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The People Skills analysts need to succeed in their careers

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Slides from a webinar that I presented. Sharing some of the highlights of my one-day online training course on People Skills for Analysts, including my 9-step-model for effective analysis. Further details available from: http://laughlinconsultancy.com

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The People Skills analysts need to succeed in their careers

  1. 1. Paul Laughlin, Chief Blogger & Managing Director, May 2020 The People Skills Analysts need to make an impact in their organisations
  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. Background “Helping exceptional teams master the people side of analytics” 2
  3. 3. Businesses need Analytics that works Relevance trumps sophistication 3
  4. 4. Focus is on technical skills Most education & training is focussed on technical capabilitiesEDSF 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 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 Source: EDISON Data Science Framework (2017) 4
  5. 5. But Leaders say other gaps matter more Experienced Data/Analytics leaders point to need for People Skills 5
  6. 6. Use appropriate methods Engage with stakeholders Communicate clearly Address real business need “Delivering” express insight in clear business actions needed “Commercial Awareness” what is relevant to your business now? 9 Step Model for effective analysis The People Skills needed at each stage to achieve impact 6 “Contracting” translate business need into data & analytical question (1) Questioning (4) Data (5) Analysis (6) Insight (2) Planning (8) Visual Storytelling (9) Solution (3) Buy-in (7) Sign-off
  7. 7. Use appropriate methods Engage with stakeholders Communicate clearly Address real business need “Delivering” express insight in clear business actions needed “Commercial Awareness” what is relevant to your business now? 9 Step Model for effective analysis 2 quick polls to understand your strengths & weaknesses 7 “Contracting” translate business need into data & analytical question (1) Questioning (4) Data (5) Analysis (6) Insight (2) Planning (8) Visual Storytelling (9) Solution (3) Buy-in (7) Sign-off
  8. 8. Getting a better quality brief 8 (1) Questioning
  9. 9. The problem with requirements 9
  10. 10. 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 10
  11. 11. Securing buy-in from key stakeholders 11 (3) Buy-In
  12. 12. 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.” 12
  13. 13. Two-stage Stakeholder Mapping Use 360 degrees mind-mapping & then prioritisation 13 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 Managers IT Managers Finance Managers Risk Managers Legal Managers Finance Teams Risk Teams Legal Teams IT BP
  14. 14. Segmenting your Stakeholders To effectively flex your style to suit different personalities 14 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 https://businesschemistry.deloitte.com
  15. 15. Beyond Analysis, generating Insights 15 (6) Insight
  16. 16. Oliver Wendell Holmes, Sr. (American writer), 1841-1935 “A moment's insight is sometimes worth a life's experience.” 16
  17. 17. Using 4 potential sources of understanding Converging evidence 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
  18. 18. BEHAVIOUR NOW MOTIVATION BEHAVIOUR THEN WHY NOW WHY THEN Insight Generation Workshops A process to get from analysis to motivational insights 18 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…
  19. 19. How to run Insight Generation workshops Further detail available in this two-part blog post series 19
  20. 20. Storytelling with Data Visualisation 20 (8) Visual Storytelling
  21. 21. Practice effective communication The 7 Cs of traditional comms training still apply Complete Concise Considerate Concrete Clear Courteous Correct
  22. 22. Use hierarchies of communication Learn from tabloid journalists, to structure your slides 22
  23. 23. Use effective storytelling techniques Learning from what causes people to binge watch on Netflix 23 Four elements of TV dramas that create effective stories which engage audiences: 1. Proven narrative structure 2. Characters you care about 3. Good pace (brevity) 4. Visually attractive & easy to follow
  24. 24. Use Basic Charts appropriately Learn when each chart type is appropriate & design principles 24
  25. 25. Keep learning from good & bad examples We live in a ‘golden age’ of Data Viz resources, writers & events 25
  26. 26. Influencing the final outcome (action) 26 (9) Solution
  27. 27. Ella Fitzgerald (American jazz singer), 1917-1996 “It isn't where you came from, it's where you're going that counts.” 27
  28. 28. 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 28
  29. 29. Use appropriate methods Engage with stakeholders Communicate clearly Address real business need “Delivering” express insight in clear business actions needed “Commercial Awareness” what is relevant to your business now? 9 Step Model for effective analysis The People Skills needed at each stage to achieve impact 29 “Contracting” translate business need into data & analytical question (1) Questioning (4) Data (5) Analysis (6) Insight (2) Planning (8) Visual Storytelling (9) Solution (3) Buy-in (7) Sign-off
  30. 30. Use appropriate methods Engage with stakeholders Communicate clearly Address real business need “Delivering” express insight in clear business actions needed “Commercial Awareness” what is relevant to your business now? 9 Step Model for effective analysis Poll about your focus, out of the 5 steps covered today 30 “Contracting” translate business need into data & analytical question (1) Questioning (4) Data (5) Analysis (6) Insight (2) Planning (8) Visual Storytelling (9) Solution (3) Buy-in (7) Sign-off
  31. 31. Take action in the next 2 weeks Action-orientated learning 31 ? What one thing will you do differently (within the next 2 weeks) as a result of this webinar?
  32. 32. Further details are available How to contact me… 32 @LaughlinPaul +44 (0)7446 958061 linkedin.com/in/paullaughlin paul@laughlinconsultancy.com
  33. 33. Any questions?

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