Understanding Business Data Analytics
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The document discusses data analytics from the perspectives of different stakeholders and outlines some of the challenges that can arise from a lack of alignment. It notes that when vendors, technology teams, business analytics, and executive leaders do not share a unified vision of data analytics, it can lead to issues like unhealthy competition, redundancy, and hindered collaboration. The document emphasizes the importance of good management of data analytics to impact business goals and establish consistent performance metrics across teams.
Understanding Business Data Analytics
- 1. 1 Table of Contents • Analytical Challenges • Imperatives • Road Map • Functions • Data Integration and Validation • Improvement Cycles Understanding Business Data Analytics Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 2. 2 Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 3. Vendors ◦ Software BI companies use the term Data Analytics to enhance the value and outline certain functions and capabilities of their products. Technology ◦ IT organizations relate to Data Analytics through the lens of enterprise solutions, technology architecture, data management optimization, business users requirements and data warehousing. Business Analytics ◦ Relate to Data Analytics through data analysis to provide business insights, value and ongoing support to their business customers Executive Leaders ◦ Relate to Data Analytics through results and insights from data analysis and reports that helps them gain a competitive edge, predict, manage and strategize the business 12/15/2016Copyright © 2013 www.DataMeans.com 3
- 4. 12/15/2016 Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com 4 Executive Leaders Business Analytics Vendors Technology Lack of alignment on Data Analytics philosophy , roles and strategy leads to duplication, increases cost and organizational grid lock Don’t get the all the insights that they need Don’t have accurate access to data, resources or collaboration to answer important business questions Competing roles with Business Analytics, lack of time and focus to peel the onion for answers Solution is not optimized or not well spec. Not aligned to support clients business grow. Happy and unhappy customers Small analytics convergence=Small Benefits Lack of Analytics Vision Convergence has a Detrimental Effect
- 5. 5 Data silos Hard to get data Long turn around times and high cost Unable to meet business needs on time Too many cooks cooking the data Efficient Access to the data Quick turn around on data analysis Focus on Answering business questions vs getting and fulfilling requirements and specs Advanced Analytics to Drive Business Grow Build Efficiencies and reduced waste Build partnerships with IT and business units Excellent Business, technical and data analytics skills Operationalized analytical findings • Too much emphasis on company data platform and adherence to use of IT tools, policies and procedures • Too much reliance on specs and requirements • If it is not in IT scope of work it won’t happen • Every variation of work is associated with additional cost and approvals Analytics organizations are structured: • For quick response to the business • To get the job done independently of tools or platform • To adapt to changing business needs • To address a problem from a business perspective ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 6. Lack of Analytics Vision Convergence Creates Unhealthy competition for resources and attention Competing visions about data assets management, technology imperatives and transfer of knowledge Lack of unified vision of key business performance metrics Redundancy Sprout of data silos Struggle for control of data assets Hinders collaboration among teams 12/15/2016Copyright © 2013 www.DataMeans.com 6
- 7. Good Management of Data Analytics is Paramount to: Impact the Bottom line and sustain business grow Establish consistent versions of business Key Performance Indicators KPIs Build synergies and efficiencies Reduce redundancy and cost 12/15/2016Copyright © 2013 www.DataMeans.com 7 Executive Leaders Business Organizations Technology Organizations Technology Partners Analytics Driving Business
- 8. 8 Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 9. 9 Drive strategic outcomes, business insights and answer business questions Balance analysis with information needs to find opportunities Develop sustainable and transferable analytical knowledge Define performance metrics, drive change & synergies Manage change to increase efficiencies and profitability Manage, recruit & staff Analytical organizations. Develop technical analytical capabilities. Establish a single representation of business true reality. Integrate data from multiple Sources. Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 10. 10 Building & Management Analytics Practice Promotion Response Models/Predictive Models Customer Segmentation/Data Analysis/ROI Study Design/Pre and Post Change Management Analytics Sales Force Effectiveness/Field Force Expansion/Call Plan Custom Turnkey Analytical Solutions Multi Channel Marketing Analytical Support Data Integration, Data Marts, Automation & Validation Reporting Solutions / Reports Automation & Rationalization Digital Analytics ©2015 Data Means
