Case Studies: Enterprise BI vs Self-Service Analytics Tools: Real Life Considerations When Choosing a BI Tool

Real Life Considerations When Choosing a
BI Tool
CASE STUDIES: ENTERPRISE BI VS SELF-SERVICE
ANALYTICS TOOLS

• INTRODUCTION
• STATE OF THE MARKET
• DEFINITIONS & EXAMPLES
• MAJOR FEATURE COMPARISONS
• FOUR CASE STUDIES
• ADDITIONAL RESOURCES
AGENDA
2Copyright 2016 Senturus, Inc. All Rights Reserved.

PRESENTERS
Copyright 2016 Senturus, Inc. All Rights Reserved.
Albert Valdez
Vice President of Learning Solutions
Senturus, Inc.
John Peterson
CEO and Co-Founder
Senturus, Inc.
3

MORE FREE RESOURCES:
HTTP://WWW.SENTURUS.COM/RESOURCES/
Copyright 2016 Senturus, Inc. All Rights Reserved. 4

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Enterprise BI vs Self-Service Analytics Tools
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platforms-vs-self-service-analytics-tools/
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BUSINESS ANALYTICS:
ARCHITECTED TO SCALE
SENTURUS OVERVIEW

• Dashboards, Reporting &
Visualizations
• Data Preparation & Data
Warehouses
• Self-Service Business Intelligence
(BI)
• Big Data & Advanced Analytics
• Enterprise Planning Systems
Focused on Business Analytics

Business
Requirements
Analysis
Ready
Data
DASHBOARDS
VISUALIZATIONS
AND REPORTING
DECISIONS
&ACTIONS
We Bridge the Gap Between Data and Decision
Making

900+ Clients, 2000+ Projects, 16+ Years

STATE OF THE MARKET
ENTERPRISE BUSINESS INTELLIGENCE VS.
SELF-SERVICE ANALYTICS

STATE OF THE MARKET
• According to Gartner (February 2016):
– “The business intelligence (BI) and analytics market has passed
a tipping point as it shifts away from IT-centric, reporting-
based platforms and toward modern BI and analytics
platforms that enable smarter analytics and greater agility.”
• What This Really Means:
– Analysts are further subdividing the estimated $16.9 billion
market in order to provide “Apples to Apples” comparisons
– You will see platforms scored against these different groups of
criteria in places like the Gartner Magic Quadrant
– Watch out for the loaded terms like “modern” and “agility”
and use your best judgement to determine:
– Which is the RIGHT tool for the job

NUMBER OF TOOLS USED
• Your Responses (at registration):
– 234 unique companies responded
– 35% using more than one solution
– 10% using more than two solutions
– Average is 1.5
• Industry Survey (from https://www.trustradius.com/articles/business-intelligence-survey-results):

OUR DEFINITIONS
• Enterprise Business Intelligence:
– Sometimes referred to as the “traditional” approach,
Enterprise BI is centrally managed, typically by a shared-
services IT department
– Infrastructure and data models are centralized, end-user
access is typically via zero-footprint, browser-based tools
– Data sources are enterprise-class and monolithic, such as data
warehouses and data marts which require long lead times and
IT engagement to adjust to changes in business requirements
– Data is governed, secured, and tested for accuracy
– Supports large-scale deployments to 1,000s of users, high-
volume, automated delivery of reports and dashboards, and
auditable, trusted, and consistent definitions of data

OUR DEFINITIONS (CONTINUED)
• Self-Service Analytics:
– Sometimes referred to as “modern,” Self-Service Analytics
solutions are typically managed at the department level, and
are considered to be more “agile”
– Agility comes from proximity to, or ownership by, the business
– Tools can be deployed on individual desktops and don’t
require engagement with shared services or IT
– Data sources are more varied, including desktop-based files
such as spreadsheets, enterprise source as well as cloud-based
and semi-structured sources
– Data acquisition and blending are managed by the business
users, resulting in quicker time to decision, but potentially
sacrificing data governance, lineage, and security

