Ericsson s journey with SAP Data Services & Information Steward

Transcription

1 Ericsson s journey with SAP Data Services & Information Steward Göran Domeij, Manager Informa2on Management Modeling & Tools, Ericsson goran.domeij@ericsson.com Tobias Nilsson, Informa2on & Master Data Management, Acando tobias.nilsson@acando.com

2 content Introduction Master Data Automation ETL/DQ background and purpose Information Steward implementation project Comparison IS/DS and old solution Lessons learned (so far ) Ericsson SBN Implementing Information Steward Ericsson Internal Page 2

3 This is Ericsson NETWORKED SOCIETY A connected world is just the beginning

4 at a glance #1 MOBILE INFRASTRUCTURE OPERATIONS & BUSINESS SUPPORT SOLUTIONS SERVICES TV & MEDIA DELIVERY 35,000 25,000 32B SEK Patents R&D Employees In R&D 1 billion 2.5 billion 64,000 Subscribers managed by us Subscribers supported by us Services professionals Net Sales 227B SEK 2013 LTE smartphone traffic 50% handled by our networks Countries with 180 customers 114,000 Employees Ericsson SBN Implementing Information Steward Ericsson Internal Page 4

5 The ripple effect ICT progress builds positive change for society, business and people SOCIETY BUSINESS 80 jobs are created for every 1000 broadband connections Source: Ericsson, AD Little PEOPLE 20 million Spotify users in 4 years, accessing 20 million songs creating 1 billion playlists Source: Spotify 6.3 million concurrent players on the Steam online gaming platform Source: Valve Ericsson SBN Implementing Information Steward Ericsson Internal Page 5

6 Progress years 50 billion connected devices Connections (billion) years 5 billion connected people years 1 billion connected places 1875 Ericsson SBN Implementing Information Steward Ericsson Internal Page

7 Three key shifts Mobility broadband Cloud Ericsson SBN Implementing Information Steward Ericsson Internal Page 7

8 Information management ways of working to cope with trends External environment and trends In-memory computing Mobility Collaboration Big data Security Cloud Internet of things Key implications for IM Need to establish and execute new common frameworks, governance, tools & WoW Engage and educate business and the global IM-community Need to structure critical unstructured data The information flows visualized and optimized Drive improvements from insights, trends, analysis Business demand for central team with strong modeling skills Reuse of information and information models Internal requirements and trends ICT Company Global skill & scale Faster development Data based decisions New Business models Automation Value flows Ericsson SBN Implementing Information Steward Ericsson Internal Page 8

9 MDM maturity The Maturity of MDM in global enterprises is essential for Operational Excellence Ericsson s Position Reference Gartner report: Use the Gartner MDM Maturity Model to Create Your MDM Road Map by John Radcliffe and Andrew White, August 2009 Ericsson SBN Implementing Information Steward Ericsson Internal Page 9

10 MD at Ericsson MD Automation Will Improve Ericsson s Operational Excellence Current MD Trends and Challenges require a more automated and standardized approach to improve Operational Excellence Ericsson MD Trends & Challenges Complexity product configuration changes (e.g. espp) Data Volumes Data Volumes are constantly increasing with new product releases Supply Chain Excellence Increase agile management of MD and reduce lead times in delivering master data to the business and at the same ensuring high quality Mergers & Acquisitions Increased complexity of master data convergence Tool Standardization Variety of master data tools evolved through time require standardization Integration of processes Ensuring integration of demand and supply planning in a timely manner Compliance Meeting regulations and standards Financial Analysis High quality and fast availability of data to ensure efficient Spend Analysis Operational Excellence The goal is to improve the speed and agility of MD to meet current trends, while reducing costs and improving quality Ericsson SBN Implementing Information Steward Ericsson Internal Page 10

11 MD automation Decide on when and What to Automate High Data Volumes Stable Process Value Add to Business Standardized Rules When to automate? Automate Validation and Load of Master Data PRIM ONE ECP What to automate? Verdi XXX Supplier Product/cost/price Customer Finance Ericsson SBN Implementing Information Steward Ericsson Internal Page 11

12 Implementing information steward

13 ETL Tool Background and Purpose Background A lot of different custom tools have been developed to extract/load data from/to different source systems in the Ericsson Landscape The extracted data is joined and transformed for validation, reporting and loading purposes in specific tools. The data is stored in silos between the tools and requires a lot of manual handling Any changes to reporting and data validations require modification of complex custom code in the tools Purpose The goal is to create an enterprise view of data instead of siloed applicationcentric approach by reducing the amount of different tools and manual steps The implementation of an Extraction, Transformation, Load (ETL) Tool will allow: Automate quality controls across master data objects and across systems Improve transparency and traceability by showing where the data comes down to field level. e.g. a BW report should give full traceability Improve quality of new data loads by validating against business rules. Ericsson SBN Implementing Information Steward Ericsson Internal Page 13

