The Need To Use Business Intelligence For Iterative Exploration of Knowledge

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1 The Need To Use Business Intelligence For Iterative Exploration of Knowledge Ela Upadhyay 1, Nikhil Govil 2 1 B. Tech. (CSE) IV Year, GLNA Institute of Technology, Mathura, Uttar Pradesh, India. 2 Asst. Professor, Department of CEA, GLA University, Mathura, Uttar Pradesh, India. 1 ela.upadhyay29@gmail.com 2 nikhilgovil@gmail.com Abstract Business Intelligence delivers a rich set of benefits that drive significant and tangible return on investment. It removes the complexity of converting raw data into meaningful business intelligence by giving organizations the power to transform data from multiple sources into accurate, consumable information that can be shared securely throughout the enterprise. It enables users to make informed business decisions quickly and confidently by providing the query and reporting tools they need to find, share, manage, publish and analyze information. The goal of Business Intelligence is to enable management to make more intelligent decisions on the basis of knowledge extracted from data. Keywords: BI, ETL, EDW, OLAP etc. I. INTRODUCTION. Business intelligence (BI) has two basic different meanings related to the use of the term intelligence. The primary, less frequently, is the human intelligence capacity applied in business affairs/activities. Intelligence of Business is a new field of the investigation of the application of human cognitive faculties and artificial intelligence technologies to the management and decision support in different business problems.the second relates to the intelligence as information valued for its currency and relevance. It is expert information, knowledge and technologies efficient in the management of organizational and individual business. Therefore, in this sense, business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all of the factors that affect the business. It is imperative that firms have an in depth knowledge about factors such as the customers, competitors, business partners, economic environment, and internal operations to make effective and good quality business decisions. Business intelligence enables firms to make these kinds of decisions. A specialized field of business intelligence known as competitive intelligence focuses solely on the external competitive environment. Information is gathered on the actions of competitors and decisions are made based on this information. Little if any attention is paid to gathering internal information.business intelligence (BI) is defined as the ability for an organization to take all its processes and capabilities and then convert these into knowledge, ultimately getting right information for the right people, at the right time.ion. BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive BI tools are a type of application software designed to retrieve, analyze and repot data. The tools generally read data that have been previously stored, often, though not necessarily, in a data warehouse. According to Hancock & Toren, 2006, BI is a set of concepts, methods, and technologies for turning separated data in an organization into useful information in order to improve business performance. In a Business Intelligence environment, data from various sources are extracted, transformed and loaded (ETL) into an Enterprise Data Warehouse (EDW) and, from EDW, they are used for generation of reports across the organization. Data quality plays an important and critical role in BI success, since poor data quality can affect business decisions at all levels of the organization and such poor quality data also hinders the growth of organization. The fact is that the business runs on data. A company cannot understand its customers, suppliers, competitors or its own people, processes, and company performance without good data. II. BUSINESS INTELLIGENCE PREDICATIVE ANALYSIS. Golfarelli et al. (2004) defined BI t Stackowiak et al. (2007) define Business intelligence as the process of taking large amounts of data, analyzing that data, and presenting a high-level set of reports that condense the essence of that data into the basis of business actions, 297

2 enabling management to make fundamental daily business decisions. The concept of Business Intelligence (BI) is brought up by Gartner Group since It is defined as the application of a set of methodologies and technologies, such as J2EE, DOTNET, Web Services, XML, data warehouse, OLAP, Data Mining, representation technologies, etc, to improve enterprise operation effectiveness. Zeng et al. (2006) define BI as The process of collection, treatment and diffusion of information that has an objective, the reduction of uncertainty in the making of all strategic decisions. Experts describe Business intelligence as a business management term used to describe applications and technologies which are used to gather, provide access to analyze data and information about an enterprise. Tvrdikova (2007) describes the basic characteristic for BI tool is that it is ability to collect data from heterogeneous source, to possess advance analytical methods. Zeng et al. (2006) categorized BI technology based on the method of information delivery; reporting, statistical analysis, adhoc analysis and hat includes effective data warehouse and also a reactive component capable of monitoring the time critical operational processes to allow tactical and operational decision-makers to tune their actions according to the company strategy Gangadharan and Swamy (2004) define BI as the result of in-depth analysis of detailed business data, including database and application technologies, as well as analysis practices. Berson et.al (2002); Curt Hall (1999) BI includes several software for Extraction, Transformation and Loading (ETL), data warehousing, database query and reporting, OLAP, data analysis, data mining and visualization. III. TYPES OF DATA PROBLEMS