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Trends in business intelligence 2012

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Transformational changes that take place in the digital world definitely change the nature of business intelligence and represent an new normal. The Internet is the societal operating system of the 21st century and its underlying infrastructure - the cloud computing model - represents a "disruptive" change. A networked infrastructure, big data from disparate sources and social media among other trends as predictive analytics, the self-service model and collaboration are changing the way BI-systems are deployed and used.
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  • 1. Trends inin Business Intelligence Studie en Advies Johan Blomme Data Consulting Services www.the-new-bi.be
  • 2. Transformational changes that take place in the digital world definitelychange the nature of business intelligence and represent a new normal.The Internet is the societal operating system of the 21st century and itsunderlying infrastructure – the cloud computing model – represents a« disruptive » change. A networked infrastructure, big data from disparatesources and social media among other trends as predictive analytics, theself-service model and collaboration are changing the way BI systems aredeployed and used. 2
  • 3. Trends in BIIntroduction 3
  • 4. • In today’s marketplace, change is a constant.• Products are increasingly commoditised, development cycles have shortened and expectations of consumers are rising. To achieve a sustainable competitive position, companies must react in an agile way to changing market conditions.• The current business environment evolves from a transition towards globalization and a restructuration of the economic order. The pace of technological changes that allow instant connectivity and the current era of ubiquitous computing that resulted from it, represent « the new normal in business intelligence». 4
  • 5. • As an industry, business intelligence has to adapt to environmental changes.• The evolution of the Internet as a new societal operating system, reshapes the future of business intelligence.• The Internet evolves as a platform for the use of interoperable resources (storage, computing, applications and services) and drives the development of information intensive services in the 21st century. Increasingly, the cloud becomes the vehicle for the Internet of Services.• The business ecosystem generates a huge amount of data in terms of volume, variety and velocity, and requires businesses to take on a data-driven approach to differentiate. It’s about gaining actionable insights faster than the competition by reducing the data-to-decision gap.• This highlights the integration of structured and unstructured data (esp. social media content) to derive actionable insights from « big data » and the leverage of predictive analytics for agile decision-making. 5
  • 6. • The exponential growth of data and the increased reliance on insights derived from data for decision-making, causes a shift in the focus of business intelligence. BI is more than an IT- function and is about people and business decisions.• Therefore, the emphasis of next-generation BI should be on designing solutions that focus on answering business questions of the end user. In the field of BI the finished product is not a dashboard displaying metrics but actionable intelligence answering the business question at hand. Users want seamless access to information to support decision-making in their day-to- day activities.• The future direction of BI will thereby be shaped by the new age of computing. In both their personal and professional lives, Web-savvy users have adopted the principles of interactive computing and have come to demand customizable BI-tools with high responsiveness. Business intelligence, and the insights it delivers, evolves towards an enterprise service that follows the lines of a self-service model with business users producing their own reports in an interactive way and performing analytics on demand. 6
  • 7. • Furthermore, Web 2.0 and social networks function as catalysts for highly intuitive user interfaces and the collaborative features of computing allow users to share insights, which transforms BI from a solitary to a collaborative activity.• Companies are exploring the connection between analytical activity and knowledge sharing. Combined with collaborative technologies that « crowdsource » intelligence from various partners of the extended enterprise, this approach provides the context for better and faster decision-making. 7
  • 8. The factors that constitute the new normal in BI can be summarised as follows : The Future Internet Predictive Analytics Big Data Trends in Social Media Analytics Cloud Computing BI Collaborative BI Embedded BI User Empowerment / Self-Service BI 8
  • 9. Trends in BI1. The Future Internet 9
