towards advanced HR Analytics by Arie-Jan Baan and Bram Eigenhuis

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1 towards advanced HR Analytics by Arie-Jan Baan and Bram Eigenhuis

2 Content #1 advanced Data Analytics (?) #2 data Science Process #3 a case study #4 your case #5 Q&A

3 Who is who? and what is your expectation?

4 There are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns the ones we don't know we don't know.

5 Some myths Everyone Is Ahead of Us in Adopting Big Data We Have So Much Data, We Don't Need to Worry About Every Little Data Flaw Big Data Technology Will Eliminate the Need for Data Integration It's Pointless Using a Data Warehouse for Advanced Analytics Data Lakes Will Replace the Data Warehouse

6 What we hear and read Data Science Artificial Intelligence Data Mining Statistics Statistical Learning Business Analytics Machine Learning Deep Learning.

7 New data-analytics Statistics Teaching humans what has happened/is happening by looking at data Data collected to answer a given question Questions come first, data come second Data analyzed by people with the aid of computers More focus on inference (assuming a hypotheses before analyzing the data) Data Science Teaching computers to predict the unkown by learning from data Data collected electronically for possibly future use Data come first, questions come second Data processes by computer algorithms with the aid ofpeople More focus on prediction (without assuming a GDP)

8 Big data - Analytics landscape Rapidly growing Diverse and fragmented Where to start?

9 4 v s

10 Analytics Complexity From reporting to prescriptive analytics Prescriptive Predictive Forecasting Monitoring Prescriptive Analytics What should I do (to make it happen)? decision automation Predictive Analytics Based on historic data, what may happen? What is likely to happen? decision support Machine Learning Regression Analysis Simulation Optimization (Operation Research) Deep learning Analysis Reporting Descriptive and Diagnostic Analytics What happened? Why did it happen? What s happening now? decision evaluation Analytics Complexity Data mining Data exploration Data visualization

rule-based decision making statistical reasoning the case of today machine learning artificial intelligence Boolean data (yes/no) Examples phone notification time- or threshold based

classification tasks Arbitrary data (that needs to be abstracted into numbers) Examples Identification of relevant features from large datasets Quality control using various metrics

11 rule-based decision making statistical reasoning the case of today machine learning artificial intelligence Boolean data (yes/no) Examples phone notification time- or threshold based alarms simple pattern matching every programmer simple regression Numerical data (allowing for curve fitting) Examples extra- and interpolation outlier detection predictive maintenance classification tasks Arbitrary data (that needs to be abstracted into numbers) Examples Identification of relevant features from large datasets Quality control using various metrics dynamic adaptation Arbitrary data (autonomous selection of best methodology) Examples Autonomous vehicles Human-like conversational skills Intelligent digital assistant data science complex systems experts

12 Data Science process 1. Ask interesting questions (understand the business) Start 2. Start with with the data sources Which internal and external sources are available? Get Get the the data data 3. Get and understand the How is the data logged? How Which is the data data is relevant logged? How is Are the Which there data data logged? privacy is relevant issues? Which data is relevant? Are there Are there privacy privacy issues? issues? 4. Prepare, integrate and explore the data Are there patterns, correlations, anomalies? 5. Model the data Build a model, fit the model, validate the model 6. Visualize the data Create appealing graphs and let the data speak 7. Communicate insights Build a compelling storyline: what did we learn? Do the results make sense. and start again

13 Ask interesting questions (understand the business and data) 1 Determine business objectives Start with broad questions at the beginning while gathering the data, Focus on data-centric question, they start with where, when, how or how often. They allow to focus the search for data within a specific set of parameters. They are more likely to provide data that lends itself to be visually represented NB. Asking a questions is a process as such and not necessarily a task that must be done at the beginning. When having a better understanding of the data new questions arise and finally we are able to ask a good question

14 Start with the datasources 2 2 Which internal and external sources are available? Is the data available? Do you need partners NB. Don t forget open data

15 Several types of sources 1. Internal Data ERP, CRM, financial, HRM, support, product, services 2. Open Data Governmental; census; demographic; federal; environmental; company; financial 3. Commercial Data Profile brokers; marketing agencies; web profiles; demographic profiles; location; mobile; 4. Web Data Social media, websites, webdocs 5. IoT Data Devices, machines, sensors

