The Usage of BI Tools for Decision Making in An Organization: A survey

Transcription

1 e-issn Volume 2 Issue 6, June 2016 pp Scientific Journal Impact Factor : The Usage of BI Tools for Decision Making in An Organization: A survey Mrs. Krantee Jamdaade 1, Onkar A. Jangam 2 1 Lecturer, MCA Department, L.B.H.S.S. T s I.C.A., Mumbai University, Bandra (E) 2 T.Y. M.C.A. Student, MCA Department, L.B.H.S.S. T s I.C.A., Mumbai University, Bandra (E) Abstract This research paper gives information about Business Intelligence space, how it is implemented in organizations and its usefulness in Decision making process of organization and overview of some popular tools. It covers various aspects of BI implementation and how it proves useful to various stakeholder of organization Keywords Data Warehouse, Data Mart, BI Modules, Tableau, Qlikview I. INTRODUCTION There are three aspects of data to any organization these are data, information and knowledge. Processing of huge data results into useful information. Processing of information results into knowledge which in turn helps management to make strategy decisions. This journey from raw data to knowledge generation is carried out with the solutions which are known as BI in modern era. This data can come from multiple sources with multiple structures & it is very important to process this data appropriately in order to turn it into knowledge.bi solutions typically helps you to integrate data from various sources transform it and turn it into a valuable asset which helps in fulfilling demand of business users. Typically, BI solutions are implemented with three tiers Architecture called Extract, transform, Load which involves fetching data from multiple sources transform it to achieve data quality & business requirements & load to generate visualization with various dimensions. II. BASIS FOR BI IMPLEMENTATION Organizations generate huge amount of data in order to process that data effectively all the data needs to be in one place that place is typically recognized as data ware house from which data is taken for further analysis to generate knowledge Data Ware House Data ware house give foundation for any BI solutions it hosts data from various data sources it contains historical data and may contain data in aggregated format as well. Data can be in different format or structures depending upon source Data Marts Data warehouse typically consist of various data marts which resembles to independent area of functions in the organization Some characteristics of data mart 1. Stores relevant data it extracts data only significant data and processes it All rights Reserved 295

2 2. Data is updated with a fixed frequency- Data mart loads data with a defined data update schedule. 3. Defines clear Meta data. Data from different sources is taken and stored into the data warehouse using the Extraction process the useful data is extracted from the data warehouse, transformed as required and standard output is generated using which we generate various reports using business intelligence tools. Fig.2.1 explains us the OLAP technology, complex solution-support of decision making, also called Business Intelligence [Source adapted from [1] III. MODULES IN BI APPLICATION There are three typical modules in BI solutions; these are Executive information system, Customer Relationship Management, Corporate performance management. All these modules provide multidimensional view of data Executive information system. BI Solutions in these modules typically translates voluminous primary data of transactional systems into logical information which helps in decision making process CHARACTERISTICS OF EIS 1. Provides Multidimensional data. 2. Extracts data from various sources. 3. Directly interacts with external data sources. 4. Use Modern Analytic practices. 5. Support Parallel Access in All rights Reserved 296

3 BENEFITS OF EIS 1. EFFECTIVE PROCESSING Processes Data from Heterogeneous Data Sources, processing of non-standardized data, identification of missing and zero values. 2. DIMENSION MANAGEMENT All the functions work generically for all dimensions, supports cross dimensional operations, generates different views of data by identifying relationships among dimensions Customer Relationship Management CRM Applications are typically focused to satisfy customer needs. It helps to build a strategy towards customer management. It extracts data from various company information systems transforms data into logical process or data insights which helps in better customer management. It has two aspects of data processing 1. Analytical 2. Operational 1. Analytical Analytical aspect includes analysis of customer behavior their transaction patterns from company s operational data to optimize/generate new strategy for company operations. 2. Operational It focuses on business operations for interacting with the customers. This typically involves improving ease of customer interaction with organization through various electronic sources and focus on ways to manage customer relationships Corporate performance management CPM BI Solutions are delivered in order to support company operations. It focuses on information which is the key for managing Performance, Execution of Strategies in an Organization FEATURES 1. It can be used to create a model which identifies various relationships among company processes, monitor and measure them. 2. It can be used to build a solution for planning aspect of an organization which are related to goals and vision of an organization. 3. It can also provide solutions that focus on budgeting part of various organization functions and provides need to compare with actual data. 4. It can also provide solutions which typically involves monitoring and measuring various KPI s of an organization. Example- Executive dashboards, Score Card Summaries. 5. It can also be used to unlock data insights by analyzing the data. It explores various trends and patterns in Business All rights Reserved 297