  • 10. • The main objective of enterprise computing is to be adaptive to change.• The new generation of enterprise computing must enable pervasive BI deployments : – spreading BI to more users and more devices : • consumerization of IT : enterprise computing aligns with consumer-class technologies ; • BI-tools are more and more organized around the user’s experience to interactively discover hidden relationships, trends and patterns and to create new information and relate it with external data sources ; – using multiple data sources : the use of structured as well as semi- and unstructured data sources (e.g. social media content) extends the playing field of BI. 10
  • 11. • The new generation of enterprise computing needs to be developed within the perspective of the future Internet : – the Internet as data source : • BI applications no longer limit their analysis to data inside the company and increasingly source their data from the Internet to provide richer insights into the dynamics of today’s business ; – the Internet as software platform : • BI applications are moving from company-internal systems to service-based platforms on the Internet. 11
  • 12. Web-based technologies enable BI-applications are deliveredthe implementation of user-configurable as a service on the Web or BI applications connecting to a wide hosted in the cloud arrangement of data INTERNET-ENABLED NE ING XT IT-INFRASTRUCTURE UT -G MP E CO NE RA ISE TIO R PR TE N EN 12
  • 13. • The Internet of the future gives rise to a new Business business model that allows enterprises to form business networks : Networks – in the knowledge economy economic activity is based on highly networked interactions ; The Future – the amount of digital collaboration is increasing Internet among people, things and their interactionsInt rvic (through the Internet of People and the Internet Se ern es of Things, networking is expanding not only in ta et Da person-to-person interactions, but also in of person-to-machine and machine-to-machine g Bi interactions). 13
  • 14. Globalization T he ng ndi Con sum xpa r e E as m fo es of IT rization eb e g e W osyst chan Th Ec x s sE ine Bus Device-Indepen Information AcceDemographic Shifts Drivers of Workforce NETWORKED INFRASTRUCTURE dent ss Hyp e Soc r Adop tive ora ial N tion lab logies Tec etworki of Col hno hno logy ng Tec Bandwidth Cloud Computing & Connectivity 14
  • 15. • Business networks take on a data-driven approach to differentiate and apply fact- Business based decision-making enabled by advanced Networks analytics: – economic interactions are based on the principle of scarcity and in the knowledge economy the concept of scarcity applies to information ; The Future – information in itself does not create competitive Internet advantage (access to lots of information hasInt rvic already become ubiquitous) ; competitive Se ern es advantage is defined as access to information, ta the decisions based on that information and the et Da actions taken on these decisions ; of g Bi – business networks manage data in real-time, support anywhere, anytime and any device connectivity and provide the appropriate information to users across and beyond the enterprise (business users, partners, suppliers, customers). 15
  • 16. • The Internet serves as a platform for a Business service-oriented approach that changes the Networks way of enterprise computing. With BI- applications moving to the web, the Internet emerges as a global SOA that is referred to as an Internet of Services. The IoS serves as the The basis for business networks. Future Internet • The new BI requires technologies that integrateInt rvic multiple data sources, address business needs Se ern es in a dynamic way and have a short time to ta et Da deployment. of g Bi • Contrary to large scale application development of traditional BI, the new BI moves towards smaller and flexible applications that can adopt quickly and are supported by a service-oriented architecture. 16
  • 17. • SOA is an architecture whereby business applications use a set of loosely coupled and reusable services that can be accessed on a network.• Often implemented by Web services, a SOA is a building block for flexible access to multiple data sources and the very nature of services that can be reused and integrated with each other allows business processes to be adopted in an agile way to adjust to changing market conditions and to meet customer demands.• With cloud computing, this service model is delivered on demand. The delivery model is no longer installed software but services. 17
  • 18. Internet of Services and BIUser empowerment / Self-service Cloud computing Embedded BI Cloud computing emerges as a new Users expect to have access to deployment model of BI by thebusiness information in the same way BI moves into the context of business adoption of a service-oriented as they use the Internet and search processes and transforms from a architecture and drives a the Web. Self-service BI is the reactive to a proactive decision- transformation in application implementation of this service- making tool by monitoring architectures through using “the Web orientation at the end-user level. performance and the prediction of as a platform” for interoperable future events. This change in the use applications and services. and delivery of software is guided by the adoption of a service-oriented approach. 18
