Data Virtualization An Agile Approach To Improving Profitability - PowerPoint PPT Presentation

Data Virtualization – An Agile Approach To Improving Profitability Mike Ferguson Managing Director Intelligent Business Strategies Denodo Executive Briefing Brussels, November 2016

About Mike Ferguson Mike Ferguson is Managing Director of Intelligent Business Strategies Limited. As an analyst and consultant he specializes in business intelligence, data management and enterprise business integration. With over 35 years of IT experience, Mike has consulted for dozens of companies, spoken at events all over the world and written numerous articles. Formerly he was a principal and co-founder of Codd and Date Europe Limited – the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing Director of DataBase Associates. www.intelligentbusiness.biz mferguson@intelligentbusiness.biz Twitter: @mikeferguson1 Tel/Fax (+44)1625 520700 2

Topics  Improving profitability  The increasingly complex data landscape  The impact on the business of distributed data  What is data virtualisation?  Improving business performance and agility using data virtualisation  Reducing time to value and increasing revenue in the logical data warehouse  Conclusions 3

Improving Profitability Is The Goal Of Every Business Profit Margin 4

Reducing Cost – How?  Reduce business complexity • Reduce location complexity • Reduce business function complexity e,g. across business units • Reduce management complexity – flatten hierarchies • Reduce product complexity • Reduce process complexity • Reduce IT system complexity AND modernise IT systems • Reduce data complexity  Automation and digitalisation across channel and lines of business  Improve performance management • Map cost centres and cost types across the value chain and all entities • Track the business impact of new initiatives  Optimise operations to reduce unplanned down time and costs 5

Increasing Revenue – How?  Customer centric organisation and operating model  Allow customers to configure products and services to meet their needs • Then build and/or ship to order  Complete 360 o insight of a customer  Targeted precision marketing for cross-sell / up-sell  Predictive and prescriptive analytics  Highest quality of customer service  Customer self-service via digital channels  Customer centric data integration  Complete view across value chain 6

Topics – Where Are We?  Improving profitability  The increasingly complex data landscape  The impact on the business of distributed data  What is data virtualisation?  Improving business performance and agility using data virtualisation  Reducing time to value and increasing revenue in the logical data warehouse  Conclusions 7

The Data Landscape Is Becoming Increasingly Complex And Lack of Integration Are Working Against Business  Line of business IT initiatives when there is a need for enterprise wide common infrastructure Customer Marketing Service System  Multiple copies of data Sales System System  Processes not integrated HR Gen. Gen. Ledger Ledger  Different user interfaces Billing system Fulfilment  Server platforms complexity Procurement System system  Duplicate application functionality  Point-to- Point “Spaghetti” application integration 8

Trends – More And More Appliances Appearing On The Market Causing ‘Islands’ of Data Oracle Exadata Pivotal Greenplum DCA IBM PureData Teradata System for Analytics 9

Big Data Is Also Now In The Enterprise Introducing More Data Stores e.g. Hadoop, NoSQL, Analytic RDBMS users business analysts developers Custom Graph Spark & MR Search based BI tools Analytic apps analytics tools BI tools BI tools real-time SQL indexes Graph MPP Analytical DBMS RDBMS DW actions Stream Enterprise Information Management processing event streams Unstructured / semi-structured content OLTP data clickstream social graph Files RDBMS Web logs social data IoT, markets data 10

Complexity Is Increasing Further As Companies Adopt and Deploy A Mix of On-Premise, SaaS and Cloud Based Systems customers partners employees Mashups Enterprise Portal Office Applications Enterprise Service Bus On-Premise Systems Within the Enterprise OLTP Off-premise Systems BI/DW OLTP apps SaaS BI Systems corporate firewall Private or public cloud Private cloud Data is now potentially fractured even WWW more than before 11

Hundreds of New Data Sources Are Emerging - The Internet of Things (IoT) High velocity, high volume data 12

The Challenge Of Fractured Data  Data in different locations  Data in different data storage Web content Office documents technologies Legacy applications ECMS Flat files  Data in different data structures E-mail  Different data definitions for the Cloud based same data in different data stores applications “Where is all the Big Data applications Customer Data?”  Some data too big to move  Different APIs and query languages BI systems <XML>Text</XML> needed to access data Packaged RDBMSs applications Digital media  Excessive use of ETL to copy data Accessing, governing and managing data • Expensive and not agile is becoming increasingly complex as it becomes more distributed  Synchronization nightmare 13

Topics – Where Are We?  Improving profitability  The increasingly complex data landscape  The impact on the business of distributed data  What is data virtualisation?  Improving business performance and agility using data virtualisation  Reducing time to value and increasing revenue in the logical data warehouse  Conclusions 14

Core Business Processes Often Now Execute Across A Hybrid Computing Environment Process Example - Manufacturing Order to cash credit order check schedule fulfil package ship invoice payment Order Finance Production CAM Inventory Distribution Billing Gen Ledger entry credit planning & system system system system control scheduling system system Orders data Customer data Product data This makes data difficult to track, maintain, synchronise and manage 15

Many Companies Have Business Units, Processes & Systems Organised Around Products and Services Enterprise Customers/ Product/service line 1 Channels/ Prospects Outlets Product/service line 2 XYZ Corp. Product/ service line 3 Order (product line 1) order credit fulfill package ship invoice payment check Order (product line 2) credit order fulfill package ship invoice payment check Order (product line 3) credit order fulfill package ship invoice payment check 16

Business and Data Complexity Can Spiral Out Of Control if Processes And Systems Are Duplicated Across Geographies Product line 1 Product line 1 Product line 1 Product line 2 Product line 2 Product line 2 Product line 3 Product line 3 Product line 3 Product line 1 Product line 2 Product line 1 Product line 3 Product line 2 Product line 3 Partners Customers Products/ Employees Services Suppliers Accounts Materials Assets 17

Business Implications Of Product Orientation and Fractured Customer Data In A World Where Customer Is Now King  Different marketing campaigns from different divisions aimed at the same customer  Different sales teams from different divisions selling to the same customer  Customer service is hard • e.g. “What is my order status for all products ordered?”  Cost of operating is much higher due to duplicate processes across product lines  Can’t see customer / product ownership  Can’t see customer risk and customer profitability  Higher chance of poor data quality  Difficult to maintain customer data fractured across multiple applications 18

This Makes It Difficult To Access And Report on Data Across The Process To Manage Business Operations What order changes in the last 10 mins? What shipments are impacted by the changes e.g. lack of inventory or shipping capacity? Which customers are affected? Order-to-Cash Process Orders credit order fulfill package ship invoice payment check Operational Problems not seen until Inability to respond reporting is long after they happen quickly to problems not timely e.g. incorrect shipments Business impact Inability to see across multiple Operational oversights cause instances of a system can cause processing errors & errors & duplication of effort unplanned operational cost 19

Planning Also Requires Data From Across A Value Chain Need to see sales, inventory, shipments, manufacturing capacity, resources and forecasts SCM ERP CAD ERP Manufacturing Shipping CRM Planning Product, Fore- SCADA execution system system system Materials casting systems Supplier Master data Plans are too No flexibility, not Planning slow and is Business dynamic, no scenarios resource out-dated by the time it impact intensive is finalised or simulations 20

Multiple Data Warehouses Are Also Common Management reporting? KPIs? SCM ERP CAD ERP Manufacturing Shipping CRM Planning Product, Fore- SCADA execution system system system Materials casting systems Supplier Master data Sales & mktng Manufacturing Finance DW DW volumes & inventory DW 21