4 IV. DIFFERENT TYPES OF BI SOLUTIONS There are four different types of BI solutions; these are Real time BI Solutions, Tactical BI Solutions, Investigative BI Solutions, and Traditional BI Solutions 4.1. Real time BI Solutions This focus on building of dashboards or reports which are updated with live data these applications are typically connected with OLTP Base systems. However, generating insights from real time data has major scope for improvement depending upon advanced technology coming in Tactical BI Solutions These BI Solutions are typically focused on improving strategy /Operational Performances of particular function in organizations. It involves analysis of historical data exploring patterns. Based on these strategical changes in organization can be directed Investigative BI Solutions These BI Solutions focus on exploiting trends which can be typically used for RCA, controlling financial metrics, exploring inconsistency in operations. These types of solutions can help to achieve cost controls, better utilization of work force Traditional BI Solutions Traditional BI typically involves dashboards, reports etc. It is a standard way of tracking KPI s of Organization, providing reports for operational activities in the organization. V. BI FOR DECISION SUPPORT PROCESS IN ORANIZATION According to A Good BI implementation in an organization enhances following five aspects of BI 1. Agile Centralized BI Provisioning Supports an agile IT-enabled workflow from data to centrally delivered and managed content using the self-contained data management capabilities of the platform. 2. Decentralized Analytics Supports a workflow from data to self-service analytics. 3. Governed Data Discovery Supports a workflow from data to self-service analytics, to systems-of-record, IT-managed content with governance, reusability. 4. Embedded BI Supports a workflow from data to embedded BI content in a process or application. 5. Extranet Deployment Supports a workflow similar to agile centralized BI provisioning for the external customer or, in the public sector, citizen access to analytic All rights Reserved 298

5 Following are the capabilities provided to an organization with a BI solution We have categorized these capabilities into different areas 5.1 Infrastructure 1. BI Platform Administration- Capabilities that enable scaling the platform, optimizing performance and ensuring high availability and disaster recovery. 2. Cloud BI- Platform-as-a-service and analytic-application-as-a-service capabilities for building, deploying and managing analytics and analytic applications in the cloud, based on data both in the cloud and on-premises. 3. Security and User Administration- Capabilities that enable platform security, administering users, and auditing platform access and utilization. 4. Data Source Connectivity- Capabilities that allow users to connect to the structured and unstructured data contained within various types of storage platforms, both on-premises and in the cloud. 5.2 Data Management 1. Governance and Meta- data Management- Enable users to share the same systems-of-record semantic model and meta-data. These should provide a robust and centralized way for administrators to search, capture, store, reuse and publish meta-data objects, such as dimensions, hierarchies, measures, performance metrics/key performance indicators (KPIs) and report layout objects, parameters and so on. Administrators should have the ability to promote a business-userdefined data model to a system-of-record meta-data object. 2. Self-Contained Extraction, Transformation and Loading (ETL) and Data Storage - Platform capabilities for accessing, integrating, transforming and loading data into a self-contained storage layer, with the ability to index data and manage data loads and refresh scheduling. 3. Self-Service Data Preparation - The drag-and-drop, user-driven data combination of different sources, and the creation of analytic models such as user-defined measures, sets, groups and hierarchies. Advanced capabilities include semantic auto discovery, intelligent joins, intelligent profiling, hierarchy generation, data lineage and data blending on varied data sources, including multi structured data. 5.3 Analysis and Content Creation 1. Embedded Advanced Analytics - Enables users to easily access advanced analytics capabilities that are self-contained within the platform itself or available through the import and integration of externally developed models. 2. Analytic Dashboards - The ability to create highly interactive dashboards and content, with visual exploration and embedded advanced and geo spatial analytics, to be consumed by others. 3. Interactive Visual Exploration - Enables the exploration of data via the manipulation of chart images, with the color, brightness, size, shape and motion of visual objects representing aspects of the dataset being analyzed. This includes an array of visualization options that go beyond those of pie, bar and line charts, to include heat and tree maps, geographic maps, scatter plots and other special-purpose visuals. These tools enable users to analyze the data by interacting directly with a visual representation of All rights Reserved 299