  • 19. Trends in BI2. Big Data 19
  • 20. 20
  • 21. VARIETY VELOCITYVOLUME 21
  • 22. Major sources of « big data » 22
  • 23. The evolution of the Internet and the proliferation of dataData 3V The Cloud The Web The Internet Semantic Web Social Web Desktop/PC era Static Web Internet of People Internet of People and Things producer generated content user generated content. system generated content time 23
  • 24. • As connectivity reaches more and more devices, the volume, variety and velocity of data from clickstreams, social networks and the Internet of Things (through which the physical world itself becomes an information system) creates a new economy of data.• Traditionally, BI applications allow users to acquire knowledge from company-internal data through various technologies (data warehousing, OLAP, data mining). However, the typical pattern of cleaning and normalizing proprietary information through an ETL process into a data warehouse is challenged by the transition to big data that is marked by greater accessibility, interoperability and 3rd party leverage of online data.• For businesses to become responsive to market conditions, it is necessary to look at the whole ecosystem by connecting internal business data with external information systems. BI- applications must access data from disparate sources inside and outside the firewall, consider qualitative and quantitative data and include structured as well as semi-structured and unstructured data. 24
  • 25. • Data from the Web is feeding BI applications : – BI applications no longer limit their analysis to data inside the company, but also source data from the outside, especially data from the Web. The Web is a data repository. – An important challenge is the extraction, integration and analysis from hererogeneous data sources.• BI applications move to the Web : – BI applications are increasingly accessible over the Web : BI is consumed as a service from the cloud. – The challenge here is the development of Web-based applications that access and analyze both historical enterprise data and real-time data, especially from the world wide market and making the information available on a variety of devices. 25
  • 26. The increasing volume and complexity of data The 3 V’s represent the common has forced organizations to look at new data dimensions of big data, but the real management and analytic tools to optimize challenge lies in extracting actionable performance, improve service delivery and insights from it. discover new opportunities. Variety Database TechnologyVelocity Analytics Volume Services 26
  • 27. • Heterogenous datasets are no longer manageable by a traditional relational database approach.• Requirements for next-generation BI-tools include : – connect directly to the underlying data sources to capture distributed data ; – schema-free : relationships between data are discovered dynamically ; – anytime, anywhere access with multiple devices ; – real-time visibility of what is happening now is needed and analytics must be used in the stream of business operations. 27
  • 28. • New approaches such as in-database analytics, massive parallel processing, columnar databases and « No SQL » will increasingly be used for the analysis of structured as well as unstructured data. 28
  • 29. • Traditional RDBMS and SQL-based access languages are unfit to the new world of unstructured information types.• NoSQL (« Not only SQL ») is a database management system that is more versatile than traditional database systems. – Map Reduce and Hadoop, for example, are currently the most widely known NoSQL approaches. – Data is stored without a pre-defined schema and big data sets are analyzed in parallel by assigning them to different servers. – Results are then collected and aggregated and can be further used in conjunction with relational database systems. 29
  • 30. • BI has evolved from historical reporting to the pervasive analysis of (real-time) data from multiple data sources. Transactional data is analyzed in combination with new data types from social, machine to machine and mobile sources (e.g. sentiment, RFID, geolocation data). 30
  • 31. • Organizations that embrace a « socialization of data »-approach by incorporating and converging disparate data sources into their BI-platforms, acquire a holistic view that provides them with the opportunity to derive actionable insights, e.g. – analytics of real-time customer sentiment and behaviour yield indicators of product or service issues ; – geospacial information of customers can be combined with transactional data to make targeted product or service offerings ; – combining internally generated data with publicly available information can reveal previously unknown correlations.• In its focus on the user experience, BI embraces Web 2.0-technology that focusses on intuitive user interfaces. Organizations must master visualization tools that let business users interactively manipulate data to find tailored insights that can be shared with other stakeholders (customers, partners, suppliers). 31