A. Defective data. 1.Types of Data Defects: There are many types of data defects. Inaccurate, incomplete, unavailable or obsolete data is defined as defective data. For example, manually entered data is often full of spelling errors, and perhaps falsified. Sometimes people neglect to fill in all the fields or enter correct data in the wrong fields. Data defects can also appear when a system is moved from one platform to another, an old application is replaced with a new one. 2.Fixing Data Defects: The problem with defective data is that it is difficult to find out once it s entered into the system. The best way to prevent defective data is to prevent it from being entered in the first place, so for this the businesses should invest in systems that validate and fix data at the source when it s entered into the system or moved between systems through an application interface. To fix defective data already in the system, the business must invest in data profiling and cleansing tools to cleanse and validate data sets before they are loaded into data warehouses. B. Inconsistent data. 1.External Data: Inconsistency in data occurs over time as the data is either duplicates or out dated. For example, customer data degrades over time as people marry, divorce, die, move, or change their names, so, this results in inconsistent data. 2.Internal Data: Internally, companies fragment data to suit their needs, largely because of the way they are organized and also because they lack centralized data management. IV. COMPONENTS OF BI OLAP (online analytical processing): Online Analytical Processing or OLAP provides multidimensional, summarized views of business data and is used for reporting, analysis, modeling and planning for optimizing the business. OLAP techniques and tools can be used to work with data warehouses or data marts designed for sophisticated enterprise intelligence systems. Other BI tools are used to store and analyze data, such as data mining and data warehouses; decision support systems and forecasting; document warehouses and document management; knowledge management; mapping, information visualization, and dash boarding, management information systems, geographic information systems, Trend Analysis, Software as a Service (SaaS). V. DATA WAREHOUSE AND DATA MARTS. The data warehouse is the significant component of business intelligence. It is subject oriented, integrated, time varying and non volatile. It can also contain the operational data which can be defined as an updateable set of integrated data used for enterprise wide tactical decision-making of a particular subject area. It contains live data, not snapshots, and retains minimal history. Data sources can be operational 298

3 databases, historical data, and external data. A data mart as described by (Inmon, 1999) is a collection of subject areas organized for decision support based on the needs of a given department. Finance has their data mart, marketing has theirs, and sales have theirs and so on. Similar to data warehouses, data marts contain operational data that helps business experts to strategize based on analyses of past trends and experiences. VI. DATA SOURCES. Data sources can be operational databases, historical data, external data for example, from market research companies or from the Internet), or information from the already existing data warehouse environment. Fig 1: Business Intelligence stages as data quality sources VII. ARCHITECTURE OF BI Business intelligence architecture is a framework for organizing the data, information management and technology components that are used to build business intelligence (BI) systems for reporting and data analytics. The underlying BI architecture plays an important role in business intelligence projects because it affects development and implementation decisions. Fig 2: Incremental and Iterative process followed in BI architecture Top Ten Signs of Effective Business Intelligence Managers and analysts have direct, nearly instantaneous access to data; they never argue over whose numbers are accurate. Information workers spend their time analyzing data and understanding its implications rather than collecting and formatting data. Managers focus on improving processes and business performance, not culling data from laptops, reports, and transaction systems. A hypothesis can be quickly analyzed and tested without a lot of manual preparation. Data is managed from an enterprise wide perspective throughout its life cycle, from its initial creation to archival or destruction. Rather than have data warehouse or business intelligence initiatives, companies manage data as a strategic corporate resource in all business initiatives. Both the supply and demand sides of the business rely on forecasts that are aligned and have been developed using a consistent set of data. High-volume, mission-critical decisionmaking processes are highly automated and integrated. Data is routinely and automatically shared between the company and its customers and suppliers. Reports and analyses seamlessly integrate and synthesize information from many sources. Business intelligence, analytics and high performance As part of Accenture s research into high-performance businesses, we have found that a growing number of companies have recognized the power of leveraging data-driven insights through the use of business intelligence. Some forward-thinking companies have gone a step further and are building their competitive strategies around analytics that is, the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. VIII. ISSUES IN BUSINESS INTELLIGENCE Experts View: These experts treat BI as technology platform for decision support application. The Opinion is that to data mining experts BI is set of advanced decision support systems with data mining 299

