Emerging Trends In Business Intelligence

Published Date: 02 Nov 2017

BI is an essential utility that enables businesses gain competitive advantage from analyzing data available to the organization. While business intelligence is still regarded as a technology that is not new, its use has increased steadily over time, with an average 4% growth rate in the BI market since the beginning of the recession of 2008 (Trujillo, 2011).

While traditional technologies for supporting BI such as data warehousing, OLAP, data mining, etc. query data from the inside of the organization, novel trends of BI (BI 2.0) that focus on the analysis of external data have emerged. The outcome of having a wider data pool is that the analysis is more comprehensive, and provides better platform and support for decision making.

Modern business intelligence is only starting to mature and realize its full potential, and this paper will look to discuss much of this referred potential.

This field of research has been chosen because of the interests of the student researchers in finding key determinants in identifying the role of proper data and knowledge management in establishing sustainable competitive advantage in business.

Introduction

An organization that cannot effectively identify the important information required at all levels will inevitably be forced out of business by competitors who can (Ackoff, 1967). A vital form of information systems (IS) is the Decision Support System (DSS), which can be defined as "systems that support non‐routine decision making with a focus on unique problems that are rapidly changing, and for which the procedure for arriving at a solution may not be fully defined in advanced" (Laudon, 2012) this literally means they are systems that support better decision making at all levels and this is precisely what Business Intelligence is geared towards.

Business intelligence (BI) is a contemporary term, given to the ability of an organization to collect, maintain, reorganize and present knowledge to enable decision making. BI typically creates information in huge quantities that can assist in identifying business opportunities. By using these said opportunities, and a strategy that is effective, it is possible to achieve long‐term stability and competitive advantage. This is because BI technologies provide historical, current and predictive views of business operations (Keen, 1980). Some characteristics of BI technologies include data mining, process mining, reporting, predictive analytics, online analytical processing (OAP), analytics, complex event processing, prescriptive analytics, business performance management, benchmarking, text mining, (Laudon, 2012).

The eventual expectations that drive the deployment of business intelligence in today’s world is the need to offer better decision making support for businesses. The challenge however is that BI isn't just analyst‐driven anymore; it's pervasive, Innovation is coming from nontraditional and emerging vendors. New technologies are coming for information access, analysis and presentation capabilities (Trevino, 1999). There is therefore a need for Innovation to occur with sourcing options in addition to technology. This paper looks to address emerging technologies that are showing up in the scene of Business Intelligence; trends like pervasive BI, mobile BI and BI 2.0.

Pervasive BI is one of those concepts that is intuitively recognized as positive and having a lot of potential. The benefits of pervasive BI extend the benefits of business intelligence to an entire organization. The pace of business is rapidly accelerating, so being knowledgeable about one’s today business is more important than ever. Keeping everyone in the organization informed is what pervasive BI is all about — and it will impact performance, understanding, and loyalty. For the vast majority of organizations, BI solutions have yet to become fully pervasive — BI still has a ways to go before it is pervasive.

In a world that is increasingly going mobile and instantaneous, it would be surprising if the Business arena would not take a cue from it. Recent emerging trends indicate that work‐place analytical capabilities are being deployed via mobile devices to improve job performance, decision making and Just-In-Time (JIT) decisions. This is possible as the frontiers of social networking tools and the ever increasing processing power and miniaturization of mobile devices are being extended to bring about limitless opportunities to be harnessed (Ramakrishna, 2008). Some of the possibilities are due to new technologies that have changed the way BI (Business Intelligence) operates which include (CEP) Complex Events Processing and In‐Memory Analysis. These allow for Predictive Analytics and Real Time Analytics which are going to be instrumental to next‐generation Business Intelligence (Bedell, 2011). Most of all, as expectation time for delivery of results keep shrinking; these mobile BI tools will keep rising to the occasion, by increasing efficiency and best of all, being available on the fly (Hatch, 2008).

