[Webinar Slides] Tapping the Power of Content Analytics – Exploring this Powerful Solution
•
0 likes•570 views
Check out these webinar slide to learn how to take the guesswork out of unstructured analytics and begin to use it to connect the dots, make smarter business decisions, and put your data to work for you. Let’s stop talking about analytics and start benefitting from it! Want to follow along with the webinar replay? Download it here for free: http://info.aiim.org/using-analytics-to-connect-dots
Recommended
More Related Content
What's hot
Similar to [Webinar Slides] Tapping the Power of Content Analytics – Exploring this Powerful Solution
Similar to [Webinar Slides] Tapping the Power of Content Analytics – Exploring this Powerful Solution (20)
More from AIIM International
More from AIIM International (20)
Recently uploaded
Recently uploaded (20)
[Webinar Slides] Tapping the Power of Content Analytics – Exploring this Powerful Solution
- 4. Underwri(en by: Presented by: Agenda § Data AnalyHcs vs Content AnalyHcs § ApplicaHons for Content AnalyHcs § Example Text AnalyHcs Approach § Example ApplicaHons: ProtecHng IP, Product Feature ExtracHon, Customer Feedback Analysis
- 5. Underwri(en by: Presented by: Data Analy(cs vs Content Analy(cs § TradiHonal data analyHcs programs are focused on structured data sources – answering “what happened” § Content analyHcs programs provide understanding of unstructured data sources – answering “why it happened” With mobile devices and mulHple channels for interacHng with customers, organizaHons need to understand and correlate structured data with unstructured content to provide a 360 degree view of customers and processes
- 7. Underwri(en by: Presented by: Applica(ons for Content Analy(cs Content AnalyHcs allows derivaHon of structure and idenHficaHon of pa(erns within unstructured content and text and analysis of “dark data”. Sample applicaHons include: § Knowledge extracHon § MiHgaHon of compliance risks § Removal of Personally IdenHfiable InformaHon (PII) § Analysis and disposiHon of “dark data” § ProtecHon of intellectual property § IdenHficaHon of pa(erns of fraud § Development of QuesHon Answering systems § Training of Intelligent Virtual Assistants and Chat bots § DetecHon of customer senHment § PredicHon of credit risks § Feature extracHon from product data
- 8. Underwri(en by: Presented by: Approach § Define objecHves § Model the domain § Define classificaHon approach § Develop rules/training sets § Run/ refine/ measure
- 9. Underwri(en by: Presented by: Comparison of Classifica(on Approaches Strengths Limita(ons Rules-based • Immediate with taxonomy • Clarity of approach • Absolute literality • Broad/subtle concepts • Term evoluHon • Diminishing returns StaHsHcs-based • No taxonomy required • Serendipity & discovery • Training sets difficult to build • Majority rules over truth • StaHsHcal “mystery” Data extracHon / EnHty extracHon • [Data] No taxonomy required • [EnHHes] Simple rules • No subjecHvity (with validaHon) • ValidaHon guidelines required • Unstructured documents require pairing with rules Structure analysis • No taxonomy required • Parse and classify based on format • Requires structured documents and/or good training sets
- 10. Underwri(en by: Presented by: Example Text Analy(cs Approach Sample output, refine algorithms Select training set and algorithm Create rule base Define classificaHon approach Design InformaHon Structures Define business objecHves StaHsHcal • Documents containing example characterisHcs • E.g.: Pa(erns represenHng fraudulent loan applicaHons, documents represenHng base product configuraHons, posiHve /negaHve reviews, etc. • Define ROT criteria (content not accessed in 3 years, near duplicate content threshold, out dated file types, etc) • Create pa(erns for matching (social security number format ###-##- ###, text preceding PII) • EnHHes to be extracted, excepHon rules • Remove ROT prior to migraHon • Eliminate PII • Extract product data from PDF’s • Analyze customer feedback • Etc. • IteraHve tesHng and refinement is key • Look for outliers • Compare manually classified/extracted to machine derived (quality, level of effort) • Develop confidence/quality scores • Develop domain model • Create metadata structures • Define enHHes and enHty lists Rules based
