Towards a Business Intelligence Cloud

Towards a Business Intelligence Cloud

University of Oldenburg

Escherweg 2, 26121 Oldenburg, Germany [norkus, appelrath]@offis.de

ABSTRACT

The requirements for Business Intelligence (BI) and reporting instruments are increasing since many years. Reporting instruments must be proactive, integrated, flex-ible and always available. They have to offer self-service functions and deal with the growing amount of data. In view of these require-ments, the characteristics of cloud computing show a revolutionary character. First ‘BI Cloud’ offers are already available on the mar-ket. However, the lack of a stand-ardized architecture as well as the lack of understanding of the fea-tures and advantages of analytical applications in the cloud causes that currently only a few compa-nies use BI in the Cloud.

To support the standardization and development of analytical applica-tions delivered as a cloud service the University of Anonymous de-signed a reference model for BI in the Cloud in an ongoing research project. From this research project this position paper reports: The topics cloud computing and BI are merged, the resulting benefit of BI in the cloud is shown and our

ap-proach for standardization is pre-sented.

KEYWORDS

Cloud Computing, Business Intel-ligence, Business Intelligence as a Service, BI in the Cloud

1 INTRODUCTION

Given the impact of the eco-nomic and financial crisis as well as globalization and in-creasing competition, compa-nies are more and more under pressure. They need to respond faster and more agile on the dynamic changes. Some experts are of the view that the charac-teristics of the crisis, such as the uncertainties, risks and the incompleteness of information will remain as general market and business-determining prop-erties beyond the crisis [1]. As a result, companies need to act dynamic and respond promptly to market and business changes to position itself successfully in the market. This requires that decisions be accelerated and

made on a sound base of infor-mation and knowledge and fur-ther that decision processes are designed to be flexible.

This results in increasing de-mands on the information tech-nology (IT). IT should support automated, flexible and cross-enterprise business processes in the form of a flexible orchestra-tion of reusable services as well as a flexible handling of peak loads. Decision support systems and in particular reporting in-struments have to become pro-active, integrated, customizable tools. This tools should be ac-cessible even with a large num-ber of accesses simultaneously. Furthermore, they should be accessible from anywhere, offer self-service functions and deal with the growing amount of data.

Looking at these challenges, cloud computing is an interest-ing technological development. Today, cloud computing is a driver of many innovations. Some cloud properties have a revolutionary character for BI applications.

Within first publications in the international science a high po-tential is attributed to BI in the Cloud [2, 3, 4, 5, 6, 7, 8, 9, 10]. First products already exist, but there are partly high un-certainties and reservations.

This is justified, that today are existing less experience and not much advices as well as not sufficient transparency of the opportunities and potentials of BI in the Cloud. Especially en-gages the fact that analytical applications in the cloud are not mature and not standardized neither in the way of the de-scription nor in its architecture yet.

Our approach is to consolidate and harmonize the architecture of analytic applications deliv-ered as a service in the context of cloud computing by building up a reference model.

This position paper reports from our ongoing research pro-ject by first bringing together the relevant terms and concepts of cloud computing and busi-ness intelligence and after that presenting the benefits and challenges of this combination. Following the current state of the discussion and technique are presented below and our approach for standardization of BI in the cloud is explained. 2 CLOUD COMPUTING and BUSINESS INTELLIGENCE 2.1 Cloud Computing

In the context of our work we understand cloud computing according to Gartner as ”a

style of computing where massively scalable IT-enabled capabilities are delivered ’as a service’ to external customers using Internet technologies” [11]. This concept of delivery is comparable with the provision of default supplies: Like water or electricity IT services should be accessible all the time and have a reliable quality [12]. A key aspect of cloud computing is the term of a cloud service. This can be defined as the pro-vision of virtual IT resources (i. e., logical resources that are mapped to physical hardware) that conforms to the following characteristics [12, 13, 14]:

1. Resource pooling: Cloud services enable the shared utilization of physical resources by means of virtualiza-tion (virtual resources instead of physical hardware) and multi-tenancy (multi-tenant data management). 2. Rapid elasticity:

Ser-vices can be immediate-ly provisioned and re-leased as demanded and the capabilities available for provisioning often appear to be unlimited. 3. Measured service:

Ser-vice utilization is

meas-ured and ideally mone-tized with a pay-per-use pricing model.

