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Objective Benchmarking for Improved Analytics Health and Effectiveness

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Achieving a high state of analytics excellence can be a daunting task. It involves mastering progressive stages of data health, technological capability, and staff readiness, all while putting out countless fires and responding to last-minute requests for analysis. Strategic progress can be slow, and charting that progress for the executive team, cumbersome and uncertain.

Join us as Denny Lengkong from Personify Implementation Partner, IntelliData, and Personify's Solution Director, Bill Connell, present a rational framework for understanding analytics health and effectiveness. This webinar will help you learn how to make targeted investments in analytics over time that everyone in your organization will understand.

Published in: Data & Analytics
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Objective Benchmarking for Improved Analytics Health and Effectiveness

  1. 1. Objective Benchmarking for Improved Analytics Health and Effectiveness Denny Lengkong and Bill Connell
  2. 2. 2 Our Speakers Denny Lengkong President, IntelliData dlengkong@intellidata.tech 1-844-237-DATA (3282) X700 Bill Connell Solutions Director, Personify wconnell@personifycorp.com 571.758.4907
  3. 3. 3 IntelliData • Northern Virginia based IT consulting company • Personify implementation partner • Specializes in association and not-for-profit implementations • Focused on business intelligence and analytics Data ► Information ► Knowledge ► Decision
  4. 4. 4 Benefits of Good Data Quality • Reduced cost  Bad data costs companies $3.1 trillion/year (1)  Salespeople wasting time dealing with erred prospect data  Service delivery people wasting time correcting flawed customer orders received from sales  Data scientists spend an inordinate amount of time cleaning data  IT spends enormous effort lining up systems that “don’t talk”  Senior executives hedge their plans because they don’t trust the numbers (1) Harvard Business Review
  5. 5. 5 Benefits of Good Data Quality • Increased customer and staff satisfaction • Increased sales • Greater confidence in analytical systems • Improved decision making “Improving data quality is a gift that keeps on giving — it enables you to take out costs permanently and to more easily pursue other data strategies” – Thomas C. Redman, Ph.D., “The Data Doc” – Harvard Business Review
  6. 6. 6 What Does Good Data Look Like • Complete • Accurate • Available • Trusted/reliable • Consistent • Up to date
  7. 7. 7 Companies with Sophisticated Data & Analytics • Better business performance • 8% higher operating margins (1) • Able to develop a “single version of the truth” about their business • Use real-time data to anticipate changes in their business and take corrective action “The goal is to turn data into information, and information into insight.” – Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co. (1) Gartner: Measuring the business value of data quality
  8. 8. 8 Data Maturity Model
  9. 9. 9 Data Aware • Multiple data sources and databases (silos) • Lack of integration • Know where the data are stored, but don't know how to retrieve them • Run basic reports • IT Dependent • Poor data hygiene (duplicates, bad data) • Need a single database (source of data) • Need a process and user training to move to the next stage
  10. 10. 10 Data Proficient • Multiple data sources and databases • Little or no integration • Know where the data are stored and know how to retrieve them • Data quality is questioned • Ready to track KPI • Run reports and manipulate data (on a separate process) • Need executive sponsorship and the know-how to manipulate or use unstructured data • Need a single database • Need to learn how to use data efficiently to move to the next stage
  11. 11. 11 Data Savvy • Few or no silos • Different data sources are nicely integrated • Data used to make critical business decisions • Executive sponsorship is in place to break down both organizational and data silos • IT must keep up by with new technologies • IT must be able store data effectively and serve up data on demand • Need to focus on building advanced capabilities such as data warehouse and predictive analysis
  12. 12. 12 Data Driven • Embed data into all business processes • No data = no decision • Objective is to scale the data strategy while continuing to reduce costs • IT and the business are functioning as a tight, cohesive unit • IT has integrated all data sources and apps and has implemented an advanced analytics platform (data warehouse, data lake, etc.) • The business has identified where and how to embed analytics in its processes
  13. 13. 13 Other Important Data Exercises (applies to all stages) •Data profiling •Data integrity •Data cleanup Data is useful. High- quality, well-understood, auditable data is priceless. – Ted Friedman, Gartner
  14. 14. 14 Data quality effects overall labor productivity (1) 20% Realize duplicate records are a concern, yet they don’t know how many duplicate records they have (2) 7% Top data quality priority is deduplication (2) 68% Failed business initiative because of poor data quality (1) 40% Cannot confirm that their data is fresh / up to date (2) 39% Do not have an official owner of their data within their organization (2) 48% Hire someone to handle data quality (2) 41% (1) Gartner, Measuring the business value of data quality (2) Ringlead, The state of data quality benchmark report
  15. 15. 15 Questions?

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