• First of all, the software world, as always, is in flux –
vendors are often playing across multiple segments – we
are not trying to nail down any one platform or vendor
and “put them in a box”
• We want to highlight the fact that certain tools are
stronger in certain areas
– Most tools are “purpose-built” for certain benefits
– Once you understand your business use case, you can
prioritize those benefits that matter to you and determine
the right approach
• There will always be experts who can make any tool do
anything, for this presentation, we are speaking about
typical mainstream users
A FEW CAVEATS

• Most organizations have one or more tools from each of
these broad categories available to them, so the
question:
– Which tool is right, and why?
• There are many different types of reporting and
analytics in all organizations, examples include:
– Standardized reports (incl. operational)
– Dashboards & scorecards (incl. KPI’s)
– Components of other docs (ex. MS PowerPoint graphics)
– Ad-hoc analyses & queries (often one-off)
– Advanced visualizations
– Predictive analytics
• No one tool fits all uses perfectly
IN OUR EXPERIENCE (ASSUMPTIONS)

• Typical business user
– We know that expert users can make any tool do what
they want, but at what cost?
– Educating the user base is crucial
• There is value in each approach, bottom line, a more
analytical (data-driven) approach to decision-making is
always better!
– 10:1 ROI is a no-brainer, so congratulations, having more
tools at your fingertips is a GOOD thing
– Identify which of the following key factors matter in your
analysis, pick up your tool, and get to work
• Investigate whether or not a given tool can do the job—
you might be surprised
OUR PERSPECTIVE

THE TOOL MATTERS…

BUT THE JOB MATTERS MORE

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ENTERPRISE BUSINESS INTELLIGENCE PLATFORMS
VS SELF-SERVICE ANALYTICS TOOLS
DEFINITIONS, EXAMPLES, SIMILARITIES
AND DIFFERENCES

ENTERPRISE BUSINESS INTELLIGENCE ARCHITECTURE

ENTERPRISE BUSINESS INTELLIGENCE EXAMPLES

SELF-SERVICE ARCHITECTURE
BYOS
IAAS

SELF-SERVICE ANALYTICS EXAMPLES

FEATURE COMPARISONS
Enterprise BI Self-Service Analytics
Supports enterprise-scale data sources including
OLAP/MDX sources
Supports most relational sources, but is
optimized for local, flat-file data sources such as
Excel or CSV files
Based on validated models of the business May require data modeling, data preparation
Models are rigid, take long time to change Rapid prototyping, effective for limited number
of data objects (sources or tables)
Very strong with operational or transactional
reporting such as detailed lists with complex
calculations (Tabular Reporting)
Ideal for dashboard-style reports with high
interactivity (inline filtering/prompts) and highly
visual/graphical
Based on widely-accepted SQL or MDX Standards Limited to vendor-specific query model
Enterprise-scale automation (scheduling) and
delivery in any format (.XLS, .CSV, .PDF, .HTML)
and report bursting
Requires scripting to automate updates to
server-based content
Data-driven delivery and alerting and Event
Lifecycle Management
Limited automation/delivery features

FEATURE COMPARISONS PART2
Billion-row, multi-terabyte database support May require data extraction to scale back sources
to support desktop processing
Complex user interfaces Intuitive user experience for first-time business
users
Dashboarding approach is based on existing
report artifacts (reliant upon professional
authors)
Analysts create dashboards on the fly,
unconstrained by the data models or other
authors to create content
MS Office integration points can be strong, direct Not as well-integrated, stand-alone solutions
Wide data sets (100s of columns) supported Local (desktop) processing may limit ability to
handle wide or long data sets
Geospatial (mapping) capabilities may be limited Strong with geospatial visualizations
Access to database functions for complex
calculations
Limited to vendor-supplied functions