4. Broad network access: Cloud services are sup-ported by ubiquitous network access and technical standardiza-tion.

5. On-demand self-service: Consumers can unilater-ally provide themselves with services as they need, due to extensive service automation on the side of the provider. Due to the provision of this services for IT infrastructure, platforms and applications we can distinguish three utiliza-tion approaches [12, 13, 14]: ’Infrastructure as a Service’ (IaaS) abstracts on the hard-ware mostly by virtualization, ’Platform as a Service’ (PaaS) offers a development and execution environment and ’Software as a Service’ (SaaS) provides dedicated software applications. With regard to their organizational assignment respectively range we can differ four cloud types [12, 13, 14]: Public Cloud (Suppliers and users do not belong to the same organi-zational unit), Private Cloud (Suppliers and users belong to the same organizational unit),

Hybrid Cloud (Mixed form of public and private cloud, nor-mal operation mostly in the Private Cloud, certain func-tions or load peaks in the Pub-lic Cloud), Community Cloud (Cloud services are exclusively provided for a community of users with common interests from different organizations). 2.2 Business Intelligence The term Business Intelligence is a broad category of applica-tions and technologies for gath-ering, storing, analyzing and visualizing information. Here, the aim is to support all special-ists and managers with in-depth knowledge to come to an elabo-rated decision. By the initially technological driven develop-ment, BI turned increasingly to an IT-based, process- and con-tent-driven analytical business management tool. From rele-vant, distributed and homoge-neous market, competition and company data BI derives and visualizes knowledge about the status, prospects and potential and makes this knowledge ac-cessible for decision-making [15,16, 17].

Specifically, conducted inter-views showed increasing re-quirements [18]: BI users today expect continuous accessibility as well as a high speed of

anal-ysis. Also short reaction times for adjustment of a parameter and short development times of new reports belongs to today's requirements. BI applications should be available from any-where and have to be achieved if many requests occur simulta-neously. Moreover, BI applica-tions should be delivered with specific role profiles and be individually customized. In ad-dition, high demands are placed on the expandability and adapt-ability. In the BI application, key performance indicators should be easily designed and visualized. BI applications have to offer self-service functions and deal with the ever-growing amount of data. In addition, the cost should be as low as possi-ble: operating costs should be minimized and investment se-curity should be maximized. 2.3 BI in the Cloud: Defini-tion and Benefit

Conventional BI usage models, such as traditional on-premise or outsourcing models, meet the increasing demands of the busi-ness forced by the ongoing globalization and under strong competition no longer suffi-ciently [2, 3, 6, 8]. Convention-al BI systems mostly are com-plex, static and expensive [9]. Upcoming reporting tools need

to be proactive, integrated, ana-lytic applications that are flexi-ble and scalaflexi-ble, offer self-service functions and deal with the increasingly large and com-plex data base.

Cloud Computing shows a high potential to satisfy the require-ments and indicates a revolu-tionary character for BI applica-tions. Analytical applications can be deployed as a cloud ser-vice, the outcome of this is Business Intelligence as a Ser-vice (BIaaS). Such BI cloud services satisfies the increased requirements for BI systems and analytical applications. Means of BIaaS analyzes and reports can be flexibly integrat-ed and orchestratintegrat-ed into pro-cesses. Especially in turbulent times, when many decision makers simultaneously access the reports and analyzes are also still available.

Analytical cloud services are permanent in reliable quality available, not only at agreed sampling times.

Analytical applications in the cloud guarantee availability and reliability. They are accessible from any devices and from any locations. The analytical appli-cation can be delivered and per-sonalized role-specific (e.g. via templates).

BIaaS provides a high level of agility: Reports and analyzes can be adapted to the needs of stakeholders and requirements of compliance. Intuitive and independent of third parties (such as administrators) BI end user can adapt reports dynami-cally according to their own ideas (e.g. change the chart type or define new metrics). BIaaS not only provides flexibility at the level of visualization and modeling, as well on the tech-nical level: changes of cubes, processes for filling or multi-dimensional databases can be made simple and flexible which is important in times of often and rapidly changing operation-al IT landscapes (e.g. due to corporate transformations). Analytical applications in the cloud promise a very high que-ry and analysis performance, especially a rapid deployment of complex reports and a rapid reactivation when changing a parameter.

By BI in the Cloud, the cost side becomes dynamic: Fixed positions are replaced by varia-ble proportions, acquisition costs are eliminated, and oper-ating costs change to cost-per-use. Thus prevents the devalua-tion of assets and increases the investment security. The costs are pre-calculated and will be

billed based on usage. So far it simplify complicated licensing issues.

