Data Flux


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  • My name is SC I run Dataflux ANZ We are a wholly owned subsiduarary of SAS We specialise in Data Management and are the leaders in Data Quality according to Gartner We provide DI and MDM solutions as well as DQ Our software sits inside many SAS products like EDI server and DI server etc Possibly you own it without realising it. Possibly you use it without knowing its full potential. Drop by our stand and get to know us. Today I only have 25 mins before I get kicked off I won’t have time to go down into the weeds What we will do is cover What Why and How of MDM.
  • Speaking of Data Stewards and Data Governance its important to understand that MDM is an evolutionary process You don’t just go there in a day Organisations range from being undisciplined through to governed Indeed right now in our experience over 80% of organisations would be in the undisciplined or reactive phases. In these phases they are really IT lead with departmentally funded projects adding to the siloed nature of their data They tend to be driven by external events like compliance reporting or the implementation of a new major CRM system There exists a chasm between the reactive and proactive phases that has nothing to do with technology This chasm can never be crossed until multiple business units together with IT collaborate around business process that spam the enterprise. These business processes will need data that is accurate and current Often the business process in question might be customer focussed – say order-to-cash and so this drives the adoption of domain specific customer MDM Other times the process might be procure-to-pay and so drive supplier and materials MDD As time goes by and this focus moves beyond individual business processes to a complete enterprise view multi-domain MDM and business process automation It is here that we see the biggest paybacks with lowered risk in things like customer churn and compliance Key element though is that this is all about getting buy in from the different LOB’s and IT to work towards our common goal of our MDM charter
  • So lets have a look at how it works at a conceptual level Here we see a customer MDM example – Apex In the various systems – Call Centre, Sales Force Automation, Enterprise Resource Planning etc various versions of Apex exist They may have different names, different addresses, ABN numbers, contact information etc The central MDM hub holds what is often called the “Golden record”. It has a strict definition. Now at this point we could talk about different models of MDM. All I am going to say is The hub can actually store this information itself Or the hub can hold references to which system holds the “golden name”, the “golden address” etc Various combinations of these approaches exist and various synchronisation schemes exist For the purpose of this presentation it is enough for us to think about this at a conceptual level and understand one key thing. The apps are still responsible for the owning and managing the transaction data i.e the Call Centre system will still be responsible for recording the customer interactions The SFA app be responsible sales The ERP for interacting with suppliers etc However they will no longer own the Master Data Why? What is the reason for this shocking truth? Well if they each do that still we will still have multiple version of the truth At its heart MDM is about decoupling the applications from the common, business wide master data and managing it as an application independent asset This can be heard to hear for some but it is the only way to fix the problem of “too many watches” As you can imagine the biggest hurdle is not technology but political. In fact the technology is straight forward The tools we need all exist and are shown on the slide Data profiling and discovery – to help us find out what exists in the source systems and what state of health its in Metadata tools – to help us define definitions Data Integration tools to interact with the source systems and merge information together Data Quality tools to cleans, deduplicate the data down to a single record The ability to render the data as a business service for consumption by the source systems and my users
  • Data Flux

    1. 1. “ The Journey to Trusted Data” Saul Cunningham DataFlux ANZ General Manager 10 March 2011
    2. 2. Welcome Saul Cunningham Managing Director, DataFlux ANZ
    3. 3. Where are you on your data journey?