1. BI 2.0

While traditional technologies for supporting BI such as data warehousing, OLAP, data mining, etc. query data from the inside of the organization, novel trends of BI (BI 2.0) that focus on the analysis of external data have emerged. The outcome of having a wider data pool is that the analysis is more comprehensive, and provides better platform and support for decision making. For example, a retail company that would typically focus its BI strategies internally, can now have access to prices of competitors, and can also have information to how the customers receive their products; this will in turn affect their strategy for the release of another product.

The internet has been the driving force for data and information dispersion over the last 10 years and will continue to be for a long time. One important thing to note is that the new trend of BI 2.0 is bidirectional: because BI applications receive and process data from the web, BI applications are starting to evolve to become pervasively web driven technologies. (See section 3). Some of the BI driven applications today are Social Networks, Cloud Computing, Interactive WebApps, SaaS (Software as a Service), Semantic Web Search, Word Clouds, Wiki’s, Collaborative Networks, e.t.c.

Fig 1: BI 2.0 Architecture (Source: Trujilo, 2011)

1.2. BI 2.0 Basic Concepts

Like any technology in use today, there are key concepts that essentially define and differentiate BI 2.0 from other data mining and processing activities.

Real-time analysis: BI 2.0 has to focus on data analysis that is on-line and real time. Because of the new found volatile nature of data, BI 2.0 has to be super flexible and super reactive in nature to process and analyze data.

Intuitive and interactive analysis: Unlike traditional data processing that typically involves homogenous data types and 1-directional analysis, BI 2.0 supports interactive analysis that acts based on a user’s preference, and that can be altered and re-analyzed if certain variables are changed.

Collaboration between decision-makers: A key concept in BI 2.0 is the collaboration that should exist between decision-makers in the manipulation of analyzed data.

Linking and enriching data: The concept of Business Intelligence itself is that of the linking of different data to present better information for decision making, and BI 2.0 takes it a notch higher by enriching this data to be more self-explanatory and therefore increases the utility of the information eventually provided.

1.3. APPLICATION OF BI 2.0

Real-Time Information

As a major application of BI 2.0, the importance of up-to-date information is crucial (Thiele et al.). Analyzed information as explained earlier needs to stay fresh so as to stay relevant; for example, an airliner needs to know they availability of seats on a flight at any given time to be able to plan supplements for their passengers, route availability, etc.

Software as a Service

Software as a Service (SaaS) is a model of distributing software where applications are normally hosted by a service provider or vendor and then made available to customers through a network, most common of which is the Internet (McHall, 2011). This means that software is consumed as a remote service (Essaidi et al). BI 2.0 is applied through the use of Service Oriented Architecture (SOA) and Service Oriented Architecture Protocol (SOAP) for platform interoperability. Because of the mainly internet based dependency of SaaS, and the substantially higher quantity of data available for analysis, it has caused a shift in modus operandum of a lot of BI systems to become BI as a Service.

Cloud Computing

As a concept related to BI 2.0, cloud computing integrates several heterogeneous elements into a network and then makes these elements available through a homogeneous platform for remote users (Armbrust et al., 2009). Cloud computing can in some way be associated as an umbrella which Software as a Service (SaaS) falls under, because typically the services which Cloud Computing offer are made available as SaaS. Cloud computing also exhibits the real time and responsive characteristics that BI 2.0 applications have, as it supports the addition of new elements at any time, be it data or service.

Collective Intelligence and Crowdsourcing

Collective Intelligence in BI 2.0 is not to be confused with the concept of crowd sourcing, because in collective intelligence, the concept of emerging behaviors is apparent as decentralized individuals are able to take decisions as a group and this promotes initiatives (Gruber et al. 2008). The generic definitions of social networks fall under this category.

Crowd sourcing is the delegation of tasks to a crowd, where every individual contributes a quota to the global goal (Howe et al. 2009). A strong force in this field are wiki’s – with Wikipedia.com being the most popular, where topics on almost anything or anyone popular can be accessed.

Social Networks

The underlining concept in social networks is that groups of people can interact with each other, collaborating and thus achieving faster goals and better outcomes than would have been the case if it were a single user. The key information that determines usefulness of a social network is the data extracted from contributions of participants; this data changes so rapidly and several trends can be identified based on the relationships of the users involved (Berthold et al; Golfarelli et al). Social networks we believe are one of the pivotal elements of BI 2.0 and the foundation to which many more BI 2.0 technologies will evolve.