3 STATUS AND

CHALLENGES OF BI IN THE CLOUD

First products offered by manu-facturers as a BI cloud solutions are already available on the market. A detailed considera-tion of various providers and services can be found in [2]. On this basis, here presented is an executive summary of BI cloud providers and offers. This summary does not claims to be exhaustive, but shows the status of the current offer and indi-cates differences.

Microsoft provides with the BI services of the Azure platform a complete cloud platform (PaaS, IaaS and SaaS) for the imple-mentation and usage of BI ap-plications. SAP offers with Business Objects BI on De-mand a BI cloud application. So SAP provides an analytical in-strument for the integration of services and visualizations. Google in cooperation with Panorama offers a BI cloud solution using its own infra-structure. Microsoft also uses own data centers. But some providers offer their BI cloud applications based on the

Ama-zon cloud - for example, the offerings of SAP, the open source BI suite of Pentaho and the BI solutions of Qliketech. IBM operates in BI cloud with the project name ‘Smart Ana-lytics Cloud’. Also on the mar-ket are offers like WebFocus of Information Builder and Busi-ness Analytics from OCO [2]. This already indicates a breadth of offerings. To illustrate the differences and similarities of existing offers exemplary pro-founder, here summarized re-sults of detailed consideration (see [3]) of Microsoft and SAP are given:

SAP uses the in-memory data-base SAP HANA. This runs in an Amazon data center whereon SAP BI on Demand and the Lumira cloud are offered. Mi-crosoft's Azure platform runs in its own data centers. Microsoft offers the BI component Power BI and complementary features of Sharepoint. With SAP a more individual visualization and graphic processing is pos-sible. Mircosoft however, is attributed more usability as Power BI is self-explanatory and clear. SAP settles accounts on the base of hours of use, Mi-crosoft on the base of resource usage. The on-demand scaling can be made in both offerings SAP und Microsoft. By SAP

only up to a pre-agreed maxi-mum allocation.

In the field of designing con-tracts and service level agree-ment both offers are similar. SAP and Microsoft make assur-ances on the topics availability, security, responsiveness and support. Data security is as-sured as well by both parties. Means to do so, for example, encrypted storage and secure transmission. In regard to data protection, the selection of the physical location must be con-sidered. At Microsoft, the loca-tion of the data can be freely chosen. There is a limitation in SAP here, because only the data centers of Amazon can be chosen.

The establishment and position-ing of the BI cloud provider takes place more and more in-tense. The summary shows var-ious differences in the offers. The current level of maturity of tenders can be considered early and heterogeneous [2, 3, 9]. Differences in the products ex-ists in the field of architecture model, visualization functions and user-friendliness as well as the billing model. Striking is also that differences in architec-ture exist in particular within authorization and security con-cept.

It is also unclear how far the current requirements to be ful-filled and the potential to be lifted. Overall, there is currently less experience with BI in the cloud and therefore not much advices. BIaaS is actual rather used for test purposes. There is a lack of appropriate architec-tures and standards as well as their understanding. This all together results in a lack of use and skepticism.

Nevertheless, the potential of BI applications in the cloud are significant. The cost savings and performance improvements and in particular the new di-mension of customer integra-tion makes the field of BI cloud very interesting and offers new opportunities. In particular therefor, it is necessary to con-solidate and standardize the architecture of BI in the cloud. BI cloud services must be de-scribable and comparable in a standard way and thus support providers by designing and presenting their offers and users by understanding and se-lecting.

Some scientific approaches are already in this direction the merger of Cloud Computing and Business Intelligence: Abadi in [5] deals with data management in the cloud, and proposes a hybrid way in that

only the current relevant data marts are in the cloud. Howev-er, an architecture or conceptu-alization is not demonstrated. Ouf and Nasr in [6] emphasize in a very general way that a layer architecture (data layer as a data source, logical layer as a model and visualization layer and access layer as a presenta-tion layer) should be in the cen-ter of the basic architecture of a cloud BI.

Chadha and Iyer in [7] show a process of cloud data staging as well as a too general and ab-stract architecture for reporting and analysis in the cloud. Mircea et al. in [8] examine the cloud as a promoter of efficien-cy in business. With a model-based return-on-investment cal-culation reveals how the in-crease in efficiency could be achieved.

Baars and Kemper in [9] postu-late a model built up on the lay-er architecture. The approach focuses SLA properties and describes some scenarios. The

paper remains at an abstract and vague level.

Grivas et al. [10] propose an event-based approach to im-plement cloud-based Business Process Management.

A comprehensive architecture with components and relations in the light of roles, authoriza-tion concepts, integraauthoriza-tion as-pects, delivery models and stages of development, taking into account the portfolio man-agement is not present yet. It is missing a consolidated and standardized reference architec-ture for BI in the Cloud as well as a universal model for de-scription and comparison BI cloud services. On that we are working in our research project whose approach is defined in the next chapter.

