Volume 2, No. 05, July 2013 ISSN 2278-1080 The International Journal of Computer Science & Applications (TIJCSA) RESEARCH PAPER Available Online at http://www.journalofcomputerscience.com/ Business Intelligence Impact and Future Trends Prof.B.Lakshma Reddy 1, Dr.T.Bhaskara Reddy 2, M.Victoria Hebseeba 3 1 Director,Department of Computer Science,Garden City College, Bangalore,Karnataka Prof.reddy99@gmail.com 2 Department of Computer Science, S.K.University,Andhra Pradesh bhaskarreddy_sku@yahoo.co.in 3 Department of Computer Science, Rayalaseema University, Andhra Pradesh victoria.hebseeba@gmail.com ABSTRACT This paper describe Business Intelligence (BI) is a computer based technique to identifying, extracting and analyzing business data. For example senior management of an industry can inspect sales revenue by products and/or departments, or by associated costs and incomes. BI technologies provide historical, current and predictive views of business operations. So, management can take some strategic or operation decision easily. 1. Why Business Intelligence? In the aftermath of the Great Recession, an economy that is growing but uncertain has lead to greater caution and thrift among consumers. As a result, even as many small-to-midsized businesses (SMBs) plan for growth, they continue to look for ways to improve efficiency and cut costs. SMBs increasingly realize that having the right information and the tools to analyze that information are critical to making business decisions that can drive growth and improve productivity. Today, more information than ever is available about companies internal operations. This information is a by-product of companies successful efforts to automate their operations. Business intelligence (BI) solutions provide a way to harness this ever-increasing information in a way that allows companies to make better, smarter decisions of all types and ultimately outpace their competition. As a result, BI has emerged as one of the top IT priorities for SMBs. As BI solutions have evolved, they have become less expensive and easier to implement and deploy. Indeed, BI vendors today, facing stagnant and oversaturated enterprise markets, are increasingly shifting their focus to serving the SMB market. This is good news for SMBs looking for BI solutions. Yet challenges remain in terms of information accessibility, ease of use, and implementation[2]. This series of white papers looks at how the BI industry will evolve over the next decade to address these challenges and how emerging technology is likely to enable vendors to further improve BI capabilities. In this series of three papers, the following areas will be covered: 1. High-level trends that have led to SMBs increasing adoption of BI and how enterprise and accounting software applications will need to change to provide better access to information 2013, http://www.journalofcomputerscience.com - TIJCSA All Rights Reserved 31
2. How BI technology and capabilities are likely to evolve over the coming decade to better address SMB s information requirements 3. How trends and advances outside the BI sphere will impact BI technology BI is used for reporting, online analytical processing, data mining, process mining, complex event processing, business performance management, benchmarking, text mining and productive analysis.by using BI, management can monitor objectives from high level, understand what is happening, why is happening and can take necessary steps why the objectives are not full filled. Business intelligence aims to support better business decision-making. Thus a BI system can be called a decisions support system (DSS). Before going to launch any product, company need to understand of market trend. Company uses BI to analyze market data and understand which product or which business is suitable for the current time in the market. In a word, BI gives you right information, right time in right format. 2. How Business Intelligence Works? BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes. The technical flow of BI is as shown in figure 1. Data warehousing and BI Often BI applications use data gathered from a data warehouse or data mart. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse. In order to distinguish between concepts of business intelligence and data warehouses, Forrester Research often defines business intelligence in one of two ways: Boarder Definition: "Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making. When using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the Information Management segment. Therefore, Forrester refers to data preparation and data usage as two separate, but closely linked segments of the business intelligence architectural stack[1]. Forrester defines the latter, narrower business intelligence market as "referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards." 2013, http://www.journalofcomputerscience.com - TIJCSA All Rights Reserved 32