  • 32. Trends in BI3. Cloud Computing 32
  • 33. # apps / # users ING PUT COM UD CLO GE O MA OTC  virtualized connected ET/D RN environment INTE  Internet-based data VER access & exchange SER  eCommerce NT- CLIE  « as a service »-  service-oriented paradigm architecture  networking PC  Web 2  office automation  data warehousing INI E/M INFRA M  desktop computing MA  centralized automation 1970s 1980s 1990s 2000s 2010 & beyond 33
  • 34. • As the competitiveness of businesses increasingly depends on adapting to changing market conditions, companies outsource tasks and processes to external providers.• This trend can be linked to the creation of business ecosystems in The Future Internet with vendors offering their services.• Software-as-a-Service (Saas), for example, is a type of cloud offering for software delivery. Applications are hosted by a provider and made available on demand.• Cloud computing is the backbone for the Internet of Services and provides resources for on demand, networked access to services. Infrastructure as a service Platform as a service Software as a service Data as a service ERP Analytics as a service 34
  • 35. “Cloud computing is enabling the consumption of IT as a service. Couple this with the “bigdata” phenomenon, and organizations increasingly will be motivated to consume IT as anexternal service versus internal infrastructure investments”.The 2011 Digital Universe Study : Extracting Value from Chaos, IDC, June 2011 35
  • 36. • Cloud computing alters the way computing, storage and networking resources are allocated. Through virtualization, the traditional server-centric architecture model in which applications are tied to the underlying hardware is altered to a service-centered cloud architecture. Applications are decoupled from the physical resource which implies that services (computing resources, e.g. processing power, memory, storage, network bandwidth) in a cloud computing environment are dynamically allocated to on demand requests.• In addition to a better utlization of IT resources, hardware cost reduction and greener computing, cloud computing provides an agile infrastructure to respond to business needs in a flexible way. 36
  • 37. The commoditization of analytics The trend towards the hosting of services, leads to the commoditization of analytics. As a result, the creation of a competitive advantage depends on 2 factors.The management of large Analytics in itself don’tdata volumes (data integration, guarantee a competitivedata quality). As data fuels advantage. The insights,analytic processes, big data communications and decisionsbecomes increasingly important.. that follow analysis become more important. This stresses the role of self-service and collaboration. 37
  • 38. In the pre-cloud world, the implementation of data warehouses needed serious upfront costs and designing database schemas was time consuming. Moreover, database schemas have their limitations because some data types (e.g. unstructured) don’t fit the schema. Combined with the need to manage big data volumes new database technologies (e.g. NoSQL) are used. For example, in the case of a Hadoop cluster that runs in parallel on smaller data sets, multiple servers are needed. Making use of cloud computing services in a pay-for-use formula is appealing. Furthermore, a service-oriented cloud architecture is ideally suited to integrateCloud computing data from various sources (e.g. « mash up » enterprise data with and big data public data). 38
  • 39. Cloud computing gives a new meaning to the consumerization of IT. The convergence of cloud computing and connectivity is changing the way technology is delivered and information is consumed. Cloud applications are available on demand and developed to meet the immediate needs of users. Cloud computing is an important catalyst for self-service BI. Users do not need to be concerned with the technical details of software and hardware when using services. User-friendly interfaces and visualization capabilities make the generation, sharing and acting on information in real-time easier. This permits faster and better decision-making as well as greater collaboration internally and Cloud computing outside the firewall.and self-service BI 39
  • 40. Trends in BI4. Embedded BI 40
  • 41. As the market changes faster and faster, BI has to adopt to support decisions in day-to-day operations. The role of BI has changed beyond its original purpose of supporting ad hoc queries and analysis of historical information. With changing market dynamics there is aThe Need for Agile BI growing need to monitor performance using the latest data available and to predict future events. The new BI delivers information to users within the context of operational activities. Rather than reporting on the business, BI moves into the context of business processes. Data is analyzed in the flow of transactions to produce real-time
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