A talk I gave at #DataVizLive online event in Nov 2020. Introducing the Laughlin Consultancy 9-step model for Softer Skills needed by Analysts & previewing some of those steps (beyond data visualisation & storytelling skills).

1. Paul Laughlin, Host of Customer Insight Leader podcast & Founder of Laughlin Consultancy
The other Softer Skills analysts need
Beyond the importance of data visualisation skills for analysts

2. Client-side to Agency-side
Created and lead data & analytics
teams, for all general & life
insurance businesses across
Lloyds Bank Group, over 13 years.
Added over £11m incremental
profit to bottom line annually.
Developed team of 44 analysts &
mentored future leaders.
My Career Journey
“Helping exceptional teams master the
people side of analytics”
2

3. 3

4. 4

5. Customer Analytics delivers ROI
Research & my own experience confirm analytics delivers profit
5

6. To achieve that needs Commercial Focus
Data & Analytics leaders confirm relevance trumps sophistication
6

7. There’s a focus on developing skills needed
But all too often that focus is solely on Technical Skills
7
EDSF Release 2: Part 1. Data Science Competence Framework (CF-DS)
Table 4.2. Identified Data Science skills related to the main Data Science competence groups
SDSDA
Data Science
Analytics
SDSENG
Data Science
Engineering
SDSDM
Data Management
SDSRM
Research Methods
and Project
Management
SDSBA
Business Analytics
SDSDA01
Use Machine Learning
technology,
algorithms, tools
(including supervised,
unsupervised, or
reinforced learning)
SDSENG01
Use systems and
software engineering
principles to
organisations
information system
design and development,
including requirements
design
SDSDM01
Specify, develop and
implement enterprise
data management and
data governance
strategy and
architecture, including
Data Management Plan
(DMP)
SDSRM01
Use research methods
principles in developing
data driven applications
and implementing the
whole cycle of data
handling
SDSBA01
and Business
Intelligence (BI)
methods for data
analysis; apply
cognitive
technologies and
relevant services
SDSDA02
Use Data Mining
techniques
SDSENG02
Use Cloud Computing
technologies and cloud
powered services design
for data infrastructure
and data handling
services
SDSDM02
Data storage systems,
data archive services,
digital libraries, and their
operational models
SDSRM02
Design experiment,
develop and implement
data collection process
SDSBA02
Apply Business
Processes
Management (BPM),
general business
processes and
operations for
organisational
processes
analysis/modelling
SDSDA03
Use Text Data Mining
techniques
SDSENG03
Use cloud based Big Data
technologies for large
datasets processing
systems and applications
SDSDM03
Define requirements to
and supervise
implementation of the
hybrid data management
infrastructure, including
enterprise private and
public cloud resources
and services
SDSRM03
Apply data lifecycle
management model to
data collection and data
quality evaluation
SDSBA03
Apply Agile Data
Driven
methodologies,
processes and
enterprises
SDSDA04
Apply Predictive
Analytics methods
SDSENG04
Use agile development
technologies, such as
DevOps and continuous
improvement cycle, for
data driven applications
SDSDM04
Develop and implement
data architecture, data
types and data formats,
data modeling and
design, including related
technologies (ETL, OLAP,
SDSRM04
Apply structured
approach to use cases
analysis
SDSBA04
Use Econometrics for
data analysis and
applications
EDSF Release 2: Part 1. Data Science Competence Framework (CF-DS)
Table 4.3. Required skills related to analytics languages, tools, platforms and Big Data infrastructure 6
DSDALANG
Data Analytics
and Statistical
languages and
tools
DSADB
Databases and
query
languages
DSVIZ
Data/Applicatio
ns visualization
DSADM
Data
Management
and Curation
platform
DSBDA
Big Data
Analytics
platforms
DSDEV
Development and
project
management
frameworks,
platforms and tool
DSDALANG01
R and data analytics
libraries (cran,
ggplot2, dplyr,
reshap2, etc.)
DSADB01
SQL and
relational
databases (open
source:
PostgreSQL,
mySQL, Nettezza,
etc.)
DSVIZ01
Data visualization
Libraries
(mathpoltlib,
seaborn, D3.js,
FusionCharts,
Chart.js, other)
DSADM01
Data modelling
and related
technologies (ETL,
OLAP, OLTP, etc.)
DSBDA01
Big Data and
distributed
computing tools
(Spark,
MapReduce,
Hadoop, Mahout,
Lucene, NLTK,
Pregel, etc.)
DSDEV01
Frameworks: Python,
Java or C/C++, AJAX
(Asynchronous
Javascript and XML),
D3.js (Data-Driven
Documents), jQuery,
others
DSDALANG02
Python and data
analytics libraries
(pandas, numpy,
mathplotlib, scipy,
scikit-learn,
seaborn, etc.)
DSADB02
SQL and
relational
databases
(proprietary:
Oracle, MS SQL
Server, others)
DSVIZ02
Visualisation
software (D3.js,
Processing,
Tableau, Raphael,
Gephi, etc.)
DSADM02
Data Warehouse
platform and
related tools
DSBDA02
Big Data Analytics
platforms
(Hadoop, Spark,
Data Lakes, others)
DSDEV02
Python, Java or
C/C++ Development
platforms/IDE
(Eclipse, R Studio,
Anaconda/Jupyter
Notebook, Visual
Studio, Jboss,
Vmware, others)
DSDALANG03
SAS
DSADB03
NoSQL Databases
(Hbase,
MongoDB,
Cassandra, Redis,
Accumulo, etc.)
DSVIZ03
Online
visualization tools
(Datawrapper,
Google
Visualisation API,
Google Charts,
Flare, etc)
DSADM03
Data curation
platform,
metadata
management (ETL,
Curator's
Workbench,
DataUp, MIXED,
etc)
DSBDA03
Real time and
streaming
analytics systems
(Flume, Kafka,
Storm)
DSDEV03
Git versioning system
as a general platform
for software
development
DSDALANG04
Julia
DSADB 04
Hive (query
language for
Hadoop)
DSADM04
Backup and
storage
management
(iRODS, XArch,
Nesstar, others)
DSBDA04
Hadoop
Ecosystem/platfor
m
DSDEV04
Scrum agile software
development and
management
methodology and
platform
DSDALANG05
IBM SPSS
DSADB 05
Data Modeling
(UML, ERWin,
DDL, etc)
DSBDA05
Azure Data
Analytics
platforms
(HDInsight, APS
Source: EDISON Data Science Framework (2017)