- 11. 11 Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 12. 12 TV & Journal Ads Email & DM A 360 Degree view of customers is critical for business grow Sales Digital Impressions Sales Force Activity Coupons & Vouchers Costumer Surveys Costumer Master File POS Distributors Financial & Cost ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 13. 13 • Customer satisfaction • Life Time Value • Segmentation • Circle of influences • Demographics • Attributes • Email & DM Campaigns • Engagement Programs • Digital Impressions • Coupons & Vouchers • Loyalty Programs TheCustomer• Sales Force Effectiveness • Call Planning • Incentive Compensation • Territory Alignment • Sampling • Lunch & Learn Sales $ Explore Customer data to develop new insights Engage with the right message in the right channel Increase Sales & Efficiencies Reduce Cost ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 14. Analyze Target Track Report Business Grow 14 Business Performance CRM/Customer Relationship Management Recruitment Auxiliary Business Analytics Support • Data Mining • Predictive Modeling • Decision Support Analysis & Reporting ©2015 Data Means
- 15. 15 Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 16. Client has a data analysis, reporting or processing critical need or idea that can not be met through current systems or resources Data Sources Efficient Data Processing & Validation Process Final Data work with client to come up and implement the most efficient and cost effective solution for clients needs Dynamic & efficient process to conduct data analysis or reporting Analytical Functions Reporting 16 ©2015 Data Means
- 17. Defining change objective ◦ Reduce Cost ◦ Improve Profitability ◦ Increase Efficiencies Establish a quantifiable baseline Develop a change process Implement change Measure change Impact Recalibrate process 17 Objective Baseline Metrics Implement Change Measure Impact Recalibrate Process ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 18. 18 Segmentation Response Models Sizing Expansion KPIs and Dashboard Reporting Incentive Compensation Geo Alignment Effectiveness Measurement Call Plan design and execution Test & Control Geo tests ©2015 Data Means 0 20 40 60 80 100 120 140 160 1 2 3 4 5 6 7 8 9 10 Avg Sales Calls Activity Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 19. 1 2 3 4 5 6 Ideas Information Data Understand the Problem Set Goals Estimate Opportunity Build Consensus Develop Program Get Support Form Team Set Work Plan And Milestones Develop Evaluation Methodology Run Program Review Interim Results Make Program Adjustments NRx Sales Productivity Gains Adherence Evaluate & Measure 19 Inputs Prepare Execute Output EvaluateDevelop The Promotional Event Process Inputs Transformation Output Evaluation Planning Execution Results Project Cycle Analytics Functions Promotion Response ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 20. 20 Population Of Interest High Value Targets No Targeted Targeted Low Value Targets Targeted No Targeted Targeted Shift targeting to Valuable Targets • Optimized campaigns by finding the most valuable customers • Redesigning targeting strategy based on data • Measuring the impact of campaign using appropriate statistical methodology • Make recommendations www.DataMeans.com ©2015 Data Means
- 21. Repoder Groups Score Range # Subscriber # Cummulative Subscriber # Responders # Cumulative Responders Cumm % Subscriber Cumm % Responders 1 510-806 5,255 5,255 3,000 3000 10% 22% 2 806-870 4,940 10,195 2,500 5,500 19% 41% 3 870-905 4,519 14,715 2,400 7,900 28% 59% 4 905-928 3,731 18,446 2,000 9,900 35% 74% 5 928-945 3,206 21,651 1,000 10,900 41% 82% 6 945-957 2,680 24,332 776 11,676 46% 87% 7 957-966 2,628 26,959 400 12,076 51% 90% 8 966-973 2,522 29,482 300 12,376 56% 93% 9 973-978 2,417 31,899 200 12,576 61% 94% 10 978-981 2,050 33,949 100 12,676 65% 95% 11 981-985 1,944 35,893 80 12,756 68% 96% 12 985-987 1,944 37,837 90 12,846 72% 96% 13 987-988 1,944 39,782 100 12,946 76% 97% 14 988-990 1,944 41,726 90 13,036 79% 98% 15 990-991 1,944 43,671 80 13,116 83% 98% 16 991-992 1,892 45,563 70 13,186 87% 99% 17 992-993 1,839 47,402 60 13,246 90% 99% 18 993-994 1,787 49,189 50 13,296 94% 100% 19 994-995 1,734 50,923 30 13,326 97% 100% 20 995+ 1,629 52,552 22 13,348 100% 100% Total 52,552 13,348 Score models are used to predict the likely hood that a customer will respond to an offering or event. The score produced by the model is used to rank customers. The lower the score the higher the likelihood to respond 10% 19% 28% 35% 41% 46% 51% 56% 61% 65% 68% 72% 76% 79% 83% 87% 90% 94% 97% 100% 22% 41% 59% 74% 82% 87% 90% 93% 94% 95% 96% 96% 97% 98% 98% 99% 99% 100%100%100% 0% 20% 40% 60% 80% 100% 120% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Score Targeting strategy Cumm % Subscriber Cumm % Responders By targeting 35% of the subscribers we capture 75% of the responders With scoring model client will be reaching about a more profitable groups of customers at a lower cost 21 ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 22. 22 Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 23. 23 Business Intelligence + Data Warehousing + Inventory Management + Data Mining + Marketing Optimization + Forecast + Marketing Automation + Predictive Modeling + Analytical Evolution ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 24. Data Integration & Validation Analytics & Reporting Rx Data Calls & Samples Alignment Demographic Promo & Third Party Call Plan Automated Data Process Data Standardization DataMart Targeting Promotion Response Samples Optimization Segmentation Customer Life Time Value Ad Hoc Brand Reviews Marketin g Executiv e Manage ment Field Force Support Call Plan The Data The Data The Processes The AnalyticsThe Reports 24www.DataMeans.com ©2015 Data Means
