Join Howard Dresner, founder and chief research officer of Dresner Advisory Services, as he shares his research on the analytics market from surveys with over 5,000 organizations. You’ll learn about the key drivers to business success with analytics and how predictive analytics can drive revenue.

1. The Most Effective Analytics
Trends of 2019
Howard Dresner Brittany Shear
With: Moderated by:
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1

2. Developer Grade Embedded Analytics
Leverages existing
investment and
infrastructure
Ultimate Control
Over the analytics
experience
Exclusively focused
on analytic
applications

3. 3
Click on the Questions panel to
interact with the presenters

4. About Howard Dresner
Howard Dresner is one of the foremost thought leaders in business intelligence and performance management, having
coined the term Business Intelligence in 1989. He has published two books on the subject, The Performance
Management Revolution: Business Results through Insight and Action (John Wiley & Sons, Nov. 2007) and Profiles in
Performance: Business Intelligence Journeys and the Roadmap for Change (John Wiley & Sons, Nov. 2009). He
lectures at forums around the world and is often cited by the business and trade press. Prior to Dresner Advisory
Services, Howard served as chief strategy officer at Hyperion Solutions and was a research fellow at Gartner, where
he led its business intelligence research practice for 13 years.
About Brittany Shear
Brittany went to Emmanuel College, where she majored in Writing, Editing, and Publishing and minored in Marketing,
Psychology, and Communications. She now works with Aggregage as an editor and webinar host on sites including CTO
Universe, Product Management Today, and Connected Health Pulse.
The Most Effective Analytics Trends of 2019
4

5. • Founded in 2007 to provide an alternative view of the market − driven by
“voice of the customer”
• Generated over 1,600 pages of independent, objective, primary research −
covering the landscape of BI / analytics, information management,
performance mgmt., etc.
• Annual Real Business Intelligence thought leadership event on the campus of
MIT in Cambridge, Massachusetts (May 5-6, 2020)
• More information at www.dresneradvisory.com
5
About Dresner Advisory Services
Copyright 2019 Dresner Advisory Services, LLC

6. • Demographics
• Business Intelligence Trends
• Data Science and Machine Learning
• Embedded BI / Analytics
• Conclusions
Agenda
6
Copyright 2019 Dresner Advisory Services, LLC

7. Research Demographics (1 of 2)
7
63.0%
25.7%
7.7%
3.6%
0%
10%
20%
30%
40%
50%
60%
70%
North America Europe, Middle
East and Africa
Asia Pacific Latin America
Geographies Represented
23.6% 22.8%
18.9%
8.8%
5.2% 5.1%
3.7%
2.1% 1.9%
7.3%
0%
5%
10%
15%
20%
25%
Functions Represented
Copyright 2019 Dresner Advisory Services, LLC

8. Research Demographics (2 of 2)
8
16%
9%
7% 7%
6% 6% 6% 5%
3% 3% 3% 2% 2% 2% 2% 2% 2% 1% 1% 1% 1% 1% 1%
5%
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
Vertical Industries Represented
30.6%
33.7%
8.3% 8.6%
5.4%
11.7%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Organization Sizes Represented
Copyright 2019 Dresner Advisory Services, LLC

9. Business intelligence (BI) is “knowledge gained through
the access and analysis of business information.
Howard Dresner, The Performance Management Revolution: Business Results Through Insight and Action (John Wiley & Sons, 2007)
9
Business Intelligence / Analytics Trends
Copyright 2019 Dresner Advisory Services, LLC

10. • On average, how often do you think people use
data in decision-making?
• All of the time
• Most of the time
• Some of the time
• Infrequently
• Never
10
Question

11. 11
The Majority Use Data in Decision-Making
0%
10%
20%
30%
40%
50%
All of the time Most of the Time Some of the time Infrequently Never
Use of Data in Decision-Making
Copyright 2019 Dresner Advisory Services, LLC

12. BI / Analytics Driven by Operations and
Executive Management
12
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Manufacturing
Human Resources
Research and Development (R&D)
Competency Center/Center of Excellence
Strategic Planning Function
Marketing
Information Technology (IT)
Sales
Finance
Executive Management
Operations
Functions Driving Business Intelligence
Always Often Sometimes Rarely Never
Copyright 2019 Dresner Advisory Services, LLC