14 The Variety of Custom Built Tools Leads to inefficient Data Handling Business Challenge Different ways of working for data extraction, transformation and load The extracted data is spread across different tools, to achieve transparency complex joins are needed Any changes to the master data structure or reporting requirements requires modification of complex code in the tools ONE 16 custom built tools with Silo View of data Tool1 MDM Custom Report1 Verdi ECP 48 Tables - Complex crossapplication extractoins/loads Tool2 Tool Tool 16 Changed reporting requirements lead to complex Tool modification Custom Report2 Custom ReportN XXX Ericsson SBN Implementing Information Steward Ericsson Internal Page 14

15 An ETL Tool will Achieve a DATA view Cross Applications Business Value Creation of an enterprise view of data instead of siloed application-centric approach 1 way of working for all data extraction, transformation and loading Automate quality controls and measurements across master data objects and across systems Make data transparent by showing where the data comes down to field level. ONE MDM Validation, Cleaning, Transformation of load files Standardized Reporting Verdi ECP Automated Extraction/Loads ETL Sporadic Reports OLAP Reporting XXX Business Rules Rule 1, Rule 2, Rule N Dashbords Ericsson SBN Implementing Information Steward Ericsson Internal Page 15

16 ETL/DQ Tool Summary An enterprise wide view of data consolidated in 1 spot will ensure high quality, speed, traceability and transparency ETL will provide an enterprise wide view with a process to acquire data and convert it into useful, compatible and accurate data ONE MDM Verdi ECP XXX ONE MDM Verdi ECP XXX Tool1 48 Tables - Complex cross-application extractoins/loads Custom Report1 Tool2 Tool Tool16 Changed reporting requirements lead to complex Tool modification Custom Custom Report2 ReportN 1 Data View cross systems Faster Downloads Maintenance and flexibility 16>Tools to 1 Tool Many Tool Owner to 1 Tool Owner Validation, Cleaning, Transformation Standardized Reporting Automated Extraction/Loads ETL Sporadic Reports OLAP Reporting Business Rules Rule 1, RuleN Dashbords Ericsson SBN Implementing Information Steward Ericsson Internal Page 16

17 From single etl/dq to more holistic view Pre-study change its initial thesis to have only one shared ETL/DQ platform insights in capabilities, ways of working and strategic roadmaps concluded we should have multiple platforms where SAP Information Steward is better suited for Data Quality and will further enhance our SAP HANA implementation Information Steward fits Ericsson & SAP strategy and roadmaps Roadmap for SAP HANA (in-memory platform), MDM & next-generation data quality Easy for end-user to custom-build quality reports (quality on demand without coding) Strategy to share metadata between ETL/ DQ platforms will give us best analytics solution where best applied Ericsson SBN Implementing Information Steward Ericsson Internal Page 17

18 Implementation project Establish project, onboard stakeholders and conduct training Identify reports to convert and business rules to implement Define Architecture, Roles & Authorities, Interfaces Information Steward and Data Services Implementation Tool Sep Oct Nov Dec Week Project Establishment Setup the server Installation of SAP IS/DS 2. First meeting with the project team (19 persons) Present the project goals Present the requirements (from the team) 3. Specify the systems and business rules The systems, business rules and the reports Specify use cases / workshop with the team Workshop and meetings with the team regarding use cases 4. Training course in SAP IS Specify a training course which cover our needs Training course for the project team Coaching, training, brainstorming (Jandy/Sam/The team) 5. Specify the user, user groups and roles (IS guidlines) Determine the user groups and their roles Define the users and their user groups 6. Apply/Define the systems/business rules (Back- end) Define connections/select tables/define the rules Create reports 7. Workshop with the project team Verify/Validate the content in IS 8. Stakeholders/Project team approval Meeting with the project team Phase 2 Q Governance & Processes Reporting & Dashboards New use-cases/quality reports Ericsson SBN Implementing Information Steward Ericsson Internal Page 18

19 Starting Load strategy SAP Data Services used to load from source to Landing Area (via HANA) in Staging Database where also reference data is loaded Landing Area loads Data Mart and group the data into views Data Mart is accessed via projects in SAP Information Steward where rules are created and reports run Complex reports requiring multiple joins and cross data validation are built in SAP Data Services, re-using rules from SAP Information Steward Other sources ONE MDM SAP Data Services Verdi ECP XXX ETL (Initial Load and Incremental) Landing Area Staging Database Data Mart Data Quality Control SAP Information Steward Quality Reports Ericsson SBN Implementing Information Steward Ericsson Internal Page 19