6 4. Mobile Exploration and Authoring - Enables organizations to develop and deliver content to mobile devices in a publishing and/or interactive mode, and takes advantage of mobile devices' native capabilities, such as touch screen, camera, location awareness and natural-language query. 5.4 Sharing of Findings 1. Embedding Analytic Content - Capabilities including a software developer's kit with APIs and support for open standards for creating and modifying analytic content, visualizations and applications, embedding them into a business process, and/or an application or portal. These capabilities can reside outside the application (reusing the analytic infrastructure), but must be easily and seamlessly accessible from inside the application without forcing users to switch between systems. The capabilities for integrating BI and analytics with the application architecture will enable users to choose where in the business process the analytics should be embedded. 2. Publishing Analytic Content - Capabilities that allow users to publish deploy and operate analytic content through various output types and distribution methods, with support for content search, storytelling, scheduling and alerts. 3. Collaboration and Social BI - Enables users to share and discuss information, analysis, analytic content and decisions via discussion threads, chat and annotations 6.1 Tableau VI. Review of Different BI Tools Tableau is one of the Market Leader and widely accepted BI platform for modern edge BI needs. It provides the complete BI solutions to the organizations to unlock hidden insights of their data. It is being characterized for its ease of implantation with its feature like no-code data query; translate queries to visualizations and drag-drop facilities to build dashboards. It has wide range of native data connectors to interact with variety of data sources. It also provides good distribution capabilities to share dashboards, publish reports, access on mobile. Its variety of visualizations helps in building dashboards that provides meaningful insights & analysis of KPIs. 6.2 Qlikview Qlikview is one of the Highest Rated and Popular tool in BI Space. It is being recognized for its distinguished features like In-memory analysis and Associative Nature. Qlikview is very user friendly and easy to implement. It achieves faster processing of data due to its in memory feature. It is being also supported with wide variety of Visualizations that provides in depth analysis of Organizational data. Qlikview also achieves High compression of data and stores it into native format of Qlik view data (Qvd s) which also helps in faster processing. Qlikview has also come up with its new Product Qlik Sense which serves as Self-service platform for cutting edge BI solutions. Qlik Sense come with all advantages of Qlikview and also has additional features like Story-Telling which helps to look insights from data. 6.3 Microsoft BI Microsoft Provides Complete package of BI solutions with the Robustness and Trust of Microsoft. MSBI covers all the aspects of BI solutions comprising of ETL, Analysis and reporting through its various products namely SSIS, SSAS, and SSRS.SSIS provides High performance of data integration solutions. It interacts to extract data from various sources and has wide range of transformation functions to provide complete ETL suite. SSAS provides you good platform to analyze data through its multidimensional analysis capabilities. It also provides capabilities of Slice and Dice, Drill down & Roll Up which are very essential for analyzing voluminous data.ssrs Provides Traditional Reporting capabilities with a flavor of cutting edge BI needs. It provides reporting over the Web or through custom applications as per need of business. It also provides multidimensional analysis, All rights Reserved 300

7 hoc reporting, export to various formats and includes variety of visualizations to make repots more meaningful. VII. COMPARISON OF DIFFERENT BI TOOLS ACCORDING TO Different tools are compared on the scale of completeness of vision to ability to execute Fig 7.1 shows us the Magic Quadrant for Business Intelligence and Analytics Platforms [Source adapted from [5]. Tableau, Qlikview, Microsoft BI are the leading BI tools so they are known as Leaders and the other categories seen in above diagram are Visionaries and Niche players. VIII. CONCLUSION Through the course of building this paper we have explored the BI Space which is essential part in functioning of organizations. We have seen what BI is exactly meant, what infrastructure in needed to implement BI. We have also seen here how BI can be implemented and prove beneficial to various functions and stakeholders of organizations. We have also explored the capabilities of BI and its features that any organizations can leverage in order to do business in smarter manner, at the end of it we have also taken overview of some Popular BI tool which is known to provide quality BI solution. To summarize, we have tried to take quick look at modern BI space on its aspects of implementation, usage and benefits. REFERENCES [1] Milena Tvrdikova, Support of Decision Making by Business Intelligence Tools, CISIM 07, IEEE, [2] Savoska Snezana, Manevska Violeta, Business Intelligence Tools for Statistical Data Analysis, Cavtat, Croatia, Proceedings of the ITI 2010, IEEE, [3] A. Martin, T.MirandaLaxmi, V.Prasnna Venkatesan, An analysis on Business intelligence models to improve business performance, ICAESM-2012, All rights Reserved 301

8 [4] Mohan S. Gounder, Vani Vasudevan Iyer, A survey on Business intelligence tools for university Dashboard Development. [5] All rights Reserved 302