13. More Users Have Access Than Ever Before
13
0%
5%
10%
15%
20%
25%
30%
35%
40%
Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more
Penetration of Business Intelligence Solutions
2015-2019
2015 2019
Copyright 2019 Dresner Advisory Services, LLC

14. 14
Organizations are Bullish on BI…
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
In 36 Months
In 24 Months
In 12 Months
Today
Expansion Plans for Business Intelligence through 2022
Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more
Copyright 2019 Dresner Advisory Services, LLC

15. 15
…and Budgets Are Increasing
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
2017
2018
2019
Budget Plans for Business Intelligence 2017-2019
Increasing over last year Staying the same as last year Decreasing over last year
Copyright 2019 Dresner Advisory Services, LLC

16. 16
Investments in BI Pay for Themselves
Very high,
transformative 24%
or higher
21%
High, significant
achievement 12%
27%
Good, clear return 6%
25%
Fair, paid for itself 3%
8%
Flat 0%
5%
Negative, but contained -3%
1%
Costly for the department -6%
1%
Severely Negative -12%
1% Extremely Negative -24% or
worse%
1%
Don't know
10%
Return on Investment for Business Intelligence
Copyright 2019 Dresner Advisory Services, LLC

17. Achievements versus Objectives
17
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Better Decision-making Growth in Revenues Improved Operational
Efficiency / Cost
Savings
Increased Competitive
Advantage
Enhanced Customer
Service
Compliance / Risk
Management
BI Achievements versus Objectives
Objectives
Achievement
Copyright 2019 Dresner Advisory Services, LLC

18. Information Used is Correlated to Success
18
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Completely
Successful
Somewhat
Successful
Unsuccessful &
Somewhat
Unsuccessful
Completely
Successful
Somewhat
Successful
Unsuccessful &
Somewhat
Unsuccessful
Completely
Successful
Somewhat
Successful
Unsuccessful &
Somewhat
Unsuccessful
Completely
Successful
Somewhat
Successful
Unsuccessful &
Somewhat
Unsuccessful
"Closed loop" - Information is shared, teams work to process
it and act in a timely fashion. No formal boundaries
Ad hoc (informal) action on insights across functions Uncoordinated/ self-serving action (sometimes at the
expense of others)
Insights are under-leveraged
Business Intelligence and Action on Insight by Success with BI
All of the Time Most of the Time Some of the Time Rarely
Copyright 2019 Dresner Advisory Services, LLC

19. High Usage Leads to High Success Rates
19
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Under 10% 11 - 20% 21 - 40% 41 - 60% 61 - 80% 81% or more
Penetration of Business Intelligence Solutions Today by BI Success
Completely Successful Somewhat Successful Unsuccessful & Somewhat Unsuccessful
Copyright 2019 Dresner Advisory Services, LLC

20. How Organizations Monitor Success
20
81.0%
50.8%
41.6% 41.2%
34.9%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
User feedback/satisfaction Customer
feedback/satisfaction
System/application activity Return on investment (ROI)
model
Numbers of deployed users
Measures of Success with Business Intelligence
Copyright 2019 Dresner Advisory Services, LLC

21. • What are the top obstacles to success
organizations face related to BI / analytics?
• Industry instability
• Internal politics
• Software costs
• Data and analytics ecosystem fragmentation
• Services / talent availability
21
Question

22. 22
Talent is the Top Obstacle
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Industry instability
Internal politics
Software costs
Data and analytics ecosystem fragmentation
Services/talent availability
Top BI Challenges
Urgent/Immediate Very Important/Near Term Important/Longer Term Not an Issue
Copyright 2019 Dresner Advisory Services, LLC

23. Data Science and Machine Learning includes statistics,
modeling, machine learning, and data mining to analyze facts to
make predictions about future or otherwise unknown events.
23
Data Science and Machine Learning (ML)
Copyright 2019 Dresner Advisory Services, LLC