20 Connections and rules Landing Area One data mart for each operational area MDM Vendor Master Data Supply Order Flow Data One connection for each data mart in IS Data Mart Rules in IS that are approved become global for any project to use Non-approved rules may exist locally in a project Project SSO MDM Vendor Master Data Vendor Master Data User Group Rule Project SSO MDM HR Master Data Supply Order Flow SSO MDM Tool&General Project Rule Rule Rule Project Approved business rules Supply Order Flow User Group Rule Project Not approved business rules Quality reports Ericsson SBN Implementing Information Steward Ericsson Internal Page 20

21 Rule Setup with IS/DS The valida2on rules for the Supplier project requires a single database table that is joined together from SAP data and other reference data. This requires a large number of queries to be built in a complex data flow. It results in a quality report with a business oriented structure. Adding a new valida2on rule requires this single table and its queries to be updated. An evalua2on must be made to see how new rule objects impacts this report structure. Total rule crea2on 2me is also dependent on network performance. Object may contain millions of records which makes the system slow to respond. Create/update reference table in SQL Server if needed (~2 hours per rule) Create/update queries to join data to single table in SQL Server (~2 hours per rule) Create and test validation rule in IS (~3 hours per rule) Ericsson SBN Implementing Information Steward Ericsson Internal Page 21

22 Query complexity Ericsson SBN Implementing Information Steward Ericsson Internal Page 22

23 Rule Setup with Access The Access tool is inflexible and very hard to fully understand. Consequence: Very difficult, if not impossible, to add new rules and maintain old ones The creator of the Access tool is no longer at Ericsson Consequence: No expert to share knowledge and help make updates The rules in the Access tool are different from what s currently used by MDM. They have been improved in IS. Consequence: The Access tool is unusable with the new rules IS requires queries only for combining tables. Access, having no built in validation tools, requires queries both for combining tables and for performing the actual data validation. Consequences: Even more complicated query flows are needed for Access tools than what s needed for IS Harder to maintain and more time consuming to build tools using Access Ericsson SBN Implementing Information Steward Ericsson Internal Page 23

24 ACCESS vs IS/DS Execution Time Table extraction from SAP (~4 hours can possibly be optimized) Run Access tool - a series of queries and macros run in the background (~2 hours) Total execu2on 2me: ~6 hours Extract report (~5 mins) Table extraction from SAP (~1 hour) Data transfer to SQL Server (~10 mins) Generation of single table (~5 mins) Execution of validation rules (~10 mins) Total execu2on 2me: ~1.5 hours Export final report (~5 mins) MS Access slower to run than the IS/DS solu2on although the table extrac2on could poten2ally be op2mized by reducing the number of tables to download Main boqleneck is the extrac2on of SAP data which can poten2ally be improved with HANA in the future Ericsson SBN Implementing Information Steward Ericsson Internal Page 24

25 Comparison Solu-on Data Volume Per MD area Execu-on Time Rule Matching Business Rule Logic Complexity Rule Maintenance Complexity IS/DS > 5GB Short High High Low Yes ACCESS < 2GB Long Low High High No Rule Management Process Remarks Ø ACCESS database has 2GB file size limita2on Ø IS/DS depends on SQL server disk space Ø ACCESS needs half day at least Ø IS/DS needs less in 2 hours Ø ACCESS rules too hard understand to update Ø IS/DS rules are all confirmed with supplier team during edi2ng Both rules logic are all complex Ø ACCESS rule hardly maintain due to its structure unclear Ø IS/DS rule easily maintain due to clear structure Ø ACCESS rule without management process Ø IS/DS has MDQM process to manage update Ericsson SBN Implementing Information Steward Ericsson Internal Page 25

26 Issue list Of 38 issues raised during implementation 37 has been fixed and the last remaining one is on the way to be resolved. Ericsson SBN Implementing Information Steward Ericsson Internal Page 26

27 Lessons learned (so far) Architecture and Interfaces Migration and Metadata Identify source systems and start setting up interfaces/connections as early as possible Involve IT Security early to review and compare Roles & Authorities structures Understand the different capabilities between Information Steward and Data Services Technical parts (installation & setup) among the easier parts Have information model ready when creating cross system reports Have well defined ownership of business rules and the rules documented before converting them Governance and Change Management Most effort when implementing Information Steward lies in changes to ways of working Think of type of governance model in advanced if going from central governance to combination of central and federated governance Have sandbox environment to work in and develop processes/ framework as you learn using the solution Ericsson SBN Implementing Information Steward Ericsson Internal Page 27

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