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Data driven decision making

SHAHZAD M. SALEEM
SHAHZAD M. SALEEM

Data analytics is used to make better business decisions by combining data and insights. There are four aspects to an effective data analytics framework: discovery, insights, actions, and outcomes. Discovery involves defining problems, developing hypotheses, and collecting relevant data. Insights are generated by exploring and analyzing the data. Actions link the insights to recommendations and plans. The desired outcomes are improved decisions and performance. Different types of analytics include descriptive (what happened), diagnostic (why), predictive (what could happen), and prescriptive (what should be done). Tools used include SQL, Hadoop, machine learning libraries, and optimization or simulation software.

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DATA DRIVEN DECISION MAKING Prepared by: Shahzad M. Saleem
List Of Topics 1. An Introduction to Data Analytics 2. The Data and Analytics Framework 3. Using Data to make decisions
1. INTRODUCTION TO DATA ANALYTICS Collection of facts Can be structured & / or Unstructured What is data? Science of examining raw data impacting organizations decisions What is analytics? Enabling new products and enabling new markets(e.g. Uber, Careem) Disrupting existing markets Increased efficiency Manage risks & drive innovation How data analytics affecting business?
ChallengesinData? Variety Velocity Volume PWC Survey Data Driven Org 3 Times better in decision making Data increasing 40% for most organizations
Solving Business Problems using Data Analytics How to better combine the Art(i.e. intuition) and Science of Decision Making? Combining a more effective use of Data with the ability to extract insights Embedding analytics in the decision making culture Case Scenario PwC Client: An airline with issue of Flight delays due to maintenance PwC prepares an Analytics model that predicts 30% maintenance delays saving millions of dollars for client. This analytical model uses: 1. Fleets Message Sensor Data 2. Maintenance log Information
Making Business-defining decisions(BIG Decisions) using Data Analytics Organizations make day to day operational decisions but they lack clarity and speed needed for Competitive advantage while making BIG Decisions due to circumstances beyond control such as: - Deadlines - Technology - Disruption - Climate Change BIG Decisions are critical as they can shift the course of business or industry and even shape the world we live in. PWC Survey Nearly 33% executives value their BIG Decisions at $1 Billion + 50% executives expect to make a BIG decision at least ONCE per month
The Data and Analytics Framework WHY NEED FRAMEWORK? - Organized Data Analytics & Process of solving problems -Focus on outcomes first enabling actions/decisions & Identify where value is generated Conclusion: Structure of discussion with clients and follow path that leads to actionable insights and business outcomes. Discovery Decisions/Actions OutcomesInsights 4 Aspects of Data Analytics Framework
Discovery Define the problem What is the key opportunity? Engage stakeholders for perspective and concerns Develop Hypotheses Answer what is likely to happen? Use information from stakeholders and other knowledge to refine hypotheses Choose the hypothesis for which the best data exists Collect Data Collect relevant internal and external data sets Validate the accuracy of the data Insights Explore Data Explore data sets to understand how they would help in accepting or refuting the hypotheses Analyze Data Use Qualitative and Quantitative analysis techniques to use data to validate the hypotheses Convert outputs into user friendly formats and visualizations that will help different stakeholders understand the analysis Actions Link Insights Use actionable data insights to explain past outcomes and predict the future landscape Link insights to financial and operational metrics to specify impact and aid decision making Provide Recommen dations Prioritize insights to build actionable plans Provide solutions that help business to address future challenges Link Insights Use actionable data insights to explain past outcomes and predict the future landscape Link insights to financial and operational metrics to specify impact and aid decision making Provide Recommen dations Prioritize insights to build actionable plans Provide solutions that help business to address future challenges Outcomes
TYPES OF ANALYTICS It replies the question What has happened i.e. current and past Descriptive Analytics It replied the question Why it happened Diagnostic Analytics It replies the question What could happen in future Predictive Analytics It replied the question What should be done Prescriptive Analytics It replies the question How to adapt change Adaptive/Autonomous Analytics
TYPE OF ANALYTICS EXPLAINED BY PWC
TOOLS & TECHNIQUES FOR DIFFERENT TYPES OF ANALYTICS Analytics Name Tools & Techiniques Descriptive & Diagnostics - SQL / OracleDB - Hadoop / Spark - Tableau - QlickView - MS Access - SAS, R and Python Predictive & Prescriptive - SAS, R, SPSS, Python - Optimization Tools - Gurobi, ILOG, River Logic - Simulation Tools - Vensim, Anylogic, STELLA - Machine Learning Tools - Scikit, TensorFlow, Caffe, Theano - Natural Language Processing Tools - NLTK and Open NLP
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Data driven decision making