4 techniques and applications of algorithms. Analysis of Right Information: Several surveys including Gartner, Forrester and International Data Centre report that most of the firms throughout the globe are interested in investing in BI. It is to be noted that despite major investments in enterprise resource planning (ERP) and customer relationship management (CRM) over the last decade businesses are struggling to achieve competitive advantage. It is due to the information captured by these systems. Any corporate would look forward for one goal called right access to information quickly. Hence, the firms need to support the analysis and application of information in order to make operational decisions. IX. AGILE BUSINESS INTELLIGENCE Agile business intelligence is an approach that combines processes, methodologies, organizational structure, tools, and technologies that enable strategic, tactical, and operational decision-makers to be more flexible and more responsive to the fast pace of changes to business and regulatory requirementsthe future of agile business intelligence: In the past, BI vendors and BI application developers focused on business and operational functionality and architectural robustness. BI vendors and developers now need to concentrate on technologies that are categorized as "agile" and refer to four major subcategories of agility: automated, pervasive, unified and limitless. Each of these new technologies stands on its own and is independent of the others. Today, different BI tools address various BI use cases, such as historical and predictive or batch based and real time BI applications. Next-generation BI brings all of them together in a unified platform. Make enterprise BI applications available wherever and whenever strategic, tactical, and operational decisionmakers need to analyze information, make decisions and act. X. CHARACTERIZING DATA QUALITY IN BI Completeness: deals with to ensure Is all the requisite information available? Are some data values missing, or in an unusable state? Completeness measures to which extent data should have been recorded in a table is effectively present and also checks whether it is according to the warehouse specifications. We refer to vertical incompleteness when we measure completeness of data in a column. Horizontal incompleteness refers instead to the quantity or percentage of entire tuples. Extensionally, completeness for cells is expressed as a binary value (i.e., true/false).consistency: Consistency denotes the uniformity of the information in a given table. Syntactic consistency refers to uniformity in the data format for a specific field. This is typically something that is detected and corrected at data cleaning time (e.g. via normalization). Semantic consistency refers to the satisfaction of semantic rules defined over a set of data items. Validity: Validity refers to the correctness of data. Conformity: Conformity refers to expectations that data values conform to specified formats. Accuracy: Incorrect spellings fields and outdated data can impact operational and analytical BI applications. Confidence describes the perceived accuracy and precision of the data, or the degree of trust that the data present in a table or set of tables is accurate. Integrity: The inability to link related records together may actually introduce duplication across the systems. Some other data quality characteristics are Assurance, Delivery, Openness / Transparency, Responsibility. XI. FUTURE OF BUSINESS INTELLIGENCE In this rapidly changing world consumers are now demanding quicker more efficient service from businesses. To stay competitive companies must meet or exceed the expectations of consumers. Business intelligence users are beginning to demand Real time Business Intelligence] or near real time. Analysis relating to their business, particularly in frontline operations. They will come to expect up to date and fresh information in the same fashion as they monitor stock quotes online. Monthly and even weekly analysis will not suffice. In the not too distant future companies will become dependent on real time business information in much the same fashion as people come to expect to get information on the internet in just one or two clicks. Also in the near future business information will become more democratized where end users from throughout the organization will be able to view information on their particular segment to see how it's performing. So, in the future, the capability requirements of business intelligence will increase in the same way that consumer expectations increase. XII. BENEFITS OF BI BI provides many benefits to companies utilizing it. It can eliminate a lot of the guesswork within an organization, enhance communication among departments while coordinating activities, and enable companies to respond quickly to changes in financial conditions, customer preferences, and supply chain operations. 300

5 The firms have recognized the importance of business intelligence for the masses has arrived. Some of them are listed below: With BI superior tools, now employees can also easily convert their business knowledge via the analytical intelligence to solve many business issues, like increase response rates from direct mail, telephone, , and Internet delivered marketing campaigns. With BI, firms can identify their most profitable customers and the underlying reasons for those customers loyalty, as well as identify future customers with comparable if not greater potential. Determine with attrition and churn analysis why customers leave for competitors and/or become the customers. Detect and deter fraudulent behavior, such as from usage spikes when credit or phone cards are stolen. XIII. BI-TODAY AND TOMORROW. BI has been one of the most important business initiatives providing positive impact on the health of organizations. Usually, questions are raised on the maturity of the BI initiative and the technology backbone to yield business benefits. And after getting the desired benefits from the BI initiative, the obvious question has been What next? XIV. BI TECHNOLOGY ADVANCEMENTS Along with traditional tools