart or report ID is for
 Avoid scrolling as much as possible

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Layout Considerations
 Consistency of color palette
 Naming conventions (i.e., sales vs revenue)
 Having viable descriptions
 One version of truth for calculations
 Direct the user to what is important
 Use conditional formatting (colors and shapes)
 Use annotations and reference lines

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 Minimize the number of CorVu analytic queries
• Utilize these mainly when merging from heterogeneous data sources
 Create and leverage dictionary correlatives to pull data from multiple
related tables
• TRANS(‘CUSTOMERS’,CustomerID, ‘Region’, ‘X’)
• f;8(tterritories;x;;2);(tsalesreps;x;;1)
• A and S types in UniVerse are interpreted and not compiled so this may be less
efficient than utilizing CorVu Analytic Queries
 When data latency is not as important, leverage scheduled cached
queries
CorVu Specific Tips

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 Utilize native pre- and post-processing features
• SELECT ORDERS WITH INVOICE.DATE # ""
• select trx.mst with Invoice.Year NE "" AND with Sales.Rep NE ""
• GET-LIST ClosedOrders
 For complex data access and rules, consider creating a Virtual Data
Source via a web service call
• Reuse existing logic from your basic programs for data retrieval
• Web services could also be used for actionable BI and database write-backs
CorVu Specific Tips

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KPIs at Highest Aggregation
KPIs Mid-Grain Aggregations
Lowest Grain i.e., Transactional Data
Use of Aggregations
PassContext
Dimensional examples
• KPI/Metrics by Year,
QTR, Country, Region,
Product Line
• KPI/Metrics by Year-
QTR-Month, Country-
Region-State, Line-
Category
Define appropriate Indexes

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Aggregate Tables

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Use Subscriber for BI and Reporting
Business intelligence and reporting
uses 10X resources than average
application
Move reporting and analysis to
Subscriber/Hot Backup
Save valuable computing resources
on production

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BI Account
MV Database Server
MV DB
MV HADR/HotBackup/Report Server
MV DB
HADR/HOT Backup/Reporting Server
 Replicate to Secondary Server
Triggers or
Automated
Batch
Columnar
Database
 Leverage Triggers or scheduled process
to prepare and aggregate data
 Aggregate data into another MV
Account, add indexes, stratify files etc.
Create BI specific dictionaries
 Data Options
 Load data into an OLAP data Cube
 Load data into a Columnar data-store

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 Query to Visual Report
wizard
 Highly formatted reports
• Cover sheets, summary
pages and
report annexes
 Report scheduling and email
 Multiple data sources
 Output graphical reports in
Excel, HTML or PDF
 Deploy to web users
CorVu NG Report Summary

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Reporting
Scheduling and distribution
Report bursting

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CorVu vs. Discover
CorVu Discover
Developer/IT focused End user focused
High value add for partners Lower value add
IT/partner pre-build content and deploy Can have base templates/then user
builds
Pixel perfect control Limited layout control
Monitoring/dashboard/reporting Analysis/exploration focused
Pre-defined navigation (by developer) Flexible drill/navigation
Embeddable BI (operational) N/A
Can embed text entry fields Strong collaboration (live chat…)

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Additional Resources
 https://www.rocketsoftware.com/solutions/bi-and-analytics
 MVU@rocketsoftware.com

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Summary
 Types of Business Intelligence
 Some key Business Intelligence best practices
 Dashboards types and design principals
 Performance considerations and best practices
 MultiValue specific best practices with CorVu NG
 CorVu NG vs. Discover

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Disclaimer
THE INFORMATION CONTAINED IN THIS PRESENTATION IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY.
WHILE EFFORTS WERE MADE TO VERIFY THE COMPLETENESS AND ACCURACY OF THE INFORMATION CONTAINED
IN THIS PRESENTATION, IT IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED.
IN ADDITION, THIS INFORMATION IS BASED ON ROCKET SOFTWARE’S CURRENT PRODUCT PLANS AND STRATEGY,
WHICH ARE SUBJECT TO CHANGE BY ROCKET SOFTWAREWITHOUT NOTICE.
ROCKET SOFTWARE SHALL NOT BE RESPONSIBLE FOR ANY DAMAGES ARISING OUT OF THE USE OF, OR
OTHERWISE RELATED TO, THIS PRESENTATION OR ANY OTHER DOCUMENTATION.
NOTHING CONTAINED IN THIS PRESENTATION IS INTENDED TO, OR SHALL HAVE THE EFFECT OF:
• CREATING ANY WARRANTY OR REPRESENTATION FROM ROCKET SOFTWARE(OR ITS AFFILIATES OR ITS OR
THEIR SUPPLIERS AND/OR LICENSORS); OR
• ALTERING THE TERMS AND CONDITIONS OF THE APPLICABLE LICENSE AGREEMENT GOVERNING THE USE OF
ROCKET SOFTWARE.

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Trademarks and Acknowledgements
The trademarks and service marks identified in the following list are the exclusive properties of Rocket Software,
Inc. and its subsidiaries (collectively, “Rocket Software”). These marks are registered with the U.S. Patent and
Trademark Office, and may be registered or pending registration in other countries. Not all trademarks owned by
Rocket Software are listed. The absence of a mark from this page neither constitutes a waiver of any intellectual
property rights that Rocket Software has established in its marks nor means that Rocket Software is not owner of
any such marks.
Aldon, CorVu, Dynamic Connect, D3, FlashConnect, Pick, mvBase, MvEnterprise, NetCure,
Rocket, SystemBuilder, U2, U2 Web Development Environment, UniData, UniVerse, and
wIntegrate
Other company, product, and service names mentioned herein may be trademarks or service marks of
others.

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