24. Data Science and ML Reach an All-time High on Importance
24
1
1.5
2
2.5
3
3.5
4
4.5
5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2014 2015 2016 2017 2018 2019
Importance of Data Science and Machine Learning 2014-2019
Critical Very Important Important Somewhat Important Not Important Weighted Mean
Copyright 2019 Dresner Advisory Services, LLC

25. • On average what percentage of organizations have
deployed data science and machine learning?
• Less than 20%
• 20%
• 35%
• 50%
• More than 50%
25
Question

26. 26
Data Science / ML Deployments Are Nascent But Growing
24% 23%
28% 33%
1.00
1.50
2.00
2.50
3.00
3.50
4.00
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2016 2017 2018 2019
Deployment of Data Science and Machine Learning 2016-2019
Yes, we use data science and machine learning today We are currently evaluating data science and machine learning software
We may use data science and machine learning in the future No, we have no plans to use data science and machine learning at all
Weighted Mean
Copyright 2019 Dresner Advisory Services, LLC

27. Organizations are More Successful with Data Science & ML
27
1
1.5
2
2.5
3
3.5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Completely Successful Somewhat Successful Somewhat Unsuccessful & Unsuccessful
Deployment of Data Science & Machine Learning by Success with BI
Yes, we use data science and machine learning today
We are currently evaluating data science and machine learning software
We may use data science and machine learning in the future
No, we have no plans to use data science and machine learning at all
Copyright 2019 Dresner Advisory Services, LLC

28. Deployment of Data Science and Machine Learning is
Correlated with Larger Budgets
28
1
1.5
2
2.5
3
3.5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Increasing over last year Staying the same as last year Decreasing over last year
Deployment of Data Science and Machine Learning by BI Budget Plans
Yes, we use data science and machine learning today
We are currently evaluating data science and machine learning software
We may use data science and machine learning in the future
No, we have no plans to use data science and machine learning at all
Copyright 2019 Dresner Advisory Services, LLC

29. Marketing and Sales Place a Premium on DS & ML
29
1
1.5
2
2.5
3
3.5
4
4.5
5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Marketing & Sales Business Intelligence
Competency Center
Research and
Development (R&D)
Executive Management Information Technology
(IT)
Finance
Importance of Data Science and Machine Learning by Function
Critical Very Important Important
Somewhat Important Not Important Weighted Mean
Copyright 2019 Dresner Advisory Services, LLC

30. Inferential Statistics as a Top ML Technique
30
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Video analysis
Ensemble learning
Vector machine (SVM) approaches for classification and estimation
Various approaches to CART (e.g., ID3, C4.5, CHAID, MARS, random forests, gradient boosting)
Neural networks supported
Geospatial analysis
Text analytic functions and sentiment analysis
Automatic feature selection like principal component analysis (PCA)
Bayesian methods, including Naïve Bayes and Bayesian Networks
Model management and governance
Recommendation engine included
Textbook statistical functions for descriptive statistics
Hierarchical clustering, expectation maximization, k-Means, and variants of self-organizing maps
Range of regression models, from linear, logistic to nonlinear
Features for Data Science and Machine Learning
Critical Very Important Important Somewhat Important UnImportant
Copyright 2019 Dresner Advisory Services, LLC

31. Usability Favors Data Scientists
31
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
A specialist NOT required to create analytical models, test and run them
Pre-built drag-and -drop macros and tools from R that require no scripting…
Automatic creation of models from data
Support for entire process in a single application/user interface
Support/guidance in preparing data analytical models
Simple process for continuous modification of models
Fast cycle time for analysis with data preparation functions
Support for easy iteration
Access to advanced analytics for predictive and temporal analysis
Usability for Data Science and Machine Learning
Critical Very Important Important Somewhat Important UnImportant
Copyright 2019 Dresner Advisory Services, LLC

32. Embedded business intelligence is the technological
capability to include BI features and functions as an
inherent part of another application.
32
Embedded Business Intelligence
Copyright 2019 Dresner Advisory Services, LLC

33. Importance of Embedding Continues to Grow
33
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2014 2015 2016 2017 2018
Importance of Embedded BI 2013-2018
Critical Very Important Important Somewhat Important Not Important Weighted Mean
Copyright 2019 Dresner Advisory Services, LLC