Business intelligence and business analytics Figure 1:- Business Intelligence Flow Business Intelligence can be applied to the following business purposes (MARCKM), in order to drive business value: MARCKM means Measurement, Analytics, Reporting/Enterprise Reporting, Collaboration/Collaboration Platform, and Knowledge Management. In addition to above, Business Intelligence also can provide a pro-active approach, such as ALARM function to alert immediately to end-user. There are many types of alerts, for example if some business value exceeds the threshold value the color of that amount in the report will turn RED and the Business Analyst is alerted. Sometimes an alert mail will be sent to the user as well. This end to end process requires data governance, which should be handled by the expert. Semi-structured or unstructured data Businesses collect a huge amount of valuable information. These information included in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, videofiles, and marketing material and news. BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making. It is very difficult to identify which information is in unstructured data. Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, it refers to data that cannot be stored in columns and rows. It must be stored in a BLOB (binary large object), a catch-all data type available in most relational database management systems. Since it is difficult to search information from unstructured data so, what will organization do to extract information from unstructured data? Metadata is only way to search information from unstructured data. Metadata is actually data about data. Metadata can include information such as author and time of creation. This metadata can be stored in a database. So, it is easy to search by this metadata. To solve problems with search ability and assessment of data, it is necessary to know something about the content. It is more useful would be metadata about the actual content e.g. summaries, topics, people or companies mentioned [1]. There are many challenges to develop BI with semi-structured and structured data. Those are: - Accessing unstructured data because it is stored in a variety of format. - There is no standard terminology. - Volume of data is so high - Search ability of unstructured data is not easy. 2.1 Impact of Business Intelligence Business intelligence technologies encompass a broad range of applications and practices for the collection, integration, analysis, and presentation of business information, with the overriding objective to support better business decision making. In the past, BI tools like decision support systems were only available to senior 2013, http://www.journalofcomputerscience.com - TIJCSA All Rights Reserved 33
executives. With the advent of Internet and proliferation of Web 2.0 applications, business intelligence has been made accessible to employees at lower levels. While senior managers and analysts have access to more specialized BI tools like digital dashboards, OLAP and data mining, more junior employees can now also use search engines and subscribe to RSS feeds to monitor competitors actions (e.g., press releases) and customers feedback on new media such as blogs. Coupled with the access to fee-based subscription databases (e.g., industry indicators, statistics and technological developments news), the acquisition process of external information has been markedly increased over the years. Besides, most organizations have in placed knowledge management systems (KMS) to integrate internal information with externally-acquired information. With the advancements in groupware technologies such as Lotus Notes and Microsoft Office Share point, organizations can now more effectively engage in knowledge sharing, assimilation and exploitation. Expectedly, the use of such BI technologies would enhance the information-intensive absorptive capacity processes significantly. We thus posit that the greater the use of BI technologies in the organization, the higher the degree of organizational absorptive capacity will be Innovation has been regarded as an importance outcome of enhanced organizational learning and absorptive capacity. The building of exploitative innovation competence is necessary for firms to respond to current opportunities and threats to survive in the short term [3]. Concurrently, they must develop exploratory innovation competence that provides strategic options for meeting future demands. Organizational survival requires a balance of engaging in sufficient exploitation for current viability, and at the same time, devoting enough energy to exploration to ensure future viability. Ambidextrous organizations have the capacities to compete in both mature markets (where cost and efficiency are essential) and develop new products and services for emerging markets (where innovativeness and speed are critical). In increasingly turbulent markets, organizations need to be flexible so that they can respond quickly to competitive threats yet remain stable so they can learn and grow based on their strengths. By taking a process oriented view of absorptive capacity as three sequential constituent processes of exploratory learning, transformative learning, and exploitative learning, we expect that enhanced capability in these three ACAP processes would result in the nurturing of both exploitative and exploratory innovation competences. Hence, we posit that: sets and capabilities that are hard to imitate, less visible, non-substitutable, and valuable, are regarded as sources of competitive advantage. Competitive advantage may be accrued when the value derived from the production of products and services surpasses that of the opportunity costs incurred by the organization in the utilization of the