Linked Data

The concept of linked data deals with the relationships of semantically tagged data, which allow for reasoning and inference of knowledge (Berlanga et al.). Knowing what relationship exist between every unique piece of data is key, and this will lead to the automatic obtaining of knowledge for the data (Bizer et al. 2009)

Opinion Mining

This strong application of BI 2.0 leverages on the general feelings a group of people have towards a certain good or service (Balahur et al). Based on several data inputted, the (often) unstructured data is analyzed, and then a conclusion is obtained. This application of BI 2.0 places high relevance to the customer’s perception of a good or service and is important for all customer driven businesses. An example of opinion mining is the user powered ratings systems that most websites now use to rate items or services, based on the feedback gotten from other users. Usually, the harnessed intelligence gotten from the crowd can often be more relevant than relying on the sole opinion of an expert.

Process Oriented BI

This concept deals with the focus on Business Processes and their logic, and tries to compare the documented process expectations to the stored data and the actual process performance. This allows businesses identify and restructure business processes while isolating potential and actual problems (Golfarelli et al. 2004).

1.4. Comparison of Available BI 2.0 Tools

Deciding which tool or service to use is never an easy decision, and organizations looking to fully tap into the benefits of BI and BI 2.0 will need to be fully aware of the wide range of tools at their disposal.

A vast majority of BI tools recognize the importance of the cloud and thus support cloud computing by using a SaaS or BI as a service approach. Some tools also support the use of the cloud for specific tasks; for example, Microsoft supports deploying the data integration and the entire system into Azure Cloud.

Because BI tools are used to aid business strategy and decision making, it is therefore important to keep a clear idea of what this business strategy is, and most tools available today offer this through the use of dashboards. While some of the dashboards available will serve for today, more can still be done to make dashboards more collaborative, interactive and intuitive.

BI Tool

Advantages

Drawbacks

Pentaho

Open source, platform-independent, rich web interface Dashboards for showing data links

Predictive algorithms

Includes collaborative features when integrated with LifeRay

Designing and integrating dashboards requires too much effort

Interactivity and data enrichment can be improved

Inadequate Integration of business processes

Cognos (IBM)

Interactive Web Interface

Collaborative Support and data enrichment

Support for Mobile BI

Dashboards and Scorecards

Predictive Analysis Support

Does not fully reflect business processes

Microsoft BI

Integration of several tools such as: Excel, PowerPivot, SQL Server, Sharepoint.

Includes:

An interactive web interface

Dashboards and Scorecards are available in mashups

Elements can be linked and can interact with other users

Tags addition to profile in Sharepoint supported

Can include data from the Web

Analysis through Excel and PowerPivot

System not optimally designed, not intuitive enough

The predictive analysis is limited to only Excel functions

Does not possess some collaborative functions

Direct interaction between users not available

SAP

Provides complete support for analyzing the

business strategy combining desktop

applications with web applications

Includes:

Web interface for ad-hoc analysis.

Mobile BI support

Dashboards and scorecards

Complete workbench for data mining tasks

Compatibility with other vendors tools thereby empowering analysis capabilities (Tools like Excel and other enterprise applications)

The collaboration between users is limited

Lacks support for enriching data

Interaction between users not available

Table: Comparison of some available BI 2.0 tools

2. Mobile BI

2.1. Introduction

Being Mobile has become part of our lives. This trend of mobility has also been changing the business world and the way information is handled over the past years.

The idea of mobile BI can be traced back to the invention of the first mobile phones and laptops in the 1990s. It started with exchanging emails and PDFs. Nevertheless the real breakthrough happened years later with the strong market penetration of smartphones and tablet computers.

With the emergence of mobile broadband and new and powerful hardware platforms by mobile phone providers such as the Android, Windows Mobile, iPhone, Blackberry, etc. in 2010, the possibility of running various BI solutions on mobiles became visible. Solution providers like GroupData and SAP have begun to develop interactive mobile applications which are now been used by decision makers irrespective of their location.