- 13. Underwri(en by: Presented by: Products & Services Pricing & Transparency Contact Experience Likelihood to Recommend Reliability Terms & CondiHons Corporate Social Responsibility Integrity Ease of Use FuncHonality CompeHHve Pricing AdverHsing On-Boarding Gemng Help Repair I am able to use the products when I want The products are simple and easy for me to use I can use the products in my life I know I'm gemng a fair price--especially when I comparison shop Billing is clear, simple and accurate I feel the terms and condiHons are fair and flexible I can feel proud to be a customer of ABC: they have great ads ABC is there for me every step of the way as I become a new customer ABC is there for me every step of the way when I need help ABC is there for me every step of the way when I need a repair I can feel proud to be a customer of ABC: they are responsible I can feel proud to be a customer of ABC: they are a good corporate ciHzen Billing Feedback text (concept) Value subcategory Example Applica(on: Text Analy(cs Applied to Customer Feedback ReputaHon & Brand Image Value category
- 15. Underwri(en by: Presented by: Example Applica(on: Let There Be Light (on Dark Data) “Dark Data” – enormous amounts of unstructured content living on file shares, in applicaHons and on desktops • Is there value in this informaHon? • Are there risks in keeping it? • Are there risks in deleHng it? • How can we find out what is there? • How do we separate low value from high value content? Contracts 7 year retenHon Purchase Orders 10 year retenHon Everything over 10 years Delete Duplicate content Archive ClassificaHon rules for disposiHon EnHty extracHon for tagging
- 16. Underwri(en by: Presented by: Classes of Analy(cs § DescripHve – What happened? RetrospecHve point of view § PredicHve – What is likely to happen? Future oriented based on trends and prior behaviors § PrescripHve – What should happen? Helps to guide the business in opHmizing an outcome
- 19. Underwri(en by: Presented by: Copyright © 2017 Earley InformaHon Science Measuring here (business outcomes) Measuring here (process indicators) Enterprise Strategy Business Unit ObjecHves New Business Opportuni(es Average Order Size Total Account Revenue Business Processes Site Traffic Search Relevance Search Digital Content Working & Measuring here (content, IA, taxonomy, search, data fill, etc.) Web Content CRM Processes enable objectives LINKAGE Leads Revenue Growth Content supports processes Objectives align with strategy CEO: “How will this increase revenue?” Conversion Content Scorecards Process Scorecards Outcome Scorecards CTR Fill Rate Content Quality etc. Digital Team: “How do I know taxonomy / content / search is working?” Determining AnalyHcs Context 19
- 21. Underwri(en by: Presented by: Instant Poll § What is your biggest challenge related to collecHng data? a) streamlining processes b) web & mobile connecHvity c) changing compliance needs d) escalaHng costs e) improving your customer experience
- 23. Underwri(en by: Presented by: Automa(ng Handling of Customer Data § Challenge of Digital TransformaHon § Command & Control § Five Big Challenges § Barriers to AutomaHon § Power of Content AnalyHcs § AI Manager: Business Process Dashboard § Insight: Company ConnecHons
- 24. Underwri(en by: Presented by: “Business process pros must combine digital transformation efforts with customer-centric process management approaches that provide the greatest flexibility to continuously improve CX.” The BPM Playbook Is Your Guide To Customer-Centric Process Change, Forrester, April 15, 2016 Digital Transformation
- 26. Underwri(en by: Presented by: • Integrate capture, including mobile, into your workflow • Centralize admin, deployment & updates • Merge documents & data • Seamless integration into business applications • Batch & on-demand capture Web & Mobile Connectivity Streamline Processes Changing Compliance Needs Escalating Costs Improve Customer Experience • Control access • Ensure chain of custody • Make fraud detection & compliance easier • Scalable • Reduce licensing upgrades & training costs • Drive ROI • Focus on your customer • Ease of use for employees • Speed up document processing • Happy customers & staff 1 2 3 4 5 Five BIG Challenges
- 32. Underwri(en by: Presented by: CRM ERP ES/KB
- 33. Underwri(en by: Presented by: Cost to recreate a misfiled document is $120
- 34. Underwri(en by: Presented by: CRM ERP ES/KB