FEATURE COMPARISONS PART3
Mobile delivery of stand-alone reports Mobile access requires connectivity to server-
based content
Designed to leverage existing enterprise
architecture such as data warehouse,
authentication providers, and web servers
Designed for desktop-based analysis, may or may
not integrate smoothly with existing
infrastructure or other IT assets
Flexibility to define various relative time periods
in the same report/query (may require complex
calculations)
Lack of query model control limits ability to
report on multiple time grains
Robust metadata layer can support unlimited
number of data sources
Designed for small number of data sources
(remember each database TABLE is treated as a
data source)
Central metadata can store business logic which
can be leveraged consistently across all reports
Each individual user is responsible for managing
business logic, potentially resulting in
inconsistent definitions across users
Purpose-built for operational, pixel-perfect
reporting, data discovery features typically an
add-on or afterthought and may not be well
integrated
Purpose-built for data visualization and data
discovery, encouraging users to ask questions

• The traditional model is based on highly-governed,
centralized data sources, models are rigid and not
designed to be changed in real-time
• There always have been, and always will be,
significant benefits of this model:
– “Single version of the truth”
– Data governance
– Crucial business rules and logic are centrally managed,
providing a single version of the truth
– Enterprise-scale, central administration for security
– Strengths in operational (managed) reporting
• But what happens when…
PROS & CONS

• You need to analyze data that is not
available in the centralized data
source?
• You need to perform self-service
analytics in real-time, without
assistance from IT?
• Self-Service analytics tools
complement the enterprise model
with one that delivers information
to decision makers much more
quickly
PROS & CONS (CONTINUED)

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ENTERPRISE BI PLATFORMS VS
SELF-SERVICE ANALYTICS TOOLS
THE RIGHT TOOL FOR THE JOB USE CASES*
*Use cases are generic, but are based on real-life projects. No
client-specific references should be implied.

USE CASE #1 - LARGE HEALTHCARE ORGANIZATION

Factors
• “Single source of the truth” (for all departments and all
levels of the org) is most important factor
• Data sources are centralized, highly structured and
governed
• Business requirements and definitions well defined and
locked by 3-5 year plan
• Metrics and data is broadly applicable across the entire
organization (crosses the departmental boundary)
• Group wishes to leverage existing enterprise tool
• Security, controlled distribution, and reusable report
widgets are critical
USE CASE #1– TRADITIONAL BI

USE CASE #2 – ONLINE E-TAILER

USE CASE #2– SELF-SERVICE ANALYTICS

Factors
• Data sources are varied; not all sources in scope
necessarily relevant outside of this particular group
• Time to decision is critical
• Business requirements are fluid
• Group doesn’t have access to enterprise tool
• Modern visualizations showing performance across sets of
data
USE CASE #2– SELF-SERVICE ANALYTICS

USE CASE #3 – CAPITAL EQUIPMENT MANUFACTURER

USE CASE #3 – HYBRID SOLUTION USING
SENTURUS ENTERPRISE BI CONNECTOR
Tableau users can connect directly to the BI modeling
layer, giving them instant access to high quality data

Factors
• Highly-governed and trusted data is crucial
• Flexibility to blend enterprise data with additional data
that is only specific to certain requirements
• Existing enterprise BI implementation with rich metadata
• But there is a need for more advanced visualization, end-
user customization, and ad-hoc analyses than available in
the current tool
• User base and presentation requirements well-suited for
purpose-built, self-service visualization tools
USE CASE #3 – HYBRID SOLUTION USING
SENTURUS ENTERPRISE BI CONNECTOR

USE CASE #4 OMNI-CHANNEL CLOTHING MANUFACTURER/RETAILER

Factors
• Lots of complex data sources needing to be blended
• Need to significantly transform and align data
• Need to “enrich” all reports and analyses with additional
info: attributes, rollups, hierarchies, plans/targets, etc
• Data useful to all parts of the company (crosses the
departmental boundary)
• Data must be timely and “single source of truth”
• Very broad set of reports and analysis types are necessary
USE CASE #4 –MULTIPLE TOOLS & DATA WAREHOUSE
MAIN REQUIREMENT: WELL PREPARED DATA

TAKEAWAYS
• Determine what the jobs are
• Then, select the right tool
• But make sure to properly prepare the data

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Copyright 2016 by Senturus, Inc.
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