- 25. Current Database New Database Both files Current and new matched It is only in the current database It is only in the new database Data Migration Making Sure that your Data is Right run freqs on matching variables List and compare a few raw records form bad files to get an idea of the source of mismatches For large data warehouses migration validating the data is a daunting process 25 Data Integration & Validation www.DataMeans.com ©2015 Data Means
- 26. Data Validation Process Develop process, for series of files, in anticipation of file delivery. A batch of files to be compared is delivered Run QC Programs on the batch files Assemble report on batch files (concurrent w/ run) QC Programming Review/ annotate FAIL Investigate / fix action items If files are close user runs reports with new file and compares results Pass log as file done 26 ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 27. 27 Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 28. 12/15/2016Copyright © 2013 www.DataMeans.com 28 Excellence on Data analytics is not about • Getting state of the art technology to harness the value of big data (Hadoop, Phyton, SAS, R…etc…) • Data warehousing with the best breed data base platform • Data mining to uncover unknown relationships hidden in the data • Contracting with the smartest software vendors, experts or analytics companies Excellence on Data Analytics is about • Building the foundation to gain business insights using the available data in an accurate and timely fashion • Applying business knowledge and sound data analysis expertise to answer specific business question • Having the rigor and knowledge to systematically manage data assets and transform insights into actionable results • Continuous development of collaborative relationships with the business, IT, Vendors and other partners
- 29. 2912/15/2016Copyright © 2013 www.DataMeans.com Data Analytics Evolution and Maturity Cycle
- 30. 30 Analytical Engagement www.DataMeans.com ©2015 Data Means
- 31. 31 The Big Picture Goals & Resources How & When Improve Improve •Integration •New Products Launch •Field Force Restructuring •Hiring Freeze •Reorganization •Recruitment •Documented •Validated •Efficient •On Time •Within Budget •Flexible Improve •Find •Screen •Recruit •Present •Engaged Resources Needs ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com
- 32. 12/15/2016Copyright © 2013 www.DataMeans.com 32 Important Elements of a Data Analytics Organization • Adequate # of Staff • Analytical Skills (Stats, critical and outside the box thinking) • Technical skills (data management, programming skills, problem solver) • Availability of appropriate technology tools • Business knowledge and Excellent communications Skills • Efficient access to data • Collaboration • Clear vision of the future and ability to rally others around the vision
- 33. 12/15/2016 33 Analytical Skills Data Accessibility YES NO YES NO NO YES NO YES Collaboration Technical Skills Adequate # of Staff Cross Functionality Processes & Standardization in Placed Business Knowledge Copyright © 2013 www.DataMeans.com #1 •Data silos/Managed differently. Some not managed but stored •Different business rules /Poor documentation •Data is not normalized •Manual creation of reports •Kept in different formats(Excel, Access, SQL server, Oracle, DB2, Cobol, txt, SAS….etc) •No efficient data access •No systematic data QC #1 •Able to use properly statistical methods to answer a business question •Able to create business story from data results •Draws business implications from data analysis and reports •Generates the urgency to react and act based on data results #2 •Sound process to standardized, normalized, aggregate, combined, validate and QC data at different levels •Creation of periodic reports must be automated •Centralized analytical data mart #3 •Understands the business and market trends •Knowledge about products and competitive landscape •Understand sales and marketing channel and sale force customer interactions #3 •No collaboration with IT partners •No transfer of knowledge •No sharing of best practice, tools and lessons learned •No responsive to the business partners and continuous changes of requirements and questions #4 •Appropriate data analysis and reporting technology platform •Strong data management and analysis programming skills •Likes to learn new things and welcomes challenges •Excellent communications skills •Team player •Good management skills #2 •Lack of technical, analytical or managerial staff. •Projects under staff •Unable to maintain ongoing and take on new projects at the same time The 3 ChallengesThe 4 Achievements
- 34. 12/15/2016Copyright © 2013 www.DataMeans.com 34 Optimum Capabilities Extremely Valuable for the Business Stagnation/ Knowledge, Technology and Process Dissemination Middle Capabilities Adds Significant Value to the Business Getting loss in the corporate organization shuffle/Opportun ities to Optimize Analytics No Capabilities Provides Some Value to the Business Becoming Irrelevant/Signific ant Opportunities to Become a Shining Star Value Risks Opportunities
- 35. 12/15/2016Copyright © 2013 www.DataMeans.com 35 Developing and maintaining talent is critical for an analytics organization • Have a pipeline for new talent • Career path and career development for existing talent • Encourage Innovation and out of the box thinking • Build internal and external partnerships for talent acquisition and development Senior MiddleJunior Diverse experience levels are important for success
- 36. 36 Know +What… +When…. Understand +How…. Optimize Process +Do it better +Grow the market +Increase sales Organization’s Analytical Evolution If organization knows and understands, there is no limit to improve in making better business decisions ©2015 Data Means Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com