Predictive Analytics for the Business Analyst. Fern Halper July 8,

Transcription

1 Predictive Analytics for the Business Analyst Fern Halper July 8,

2 Sponsor

3 Speakers Fern Halper Research Director for Advanced Analytics, TDWI Allen Bonde VP, Product Marketing and Innovation, Actuate 3

4 Agenda Brief overview of predictive analytics Predictive analytics trends Skills required Getting started 4

5 The Analytics Spectrum Visualization Advanced Analytics Other BI Dashboards and reports Excel Often becomes more algorithmic 5

6 Predictive Analytics A statistical or data mining solution consisting of algorithms and techniques that can be used on both structured and unstructured data to determine outcomes. 6

7 Some Use Cases Marketing and sales** Healthcare Fraud detection Human resources Operations maintenance And many, many more!

8 Popular Methods Decision Trees Regression Cluster Analysis <$100 Total Monthly bill >$100 Length of time customer No. of phones on account > 2 yr < 2 yr Etc 90% probability No Call center calls 8

9 Trends Ease of use Disparate data types Operationalizing and embedding advanced analytics Prescriptive 9

10 1. Ease of Use Graphical UI Automation Solutions 10

11 Ease of Use New builders Predictive Analytics 11

12 New Users are Emerging Statistician/Modeler Moving towards critical thinker with knowledge of the business -- e.g. a business analyst

13 The Business Analyst Rules (source: TDWI Best Practices Report Predictive Analytics for Business Advantage, 2014) 13

14 With an Evolving Skill Set This ranks low 14

15 2. Data, Data New Data Types Predictive Analytics 15

16 New and Big Data Types for Analysis Disparate data sources 16

17 3. New Deployment Models Operationalizing Embedding Predictive Analytics 17

18 Embedded Analytics An example: 18

19 Example: Fraud Objectives: Reduce fraud Improve customer experience Benefits Speed up process Reduce false alerts Save money 19

20 4. Prescriptive Analytics Whereas predictive analytics helps to determine what might happen, prescriptive analytics takes this further to either suggest or automatically initiate a subsequent action based on this output. 20

21 Skills Needed (1) 3. Domain Expertise 1. Critical Thinking 2. Data Sense Framing the problem 21

22 1. Critical Thinking Ability to formulate a question Comfortable creatively thinking in numbers and attributes Interpretation skills Inference Above all: Questioning 22

23 2. Domain Expertise Helps in: formulating good questions understanding objectives assessing the model and taking action on it Understanding relevant data Dealing with data outliers, missing data, etc. 23

24 3. Understanding data Target vs. explanatory variables Derived variables Lots of new data types Documents, graph, location May require parsing, geocoding 24

25 Skills Needed (2) 4. Tools 5. Techniques Explain/Defend 25

26 4. Understanding the tools! 26

27 5. Understanding the techniques A basic understanding is necessary 27

28 6. Storytelling Don t start with the techniques Begin with the business problem and the outcome. (source: vitualspeechcoach.com) 28

29 Getting started An analytics program does take time But you can get started quickly Not necessarily sequential! 29

30 Getting started Pick a problem Experiment and involve business/it POV/POC tied to metrics Decide beforehand how to integrate it into a process Balance cost of model with model building solutions 30

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32 Fast is the new Big! Actuate Corporation

33 Fast analytics enables Iterative process Better questions Instant feedback employ A/B testing, chunk down problem what do I ask next? what can I adjust? 33

34 Staying Focused is Key Actuate Corporation

35 What s the business case? Improved cross-sell? Better customer understanding Getting all data together Pricing optimization Risk management 35

36 Picking the Right Tool Actuate Corporation

37 Access data Disparate sources, billions of records Complexity of loading, cleaning Need all data in one view Key Challenges Understand Patterns Easily profile and segment Look for trends, relationships Explore, visualize with no coding! Deliver Insights Enable non-technical business users Support iterative, collaborative work Integrate with operational systems Insights in minutes vs. days 37

38 APPROACH: Make it easy to access and integrate data To create a complete picture of customers, we need to combine insights from social channels and campaigns with Web and transactional data 10 Reasons 2014 will be the Year of Small Data, ZDNet, Dec 2013 CRM/ERP Social Columnar DB Web Other sources 38

39 and shorten time-to-value by using pre-built analytics Many vendors are trying to (make predictive analytics available to an end user in a consumable form) but in our view BIRT Analytics comes closest to getting it right, by not requiring the user to select algorithms IDC, Feb 2013 Columnar DB Exploration & Visualization Analytics & Data Mining Campaign Workflow Segmentation Profile Forecast Decision Tree Cluster 39

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41 visit my blog:

42 Questions? 42

43 Contact Information If you have further questions or comments: Fern Halper, TDWI Allen Bonde, Actuate 43