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The Softer Skills that analysts need (beyond Data Visualisation)


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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).

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The Softer Skills that analysts need (beyond Data Visualisation)

  1. 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. 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
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  5. 5. Customer Analytics delivers ROI Research & my own experience confirm analytics delivers profit 5
  6. 6. To achieve that needs Commercial Focus Data & Analytics leaders confirm relevance trumps sophistication 6
  7. 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. 8. Experienced leaders say otherwise Like me they see the need to focus on “Softer” People Skills 8
  9. 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. 10. Sharing four pieces of that puzzle In this talk I’ll introduce you to these parts of that Model 10
  11. 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. 12. The problem with requirements 12
  13. 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. 14. That’s all for now on Step 1
  15. 15. (3) Securing buy-in from the key players Identifying, prioritising and managing stakeholder relationships to ensure you manage expectations & communicate/collaborate well.
  16. 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. 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. 18. Step 2: Prioritise those who need more of your time Focus using Stakeholder Mapping
  19. 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. 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
  21. 21. How to map & segment your stakeholders to focus your efforts Further guidance is available on my blog 21
  22. 22. That’s all for now on Step 3
  23. 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. 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. 25. Converging evidence from four possible sources to spot themes Generating Insight means convergence 25 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. 26. Can use structured questioning techniques to build bridges Customer Insight Generation workshops 26 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. 27. How to run an Insight Generation workshop Further guidance is available on my blog 27
  28. 28. That’s all for now on Step 6
  29. 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. 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. 31. Where does your responsibility end?
  32. 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. 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. 34. That’s all for now on Step 9
  35. 35. That’s your preview Where might you need to develop your People Skills to be a more effective Analyst?
  36. 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. 37. Further details are available How to contact me… 37 @LaughlinPaul +44 (0)7446 958061
  38. 38. Any questions?