8. Experienced leaders say otherwise
Like me they see the need to focus on “Softer” People Skills
8

9. So, I’ve developed a model
to explain the skills needed
Introducing a model to explain
the People Skills needed at each
stage for analysts or Data
Science teams to achieve impact

10. Sharing four pieces of that puzzle
In this talk I’ll introduce you to these parts of that Model
10

11. (1) Questioning to get to
the real business need
Socratic Questioning skills to get
beneath the request to what the
business really needs and how
what is delivered will be used.

12. The problem with requirements
12

13. Getting clarity on need not want
Practice using questions to get clarity on
what they need, not just what they want:
• Concept clarification questions
• Probing assumptions
• Probing rationale, reasons & evidence
• Questioning viewpoints & perspectives
• Probe implications & consequences
Socratic questioning
13

14. That’s all for now on Step 1

15. (3) Securing buy-in from
the key players
Identifying, prioritising and
managing stakeholder relationships
to ensure you manage expectations
& communicate/collaborate well.

16. Alexander Hamilton (American ‘Founding Father’ & abolitionist), 1755-1804
“Men often oppose a thing merely because they have
had no agency in planning it, or because it may
have been planned by those whom they dislike.”
16

17. Step 1: 360-degree MindMapping consider all those impacted
Focus using Stakeholder Mapping
17
IT
Developers
Business
Architect
Finance
BP
Compliance
Competitors
CMO
CEO
CIO CRO
NEDs
City
Analysts
Your
Managers
Chairman
Your
Analysts
CFO
Regulators
Market
Tech
Vendors
Gartner/
Forrester
Benchmarks
Consumer
Groups
Customers
COO
Finance
Peers
Risk
Peers
Marketing
Peers
You
Legal
Peers
Ops
Peers
IT
Peers
Teams
supplying
data
Teams
supporting
systems
External
data
suppliers
CX ManagersIT Managers
Finance
Managers
Risk
Managers
Legal
Managers
Finance
Teams
Risk Teams Legal Teams
IT
BP