Today, mobile phones are been used to manage micropayments in the Philippines and also to track crop prices in Kenya. They are tapping into these devices to handle healthcare information in Nicaragua and oversee bakery orders in Nigeria. In fact, with an estimated three billion- plus mobile phones in use worldwide and approximately 80% of the world’s population within the reach of a cell tower, almost no corner of the globe remains untouched. (Greengard 2008)

2.2. Development of Mobile BI

Business Intelligence vendors announced the availability of BI for mobile devices in 2009. First its use was strongly restricted by the low interoperability between devices their Screen Real Estate and the hardware and software specifications of the devices. In some cases, end user could change the way they viewed the data while only a few could manipulate and do much more with the information they were viewing. Also because of the user interface that was limited, not more than a report document or a single graph could be compared or viewed at a time. (Frenkiel R. 2009)

While the easy access of some BI tools through a mobile device could be seen as adding overall value to the applications in an organization, the information provided in this access mode was still limiting. Little value was derived from the mobile application of BI despite the fact that utilities such as emailing reports or analytics could be done via mobile devices.

From 2012 to date the offerings of devices and applications in the field of mobile BI have constantly increased. One big success of the past years was to achieve a higher level of interoperability in between different devices and operating system, like viewing a word document on the iPhone that was send by a Blackberry. With the Tablet computer a bigger screen made mobile work more convenient. Moreover stronger hardware (also in smartphones) allows more applications.

2.3. The Evolution of Offerings

In order for workers and executives to access report document regardless of their geographic location, organizations focused on providing access Portable Document Formats "PDFs" through blackberries. This made information access easier for business travellers and general users. Business intelligence is gradually moving away from desktop solution as mobile technology is developing. With the rate at which mobile technology is maturing, the usage of BI is increasing rapidly which makes the possibility to look at different ways of deploying and interacting with BI became a reality. (Frenkiel R. 2009)

Even though many organizations still use traditional BI applications like date warehousing, OLAP and interactive reporting, the focus starts to shift. Companies realize that BI needs to refocus on providing its users with deeper insights and supporting decision-making.

Within the last year, i.e. 2011, solution providers for example SAP (System, Applications, Products) have announced mobile BI solutions.  There are two key differences in the present solutions compared to their predecessors:

1) Appealing user interfaces which makes the use of BI possible and much easier

2) Mobile BI which includes interaction with BI applications and functionality

2.4. Opportunities of Mobile BI

Not only can users view and share information but they can interact with and analyze data, creating reports and charts as needed.  Mobile BI offers company the ability to provide accurate and timely access to analytics needed at any point in time.

The most valuable part of mobile BI is having access to the right information at the right time. In most cases it includes giving sales representatives’ information needed before visiting their suppliers or clients, or providing executives with the needed information necessary to prepare for meetings.

Mobile BI allows accessing and using information without requiring a laptop, having to print and carry papers and being bound to a certain place. Even though it existed with the use of blackberries, the full-scale functionality and level of interaction was previously not available. (Blum A. 2010)

2.5. Trends of Mobile BI

As mobility is increasing in general BI becomes more broadly applied and valued as well. It enables organizations to access, create, distribute and edit required information whenever they need it with everyone needed. 

Several emerging trends have developed in mobile BI:

1) Customization

Since many employees already have an iPad or other mobile devices, it would be difficult to ask them to carry a second one just for business. With the preponderance of mobile devices such as iPad, iPhone and others, employees generally would not appreciate the idea of going about with two of such simply because the office requests them to. Invariably, organizations will have to face the grim reality that they just have to accommodate the mobile devices that are owned by their employees. However, the security question still comes into play: how does the business handle the security implication of a personal device that doubles as an office equipment in the matter of speaking? These devices will very likely run a mixture of corporate and personal applications on maybe even different platforms and versions. This seemingly innocuous technology has inadvertently made the job of a typical IT department much more complex and complicated than before. This is because securing a network based on a plethora of mobile devices with different Operating System platforms, and a wider variety of applications is a much different ballgame compared to maintaining and secure one based only on computers. Moreover, these mobile devices are owned by the employees as against the Organization owning the work device, making a complex situation even worse. Change is the only constant thing and so the IT department would have to face and adapt to the current realities of the ultra modern network. This would mean purchasing the necessary corporate gadgets to support development and testing as well as provide devices to employees who don’t have them already. Imperatively, that would also mean installing the necessary corporate applications on employee devices that use their devices in the corporate environment. In addition to this, a sound security and data policy needs to be in place to ensure that employees know the responsibilities attached to such convenience as well as the clear security implication of any breach on their part. (Blum A. 2010)