    4. 4. Planning is the Key
    5. 5. The Evolution of Data Awareness
    6. 6. The Current State of Data Management The top four potential barriers to coordinating data management initiatives
    7. 7. The Data Disconnect
    8. 8. Understanding the Gap <ul><li>IT Drivers for Data Management </li></ul><ul><li>Back Office Applications </li></ul><ul><li>IT Infrastructure </li></ul><ul><li>Operational Efficiency </li></ul><ul><li>Proven Technologies </li></ul><ul><li>Methodical in Approach </li></ul><ul><li>Business Drivers for Data Management </li></ul><ul><li>Front Office Applications </li></ul><ul><li>Customer Oriented </li></ul><ul><li>Revenue Generation </li></ul><ul><li>Leading Edge Technologies </li></ul><ul><li>Process Oriented </li></ul>
    9. 9. Closing the Gap <ul><li>It ’ s a question of ownership </li></ul><ul><li>Business owns the processes </li></ul><ul><li>IT owns the systems </li></ul><ul><li>It ’ s a question of sponsorship </li></ul><ul><li>Organisations can place a value on almost all assets (buildings, machinery, employees, bandwidth) </li></ul><ul><li>Data is, at its core, viewed by the business as a by-product of IT assets </li></ul><ul><li>It’s a question of technology adoption </li></ul><ul><li>Traditional data management capabilities are lacking </li></ul><ul><li>Right technology at the right time </li></ul>
    10. 10. The Data Governance Maturity Model Sales Force Automation Database Marketing IT-driven projects Duplicate, inconsistent data Inability to adapt to business changes Data Warehouse ERP CRM Line of business influences IT projects Little cross-functional collaboration High cost to maintain multiple applications IT and business groups collaborate Enterprise view of certain domains Data is a corporate asset Customer MDM Product MDM Materials MDM Employee MDM Asset MDM Business requirements drive IT projects Repeatable, automated business processes Personalized customer relationships and optimized operations MDM Business Process Automation
    11. 11. Establish Repeatable Processes
    12. 12. <ul><li>Your Data. Your Business. One Solution. </li></ul><ul><li>DataFlux enables business agility and IT efficiency by providing innovative data management technology and services that transform data into a strategic asset . </li></ul>Our Mission
    13. 13. <ul><li>Recognised as a leading provider of data quality, data integration and MDM solutions </li></ul><ul><li>Over 2300 customers worldwide </li></ul><ul><li>Been in the leadership position within the Gartner DQ MQ since 2006 </li></ul><ul><li>Single home grown platform providing DQ, DI and MDM capabilities </li></ul><ul><li>Wholly owned subsidiary of SAS </li></ul>DataFlux - Proven Market Leader in Data Management
    14. 14. DataFlux – one solution with broad application across your business “ It’s like having a “Swiss Army knife” suite of tools to characterise, clean, integrate and monitor the quality of BP’s data.” Ken Dunn (BP Data Architecture Manager)
    15. 15. Scenario 1: Business Reporting <ul><ul><li>The “classic” use of Data Quality </li></ul></ul><ul><ul><li>Access and integrate data in batch & real-time for analytics </li></ul></ul><ul><ul><li>Apply Data Quality business rules to ensure trusted data for reporting - improve User confidence in the data </li></ul></ul>Business Intelligence Legacy Systems Application Systems Data Mart DW / DM Data Warehouse Batch DQ Real-time DQ DataFlux DQ Platform
    16. 16. Scenario 2: Business Process Improvement <ul><ul><li>Improve data quality at the “Point of data entry” into your Enterprise Applications </li></ul></ul><ul><ul><li>Eliminates duplicate data entering the system </li></ul></ul><ul><ul><li>Data quality management over the life of an ERP/CRM system </li></ul></ul>Ensuring quality information at User interface Enterprise Applications Business Users DataFlux Quality Firewall DataFlux DQ Platform
    17. 17. Scenario 3: Reduce Risk in Data Migrations Legacy Systems ERP/ CRM New Enterprise Application <ul><ul><li>Migrate cleansed and standardised legacy application data into new or upgraded Enterprise Application </li></ul></ul><ul><ul><li>Reduce risk and ensure timely delivery of application go-live </li></ul></ul><ul><ul><li>Monitor DQ issues during Data Migration process to increase confidence for key stakeholders </li></ul></ul>Data Migrations Legacy Systems DataFlux DQ Platform
    18. 18. Scenario 4: Data Governance process <ul><ul><li>Meet compliance requirements with ability to trace and audit data </li></ul></ul><ul><ul><li>Consolidate enterprise data to establish a single version of customer, product or employee </li></ul></ul>Data Governance Legacy Systems MDM Application Systems Enterprise Apps Business Users DataFlux DQ Platform
    19. 19. Questions?
    20. 20. <ul><li>Improve Business User productivity with ease of use and process reuse and sharing for collaboration </li></ul><ul><li>Helps Users make decisions with confidence using trusted information </li></ul><ul><li>Flexibility to scale from small pilot projects to enterprise-wide deployments </li></ul><ul><li>Reduce costs and Total Cost of Ownership with a single easy-to-use product solution </li></ul><ul><li>Flexibility to support multiple data types and emerging technologies such as SOA and Web Services </li></ul>
    21. 21. Conceptual View of Master Data Management SFA ERP Data Warehouse Call Center Apex Equipment Pty Ltd | Newcastle Apex | Newcastle, New South Wales Apex Construction | Newcastle NSW Apex Equip & Const | Newcastle NSW Apex Equipment & Construction, Pty Ltd | Newcastle NSW 2300 Batch | Real-Time Batch | Real-Time Batch | Real-Time Batch | Real-Time Data Integration Data Quality Data Model Business Services Stewardship Console Business User Interface Data Governance Identity Management Reporting Data Profiling Metadata Discovery Business Rule Definition Entity Definition