34. Embedding Increasingly a Front Office Topic
34
1
1.5
2
2.5
3
3.5
4
4.5
5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Executive
Management
Research and
Development (R&D)
Business Intelligence
Competency Center
Operations (e.g.,
Manufacturing, Supply
Chain, Services)
Information
Technology (IT)
Marketing & Sales Finance
Importance of Embedded BI by Function
Not Important Somewhat Important Important Very Important Critical Weighted Mean
Copyright 2019 Dresner Advisory Services, LLC

35. Embedded Analytics is All About User Enablement
35
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Provide access to external users for a fee
Provide complimentary access to external users
Reduce software licensing costs
Provide interface/UI consistent with existing applications
Provide internal application users with in-context insights and analysis
Broaden access to internal users
Enhance access to existing reports/analyses
Improve self-service capabilities for end users
Objectives for Embedded BI
Critical Very Important Important Somewhat Important Not Important
Copyright 2019 Dresner Advisory Services, LLC

36. • On average what percentage of organizations have
adopted embedded BI / analytics?
• Less than 20%
• 20%
• 35%
• 50%
• More than 50%
36
Question

37. Embedded Analytics Adoption is Strong
37
47%
35%
11%
7%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Using today 12 months 24 months No plans
Adoption of Embedded Business Intelligence 2016-2018
2016 2018
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38. Operations is the Next Frontier of Adoption
38
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Research and
Development (R&D)
Executive
Management
Operations (e.g.,
Manufacturing, Supply
Chain, Services)
Business Intelligence
Competency Center
Information
Technology (IT)
Marketing & Sales Finance
Adoption of Embedded Business Intelligence by Function
No Plans 24 Months 12 Months Using Today
Copyright 2019 Dresner Advisory Services, LLC

39. Lightweight Architectures Broaden Adoption
39
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Flash API
Google Gadgets
Portlets
PHP framework
Docker Support
Frameworks (Force.com, Sharepoint)
Python API
CSS Support
Javascript API
Web services (RESTful, Soap)
Embedded BI Architecture
Critical Very Important Important Somewhat Important Not Important
Copyright 2019 Dresner Advisory Services, LLC

40. Feature Priorities Have Shifted From Static to Interactive
40
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Write-back support
Introduce user-supplied data for "mashups"
Workflow support
Apply analytical algorithms, mining, predictive
Re-skinning/customizing interface
Run invisibly in the background to provide data values to application
Alerts
Modify/create objects (e.g., reports, dashboards)
Browse/select from catalog of objects
Save and publish objects (e.g., reports, dashboards)
Open/view objects
Refresh objects/prompts
Single sign-on/security integration
Interact with objects (navigate, filter, drill)
Embedded BI Feature Priorities
Critical Very Important Important Somewhat Important Not Important
Copyright 2019 Dresner Advisory Services, LLC

41. Increasingly, Internal Applications Turn to Embedded
Analytics to Drive Adoption
41
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Electronic Medical Record
Call Center Management Applications
Workforce Management Applications
Supply Chain Management/Procurement Applications
Personal Productivity Applications
Marketing Automation Applications
Salesforce Management Applications
ERP Applications
Financial Management Applications
Web Portals
Internally Developed Applications
Application Targets for Embedded BI
Critical Very Important Important Somewhat Important Not Important
Copyright 2019 Dresner Advisory Services, LLC

42. • Business Intelligence (Fact-Based Decision-Making) continues to
expand with growing budgets, and clear success, value and ROI
• Data Science and ML continues to emerge as an important area −
leveraged by experts − for fortifying apps with enhanced insights
• Embedded BI − using lightweight interfaces − has become a key
mechanism for delivering upon “information democracy”
42
Conclusions
Copyright 2019 Dresner Advisory Services, LLC

43. 43
Q&A
Brittany Shear
With: Moderated by:
Chief Research Officer, Dresner Advisory Services, LLC
Linkedin page: /in/howarddresner/
Twitter ID: @howarddresner
Website: dresneradvisory.com/
Howard Dresner
Editor, Product Management Today
Linkedin page: /in/brittanyshear
Twitter ID: @prodmgmttoday
Website: productmanagementtoday.com
https://www.productmanagementtoday.com/upcoming-webinars/