2) Clouds

The trend of Cloud Computing is a major step towards a higher level of mobility. The more data is stored in clouds the less a user is tied to a location (e.g. desktop) or even to a particular mobile device.

3) Collaborations

Collaborations and social networking are the communication signature that has characterized the 21st century especially in the office environment. It has become so preponderant that organizations and people use it to narrow the gulf between the way they interact with technology within and outside the office. With the escalation of this trend, more organizations will commence the use of applications that support collaboration and contact across channels, divisions and geographical barriers. People now communicate in real time via text messages, chats and email to avail themselves of prospects or raise issues based on insights drawn via information availability and regular interactions inspite of location disparity.

4) Improved Customer Service

A new trend is to manage customers irrespective of location by e.g. connecting mobile devices to document management systems. That way data can be accessed and edited in every location while dealing with customers. A good example is Apple. With the paperless transactions, they bring checkout in their physical stores. For example, a cashier is replaced by an iPad as it’s able to pass the necessary information needed for the business process. Customer initiatives will continue to become more prevalent and provide a good example of how businesses are taking advantage of mobile technologies to increase and expand their reach thereby enhancing customer experience. 

5) Flexibility and Mobility

Mobile BI will offer more and more flexibility to its employees. The can access the right information at the right time within the right place – whether the right place means on a desktop, laptop or mobile device. People are becoming less attached to physical offices due to the increase in telecommuting, virtual businesses and e-commerce, travel, client engagements, supply chain management, and the like.  Consequently, people are also becoming less tied to traditional means of business management and are adopting more interactive ways of doing business. 

6) Better Sales Analysis and Visibility

Sales analysis has always been at the forefront of BI adoption. In traditional or "software as a service" SaaS-based offerings, sales analytics remains a key way by which businesses identify the success or failure of their performance.

2.6. Challenges

Challenge- Screen Size

Even with the benefits of mobile access to information, the reality is that due to the small screen real estate of mobile phones, there will always be limitations in relation to how much can be viewed or interacted with at once. People’s preferences will dictate whether mobile BI actually gains wide adoption or whether the development of these applications simply addresses the needs of internal competition among vendors.

But there are all kinds of thorny issues involved in deploying BI--or any corporate application for that matter--on a mobile platform. The most widely discussed are security, selecting a mobile platform, designing for the mobile form factor, identifying appropriate mobile use cases, and selecting an application architecture (i.e. browser-based versus device-specific). These all represent significant challenges. But one issued that hasn't been discussed much yet is whether an organization should deploy corporate mobile applications on personal or company-issued devices.

Choice of User

The promise of information access in the hands of decision makers without restrictions is appealing.  Organizations using business intelligence effectively within their companies can always choose to move beyond traditional BI use and add mobile BI to their overall usage with relative ease. 

The only real barrier to entry is whether BI users are open to changing and expanding the way they interact with their business applications.

Stolen devices

But these are small problems. The biggest issue is non-technical. What happens when devices with a corporate application installed on it are lost? If your mobile application is browser based, you only have to worry about data cached on the device, which is there to optimize performance. Ideally, your security software automatically deletes the cache every hour which minimizes (but doesn't eliminate) the risk. If you've deployed a native mobile application in which data resides on the device, you'll not only need to clear the cache, but wipe the hard drive as well, which your security software can perform remotely. (Of course, this only works if the device is turned on and connected to the network. Sophisticated thieves will hijack the data without connecting to a network.) In all likelihood, you'll need to apply all these strategies to secure sensitive corporate information. (DinuAirinei & Homocianu D. 2010)

Support Diversity

Gone are the days when corporate IT can set mobile device standards. Instead, users are increasingly bringing their own devices, forcing IT (and BI vendors) to support a broad swath of smartphones and tablets - the most promising way to support diversity.

