Gain insights into your business operations with BPM Analytics

Gain insights into your business
operations with BPM Analytics
Allen Chan
Chief Architect, Business Process Management

IBM Cloud University 2017 | October
Please Note
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The development, release, and timing of any future features
or functionality described for our products remains at our
sole discretion.
Performance is based on measurements and projections
using standard IBM benchmarks in a
controlled environment. The actual throughput or
performance that any user will experience will vary
depending upon many factors, including considerations
such as the amount of multiprogramming in
the user’s job stream, the I/O configuration, the storage
configuration, and the workload processed. Therefore, no
assurance can be given that an individual user will achieve
results similar to those stated here.

Agenda
• Digital Process Automation
• Business Monitoring
• Elasticsearch + Kibana with IBM BPM
3 9/29/17

… to deliver the end-to-end
customer journey
… built on an extensible
framework for innovation
and insight
… enabled by a platform for
rapid delivery and iteration,
led by the business
Digital Process Automation is…
IBM’s platform for rapidly creating business–led process apps
that deliver new levels of
customer experience and digital self service
Key attributes of the Digital Process Automation platform:
Customer
focus
Speed Scale

Digital Process Automation capabilities
Define customer journeys and business operations to
discover new opportunities for innovation
Design, automate and govern business
operations to deliver a scalable, consistent
customer experience
Analyze data and events
to augment knowledge work
and take the right action at the right time
Actionable
Insights
Collaborative
Discovery
Business Automation
Digital
Process
Automation

To get Actionable Insights –
we need business monitoring
• Increase profitability
– Reduce costs by ensuring timely completion of work
– Real-time insight into operational processes for
• Improved operational efficiency
• Dynamic business response
• Increase customer satisfaction
– Proactive alerts to business situations
– Access to relevant information to
resolve situations
• Increase team productivity
– Monitor overall team productivity
– Monitor individual worker productivity
– Identify high performing workers to
mentor lower productivity workers
! CICS
! IMS
! WAS
Executive!
Management!
Customer!
Service!
Risk Management 
Teams!
Finance!
and Ops!
Account  
Administration!
6

Drill down to
specific situations
Track your KPIs and get
alerted as situations
occur
Trend analysis and
prediction
Analyze a range of business data with BAM
Take action faster with real-time information on personalized dashboards
§ Drill down to investigate
why John Smith’s new
bank account request has
not been processed within
the specified service level
agreement
§ See if the average
amount of time to open
a new bank account is
within an acceptable or
desired range – from
your desktop or mobile
§ Compare new account
opening KPIs across
time periods to manage
resources more
effectively and predict
future problems
7

1. Track
2. Store
3. Report
4. Analyze
5. Respond
8 9/29/17
5 Steps to manage your Operational Intelligence

• Built-in data store for business events.
• BPM will send out business events with:
• Auto-Tracking
• Tracking Points
• Timing Intervals
• Built-in Performance Dashboard
• Team Performance Dashboard
• Process Performance Dashboard
9 9/29/17
Performance Data Warehouse

• Once turned on, will track all
process level events, e.g pre-
and post- task execution.
• Individual variable values can
also be tracked.
10 9/29/17
Auto Tracking
A database table by the
“Autotracking name” will be
created in the PDW
database.

1. Add custom Tracking Groups
with a specific set of variables.
2. Specify in the BPD where to
write Tracking Groups data
before or after a certain activity.
11 9/29/17
Tracking Points & Tracking Groups
In this case, we will write an entry in
the Tracking Group table
TG_TRAVELREQUEST

Using Intermediate Event, one
can create timing interval with
tracking groups to measure
elapsed time between any two
events.
12 9/29/17
Tracking Points

BPM has a built-in Performance
Dashboard that will allow you to
visualize the information about
process performance.
13 9/29/17
Performance Dashboard

1. Customize the information we want to show in the Dashboard?
2. Federate information across multiple BPM systems?
3. Perform data mining and analytics with business data?
4. Roll Your Own analytics solutions by using 3
rd
part analytic software?
14 9/29/17
But, what if we want to …

