Unleash the Full Power of Your Legacy Data in Modern Analytics Platforms

[Webinar] February 2022
Unleash the Full Power of
Your Legacy Data in Modern
Analytics Platforms
Paige Bartley
Senior Research Analyst
451 Research, part of S&P Global Market Intelligence
John de Saint Phalle
Senior Product Manager
Precisely
Copyright © 2022 S&P Global Market Intelligence.
Permission to reprint or distribute any content from this presentation requires the prior written approval of S&P Global Market Intelligence.

Housekeeping items
2

Today’s speakers
3
Paige Bartley
Senior Research Analyst
451 Research, part of S&P Global Market Intelligence
paige.bartley@spglobal.com
John de Saint Phalle
Senior Product Manager
Precisely
jdesaintphalle@precisely.com

Permission to reprint or distribute any content from this presentation requires the prior written approval of S&P Global Market Intelligence. 4
Data you have = data you need
Data challenges and issues
Data silos confound insight
Data culture & data management
Takeaways and considerations
Agenda

Data you have = data you need
5

More data isn’t necessarily better or
more interesting. What is interesting is
how organizations can exploit the data
they have to build the next generation
of applications […]
2022 Trends in Data, AI & Analytics, 451 Research, December 2021
6

Historical data can help defend
against competitive disruption
7
New market entrants, looking to disrupt, often
depend on new data. Historical data can provide:
• Longitudinal insights not possible with other data
• Correlation of customer trends to historical events
• Understanding of business performance over time
• Opportunities to enrich data with other available
historical datasets (demographics, etc.)

The benefits of historical data sources
Data Opportunity Key Benefits
Deep analysis of legacy data sources Historical understanding of long-term business trends
Leverage of previously unused ‘dark data’ sources Fresh insight into business operations and efficiency
Analysis of past data in context with new data sources Holistic view of business performance, in full context
Using historical data to train AI and ML Models that get more accurate and ‘smarter,’ faster
Nuances and differences in regulatory requitements Historical and legacy data may be easier to leverage
8

Data challenges and issues
9

Functional barriers remain in the attempt to be more data-driven
10
Q. What are the most significant barriers your organization faces in attempting to be more data-driven?
Please select all that apply.
Q. What are the most significant barriers your organization faces in attempting to be more data-driven? Please select all that apply. Base: All respondents (n=371)
Source: 451 Research's Voice of the Enterprise: Data & Analytics, Data Management & Analytics 2021

In analytics, data quality and legacy IT remain key pain points
11
Q. What are the biggest challenges faced by your organization in relation to its analytics initiatives?
Q. What are the biggest challenges faced by your organization in relation to its analytics initiatives? Please select all that apply. Base: Analytics respondents (n=218)

Misconception: Reactive vs.
proactive enterprise functions
12
Historically, organizations often viewed ‘reactive’
functions such as compliance as being antithetical
to ‘proactive’ functions such as analytics.
Data Protection

Reality: Reactive and proactive
functions share dependencies
13
Firm governance and control of ALL data is needed
to achieve both reactive and proactive functions: the
underpinning mechanisms are the same.
Data Protection

But perceptions about data
are incredibly persistent.
14

In trying to gain a more unified view of data, most organizations
see requirements as pain points.
15
Q. What are the biggest challenges facing your organization as it tries to create a more unified view
of data? Please select all that apply.
Q. What are the biggest challenges facing your organization as it tries to create a more unified view of data? Please select all that apply. Base: Data management respondents, abbreviated fielding (n=152)

Data silos confound insight
16

Data silos are as old as IT architecture,
and they continue to reincarnate.
Functional or technical incompatibility of data repositories/
systems makes it difficult to gain a holistic view.
• New architectural methods can spawn new ‘islands’ of data.
• Older data sources often have different structure than newer
data sources, confounding integration efforts.
• Data silos are often interdependent with ‘communication’ silos
and entrenched work processes related to org dynamics.
• Data ‘ownership’ and territorial disputes can intensify
proliferation of architectural data silos.
17

The average number of data silos? A LOT of data silos.
18
Q. How many data silos would you estimate exist across your organization?
Q. How many data silos would you estimate exist across your organization?
Base: Data management respondents (n=193)
37%
of organizations
have more than
100 data silos

Silos can make data less accessible for insight initiatives
19
Q. Of all the data your organization currently manages, what percent is used for analytics projects?
Please give your best estimate.
Q. Of all the data your organization currently manages, what percent is used for analytics projects? Please give your best estimate.
Base: All respondents (n=416)
About 43% of
businesses
use less than
51% of their
available data
in analytics
projects.

Data culture & data management
20

Steps taken to establish and support
data culture within organizations
continue to reveal key differences in
methodology between those that
already utilize data-derived insight
extensively and those that are earlier
in the data-driven maturity curve.
Divergent data cultures: Highlights from VotE: Data & Analytics, 451 Research, November 2021
21

Top steps to improve data culture often = product investment
22
Q. What steps has your organization taken to accelerate data-driven decision-making?
Q. What steps has your organization taken to accelerate data-driven decision-making? Please select all that apply. Base: Analytics respondents (n=216)

Divergent ‘data cultures’ tell a
deeper story
23
Flywheel effect: investment in data = greater
benefits = further investment
• Pressures of COVID-19 split businesses into groups
of data 'haves' and data 'have-nots.’
• Success with analytics is associated with
commitment to measuring ROI.
• Businesses that report success with analytics are
also much more likely to report self-service models.