Finally, creating a new kind of BI experience – one that is designed more for workers and less for IT.

3. PERVASIVE BUSINESS INTELLIGENCE

3.1. Introduction

Pervasive business intelligence focuses on providing timely insights where and when they are required. It aims at integrating analytical data from data warehouses and real-time transactional data, while performing on-the-fly analysis in order to deliver information to employees; focusing on primarily improving decision-making among managers, and improving cross-selling and up-selling opportunities at the sales level (McKendrick, J., 2008).

McKendrick refers to it as "BI for the masses" because it aims at facilitating a holistic enterprise-wide provision of data, offer tools and dashboards to perform real-time analysis, as well as making it available through web and mobile services to all employees of an organization.

Making use of a BI system is becoming less of a luxury and more of a matter of survival in order to become a successful enterprise (Chaudhuri et al., 2011).

 

Pervasive business intelligence refers to the evolved version of traditional business intelligence; which is made up of strategic and tactical business intelligence plus operational business intelligence (E. Turban & L. Volonino, 2012, pg. 326). This evolutionary transition follows the recent trends in BI offerings from vendors and its adoption among organizations. Both parties focus on making BI available to employees at all levels; be it strategic, managerial or operational.

Pervasive business intelligence also focuses on making insightful and actionable information available in two directions across any organization. They are vertical (top - down) and horizontal (cross-departmental).

PBI proposes to move the proprietary administration of BI infrastructure and tools from the IT department to the general populace in an enterprise. It bridges the gap between IT staff and managerial staff while also enabling operational staff to benefit from analyzed insights.

This is aimed at solving the problematic issue of department or business unit silos which are common in traditional BI implementations.

3.2. Criteria to Satisfy Pervasiveness of a BI Implementation

What makes business intelligence 'pervasive'? It could be considered as the ability of an organization to

·         integrate and provide enterprise and transactional data across all departments,

·         provide the tools, architecture and services to perform real-time analysis on operational and warehoused data,

·         provide these tools through singular dashboards or embedded in legacy systems,

·         provide these tools and services at optimal uptime through secured networks,

·         provide specific insightful or otherwise new information that is actionable to the right user,

in order to provide facts and knowledge aiding decision-making in the organization.

We shall discuss in depth the various criteria qualifying a Pervasive BI system.

It must be collaborative through enterprise-wide implementation

BI must be implemented on an enterprise level for it to be considered pervasive. It should diminish the occurrence of siloed data in departmental BI implementations across an organization. It should provide insights to all employees at all levels of the enterprise furnishing them with timely information as they are needed. It should also support both individual and group decision making processes by providing a single platform that disseminates accurate data access and similar facts enterprise-wide.

Pervasive BI must extend beyond the enterprise by incorporating external users such as partners and customers. It improves the realization of crowd-sourcing ventures, highly responsive supply chain management, real-time inventory monitoring and control, improved self-service.

It must be simple to use

Dashboards and analytical tools should be simple enough for information managers as well as frontline operational staff to use. It should be simple for the information manager to monitor and analyze data to uncover insights, find and generate reports, plan, query and provide informed predictions from BI dashboards or through embedded access. It should also be simple for field staff to monitor and drill into real-time data in order to be more productive through higher responsivity rates on their jobs.

 

It must provide near real-time data analysis

Pervasive BI should operate at a minimal latency and lag time for data access, analysis and insights provision to its users i.e. it should provide timely insights on-the-fly at optimal speeds. It should be autonomous and asynchronous enabling individuals and development groups to perform data monitoring, analysis and design reporting content irrespective of location.

This near real-time data availability must exist through the implementation or optimization of a highly responsive Active Data Warehouse (ADW) through event-based processing technologies. The ADW must be designed and optimized to satisfy best-in-class standards, rules and techniques to manage data storage.

