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MARKET UPDATE: Following the Paradigm Shift of Data Analysis

MARKET UPDATE: Following the Paradigm Shift of Data Analysis. Agenda. Changing Data Requirements: Big, Agile, Accurate Transforming Data Analytics from Search to Discovery Turning Data to Information Value Creating an Analytics-driven Culture Analytics for Non-technical Executives

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MARKET UPDATE: Following the Paradigm Shift of Data Analysis

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  1. MARKET UPDATE: Following the Paradigm Shift of Data Analysis

  2. Agenda • Changing Data Requirements: Big, Agile, Accurate • Transforming Data Analytics from Search to Discovery • Turning Data to Information Value • Creating an Analytics-driven Culture • Analytics for Non-technical Executives • New Sales Opportunities from Analytics • Q&A

  3. Changing Data Requirements: Big Data Traditional Analytics Big Data Analytics Relational Database Silos, Structured Data, Data Warehouses Unify Silos, More Data Enterprise Data Outside The DW Third-Party Data Unstructured, Semi-structured Documents New Databases & Sources

  4. The Importance of Agile Business/IT Collaboration • Organizations that have achieved lasting benefits from formal data quality improvement programs tend to take a holistic approach involving people, repeatable processes, and appropriate technology. • An agile approach is predicated upon decentralization, moving the ownership of data closest to those who understand the data and are impacted by quality control over the data. • All of this requires trust, which is fueled by increased agility of analyses and accuracy of results.

  5. A Virtuous Cycle Of Agility, Accuracy, And Trust Trust fosters collaboration between IT departments and business users, starting with the data-driven requirements gathering process which is essential to trustworthy analyses Accurate results increase trust, lowering objections to further decentralization Agile collaboration between self-sufficient business SMEs and data brokers yields better, faster results

  6. What Type of Data Manager Are You? Data Waster Data Collector Data Valuer Strategic Data User

  7. How to Become a Data Strategist • Senior-level ownership of the organization’s data strategy • Partnership with IT

  8. Analytics as an Organizational Philosophy • Constant tuning and monitoring of processes • Requires a mix of data sleuths, analytics software, reporting coupled with data management and business stakeholder involvement • Analytics that provide process guardrails, coupled with ability to discover new exceptions • Ability to quickly identify and resolve issues by business owners

  9. Putting Data to Work at Fairpoint NNE • 500,000+ customers offering services from Plain Old Telephone to Carrier Ethernet services • Converted Northern New England Verizon territory (ME, NH, and VT) in 2009 • Shifting of revenue from voice to DSL and Carrier Ethernet service required advanced data analytics

  10. Transforming Data Analytics from Search to Discovery Fairpoint NNE has evolved its data management and analytic capabilities over the past 3 years 5. Strategic Adoption Drive changes at executive level from analytics (Book to Bill) 4. Expanded Trust Spread analytics to other departments up to the CxO level 3. Data Analytics Base decisions on a single source of data (single dept.) 2. Clean Data How does it relate across systems 1. Sync Data Bring data together

  11. Creating an Analytics-driven Culture with Clear-cut ROI • Data knowledge -> trust -> greater value • Show how you can relate data across systems • Demonstrate you can deliver results in a short period of time • Ex: On many occasions turning CxO level requests regarding order activity or customer tendencies in 1-2 days. • Led to a change in Sales criteria: which customers to target for DSL service • Data control leads to better ROI • Easy to demonstrate value compared to a traditional Requirements, Design, Build, Test process • Ex: Daily analysis and improvement of data gathering regarding our customer line terms and promotions with the CMO • Led to a repository of customer data leveraged by many department that drives mail campaigns, SFDC Opportunity generation, and call center activities

  12. Qualifying Analytics Potential to Non-technical Executives • Add technology for projects with specific goals/results • Ex. Data Sync of applications was an initial use of Lavastorm yielding numerous cleanup efforts increasing revenue and order flow through • Demonstrate that additional analytics can replace or improve existing processes • Ex. Replacing 3rd Party “Scorecarding” application with one Lavastorm graph/process • Demonstrate value over and above current process, such as: • With the Lavastorm solution we could visually review the process, and sample the data at any point in the process to ensure validity • Decommissioned the old data warehouse and OBIE solution replacing it with the Lavastorm / Cyfeon solution

  13. The Difference Between Data Value and Information Value • Data value – just the facts • Ex: Retention data analytics reveals customer trends associated with what our customers do at the end of a term or promo period • Information value • Extrapolation shows what the data really means • Ex: Realize people are more likely to leave within the first 30 days after expiration of a promotion than at any time following the expiration • Business value comes from information value • Information value leads to understanding • Ex: Drive re-term and promo sales initiatives at the end of their term – we have a better chance of retaining a customer

  14. New Sales Opportunities from Analytics • Data management and analytics has yielded a single source of the truth for our company • Resulting in many expansions into the operating groups within Fairpoint • Personalized analytics by developing a web interface to address ongoing analytic requests from the operating groups • Integration with CRM/Salesforce data ties data to sales activities • Customer retention data reveals customer trends and indicators • Use Lavastorm to generate opportunities in Salesforce to drive our sales team to reach out to customers at the point we found that our customers were leaving us

  15. Summary • Changing data requirements – bigger, more agile, more accurate • Strong data analytics foundation is the key, leading to • Information that leads to business value • Use • Trust • Expansion • Demonstrations lead to executive buy-in and an analytics-driven culture • Analytics exposes greater insights, including new sales opportunities

  16. Questions, Next Steps Get Lavastorm Analytics Engine Public Edition (FREE) http://www.lavastorm.com/resources/software-downloads-trials/ Contact Us Kerry Reitnauer +1 603-656-8188 kreitnauer@fairpoint.com Mark Marinelli +1 617-948-6244 mmarinelli@lavastorm.com Brandon Smith +1 512-981-9408 bsmith@cyfeon.com

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