Takeaways and considerations
24

Full control and understanding of
enterprise data resources is essential
Businesses need full control and understanding of data if
they expect to meet both ‘reactive’ and ‘proactive’ needs
• Seek to integrate and understand all data sources in context,
including historical and legacy data sources.
• Data governance is a fundamental common denominator to
success with data-driven insight and data-driven requirements.
• A business certainly can’t leverage, or protect, data that it doesn’t
know it has.
• Focus on architecture that is flexible, and technology providers
that have diverse partnerships.
25

What Can You Do with
Modern Analytics Platforms?
CentralizedBI
andanalytics
Data discovery Data democratization with
governance
Next-gen projects –AI and
ML

What are
the
benefits
of a modern
analytics
platform?
Visibility
intoall data Sets course
for real-time pipelines
Limits
skillsgaps
Removes
data silos

Reality is not so simple
Silos of multi-
structured data
Legacy IT
infrastructure
Employees
Data
archives

Value that Data
from Legacy
Systems Brings
• Holds importanttransactiondata
• Mostcorebusinessapplicationsrunningon legacy
systems
• High volumesof data

Best practices for
legacy data
integration

Best practices
1.Breakdown the legacy data silo
2.Rethink your current approach
3.Build real-time applications
4.Limit skills gap & costs

1. Breakdown the legacy data
silo

Shipping Company requires
real-time delivery status
Toplevel mandatedriven bycustomerdemandsto:
1. Integratecustomerandshipment informationthatresides on multiplesystemsofrecord
2. Improveintegrationofmainframesystems withanalyticsplatform
3. Replicatechanges ofmainframedatatolargerbusiness in real-time
Challenge: Mainframe data not readable for
downstream
tracking dashboards

Precisely makes mainframe data
readable in Snowflake for real-
time tracking
Solution
• Connect (ETL +CDC)
• Snowflake
Results
• Power business user andcustomer dashboards with the
latest shipment information
• Report shipment information in ways that give business
competitive edge
• Integrate andreplicate hundreds ofz/OS Db2 tables to
Snowflake
• All data is integrated andreadable across platforms

2. Rethink your current
approach

Creating enterprise claims hub,
required quickly adding new targets
Strategicdecision tousedatato:
1. Improvethe claims experienceforend customers
2. Identifyofpatternsin claims toalertthe businessto unexpectedsevere claims
3. Automatethe fast-trackingoflow dollarclaims withoutthe need foranadjuster
Challenge: Current methods of integrating
mainframe data

Precisely and Databricks helps to
create high performance data hub
Solution
• Connect (ETL)
• Databricks
Results
• No downtime or reworkfor implementing a new approach to
legacy source integration
• Ability to meet requirements of high-volume processing fordata
hub
• Faster time toclose claims andimproved customer experiences

Financial Services Company needs to
build a real-time AML process
Toplevel mandatedriven byregulatorydemandsto:
1. Have consolidated,clean, verified dataforall analyticsandreporting
2. Providealertstoanysuspiciousactivityin real-time
3. Integratemainframedatatoanalyticsbutalsomaintainanunmodifiedcopyofmainframedatastored
Challenge: Disparate systems and slow time to update
mainframe data
caused major process delays in
meeting AML monitoring

Precisely and Cloudera enable AML
with timely delivery
Solution
• Connect (ETL +CDC)
• Trillium
• Cloudera
Results
• High performance AMLresults
• Faster time tovalue
• Data lake is trusted source
• Data feeding critical machine
learning-based fraud detection
Looking forward…
• Expanding toadditional Customer Engagement solutions and
applications

Credit Union looks to enable a data
hub for all lines of business
Toplevel mandatetoopenup dataacrossthe organization:
1. Toimprovecustomer bankingexperiences
2. Providetransparencyofdatato lines ofbusiness foranalyticsandBI
3. EnableAI/ML usecaseswithricherlegacy datasets
Challenge: Core banking functions run on mainframe but
lack of skills in
house incurred high development
costs and made it difficult
to scale

By the numbers…the cost of legacy
data
$95
per hour
40
hour work week
$3800
cost per week
6 months
average project
time
2
programmers
$7600
cost per week
$7600
cost per week
$197,600
cost per project
X X
X =
= =

Connect’s ETL helps to lower
costs and solve skills gap
Solution
• Connect (ETL)
Results
• Reduced costs todevelopment
• Leverage existing skills in house andenable
• Delivers all enterprise data for distribution across an proprietary
analytics platform

What you can do in the next 90
days…
• Assesshowyouarecurrentlyusing mainframeandIBM i datatoday
• Lookatwaysin which youcanleverage datafromlegacy systemstomaximizeimpact
• Keep bothbest practicesandlessons learned in mind whendeveloping yourapproach
• Remember Precisely is heretobeyourpartnerin innovation!

Questions?
46

Thank You
Contact Us:
451 Research:
US +1 212.505.3030
EUROPE +44 (0) 203.929.5700
S&P Global Market Intelligence:
US +1 877.863.1306
EUROPE +44 (0) 20.7176.1234
451research.com
spglobal.com/marketintelligence
Paige Bartley, Senior Research Analyst
451 Research, S&P Global Market Intelligence
paige.bartley@spglobal.com
John de Saint Phalle, Senior Product Manager
Precisely
jdesaintphalle@precisely.com

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