- 35. Underwri(en by: Presented by: Paper: Not Going Away § Signatures § SupporHng documents— can’t make the user digiHze § Regulated industries—must retain originals, must provide originals to others during transacHons, etc. § User experience vs. digital § Security
- 36. Underwri(en by: Presented by: Ac(onable Intelligence Lifecycle Full range of technologies to address all the phases in a document’s life. Document Capture & Analyze Store & Versioning Manage, Organize & Search Share & Collaborate Insights, Metrics & Reporting Migrate & Archive Actionable Intelligence Lifecycle
- 37. Underwri(en by: Presented by: Image Science Web server Connector Document Classification Data Extraction SQL DB Document Analytics D&B FICO Syste m of Recor d Electronic File Store Workflow Storage Capture Basic Capture
- 38. Underwri(en by: Presented by: Document Classification Electronic File Store Watched folder Data Extraction Web server SQL DB Document Analytics D&B Image Science FICO System of Record Connector Workflow Storage Transac(onal Capture
- 39. Underwri(en by: Presented by: Investment Officer Report Welcome: Nancy M. Gibeau Applicants Managed by Nancy Kevin Keller Betty Cooper Veronica Lodge Reggie Mantle Ethel Muggs ü Application date: 03.01.2017 Loan Program: 7A Loan value: $300,000 Guarantee: 75%, $225,000 Guarantee fee: 3%, $6750 Origination fee on loan value: 3% Interest rate: 4.09% Maturity: 5 years Applicant receives: 94.75%, $284,250 Applicant down payment: 10%, $30,000 Monthly payment: $5,765 Applicant: Reggie Mantle Disadvantaged: No Applicant age: 19 FICO® score: 734 Business age: 3 years Annual revenues: $500,000 Profitable: yes Application aging: 1 day Documents collected: 19/29, 66% Documents missing: 10 Documents collected missing signatures: 1 Last missing doc/signature alert sent: 03.02.2017 Package posted to LOS: No Menu • Send Doc Alert • Review Files • Post to LOS Chuck Clayton Click to view supporting doc
- 40. Underwri(en by: Presented by: Manager Report Welcome: Thomas J. Barry Officers Managed by Thomas Carol Cedrone Brennan Nancy M. Gibeau R.P. Field Rider Ryan Johnson ü 10 Loans in process (12 expected) Aggregate Loan value: $3,000,000 Aggregate Guarantees: 75%, $2,250,000 Average Guarantee fees per loan: 3%, $67,500 Total origination fees on loan value: 3%, $90,000 Average loan interest rate: 4.0% Average loan maturity: 6 years Average applicant FICO® score: 700 Lowest business age: 4 years Average application aging before posting: 3 days #Missing signatures found: 36 Packages posted to LOS: 24 Packages posted meeting SLA: 21/24, 88% < 90% threshold Menu • Send Doc Alert • Review Files • Reports • Post to LOS Reporting window: • CQ • CY • LQ • LY Officer Rollup
- 41. Underwri(en by: Presented by: CXO Report Welcome: Paul F. Flynn, Jr. 100 Loans in process (110 expected) Aggregate Loan value: $30,000,000 Aggregate Guarantees: 75%, $22,500,000 Average Guarantee fees per loan: 3%, $67,500 Total origination fees on loan values: 3%, $900,000 Average loan interest rate: 4.0% Average loan maturity: 5 years Average applicant FICO® score: 675 Lowest business age: 3 years Average application aging before posting: 4 days #Missing signatures found: 360 Packages posted to LOS: 240 Packages posted meeting SLA: 210/240, 88% < 90% threshold Menu • Send Doc Alert • Review Files • Reports • Post to LOS Reporting window: • CQ • CY • LQ • LY Manager Rollup Login as Manager Thomas J. Barry Carol Fleit Karen Michalski ü
- 43. Underwri(en by: Presented by: Who Are We SMAC The majority of the management team has worked together for almost 30 years. Kodak Alaris core businesses are market leaders for 25+ years Management Team has Built 4 Companies that Became Leaders in Their Industries Team Stability Solution Experts IT Industry Veterans Innovators Successful Entrepreneurs History of being first to market including - AcHonable Intelligence Lifecycle - Enterprise Search AutomaHon - Automated Soxware Compliance - E-Business Quality AutomaHon Computer ScienHsts and Engineers ExperHse – Document to Data Workflow AutomaHon ExperHse – Gaining Intelligence from Content 20 -30 years IT Industry Experience especially Enterprise Soxware AI Foundry
- 45. Underwri(en by: Presented by: Learn More About AI Foundry § Visit our website at aifoundry.com § Resource Center: download whitepapers and soluHon briefs § Visit our blog to read Industry trends & insights from AI Foundry leadership