It must offer freshness of data at all times

In a pervasive BI implementation, data gathered from sources must be stored in storage formats that must obey increased atomicity and granularity standards. It should also be stored in an Active Data Warehouse (ADW), which should guarantee that fresh views generated from the warehoused operational data. The Active Data Warehouse should enable pervasive BI tasks to follow an incremental and constant ongoing process as fresh data is created. This subsequently improves the efficiency of Active Data Warehouse refresh ratios through Extract-Transform-Load (ETL) technologies.

 

It must provide high data quality in storage and subsequent information outputs

The data churned and analyzed by pervasive BI systems must be of high quality. This is essential because organizations in the past have shown to have different opinions from the fairly similar datasets by utilizing departmental BI implementations arising in knowledge silos. This has resulted in disparate facts and figures and creates erroneous data and facts concurrency leading to misleading managerial decisions.

Operational data and its analysis within a pervasive BI implementation must provide uniformity and transparency at all levels, exposing the right information to the right user through BI tools enterprise-wide. This ensures concurrency and highly accurate data analysis leading to better decision making process based on 'singularly true facts'.

It must provide more insightful facts otherwise unknown to the organization

A successful pervasive BI implementation should offer immediate ROI from increased and improved employee productivity levels. Pervasive BI increases the selling power of frontline operational staff by informing them with timely product offerings and opportunities for cross-selling and up-selling as they arise. This results from the product and services data drilling and monitoring role they play.

For analysts enterprise-wide, it should uncover future predictions based on past and present organizational performance. It provides them the power to report on enterprise data analysis showing the current state of business performance measurable against preset Key Performance Indicators (KPIs).

For managers and strategists who are decision makers at various levels, it should provide insights through various reports, which uncover potential business investment opportunities that will make the enterprise more nimble and responsive to rapid changes occurring in its business environment. It may also provide investment opportunities which will move the enterprise into a new market.

It must provide adequate data security

Pervasive BI systems should be inherently secure in order not to expose the wrong data and information to the wrong user. This means that the BI system implemented should follow protocols and rules providing accurate data access and transmission standards to categorized classes of users. For example, a frontline employee should be provided with products and services information that currently show stock, availability, bundles and offers; and not be provided information exposing deep level Supply Chain Management information.

It should also obey high security standards following industry and government requirements on how its data is captured, stored, analyzed, disseminated and utilized. The BI system should these follow protocols in order to ensure that their business is accountable and prove legitimate and legal competitive advantage.

3.3. PERVASIVE BI IMPACT ON PERFORMANCE, UNDERSTANDING AND LOYALTY

Keeping everyone in the organization informed is what pervasive BI is all about — and it will impact performance, understanding, and loyalty.

Pervasive BI is geared to provide the right insight to the right person as at when needed. This provision of real-time actionable information increases an organisation's decision-making process and subsequently increases their competitive advantage positively. Pervasive BI promises to impact organisational performance positively by supporting increased productivity and through higher ROI, their profitability.

Although, pervasive BI implementation success is strongly affected by positive or negative organizational culture. User acceptance is a major factor to be considered in technological implementations in any organization, irrespective of the potential benefits being brought about by the implementation. Whether it is in form of increased productivity, performance, increased ROI, reduced costs of production and distribution or any other business benefits, it all ultimately depends on users accepting and utilizing the system (Davis, 1993).

Employees within an enterprise need to be comfortable with using the pervasive BI system. It must be available at all times and obey all usability criteria listed above i.e. simplicity, availability through legacy systems (e.g. Microsoft Excel), scalability over multiple platforms, reduced complexity through customization for each user group and offer a little to non-existent learning curve.

Through surveys conducted by the Aberdeen Group, Gartner and the BIscorecard, pervasive BI implementations still shows an unchanging rate of enterprise adoption at 24%, despite increased annual spending falling into trillions.

4. Web Influence on BI

The internet has shaped the way most technologies have evolved, and the impact it has had on BI is clearly visible. With the evolution of society, connectivity ratio is ever increasing with more people accessing the web and with more data requirements. The business environment is also evolving day by day, with new channels of doing business online opening up, thus eliminating physical barriers that businesses face. Consumers now have access to a wider range of data and services with lesser setup costs, and the customer has the added advantage of being the critic. The opinions of the several customers are typically used to sway potential customers, and opinions can easily be interchanged on several platforms (social networks, forums, reviews, etc.)

