The document discusses the importance of data management in the context of the fourth industrial revolution, highlighting potential pitfalls and recommending solutions for effective data integration. It emphasizes the need for data governance, understanding data sources, and ensuring data quality through practices like eliminating duplication and maintaining up-to-date records. Additionally, it outlines strategies for using data in marketing automation, including lead scoring and segmentation to enhance marketing efforts.

1. Fueling Your Growth with
Smart Data Management
May 15, 2018

2. Our Speakers
Div Pithadia
Head of Technology,
MDR
Anne Goto
Data Products Manager,
MDR
Dave Clemen
Director of Digital Marketing, MDR

3. Agenda
The Data Revolution
Common Data Quality Pitfalls and Winning
Solutions for Data Integration
Tying it All Together: Using Your Data for
Marketing Automation
1
2
3

4. Data as Digital Reflection

5. Knowledge Management Cognitive Pyramid
Data = Path to Decisioning/Wisdom
Adaptation of the DIKW pyramid by US Army Knowledge Managers https://en.wikipedia.org/wiki/DIKW_pyramid

6. We are in a
New Industrial
Revolution

7. Steam
Energy of the first
Industrial Revolution
Electricity
Energy of the
second Industrial
Revolution
Computing
Energy of the third
Industrial Revolution
The Fourth Industrial Revolution - Klaus Schwab

8. Data
Energy of the Fourth Industrial Revolution
The Fourth Industrial Revolution - Klaus Schwab

9. By 2025
90%
of the population will
have Smartphones
The Fourth Industrial Revolution - Klaus Schwab

10. By 2025
90%
of the population will
have Unlimited Storage
The Fourth Industrial Revolution - Klaus Schwab

11. By 2025
there will be
1 Trillion
sensors on the
Internet
The Fourth Industrial Revolution - Klaus Schwab

12. Text
Tables
Images
Videos
Streams

13. Volume
Amount of data
Velocity
Speed of data capture
Variety
Types of data

14. Behaviors for
Success in the Fourth
Industrial Revolution

15. Contextualize
Applications
Valuation
Quality, Monetary, Accuracy
Taxonomy
Automate
Data Flows
Data Analysis & Algorithms
AI/ML/DL
Protect
Privacy
Security
Compliance

16. Measure
Value of Data
Valuation of business
Accountability
Governance
Compliance
Ownership
Training
Data & Analysis Skills
Data Driven Culture

17. Treat Data as
what it
represents

18. Data will be:
• Crucial, yet commoditized
• Bigger, faster, and will demand contextualization
• Actioned by automation, not by humans
• Stolen
• Regulated
Data mismanagement will have consequences!

19. Data Quality – Take Action Now

20. What is Most Important In
Achieving Data Quality?
• Accuracy?
• Completeness?
• Currency?
• Consistency?
• Security?
• Sustainability?

21. Common Pitfalls in
Maintaining Your Data
• Focusing only on new data
• Fear of removing or inactivating
data
• Data overlay – when data differs,
how do you know what data to
keep
• Not understanding all of your
data sources
• Duplication
• Lack of data ownership

22. Winning Solutions –
Things to Consider

23. Data Governance
• Ownership for evaluation of data,
creating data rules, assessing new data
sources
• Can be a cross-departmental team,
including voices from varied
stakeholders
• Create functional data flows between
applications

24. Understand Your Data Sources
• Identify your data sources – do you
know where your data is from?
• If using third party provider – do you
know the frequency that they update
their data? How often can you get data
refreshes?
• Rank different sources of data – a one
time conference list vs a regular feed
from a trusted data partner

25. Source and Date Tracking
• Create a source management process
to track all data sources
• Date stamp records and even
individual critical data points so you
know what is current
• Drive to synthesize your data sources

26. Eliminate Data Duplication
• Understand new data sources and
how the quality ranks against your
current data
• Seek resources to match your
existing data to incoming data
• Eliminate duplication before it
happens

27. Custom Data Sets/
Parallel Fields for Source Data
• Instead of overlaying your existing data
with incoming source, let the source data
live in parallel with your in-house records
• Keep data in separate tables/objects, and
link them to your existing records with a
key field
• Once all data is in same platform, create
rules that can be executed to update your
house records, especially for missing data

28. Data Integration
• Understand all of your data
repositories and the workflows
between them
• Identify key fields that can link source
data to your existing data
• Seek outside mapping tools to
update existing records as well as
add new records

29. Recommendations to Ensure Quality
• Explore New Data Sources
• Keep existing data current
• Adding new leads
• Data Hygiene
• Talk to your data partners about
available tools that can help with your
data hygiene
• Identify outdated records
• Duplication matching
• Merge/purge
• Address cleanse
• Data appends

30. Example of Duplication Matching

31. Recommendations to Ensure Quality
• Explore Ways to Streamline
Integration
• Establish your data flow
• API - streamlined access to data
• Ease of use mapping tools

32. Data
Mappin
g Tool

33. Marketing Automation

34. Defining Your Goals and Lead Scoring
• Revisit your goals before automating.
• What is the point that must be
exceeded to begin producing a given
effect or result or to elicit a specific
response?
• Must be a collaborative effort between
Sales and Marketing teams.
• Needs to be evaluated and adjusted
over time to ensure effectiveness.

35. 3 Types of Lead Scoring Criteria
Explicit
Information taken
at face value
• Title
• Location
• Industry
• Revenue
• Lead Source
Implicit
Information from customer
actions and open to further
interpretation
• Website visits
• Phone calls
• Downloads
• Email signups
• Form completions
Negative
Deducts points for
disqualifying criteria
• Lack of response
• No defined
budget
• Periods of
inactivity
• Lack of authority
• Unsubscribes

36. Integrate With Your Existing
Inbound Marketing Strategy
• Can you integrate with your CRM
(e.g. Salesforce)?
• How can marketing automation enhance
your current inbound marketing efforts
(investment
of resources, timing, focus, qualification,
tracking
of results)?
• How can your different marketing efforts
work together (email, paid digital
advertising,
social media, direct mail, etc.)?

37. You’ve identified your goals
and segments, now what?
DON’T just blast general messages.
DO send highly targeted content to a
specific segment.
Segmentation Ideas:
• Topics/products interested in
• What stage of buying process they are currently in
• What pages they have engaged with on your website
• Demographics or other personal details of the contact in
your database (job title, geography, industry)

38. Don’t forget about your
existing customers!
You can do so much more
than a newsletter or general
promotion
• Retention (FAQ, Renewal)
• Upsell
• New Offerings

39. QUESTIONS?

40. Upcoming Webinars & Resources
Gen Z Has Arrived: Engaging these Passionate and
Effective Activists
Wednesday, June 20, 2018 | 1:30PM EDT
State of the K12 Market Overview
Wednesday, July 11, 2018 | 1:00PM EDT
Social Media Marketing to Educators
Wednesday, September 12, 2018 | 1:00PM EDT
Email Best Practices
Tuesday, October 23, 2018 | 1:00PM EDT
https://mdreducation.com/ed
ucation-marketing-free-
webinars/

41. Thank You!
Div Pithadia
Head of Technology
pithadiad@dnb.com
Anne Goto
Data Products
Manager
agoto@dnb.com
Contact us:
MDReducation.com
mdrinfo@dnb.com
800-333-8802
@MDRBiz
MDR Education Marketing
MDR Education Marketing
Connect with us on social:
Dave Clemen
Director of Digital
Marketing
clemenda@dnb.com