4 OUR APPROACH

As shown in the previous chap-ter existing offers of BIaaS dif-fer in many aspects. First scien-tific approaches suggest in sev-eral directions and address dif-ferent initial questions.

Our approach is to consolidate and to standardize the architec-ture of BI in the cloud by build-ing up a reference model. Our reusable model will be present-ing recommendations for action and targets to support the de-sign and application of analyti-cal applications as a service. To this end, we develop a refer-ence model with formal de-scribed components, their rela-tions and properties. The model promotes the development and operation of analytical applica-tions delivered as a service in the context of cloud computing. In addition, we develop a taxonomy to describe and compare existing products and

architectures. Thus, differences and similarities can be explicat-ed and characterizexplicat-ed.

Our aim is to present a proposal for the standardization and gen-eralization of the architecture and environment of BI applica-tions delivered as a service in the context of cloud computing. For this, we apply the design science research process pre-sented e.g. by Peffers et. al. [19]. The phases of problem identification followed by the target definition are already active and advanced. The de-velopment is on-going and the evaluation is the further step. As part of the development the conception of the reference

model in the corresponding framework is performed. The evaluation takes place by a fea-sibility and case study.

The environment of our refer-ence modeling is shown in Fig-ure 1 and outlined in more de-tail below.

In the first step of our approach, it is necessary to survey and to consolidate the portfolio of rel-evant models. This will be real-ized based on encountered phe-nomena of the subject area (services and architectures of the industry) as well as on the models of scientific approaches. Given the results of the survey (shown as input in Fig. 1) and consolidation in the light of the current requirements the con-ceptualization of our reference architecture will be proceeded. The context of our reference model can be described as changes in market and competi-tion, increasing requirements for BI and in particular to re-porting tools as well as the fact that conventional BI systems are too rigid to serve the re-quirements.

The methods of our reference modelling relates the construc-tion of the model. It's about answering the central question of the construction; in which steps a reference model is to be developed. For that the

spec-trum of methods consists of approaches to data modeling methods e.g. by Hars [20], pro-cess modeling methods e.g. by Schütte [21] and the object modeling methods e.g. by Schwegmann [22].

The modeling language is the concept of explication and vis-ualization the reference model. This is mainly based on the standards of the Unified Model-ing Language (UML). In addi-tion, various concepts such as multiple perspectives and vari-ant management are used. The result will be a universal, reusable model in the domain BI in the Cloud that supports the design, implementation and application of analytical appli-cations delivered as a service in the context of cloud computing. The demonstration of the feasi-bility of our reference architec-ture takes the form of an im-plemented prototype. To evalu-ate, the result is held against the research gap. The usefulness of the methodology and the result will be examined with tool sup-port in a case study.

Our reference architecture can be understood as a reference model as a formally defined terminological construct. With the reference model will be shown, from which compo-nents, relations and properties a

BI Cloud consists. Dedicated system instances can be created based on the reference architec-ture to fulfill the increased re-quirements and to take the ad-vantages.

5 SUMMARY

In the light of globalization, increased competition and the economic and financial crises, companies must react faster, immediate and more agile. De-cisions must be made on the basis of a larger and complex information base. For this pur-pose reporting tools are needed that allow and encourage this agility and flexibility. Conven-tional BI systems do not longer fit here. Reports must be proac-tive, integrated, flexible and continuously and everywhere reachable. In addition, future reporting tools must offer self-service functions and deal with the growing amount of data. The cloud has many properties, which meet the current re-quirements in particular im-posed on BI applications. Some of these properties are shown a revolutionary character for analytical applications. BI in the cloud based on the flexibility, scalability and self-service features promises a new orientation to the actual re-quirements of the user.

Premature BI cloud offerings are already available on the market. First general and gener-ic scientifgener-ic approaches are published. However, a consoli-dated, standardized architecture is missing.

Our project at the University Anonymous explores a refer-ence model for BI in the cloud. With the reference model for BI in the cloud, analytical infor-mation systems in the cloud can be designed experienced technologically. Standardized methods and languages of the reference model are used in our conceptualization. With a pro-totype, we will present the fea-sibility and evaluate our refer-ence architecture with a case study. In addition, we are work-ing on a taxonomy BI cloud services can be uniformly de-scribed and compared. Alto-gether we are working on the consolidation and standardiza-tion of the architecture and the environment of the new field BI in the cloud.

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