resources necessary to produce the outputs in question. Sustained competitive advantage arises when competitors face significant challenges in acquiring, developing, and using the resources underlying the value creating strategy. Innovations create economic value by increasing the efficiency with which current goods and services are produced, improving their quality, or by creating entirely new products for which there is market demand[7]. More precisely, exploitative innovation competence impacts competitive advantage through improvements to quality (enhancing the value of the organization s current products and services) or production efficiencies (reducing the costs of production), while exploratory innovation competence creates competitive advantage through radically new products and services that captures emerging markets and customers. First mover advantages may also be enjoyed by firms with exploratory innovations, to the extent that the innovating firm is able to protect its innovation from imitation by competitors. Exploitative innovation enhances the firm s current competitive position while exploratory innovation enhances the firm s future viability. 2.2 Future of Business Intelligence - Predictive Analytics The market is witnessing an unprecedented shift in business intelligence (BI), largely because of technological innovation and increasing business needs. The latest shift in the BI market is the move from traditional analytics to predictive analytics. Although predictive analytics belongs to the BI family, it is emerging as a distinct new software sector. 2013, http://www.journalofcomputerscience.com - TIJCSA All Rights Reserved 34
Analytical tools enable greater transparency, and can find and analyze past and present trends, as well as the hidden nature of data. However, past and present insight and trend information are not enough to be competitive in business. Business organizations need to know more about the future, and in particular, about future trends, patterns, and customer behavior in order to understand the market better. To meet this demand, many BI vendors developed predictive analytics to forecast future trends in customer behavior, buying patterns, and who is coming into and leaving the market and why. Traditional analytical tools claim to have a real 360 view of the enterprise or business, but they analyze only historical data data about what has already happened. Traditional analytics help gain insight for what was right and what went wrong in decision-making. Today s tools merely provide rear view analysis. However, one cannot change the past, but one can prepare better for the future and decision makers want to see the predictable future, control it, and take actions today to attain tomorrow s goals[3]. 3. What New Technologies and Trends Will Mean for BI In the last two white papers in this series, we talked about how the need to use information from business applications to help make better decisions is driving increased demand for BI. You learned how business applications and security will need to change to improve access to that information, advances that are likely to address current deficiencies in BI solutions over the coming decade, and likely future enhancements. In addition to advances in BI technology itself, many other trends will ultimately have an influence on how BI solutions will evolve over the coming decade. This paper will discuss some of the most critical changes and the potential impact they may have on BI. These advances include: The Internet Social media Mobility Software as a Service New tools Changes in user skill sets as a more technically savvy generation comes of age 4. Business Intelligence and the Challenges Whether they involve distributing goods to customers, collaborating with suppliers or coordinating the efforts of employees, business processes are the foundation on which rest a business s products, services and brands. They are the essential underpinning of an organization s ability to function. In successful companies, business processes align resources to support the achievement of goals[4]. Business process management (BPM) is a methodology to ensure that those processes support a common set of goals and objectives. It involves automating and/or improving the effectiveness of process activities, tasks and outcomes to accomplish particular business purposes. Its goals include not only efficiency and productivity but, beyond them, control, responsiveness and improvement. Efficiency enables individuals to maximize the time they can devote to priority business tasks and to maximize throughput. Control assures that company resources are aligned optimally to execute strategies. Responsiveness and improvement support the competitive differentiation that enables a company to excel over others. BPM, then, is about improving processes and implementing systems that enable the improvement. In pursuit of this mission, BPM starts with the following assumptions: As a business changes, so must its processes; as a result, they need to be revisited periodically. Processes are used by multiple organizations and stakeholders. 2013, http://www.journalofcomputerscience.com - TIJCSA All Rights Reserved 35
