Presentation at TEAR 2017

1. 1
A Mathematical Model for Evaluation of
Data Analytics Implementation
Alternatives
Jānis Grabis, Rūta Pirta
Department of Management Information Technology
Riga Technical University
Kalku 1, Riga, Latvia
grabis@rtu.lv, ruta.pirta@rtu.lv
TEAR: Trends in Enterprise
Architecture Research

2. 22
 Problem area and related work
 Objective and evaluation process
 Optimization model
 Evaluation example
 Conclusion
Outline
2

3. 33
 Importance of data analytics greatly
increases in enterprise information
systems
 Proliferation of technological platforms for
data analytics
Problem Area
3

4. 44
Research Question
How to select the right
platform for implementing
data analytics solutions?

5. 55
 Data analytics planning challenges
– Mohammad et al. (2014)
 Enterprise architecture and data analytics
– Koehler and Alter (2016)
– Dokhanchi and Nazemi (2015)
 Analysis of enterprise architecture models
– Goikoetxea (2004)
– Plataniotis et al. (2013)
Related Work
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6. 66
 To elaborate a model for selecting an
optimal alternative for implementation of
data analytics functionality requested by
users in accordance with EA management
principles
Objective
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7. 77
 Enterprise architecture represents
applications, data entities and
analytical components (e.g., reports),
and EA management principles
– Data entities are maintained by the
applications
– Reports are implemented on the basis
of applications
– Reports require access to data entities.
 A number of requests for new reports
has been received
 To select on the most suitable
applications for implementation of the
reports
Problem Statement
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8. 88
Architectural principles
Identify new reports required
Estimate development, maintenance, integration
costs and other parameters
Optimize reports to applications assignments
Implement the reports
Evaluation Process
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9. 99
Application Description
Enterprise applications Reporting is an internal component, which uses proprietary
technologies. Easy often end-user driven development of
simple reports with limited integration of external sources
and sharing.
Web-based reporting Reports are a part of various web pages and are developed
using open technologies such as HTML5 and JavaScript.
Often used for stand-alone and ad-hoc reports.
Data warehouse An information system specifically built for data analysis and
integrates data from various sources. The technology is
mainly intended for batch processing and requires significant
ramp-up time for new data sources.
Big data A set of technologies for developing advanced reports using
large data volumes and large variety of data formats. It often
requires customer development and specialized knowledge.
Spreadsheets Quick development of simple reports often for individual
users with limited scalability though sharing is possible using
cloud-based spreadsheet development tools.
Technological Platforms for Data
Analytics
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10. 1010
 Decision
– Assign reports (i) to applications (j) - Xij
 Objective
– Minimize costs, decentralization penalty and
maximize user preferences
 Constraints
– All reports should be implemented
– Applications should have access to data
entities (k) represented in the reports
Model Formulation: Informal
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11. 1111
 Objective function
 Constraints
Model Formulation: Formal
11
TC DC MC IC DP UP    
 11 1
1
I J D
ij j iji j
DC c R X 
  
 21 1
1
I J M
ij j iji j
MC c R X 
  
1 1
K J I
kj kjk j
IC c Y 
  
1 1
I J D
j iji j
DP p X 
  
1 1
I J U
ij iji j
UP p X 
  
, , ,ik ij kjX Y i j k  
1 1
1
I J
iji j
X 
 

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• Relative measures, e.g., planning poker
Development
cost
• EA network complexity measuresIntegration cost
• Is platform accessed by many users?
• Does platform maintain many data entities?
Decentralization
penalty
• Stated preferences
• Related systemsUser preferences
Estimation of Parameters’ Values
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 Objective is to demonstrate
application of the proposed model
and to highlight dependence of
decision-making results on
architectural and implementation
considerations
 Four new reports and five
implementation platforms
(applications)
 Focus on a fleet management
solution
– Customers
– Service request
– Delivery routes
– Invoices
– Payments
– Drivers
– Customer feedback
– Delivery confirmations
– Insurance claims
– Accident claims
– Absence
– Traffic data
– Road safety data
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Experimental Analysis

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Sample Enterprise Architecture
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Report (development and maintenance costs) CRM Portal ERP DW Big data Spreadsheet
Service quality by driver 100, 40 120, 24 150, 40 200, 20 300, 100 100, 40
Road safety impact on anxiety 500, 80 400, 40 300, 70 300, 30 300, 120 600, 80
Route execution delay causes 800, 100 400, 50 600, 70 500, 30 300, 120 600, 80
Service on-time fullfilment and customer
satisfaction
100, 40 200, 40 100, 40 300, 20 300, 100 200, 80

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# New report Entities
1 Service quality
by driver
Customers, Service request, Delivery routes,
Invoices, Payments, Drivers, Customer feedback,
Delivery confirmations
2 Road safety
impact on
anxiety
Delivery routes, Invoices, Payments, Drivers,
Customer feedback, Delivery confirmations,
Insurance claims, Accident claims, Absence, Traffic
data, Road safety data
3 Route execution
delay causes
Customers, Service request, Delivery routes,
Invoices, Payments, Drivers, Customer feedback,
Delivery confirmations, Insurance claims, Accident
claims, Absence, Traffic data, Road safety data
4 Service on-time
fulfillment
Customers, Service request, Delivery routes,
Invoices, Payments, Drivers, Customer feedback,
Delivery confirmations
Requested Reports
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 Baseline scenario
 Emphasis on meeting user preferences
– UP is multiplied by three
 Emphasis on satisfying the centralization
principle
– DP is multiplied by two
 Low cost of integration
– IC is divided by two
 Increased economies of scale
– R1 and R2 are increased two times
Scenarios
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Optimization Result
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Cost Breakdown
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0%
20%
40%
60%
80%
100%
Base Increase UP Increase DP Reduce IC Increase
reuse
DC MC IC DP UP
# Scenario DC MC IC DP UP
S1 Base 52% 16% 17% 14% 1%
S2 Increase UP 29% 11% 16% 21% 23%
S3 Increase DP 60% 14% 14% 12% 0%
S4 Reduce IC 58% 12% 13% 16% 1%
S5 Increase reuse 64% 12% 17% 7% 0%

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 Prescriptive analysis of EA
 Trade-offs among architectural pinciples
 Estimation of the model’s parameters
using EA data
Conclusion
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Thank you!
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