and technologies (i.e. ETL, OLAP, Appliances, Dashboards, Mining), new technologies have started finding their place under the BI umbrella. Growth in Data volume: Globalization, consolidation and increase in customer and product base have resulted in tremendous growth in data volume. This has led to the increased use of appliances, and data compression. Critical or important datasets can be given main computing power, while keeping the other datasets (mostly >70% of whole dataset) in inactive (not necessarily offline) state with the help of ILM (Information Lifecycle Management) products. This significantly increases system performance with the same computing power. Focus on operational efficiency: The philosophy is to have the right information available to the right person at the right time. As per the Time- Value Curve for Decision Making, business value decays with time and the definition of the right time depends purely on the optimization need in the business decision making cycle. If a call center person needs to perform the Best Action while interacting with the customer, he/she needs to know the as-of-now customer profile (e.g. customer information along multiple focus areas such as Account / Subscription, Affinity towards Products, Campaigns sent and Faults reported). In this case, the as-of now information can be with 5-10 minutes latency. XV. FUTURE TRENDS OF BUSINESS INTELLIGENCE Looking beyond the obvious trends (Social, Mobile, Cloud), I would like to focus on three trends brewing at the tectonic levels of the BI industry. In my opinion, these trends are the catalysts for unprecedented adoption down the line. In simple words, business intelligence tools need to be Simplified, Specialized and Personalized. 1. Simplification Every product needs to end up in the hands of its final user. BI s real users are the decision makers in a company, statuesquely called the Executives. BI has successfully transitioned from the obscure backrooms of IT into the realms of analysts. However, the journey will end when the Executives (whom are not very tech-savvy) can use appropriate tools to aid their primary job. 2. Specialization As humans we seek specialist medical opinion for anything more than the flu as we want expert analysis on matters critical to us as individuals. Similarly, each industry and each firm is as unique as would be their data and practices. The era of generic, domain-agnostic systems will give way to systems that have deep understanding of the client s business vertical. Specialist systems built on emerging technologies such as machine learning, knowledge engineering, artificial intelligence and the like will provide invaluable insights to the Executives on matters specific to their industry vertical to enable better decision making. 3. Personalization Most of the current solutions are too hard to use and too complicated to implement and maintain. Smart BI, with sophisticated technology that is almost transparent, resulting in personal digital assistants will unlock the vast under-tapped potential of the BI promise.. XVI.CONCLUSION The movement towards the simplification of BI is substantially enhanced by the triad of technologies associated with Data Discovery tools, mobile devices, 301

6 and the Cloud. This combination has spurred BI s departure from the backrooms of IT departments and placed its capabilities in the front offices and pockets of end users, providing an unmatched potential to access insights on-demand from anywhere. Discovery tools have lessened the need for conventionally lengthy (and time-consuming) BI reports, and significantly augmented them in cases in which they re essential. Dashboards and interactive visualizations graphically represent data and results from BI in ways in which trends are readily discernible, data mashups and in-memory analytics enable users to quickly query a variety of disparate sources, and search tools offer text-derived analysis of either structured or unstructured quantitative and qualitative data. 8. Business Intelligence Present and Future, Posted by Ravisankar S., in Buiness Intelligence & ERP, Highlight, Products & Services, Ramco Analytics Suite, The World of ERP, September Margaret Rouse, Business Intelligence Architecture, TechTarget Search Business Analytics, November Mobile technologies are extremely complementary to discovery tools; several dashboards and visualizations were specifically designed for or are easily modified for tablets and smart phones. Mobile devices bolster user familiarity particularly due to the widespread Bring Your Own Device (BYOD) policy of many organizations and provide a degree of accessibility that desktop applications can t match. Cloud computing plays a major role in the simplification of BI. The Cloud is projected to be the de facto source for Big Data analytics in the coming years, while its low cost and extreme scalability is ideal for accessing BI from mobile devices. Cloud-based BI and cloud applications in general are partly responsible for the consumerization of IT by rendering these services XVII.REFERENCES 1. Navita Kumari, Business intelligence In A Nutshell, International Journal of Innovative Research in Computer and Communication Engineering, Vol. I. Issue 4, June Jayanthi Ranjan, Business Intelligence: Concepts, Components, Techniques and Benefits, International Journal of Theoretical and Applied Information Technology, Vol.9, No. 1, Gartner Business Intelligence & Information Management Summit, Mumbai, 9 10 June The Architecture of Business Intelligence, by Accenture, Adapted from the forthcoming book Competing on Analytics: The New Science of Winning, Harvard Business School Press, March Adelman Sid, Moss Larissa and Barbusinski Les., I found several definitions of BI, DM review, Aug Jelani Harper, The Future of Business Intelligence: An Impending Realization, Data University, 9 January Daan Van Beek, The Future of Business Intelligence, Passionned Netherland B. V. Group, 24 December