Processes interact with systems and people. Those people can be employees, customers, suppliers or other stakeholders. To attain the process goals of efficiency, productivity, control, response and improvement, companies first must understand their processes, the needs and skills of the people who use them, the changes that affect them and which areas need improvement. To understand these things, they need relevant information and the ability to analyze and apply it. Business intelligence (BI) tools provide this information, its contexts and appropriate analytics. BI delivers information that, when linked with BPM, gives people the input they require to improve business processes. This last point is key: Both processes and tools are subordinate to the end of empowering people, at whatever level they occupy, to make informed business decisions that execute corporate strategy, improve performance and ultimately produce the best possible results. Separation Inhibits Decision-Making To benefit from improved process management and decision-making, however, companies first must bring together these two business enablers. Most organizations use BI and BPM technologies to serve separate purposes that seldom overlap. For the most part, BI deployments don t focus on process, and BPM technology doesn t provide metrics or an aggregate view of business. This situation reflects the predominant view that these are different technologies that each stands alone, delivering value to the business each in its own way. This view is short sighted, and soon it will be antiquated. Dynamic enterprises are beginning to forge links between processes and data, particularly in the areas of operational BI and decision-intensive processes. They are linking these two seemingly incompatible technologies in three distinct ways. First, when initially implemented, most processes are sketchily defined. BI can generate analytics that help analysts tune the process to serve its purposes better. For example, process analytics can calculate measures such as the average process cycle time, durations of individual activities and time lapses between activities. Typically, BI used in this way will report on data about both transactions and process instances. This data becomes post process feedback that can guide the process of tuning. Second, most business processes manage the flow of data through an organization and involve decision points at which human interaction is required [5]. 5. Conclusion Business intelligence is the ability of an organization to understand and use information for achieving competitive advantage. It plays a key role in helping companies optimize their decision-making process and management. Correct, prompt and visually appealing information becomes an important resource for efficacious decisionmaking and management. Only information system based on the modern information technology, which is the data warehouse concept, can provide data and information needed for management of business processes. The data warehouses are used, or being developed, in 70% of the worldwide companies. However, quality data and information alone are not sufficient. Usage of techniques and tools for extracting useful knowledge from the data warehouse is necessary for a company that has to respond to business pressures from the environment. There are two widely used enabling techniques of the knowledge discovery: online analytical processing (OLAP) and data mining. In the companies worldwide, level of the OLAP tools usage is somewhat similar to the rate of the data warehouse (60%). OLAP tools are in use in only 18% organizations, while even 38% of IS executives don't know what OLAP tools are. This result is unexpected. The data warehouse and OLAP tools are two complementary technologies, so it could be expected that the level of the OLAP tools usage is somewhat similar to the state of the data warehousing. Data mining tools and techniques are in use in only a few organizations[6]. 2013, http://www.journalofcomputerscience.com - TIJCSA All Rights Reserved 36
The question is, have the managers have realized the importance of quality information for making business decisions and management? Results show that managers are often among the initiators of data warehousing. Satisfaction with the business intelligence tools is a little higher, if the initiators of the project were a manager, but it is not statistically significant. On the other hand, CEOs and IS executives identify managers as the obstacle to introducing new, contemporary information technology. 6. References 1. Alter, Steven, Information Systems: a management perspective, Addison Wesley Longman, 1999 2. Buytendijk, Frank A. Het walhalla van de informatie-democratie (een interview met Howard Dresner), Database Magazine (DB/M), no. 8, 1997, p. 35, http://www.array.nl/dbm/art9708/howarddresner.htm 3. Hashmi, Naeem BI for sale, 2000, Information Frameworks http://www.intelligenterp.com/feature/2000/10/hashmioct20.shtml 4. Hermelink, Judith & Bilsen, Joost van IT bepaalt de score Database Magazine (DB/M), no. 8, 2000, p. 13 5. Laudon, Kenneth C. & Laudon, Jane P. Management Information Systems: organization and technology in the networked enterprise, Prenctice Hall, 2000 6. Lewis, William J. Data Warehousing and E-Commerce, Prentice Hall, 2001 7. Turban, Efrahim & Aronson, Jay E. Decision Support Systems and Intelligent Systems, Prentice Hall, 2001 2013, http://www.journalofcomputerscience.com - TIJCSA All Rights Reserved 37