With the present setup, business looking to tap into the future need to consider as much information as possible when taking decisions. Considerations need to be made as to what competitors offer, how customers perceive their product and service, and how they can move forward at any given time. One of the key concepts of BI is its real-time nature, and thus BI solutions need to provide the platform for decision makers to be agile in nature, because customer loyalty is a very volatile asset which can be lost due to the lack of responsiveness of an organization.

New modes of interfacing with BI are being introduced because data must now be accessed and analyzed on the move and from anywhere. Desktop applications are being replaced by platform-independent web interfaces, and BI is unconstrained with the advent of Mobile BI.

Information provided in up-to-date manner, not just periodic reports, as was the case before. Because reports were not interactive, and difficult to relate to business goals, intuitive dashboards present data in a real time form.

The decision making process typically was constrained to executives in an isolated manner, but through collective intelligence and crowdsourcing, BI yields better informed decisions (Berthold et al). While crowdsourcing typically involves external elements, it is also possible to tap into tacit knowledge present in employees of an organization (Golfarelli et al). Decisions can be takes through collaborative BI involving other employees, this in turn enriches the data quality and the data can then be exploited to take decisions.

Data presentation has moved away from the traditionally default tools that focus on presenting data in an aggregated form (charts, graphs, spread sheets), to presenting data that compares itself the business strategies, and determines its effectiveness, and projects a way forward.

Tendencies are also identified, as the status of the business strategy is checked from the dashboards (as a form of balanced scorecards) (Trujillo, 2011).

A new form of analysis which still keeps long-term decisions in mind, but due to the dynamic nature of the business environment now has to focus on the immediate future. These analyses are fueled by real-time information, relying strongly on predictive data mining while having a critical time restriction.

In summary, BI is being altered to fit into a dynamically structured environment by the inclusion of new interfaces that present fresh real-time information, where data is presented in a form that is more relevant to the users and focused on the analysis of trends for the immediate future (Trujillo, 2011). Another clear cut characteristic is that decision-making is no longer isolated, and stakeholders can view the expected outcomes of several decisions even before they are taken, and how it affect other internal and external stakeholders.

5. Conclusion

5.1. Research

While the strengths of current and future trends have been identified, our recommendations will be that future extended research should be done in the area of predictive algorithms, most especially time restricted. More ways to presenting data should also be harnessed, as well as improving the process intelligence and mining functions that most tools are beginning to exploit. Finally, best practices should also be identified on how the collaborative process works best in BI application.

5.2. Proposed Future of BI

Based on our research and the possibilities that exist in the realm of Business Intelligence, it is believed that the following can be done to extend BI:

The environment that data and derived insights are presented should be created through the contributions of several stakeholders, to increase the richness of facts, contexts, and eventually, decisions. Collaborative business intelligence can be achieved by using BI as a strategic tool to gain competitive advantage. A realistic way of achieving this is to incorporate narrative context into the data and reports in such a way that annotations are descriptive in nature, with results that also give insights as to what needs to be done next.

Data will become a dynamic element that is enabled to act based on its value. Business rules will be set to give holistic action plans when an exception, alert or notification is received. While data will be self-acting, it is also believed that data can act in capacity as several options to offer the user a range of next line of action. Users will no longer just be presented with data, but they will have the option to act on it.

Through the wide variety and incredibly large amount of data available, several business outcomes should be able to be played out till the end, where decision makers can see what happens if certain actions are taken. With this in mind, a library of actions and business reactions can also be derived, thereby increasing the collective global knowledge.

Complex data visualization will become easier and more intuitive in nature, as tools will create data based on what is seen and users then have the option of customizing these visualizations based on preferences. Complex patterns in data will be detected through automated analytic routines.

Rich data mining will be available, as business users will not view simple information, but trend analysis over time will be created for keywords, to enrich the value of data. Data linkage will also be made possible through several knowledge bases that seem unrelated in today’s BI realm.