18. Step 2: Prioritise those who need more of your time
Focus using Stakeholder Mapping

19. Step 3: Bring both tools together to decide where to act
Focus using Stakeholder Mapping
High Influence
Low Influence
High
Interest
Low
Interest
CMO
CEO
CIO CRO
CFO COO
Your
Managers
Your
Analysts
Business
Architect
IT
BP
Marketing
Peers
Teams
supplying
data
Finance
Teams
Compliance
Review all stakeholders
None on the Axes
Ruthless Prioritisation

20. Segment your stakeholders to better understand their styles
Flex your style to work for each Stakeholder
Spotting a Pioneer
Pioneer motto: Have
fun. It’s just work.
Spotting a Driver
Driver motto: And your
point is…?
Spotting an Integrator
Integrator motto:
Consensus Rules!
Spotting a Guardian
Guardian motto:
Changing the World, One
Spreadsheet at a Time
20 https://www2.deloitte.com/us/en/pages/operations/solutions/business-chemistry.html

21. How to map & segment your stakeholders to focus your efforts
Further guidance is available on my blog
21

22. That’s all for now on Step 3

23. (6) Generate insights to
understand behaviour
Generation of deeper insights
into motivation and triggers for
behaviour seen in analysis, using
structured questioning &
converging evidence.

24. Exploring further the context & considering what you don’t know
Generating Insight means asking questions
24
How do
Do we meetHow will we
How will we
communicate
Fig 2a
Datamine 7
How do
Do we meetHow will we
How will we
communicate
Fig 2a
Fig 2b

25. Converging evidence from four possible sources to spot themes
Generating Insight means convergence
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Media and Technology Trends
Regulatory Environment
Socioeconomic Stats
Competitor Intelligence
Market Developments
Qualitative Research
Quantitative Studies
Tracking Studies
Meeting Customers F2F
Customer Complaints
Listening in at Call Centre
Those who meet customers
Sales, Customer &
Transactional data
Communication
Evaluations
Behavioural
Data
Environm
ent
Research
Custom
er
Connection
Customer Personas/Vox pops
Customer Experience Study
Market Intel. Team
External MI Database
Data Team
Analysis Team
Research Team
Customer facing
Colleagues

26. Can use structured questioning techniques to build bridges
Customer Insight Generation workshops
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Through the steps of an Insight Generation workshop, attendees are
building a bridge from the current customer behaviour to the desired
customer behaviour, via Analytical Thinking about deeper motivations…
BEHAVIOUR NOW
MOTIVATION
BEHAVIOUR THEN
WHY NOW WHY THEN

27. How to run an Insight Generation workshop
Further guidance is available on my blog
27

28. That’s all for now on Step 6

29. (9) Ensure action as a
result to deliver solution
Follow-up on recommendations and
influence key players to ensure
appropriate action that meets the
business need.
Plus, implement feedback loops so
you continue to learn from what
happens as a result of actions.

30. Ella Fitzgerald (American jazz singer), 1917-1996
“It isn't where you came from, it's where
you're going that counts.”
30

31. Where does your responsibility end?

32. A simple segmentation to consider adjusting your style
Face the Political Reality
32
CARR YING
READING
Politically aware
Politically unaware
Acting with
integrityPsychological
game-playing

33. Focus on action not outputs
Ensure request is for action
Design analysis to be actionable
Include recommended actions
Give progress updates on action
Measure effect of actions
Change your language
33

34. That’s all for now on Step 9

35. That’s your preview
Where might you need to
develop your People Skills to be
a more effective Analyst?

36. Take action in the next 2 weeks
Action-orientated learning
36
?
What one thing will you do differently (within the next 2 weeks) as a result of this webinar?

37. Further details are available
How to contact me…
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@LaughlinPaul
+44 (0)7446 958061
linkedin.com/in/paullaughlin
paul@laughlinconsultancy.com

39. Any questions?