16 Aan de slag met data consultants, data engineers, data scientisten van NS Welke data is beschikbaar? Hoe is de data gelogd? Hoe kunnen we diverse datatabellen aan elkaar koppelen? Machinistengegevens Treindienst uitgevoerd Nabel informatie machinisten [Log-informatie FLIRT materieel] Materieel uitgevoerd Personeel uitgevoerd

17 Get and understand the data 3 3 Describe the data Select data Extract data Explore data Verify data quality $ CRM, SCM, ERP Transactional data Documents Images Audio Video Social Mobile Machine Weather GPS Open Media Data Data data Sensor data

18 Two types of data Quantitative All about numbers that can be measured and counted (e.g. # autodorp in bag) Qualitative Things you can observe but can t measure. (e.g. tast, color of autodrop in a bag) Discrete (meristic) Based on counts and can only be sertain values such as whole numbers (e.g. # autodorp in bag) Continuous Can take any value in a range, infinite specificity to be accurate (e.g. height, time, temp.)

19 scales Numerical Measurement Discrete Continuous Interval Ratio Circular When data is being grouped (e.g. the aged of visitors ) Have a meaningful zero point (e.g. temperture or weight) Time (e.g. annual dates, clock times and others)

20 The state of your data The term data is very bread and general. Data comes in many forms and state of quality. Understanding your data and prepare for data analysis RAW Data Technically correct Tidy data Aggregated data Formatted data

21 Prepare, integrate and explore the data 4 4 Clean data Construct data Integrate data Enrich data Aggregate data Format data which keys are essential to merge the datafiles?

22 Data wrangling Split cells Handle missing values Expand tables Unite tables Make new variables Subset observations

Van ruwe data naar inzichten Sourcing raw data

uitgevoerd Staging and transformations Flat data file

visuals, dashboarding Personeel uitgevoerd

23 Van ruwe data naar inzichten Sourcing raw data Machinisten-gegevens Treindienst uitgevoerd Materieel uitgevoerd Staging and transformations Flat data file Exploration and Analytics Analytics library Insights, visuals, dashboarding Personeel uitgevoerd Storingsdata Nabel informatie machinisten [Log-informatie FLIRT materieel]

24 NEXT STEPS Predicting performance Create customized education program

25 Three types of analytics

26 Model the data 5 5 Select modelling technique Generate PoC (test design) Build and train model Test and assess model Improve model with domain experts

27 Data + algorithm = machine learning data algorithms

28 What is an algorithm?

29 Algorithm - requirements In simple terms, it is possible to say that an algorithm is a sequence of steps which allow to solve a certain task. Three important characteristics to be considered valid: It should be finite: If your algorithm never ends trying to solve the problem it was designed to solve then it is useless It should have well defined instructions: Each step of the algorithm has to be precisely defined; the instructions should be unambiguously specified for each case. It should be effective: The algorithm should solve the problem it was designed to solve. And it should be possible to demonstrate that the algorithm converges with just a paper and pencil.

Three types of learning Supervised Unsupervised

train the algorithm then apply it to data without a

algorithm, hope it makes some kind of sense out of the

30 Three types of learning Supervised Unsupervised Reinforcement Use training data with right outcomes to train the algorithm then apply it to data without a correct answer no training data throw data into the algorithm, hope it makes some kind of sense out of the data no training data agent learns from environment and rewards trial and error

32 Data landscape Integration of data Using the big data stack

33 Can we predict the optimal schedule for a traindriver based on his characteristics and his experience? a customized education program for new trains? Detailed timetable data Detailed timetable data Personal characteristics Detailed timetabl e data Expert Knowlegde Detailed data of delays Detailed timetable data

34 THANK YOU Arie-Jan Baan Senior Consultant / Data Scientist a.j.baan@intellerts.com Bram Eigenhuis Consultant HR Analytics b.eigenhuis@berenschot.nl

Learning Objectives for Data Concept and Visualization

Learning Objectives for Data Concept and Visualization Assignment 1: Data Quality Concept and Impact of Data Quality Summarize concepts of data quality. Understand and describe the impact of data on actuarial