  • 1. DATA DRIVEN DECISION MAKING Prepared by: Shahzad M. Saleem
  • 2. List Of Topics 1. An Introduction to Data Analytics 2. The Data and Analytics Framework 3. Using Data to make decisions
  • 3. 1. INTRODUCTION TO DATA ANALYTICS Collection of facts Can be structured & / or Unstructured What is data? Science of examining raw data impacting organizations decisions What is analytics? Enabling new products and enabling new markets(e.g. Uber, Careem) Disrupting existing markets Increased efficiency Manage risks & drive innovation How data analytics affecting business?
  • 4. ChallengesinData? Variety Velocity Volume PWC Survey Data Driven Org 3 Times better in decision making Data increasing 40% for most organizations
  • 5. Solving Business Problems using Data Analytics How to better combine the Art(i.e. intuition) and Science of Decision Making? Combining a more effective use of Data with the ability to extract insights Embedding analytics in the decision making culture Case Scenario PwC Client: An airline with issue of Flight delays due to maintenance PwC prepares an Analytics model that predicts 30% maintenance delays saving millions of dollars for client. This analytical model uses: 1. Fleets Message Sensor Data 2. Maintenance log Information
  • 6. Making Business-defining decisions(BIG Decisions) using Data Analytics Organizations make day to day operational decisions but they lack clarity and speed needed for Competitive advantage while making BIG Decisions due to circumstances beyond control such as: - Deadlines - Technology - Disruption - Climate Change BIG Decisions are critical as they can shift the course of business or industry and even shape the world we live in. PWC Survey Nearly 33% executives value their BIG Decisions at $1 Billion + 50% executives expect to make a BIG decision at least ONCE per month
  • 7. The Data and Analytics Framework WHY NEED FRAMEWORK? - Organized Data Analytics & Process of solving problems -Focus on outcomes first enabling actions/decisions & Identify where value is generated Conclusion: Structure of discussion with clients and follow path that leads to actionable insights and business outcomes. Discovery Decisions/Actions OutcomesInsights 4 Aspects of Data Analytics Framework
  • 8. Discovery Define the problem What is the key opportunity? Engage stakeholders for perspective and concerns Develop Hypotheses Answer what is likely to happen? Use information from stakeholders and other knowledge to refine hypotheses Choose the hypothesis for which the best data exists Collect Data Collect relevant internal and external data sets Validate the accuracy of the data Insights Explore Data Explore data sets to understand how they would help in accepting or refuting the hypotheses Analyze Data Use Qualitative and Quantitative analysis techniques to use data to validate the hypotheses Convert outputs into user friendly formats and visualizations that will help different stakeholders understand the analysis Actions Link Insights Use actionable data insights to explain past outcomes and predict the future landscape Link insights to financial and operational metrics to specify impact and aid decision making Provide Recommen dations Prioritize insights to build actionable plans Provide solutions that help business to address future challenges Link Insights Use actionable data insights to explain past outcomes and predict the future landscape Link insights to financial and operational metrics to specify impact and aid decision making Provide Recommen dations Prioritize insights to build actionable plans Provide solutions that help business to address future challenges Outcomes
  • 9. TYPES OF ANALYTICS It replies the question What has happened i.e. current and past Descriptive Analytics It replied the question Why it happened Diagnostic Analytics It replies the question What could happen in future Predictive Analytics It replied the question What should be done Prescriptive Analytics It replies the question How to adapt change Adaptive/Autonomous Analytics
  • 10. TYPE OF ANALYTICS EXPLAINED BY PWC
  • 11. TOOLS & TECHNIQUES FOR DIFFERENT TYPES OF ANALYTICS Analytics Name Tools & Techiniques Descriptive & Diagnostics - SQL / OracleDB - Hadoop / Spark - Tableau - QlickView - MS Access - SAS, R and Python Predictive & Prescriptive - SAS, R, SPSS, Python - Optimization Tools - Gurobi, ILOG, River Logic - Simulation Tools - Vensim, Anylogic, STELLA - Machine Learning Tools - Scikit, TensorFlow, Caffe, Theano - Natural Language Processing Tools - NLTK and Open NLP