Essay Example on Preface Business Intelligence








Preface Business Intelligence has for the past many decades been a reliable source of business decision support for companies It is an umbrella term of IT solutions to help analyze massive amounts of data and slice and dice the data to provide relevant reports to the business users Over the years there have been many advancements in the technology available for BI but there is still a large gap to cover when it comes to implementation and complete adoption Being a Masters student in the field data science and business analytics I did not anticipate this discrepancy and I was inspired to explore further and make it the basis of my thesis I got the opportunity to observe the ground reality and substantiate my perspective that any technology related to data analysis is only as powerful and effective as its implementation application and adoption is I have a few key people to thank without whom I would not be able to accomplish this mammoth task My thesis director Professor Nicolas Prat guided me through every critical step of the process

He was instrumental in encouraging my idea and helping me formulate the proposal His long standing experience in the field of BI contributed to the quality of my thesis Professor Guillaume Chevillon our program director helped in understanding the kind of value a thesis ought to create I had several enlightening conversations with him and they helped me in creating my perspective for this thesis Professor Nikos Paragios our program director gave me the much required push in the last few weeks to be able to believe in myself and deliver the thesis I would like to express my gratitude to my manager at Schneider Electric Benoit Thooris He was always available to answer any questions I had and thanks to him I got insights into the ground reality of art and science and philosophy of business in large multinational companies In addition I would like to thank my colleagues from Sales Excellence team at Schneider Electric for contributing with their ideas my friends for the much needed moral support and last but not the least the kind hostess at the cafeteria in Schneider Electric office who made great coffee to fuel me on this mental marathon Abstract Introduction 

The past few decades have been marked with the rise and the rise of data We as individuals are the newest and the most prolific data creators we create video photos and text on our phones and personal computers and share it through various social media applications Through wearables we create data related to user fitness geo location and music preferences etc There wearables and other smart devices are a part of the onset Internet of Things era where objects have built in intelligence to accept user inputs but as well as to be able to anticipate their future actions preferences and respond to them Thanks to the Internet of things gaining traction and every other device becoming smart there is a new breed of inanimate data sources IDC forecasts that by 2025 80 billion devices would be connected Kanellos 2016 which does not seem a hyperbole when we consider all the smart devices around us ranging from thermostats refrigerators cars to entire automated factories defense systems space stations that are creating unprecedented volumes of data that did not exist before Individual data producers aside still the highest data producing capacity remains with enterprises who not only receive the data from the previously mentioned two sources actively from devices being used by individuals and passively from smart machines but also by the way of their internal processes making them major data producers and well as consumers 

To give an idea of the scale of this data revolution a well know benchmark is the amount of data that is going to be produced by 2020 The entire digital universe is going to grow exponentially to 44 zettabytes in simple words 44 trillion gigabytes which would further explode up to 180 zettabytes by 2025 Kanellos 2016 This translates into big numbers financially as well with organizations making the shift to modern BI with tenets of easy accessibility of data agile development of BI applications and the ability to arrive at deeper never realized before insights Gartner forecasts Business Intelligence and Analytics software market to grow to 22 8 billion by the end of 2020 Gartner 2017 The horizon of this data scenario with advanced BI and analytics capabilities seems bright and promising This has led to a massive influx of various tools and software and organizations rapidly trying to get onboarded to them Here there is need for the organizations to check their enthusiasm and realize two important points

One based around the fact that as much as the adoption of modern BI is essential it is not completely a new concept To mark a difference the BI so far or traditional BI is still very powerful and well tested making it a solid base for modern BI to set up The second point is an extension of the first one New BI tools might come with a promise of easy implementation and revolutionizing any organizations data woes but the value they deliver or their ease of plug and play is completely dependent on the QUALITY of data they are provided with as well as the data source layout they are presented with Research Objective The objective of this research is to closely examine the irony of the situation faced by large companies at the crossroads of the promise held by internal process improvement by technology advancement and the many challenges to implement it Furthermore to develop a logical next step and propose a path towards complete adoption of advanced BI and data analysis tools

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