BPM Analytics Tech Preview
• Enable the BPM users to gain insight of the
processes with big data and analytics
• Performance overhead ~ 10% or less
• Expose the rich information of the processes – system
view and business view
• Near real time
• Archive and restore historical data to form new monitor
model and new analysis
• Out of the box sample dashboards
• 3rd Party Analytics
• Integrate the data which do not come to the BPM system
• Publish the process data to external data and analytics
solutions provided by IBM, customer and partners
• For evaluation only, not for production
use

Process
Server
JMS
Business
Events
BPM Cell
defconfig.x
ml
BPMEvent
Emitter
Kibana
Elasticsearch
Topology reference
Process
Server
JMS
Business
Events
BPM Cell
defconfig.x
ml
BPMEvent
Emitter
Not packaged by
BPM
Event
Summary
Agent
Raw
Event
Combined
Event
BPM Pre-
defined
Dashboard
BPM Analytics components
Object
Storage
Event
Summary
Agent
Archive
Archive Restore
Restore
Optional
• Two level storages, Elasticsearch for the online storage, the
Object Storage is the archive storage
• Support multiple object storage interfaces, like:
OpenStack Swift and Amazon S3(to be ready)
• Raw events will be stored at the archive object storage
in the bulks
• Archive storage offline by accident will not impact the analytics
function
• Archive operation will avoid peak time automatically
• Archived events can be selected by the time range to re-build
the indices at the ES

Topology – option 1
• Kafka can be configured as the event cache in front of all event consumers
Process
Server
JMS
Business
Events
BPM
Cell
defconfig.
xml
BPMEvent
Emitter
Kafka
Not the OOTB
capability
Kibana
Elasticsearch
Event
Summary
Agent
Raw Event
Combined Event
Object
Storage
Event
Summary
Agent
Archive
Archive Restore
Restore

Topology – option 2
• Kafka can be the message broker for any analytics solutions
Process
Server
JMS
Business
Events
BPM
Cell
defconfig.
xml
BPMEvent
Emitter
Kafka
Not the OOTB
capability
Any Analytics Solution
Not the OOTB
capability

BPMEventEmitter Design
• If the BPM enable emit the JSON format DEF event, the “XML to JSON” will be skipped automatically
• ES and Kafka are event consumers, they can be enabled together or separately
• Events emit to the different consumers are identical, it’s easy to build up event tunnel between:
• Kafka to Analytics – customized analytics solution
• Kafka to ES – event cache in front of BPM Analytics
• Kafka to Object Storage – customized archive solution
MDB
XML to
JSON
Def event
(xml or json)
To Elasticsearch
To Kafka
BPMEventEmitter
config
Activity
transformer
Process
transformer

Activity Event
• Contains all the information from activity level events.
• One activity level event generates one Activity Event document
• One task has several Activity Event documents
Activity Summary
• Contains information that represents one task, created by combining information
from several Activity Event documents.
• Simple flat structure – simplify search query and visualzation by Kibana
20 9/29/17
Elasticsearch Indexes

Raw Event Type (immutable)
Elasticsearch
Data model at Elasticsearch
Combined Type (mutable)
Kibana
Elasticsearch Index
Activity
Event
Process
Event
Activity
Summary
Process
Summary
MDB
Transform
(aggregate)
Aggregatipn
Handler
• Aggregation is the batch processing
with low latency
• Not stream processing because
the aggregated event’s time range
can be weeks, or even months
• Data as Combined Types is mutable:
• Update the process status for
example from the running to
finished
• Data as Raw Event Types is immutable
• Support re-derive Combined
Types over again based on the
Raw Event Types
Elasticsearch
Index
Progress
Record

