The least you need to know about Big Data s Business Application in 18 minutes. Vincent Suppa.

Transcription

1 The least you need to know about Big Data s Business Application in 18 minutes. Vincent Suppa

2 The Least YOU Need to Know Oil - currency of industrial economy. Data - currency of knowledge economy. Firms rely less on internal data & more on external data People claim privacy concerns as they volunteer troves of data Ability to learn valued more than experience; intuition less critical Demand outstrips supply for HR Data scientists

3 Five against Six Waves 5 Waves Leading to Big Data 1. Metrics 2. Benchmarks 3. Dashboard Indicators 4. Predictive Analytics 5. Big Data 6 Waves of Technological Revolution Main Frame Era PC Era Internet Web 1.0 Social Media Web 2.0 Big Data Internet of things Web 3.0

4 Big Data versus Traditional Analytics Standard software & databases not suitable Volumes of data self explanatory Variety of unstructured data not scalable Velocity of data Increasing rate of real time streaming data Speed that data must be evaluated to retain its value

5 Structured versus Unstructured Data Structured Data Standard data: relational databases Linear analysis Binary data definitions Quantitative Examples HRIS Accounting systems ERP system modules Unstructured Data Free form qualitative information Amorphous data points Examples Narrative replies on surveys Social media posts Blogs Wikis s Videos Images

6 Origin of Firm s Big Data Categorize them into internal versus external Transactional systems Relational Databases Business Applications ERP systems (Resource Enterprise Systems) s Social Media LinkedIn Facebook Digital exhaust Internet of things low processing embedded chips linked to internet via wireless technology Double Click & other firms that aggregate big data

7 Internal Side of Big Data: ERP Big Data pulls data points across entire enterprise Marketing Customer Relationship Management Accounting Supply Chain Management Inventory Control Material Purchasing Production Planning Customer service R&D Operations Finance Human Resources

8 External Side to Big Data? External Data LinkedIn & Social Media Vendor s & Supplier s ERP systems s, Surveys & Expense Reports ERP Internal Data

9 You simply use the data you already have.

10 Application of Big Data in Defense Company Train thousands of employees in latest technology. Analyze (1) internal and (2) external data to determine ROI of training locations using dependent available of total costs. Variables include: Class room costs Hotel costs Instructor s cost Flight costs Forecasted locations Expected attendance

11 Application of Big Data in Silicon Valley Firm specializing in network infrastructure products Firmed used LinkedIn to track & analyze (1) skills, (2) knowledge, (3) experience & (4) career paths of: Employees Former employees Potential employees

12 Application of Big Data in Global Courier Delivery Firm Determinant for M&A Decisions Analyze aggregate workforce data from acquisition target. Cross analysis of target s engagement survey results against their own data to quantify cultural fit.

13 Taking Action Now 1. Determine decisions to derive from Big Data 2. Take inventory of data firm already has 3. Determine dependent & independent variables for hypothesis 4. Contract STEM grad students & turn them into data scientists 5. Learn coding (free on-line courses!) to learn the language. Coding is the new literacy

14 Independent vs Dependent Variable Independent variable: manipulated variable Dependent variable: measured response Independent variable is the presumed cause. Dependent variable is the presumed effect.

15 Intuition versus Data Driven Decision Decisions based on data - not intuition! Qualified decisions by seasoned professionals versus Quantified data driven decision

16 Implications of Big Data Data provided not clean or structured Signals from data usually not strong Number of systems providing data from dozens to thousands Ratio of external to internal data is high Volume of data requires a Data Warehouse

17 Illustration of Why External to Internal Data Ratio is High Firms with internal skill set databases report that employees are more conscientious at updating their set sets on LinkedIn than on their own internal company database.

18 Data Mart is to blank as Data Warehouse is to what?

19 Extracts data from source Transforms data for storing Loads data into DataMart

20 What Big Data Might Look at the C suite Once you determine what leading indicators cause the lagging indicators (results) the firm cares about present them in a Dash Board

21 Dashboard Indicators Provide at-a-glance views of relevant KPIs Enterprise wide (for CEO) or department wide. Dashboards: set with green, red or yellow thresholds Typically limited to aggregate summaries: trends comparisons exceptions.

22 Elements of Dashboard Indictors Sophistication in simplicity Minimum distractions Indicators should not correlate Supports business with knowledge Applies visual perception to information presentation Give managers views into their data marts Knowledge defined as actionable information

23 As an example..

24 Drilling Down to Specific Big Data Illustrations

25 Financial Planning Firm Looking to Pull Recruit Talent Cross pollinate current employee data (ERP) and LinkedIn s external data for same dataset. This was done to test multiple hypotheses on traits (independent variable) that cause on-the-job success. (dependent variable) Success Predictor: having a family member already working for the company. employees who were entrepreneurial had connections to large cap companies ties to community through volunteer work

26 It s about the Correlation Firm developing model to use against external data to resolve dependent & independent variables for this hypothesis. Pull Recruiting Reduces SGA costs of hiring Higher penetration rate Recruits high potential employee

27 Another Case Study Firm conducting correlation - not causation - studies between employee internal survey data & external social media data presumably posted by same data set. Premise: employees as de facto brand ambassadors social media is their megaphone. Determine discrepancies between what (1) employees say about firm on social media against what (2) they say on internal engagement surveys.

28 Big Data predicts probability of outcome Create attrition model capturing 30 + data points to predict months in advance, with high correlation, which employees are going to resign for better offers.

29 Operational Advice Predictive modeling requires historical data If firm not ready to executive analytics, do this now! 1. Take inventory of already collected data. 2. Write hypothesis you d like to test 3. List data points that would support testing of hypothesis. 4. List add l data points that still need to be captured. If your firm never goes forward, you now have a solid resume bullet point on what you ve accomplished so far.

30 Network smart, not hard!