Data Strategy: How to Handle the New Data Integration Challenges. Edgar de Groot

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1 Data Strategy: How to Handle the New Data Integration Challenges Edgar de Groot

2 New Business Models Lead to New Data Integration Challenges

3 Organisations are generating insight Insight is capital 3

4 Retailers are generating insight Insight is capital Revenue and Loyalty Responsive not enough, adaptive is imperative Prioriteit 2014: De klant opnieuw centraal plaatsen: versnelde verkopen dankzij het juiste klantenaanbod 4

Organisations are selling insight Data Monetization

customers after we have removed your name or any

have combined it with other people s data in a way

5 Organisations are selling insight Data Monetization We only provide data to our advertising partners or customers after we have removed your name or any other personally identifying information from it, or have combined it with other people s data in a way that it is no longer personally identifies associated with you.

6 Selling insight How to? Insight in behaviour and influence by more data Better insight in behaviour and influence by new data Use test & learn to improve faster Create data driven products 6

7 New Data

8 New Data is often Big Data Gigabytes, Terrabytes, Petabytes Volume Velocity Realtime Capture and Realtime Analytics Big Data Variety Unstructured data like Images, Documents, Structured data like files, tables, messages

software techniques Big data technologies describe a new generation of technologies and architectures, designed to

9 Data is a new class of economic assets, like currency and gold Source: World Economic Forum 2012 Big Data: A massive volume of both structured and unstructured data that is so large that it s difficult to process with traditional database and software techniques Big data technologies describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis

10 Capacity, Costs and Emerging Role of IT 10

11 Today s Top Data-Management Challenge Big Data and Machine Generated Data Machine- Generated Data Data Storage Human-Generated Data Time

Introduction of New Data Sources Social Network Profiles (interest, demographics) Social Influencers (comments, likes etc) Activity Generated data ( Internet of things ) Software as a Service (SaaS,

12 Introduction of New Data Sources Social Network Profiles (interest, demographics) Social Influencers (comments, likes etc) Activity Generated data ( Internet of things ) Software as a Service (SaaS, API s, External Data) Hadoop MapReduce (new generation of applications) Network & In-stream data (click streams) Legacy Documents (old systems of record) Machine generated data (logfiles, activities, locations) Because it will be impossible to move all the relevant data inside the four walls of a business, companies will instead have to create a new type of data integration that harvests insights and accesses data wherever it resides. 12

13 New Business Models lead to New Information Strategies

Roosevelt, 1933 "A regulatory regime basically crafted in the wake of a 20th century

14 An Example on Information Strategy Sunlight is said to be the best of disinfectants. Electric lights the most efficient policeman Franklin D. Roosevelt, 1933 "A regulatory regime basically crafted in the wake of a 20th century economic crisis the Great Depression was overwhelmed by the speed, scope, and sophistication of a 21st century global economy."

15 Business Requirement for New Analytical Techniques Cluster Analysis: Textual Analysis: Sentiment Analysis: Network Analysis: Data Exploration Social Analytics Decision Science 15

16 Information Strategy Everyone Makes Decisions Developers Analysts & Power Users 20 % Business Users 80 % Partners Customers And Beyond 16

17 Different kind of Analytical Talent in the organisation

18 What Do Users Want? Tools vs Apps 18

19 Data Management Strategies Cloud Storage Data Virtualization DWH Appliance BI Accelerator 19

20 New Data Integration Challenges

21 How RDC maximizes on Information Capital

22 RDC For more than 40 years the Dutch information broker in Automotive (mobility) and Finance Supports commercial mobility processes by innovative software solutions and the most reliable information in the market. One-stop-shop for dealerships and manufacturers Data available on license plate level High Value BI Application Easy-to-use & Intuitive for the business user Real-time data Mobile device aware Self-service analytical capabilities High performance responses 22

23 Data to Insight BI Accelerator and Scalable Self-Service Apps Action Dealerships, Representatives, Importers Business Consumer BI Dashboards & Applications BI Analytics & Self-Service Apps Insight Information Access Central DWH Metadata Central BI Accelerator Data Data Management Real-time Data Integration Server Data sources

24 RDC InfoApp Targeted Self service BI 24

25 BI Accelerator How does it work? Column Orientation Knowledge Grid statistics and metadata describing the super-compressed data Data Packs data stored in manageably sized, highly compressed data packs Smarter Architecture No maintenance No query planning No partition schemes No DBA Data compressed using algorithms tailored to data type

26 How Informa maximizes on Information Capital

Informa Informa is the largest publicly-owned organizer of exhibitions, conferences and training courses in the world. The Academic Information Division publishes books and journals with over 80.

27 Informa Informa is the largest publicly-owned organizer of exhibitions, conferences and training courses in the world. The Academic Information Division publishes books and journals with over titles available. Revenue: 1.4 Billion, Employees: 7.000, Offices in over 40 countries 34 Million Customers Business Requirements: InFront Marketing Application w users with batch/continuous datafeeds from SAP/SFDC Many data issues cause bad marketing campaign results and poor quality of service, cross-sell and upsell

28 Informa Informa delivers content to highly specialized niche markets under numerous brands.

29 Informa Some Metrics To date over 100 individual marketing databases have been combined Annually over events are organised Over books and publications are provided to universities around the globe academic articles are downloaded daily. That s two every second New databases are still being added to the central system Contacts come from many different sources

Informa Objectives & Goals Globally improve the quality of customer data by decreasing the total number of contacts by 25% (duplicates) and improve the quality of the other 75%.

30 Informa Objectives & Goals Globally improve the quality of customer data by decreasing the total number of contacts by 25% (duplicates) and improve the quality of the other 75%. within and across multiple databases Allow data stewards world wide to validate and cherry pick the best records to merge Give the various marketing businesses control over the process of increasing the quality of their own data Establish a fully automated and repeatable process

Informa Cleansing data to maximise matching success Connected address databases for every country in the world, updated monthly Takes into account the different ways of writing addresses in various

31 Informa Cleansing data to maximise matching success Connected address databases for every country in the world, updated monthly Takes into account the different ways of writing addresses in various countries Verifies addresses or proposes multiple options based on input Defined as a web service for real-time and online address validation Matching logic dynamically adjusted to reflect the preference of data stewards Processing approximately records an hour

Informa Results Reduced number of contact globally by 25% by removing duplicates Data Quality improvements allow for cross and up-sell opportunities across multiple brands Improved customer

32 Informa Results Reduced number of contact globally by 25% by removing duplicates Data Quality improvements allow for cross and up-sell opportunities across multiple brands Improved customer retention and loyalty due to better segmentation and personalized offers Globally increased brand reputation and recognition Real-time analysis and matching of incoming contacts (Data Quality Firewall)

33 Who is Information Builders?

34 Information Is Capital Leverage It Through Integration, Integrity, and Intelligence Market Leader in business intelligence, integration and data integrity Thousands of customer, millions of users 39 years of experience 1,450 employees in offices around the world 34

35 Information Builders Business Enablers Our Platform Business Intelligence Advanced Analytics Performance Management Data Quality Management Master Data Management Data Governance Integration Infrastructure Data Integration Universal Adapter Suite 35

36 Information Builders Partnerships and Industry Adoption Strategic ISV Partners Technology Partners Systems Integration Partners 36

Analytics in Action transforming the way we use and consume information

Analytics in Action transforming the way we use and consume information Big Data Ecosystem The Data Traditional Data BIG DATA Repositories MPP Appliances Internet Hadoop Data Streaming Big Data Ecosystem