Process Event
• Contains all the information from a Process instance as it
happens.
• One process event generates one Process Event document
• One process instance can have several Process Event
documents
Process Summary
• A single document containing the most useful information of
a process instance by combining data from several Process
Event documents
• One Process Summary document represents one process
instance
• Simple flat structure – simplify search query and visualization
by Kibana
22 9/29/17
Elasticsearch Indexes {
"cellName":"BPM1",
"eventVersion": "2010-11-11",
"eventKind": "bpmnx:PROCESS_TERMINATED",
"occurrenceTime": "2017-02-06T13:24:54.283+08:00",
"processApplicationId": "532cc340-7d17-4ce7-a1c1-6e8380fec134",
"processApplicationVersion": "2064.2142be53-2334-4338-a194-4e3861992ae2",
"processApplicationName": "Hiring Sample",
"processApplicationSnapshotName": "v5",
"startingProcessInstanceFullId":"cd974bd9-0ba2-499d-abdc-319c3be66993.2064.ca663330-
14b8-40c2-8a57-2c63a173c677.44000000435",
"processFullId": "cd974bd9-0ba2-499d-abdc-319c3be66993.2064.ca663330-14b8-40c2-8a57-
2c63a173c677.44000000435",
"processName":"Standard HR Open New Position",
"processInstanceId":44000000435,
"processState":"Terminated",
"processVersion":"2064.ca663330-14b8-40c2-8a57-2c63a173c677",
"processPOId":"cd974bd9-0ba2-499d-abdc-319c3be66993",
"processSnapshotName": "v5"
}
{
"cellName":"BPM1",
"processStartTime":"2017-02-06T13:00:00.384+08:00",
"processTerminatedTime": "2017-02-06T13:24:54.283+08:00",
"startingProcessInstanceFullId":"cd974bd9-0ba2-499d-abdc-319c3be66993.2064.ca663330-
14b8-40c2-8a57-2c63a173c677.44000000435",
"processFullId": "cd974bd9-0ba2-499d-abdc-319c3be66993.2064.ca663330-14b8-40c2-8a57-
2c63a173c677.44000000435",
"processState":"Terminated",
"processVersion":"2064.ca663330-14b8-40c2-8a57-2c63a173c677",
"processPOId":"cd974bd9-0ba2-499d-abdc-319c3be66993",
"processSnapshotName": "v5"
}
Info calculated from
several Process Event
document
Process Event
Process Summary

23 9/29/17
{
"cellName":"BPM1",
"eventKind": "bpmnx:ACTIVITY_ACTIVE",
"occurrenceTime":2017-01-25T17:32:32.084+08:00,
"performerId":"deadmin",
"performerName":"deadmin",
"activityType":"userTask",
"activityName":"HT1",
"activityMonId":"bpdid:54fb3da14f1aa466:3550961c:159b077f5d7:-7f6b",
"activityMonVersion":"2064.402aa322-1a07-4886-a848-c4af1f2a8c3e",
"processApplicationName": "PA01",
"activityFullId": "a0884aa1-5112-4142-a79a-e87657b142fb.2064.402aa322-1a07-4886-a848-
c4af1f2a8c3e.234-3-6.16",
"startingProcessInstanceFullId":"cf3e2fcc-5aba-43fb-abc5-76347b9fd7b0.2064.c7e467ab-e7b3-4307-
88e6-44fbd1b74fbf.234",
}
{
"cellName":"BPM1",
"eventKind": "bpmnx:ACTIVITY_RESOURCE_ASSIGNED",
"occurrenceTime": 2017-01-25T14:54:20.490+08:00
"taskInstanceId":"280",
"potentialPerformerId":"All Users_T_da7e4d23-78cb-4483-98ed-b9c238308a03.5e6546c5-
5ae4-429c-bc57-cdbc3bb015f5",
"potentialPerformerName":"All Users",
"potentialPerformerDocumentation":"All Users",
"activityFullId": "a0884aa1-5112-4142-a79a-e87657b142fb.2064.402aa322-1a07-4886-a848-
c4af1f2a8c3e.234-3-6.16",
"startingProcessInstanceFullId":"cf3e2fcc-5aba-43fb-abc5-76347b9fd7b0.2064.c7e467ab-
e7b3-4307-88e6-44fbd1b74fbf.234",
}
{
"cellName":"BPM1",
"taskInstanceId":"280",
"potentialPerformerId":"All Users_T_da7e4d23-78cb-4483-98ed-b9c238308a03.5e6546c5-5ae4-429c-bc57-cdbc3bb015f5",
"potentialPerformerName":"All Users",
"potentialPerformerDocumentation":"All Users",
"performerId":"deadmin",
"performerName":"deadmin",
"taskStartTime":2017-01-25T14:54:20.490+08:00,
"taskClaimedTime":2017-01-25T17:32:32.084+08:00,
"taskCompletedTime": 2017-01-25T17:34:32.084+08:00,
"taskState":"Completed",
"activityFullId": "a0884aa1-5112-4142-a79a-e87657b142fb.2064.402aa322-1a07-4886-a848-c4af1f2a8c3e.234-3-6.16",
"startingProcessInstanceFullId":"cf3e2fcc-5aba-43fb-abc5-76347b9fd7b0.2064.c7e467ab-e7b3-4307-88e6-44fbd1b74fbf.234",
}
Activity Event
Activity Summary
Info calculated from several
Activity Event documents

BusinessData
• Contains business data and KPI
24 9/29/17
{
"cellName":"BPM1",
"activityFullId": "a0884aa1-5112-4142-a79a-e87657b142fb.2064.402aa322-1a07-4886-a848-c4af1f2a8c3e.234.16",
"startingProcessInstanceFullId":"cf3e2fcc-5aba-43fb-abc5-76347b9fd7b0.2064.c7e467ab-e7b3-4307-88e6-44fbd1b74fbf.234",
"taskId":280
"activityName":"Submit position request ",
"wleGroupId": "19b73424-1bdc-4ee4-aa5a-f4ed8784d4a8",
"wleName": "Submit position request (PRE)",
"wleGroupVersion": "2064.be7c5d58-e3b6-4550-a253-09be00e5434e",
"wleGroupName": "aEmpRequisition121381434563922",
"wleTime": 2017-01-17T15:31:06.852+08:00,
"wleVersion": "2064.be7c5d58-e3b6-4550-a253-09be00e5434e",
"wleId": "-f4ed8784d4a8bpdid431b0753c33842e23d5457c0141a2fd3448-75fb (PRE)",
"kpiData":
{
"Rework.integer":0
}
"trackedField":
{
"GMApproval.boolean":false,
"NumberRequested.integer": "2",
"Location.keyword": "Chicago",
"EmploymentStatus.keyword": "Full-time",
"Department.keyword": "Finance",
"HiringManager.keyword": "Jack Smith"
}
}
Business Data sample

Out of box Dashboard – Process Performance
2
Identify total
process count
Process
statistics based
on BPM system
and state
Average process
running duration
Process instance
started per 3
hours – help to
find busy time
Process instance
completed per 3
hours – help to
find busy time
Process count
for each
process

Out of box Dashboard – Task Performance
2
Drill down for specific task:
review new position request
Identify total
task count
Task count by
BPM system
and state
Task count for
different tasks
Task started and
completed per 3
hours – help to
find busy time
26,124 out of
128,926 tasks are
“review new
position request”
Average task
running duration
increases from
35.07m to 63.86m
Average Task
Running
Duration

Out of box Dashboard – Team Performance
2
Task assigned to
each team and
team members
Average task
duration for each
team member
Task completed per 3
hours by each team

Sample Custom Dashboard – KPI
2
Average Task Time, Average
Execute Time and Average Wait
Time for each task
Average task time
change trend per 3
hours
Average task time
has a sudden
increase here. It may
indicate abnormal.

Sample Dashboard – Business at a Glance
2
• Business Data
dashboard is
different for each
process and can
only be customized
by yourself.
• Here we show a
sample business
dashboard for Hiring
Sample
How many people
are hired at each
location and what
type
How many people
are hired in each
department and
their job type
Tile map shows hiring people
at each location. Deeper
color indicates more people
are hired there
Approval rate
for each
manager’s
requests

Create “Business at a Glance”
Dashboard for Hiring Sample
• On discover tab, search
”Location:*”
• Save the save search as
“HiringBusinessData”
• On visualization tab, select “Pie
Chart”
30 9/29/17
Create Your Dashboard

• Select “HiringBusinessData”
save search
• Choose “Split Slices”
• Select “Terms” as
Aggregation
• Select “Location” as Field
• Save pie as “Hiring
Request by Location”
31 9/29/17

• On dashboard tab, add a
new dashboard
• Select “Hiring Requests by
Location”
• Save the dashboard
“Business at a Glance”
• Create other visualization
and add to the dashboard
32 9/29/17

Install BPM Analytics Technology Preview
• Install BPM 8.6
• Download add-ons from: https://github.com/ibmbpm/bpm_analytics
• Contents:
• EventSummaryAgent.tar
• Monitors the new events added in the configured elasticsearch index and
then generate summary events
• BPMDashboardKibana.zip
• Contains five Kibana dashboards
• IBM Business Process Manager Analytics.pdf
• Install instructions
CF 2017.09 (8.6)

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Gain insights into your business operations with BPM Analytics

Gain insights into your business operations with BPM Analytics

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