The Predictive Program Manager BOXSET VOL 1 & VOL 2

DEDICATION

This book is dedicated to the Divine Mother

1 INTRODUCTION

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When I was working at IBM, Some years back, I was presenting to an executive about the status of programs in my organization. When in the mid of the presentation the ‘C’ level executive asked me a question. Puneet can you predict the outcome of this program? Can you tell what will be its outcome will it overshoot its budget, if it will overshoot then by how much? Can you tell if the program is going to meet its schedule or slip?

For a moment I thought and answered him with the poise of professionalism that the question demanded. But it made me think if there was really a way to predict the outcome of programs? How wonderful it would be if program managers could tell how the program is likely to perform with some degree of accuracy.

I found this answer in Statistics and more specifically in Data science. The principles of which I have applied in Project and Program Management. Applying this wonderful methodology one can become truly Predictive Program Manager.

I now present to you the concept map on which this book is based to give you a preliminary understanding of the topic and of what lies ahead.

I always present to my book readers concept maps of highly complex subjects such the one in this book. It gives you an opportunity to get a big picture or 50000 feet view from the top on what this book covers. It also sets the expectations on what to look out for in the pages as you turn them.

The purpose of writing this book is to create a new bridge between Data science and Program Management. You may be wondering how are data science and program management linked?

It is an obvious question, but I can tell you from 17+ years of product development and 11+ years of my experience as Program Manager in working with major Multinationals like HP, IBM, and Dell that there cannot be a more suitable candidate for applying data science than program management. As I chalked out my path to moving myself out of traditional program management towards being a predictive program manager to transitioning into a Data Scientist I will in this book, take yours through the major processes of program management and on how to apply data science to it.

As a program manager, I have been called upon by executives most of the time to present on the current state of the program. This could be in the form of a Traffic light dashboard or any other such indicator to the stakeholders. Such an indicator should also have the ability to drill down to the lowest level of the business unit and show its state if the need be.

The one question which any program manager worth their salt dread to answer is:

Will this Program end as per schedule and as per the allocated budget?

Any experienced program manager can tell you that when an executive asks such a question, you need to be prepared for an answer beforehand. If you answer in the affirmative no problem for now but you will be questioned in the next meeting on what you said about the state of the program. Also even if you are not questioned it will give a lasting impression on the executive’s minds on your level of professionalism as a program manager. So a lot is at stake here.

Also what any experienced program manager will tell you is, that no matter how rigorous your risk management approach and strategy, there are bound to be some issues that crop up which have the potential to derail the entire program if not handled properly at the right time.

This makes it even harder for a program manager to answer questions which any executive may ask such as the one above.

Until Data science and predictive analytics came in there was hardly a way for a program manager to know what is the likely outcome of the program going to be. Also, is this program going to overshoot its budget or not?

Whether you are a program/project manager or not, but what you will learn from this book is how data science and big data are changing the way traditional project/program management used to be dealt with.  With the advent of new technology and the emergence of new science, it is giving the traditional program manager a run for their money, by bringing the ability to predict from data science to program management. So sit back take a deep breath and relax as you enjoy this book!

2 What is Data Science?

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I am sure you would have heard of Data Science by now but for the uninitiated, I will explain what it is. Wikipedia defines data science as follows: Data science, also known as data-driven science, is an interdisciplinary field about scientific processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured.

Data science comprises two words data and science. Data comprises of symbols, letters, and numbers in digital or non-digital format. When such data is used to find hidden patterns which are scientifically validated using techniques of statistics or machine learning it gives rise to the technique of data science.

Well, it is not as simple as it sounds in this definition. Data science as a field is much more complicated than it appears. Underneath are the fundamentals of statistics and Artificial intelligence which enable it with the ability to predict.

You may be thinking what is a prediction? And how can prediction happen with data science?

The postulate that governs all data science capabilities is Future follows the Past. What I mean by this is that data science assumes that whatever has happened in the past will be repeated in the future as well. Data science uses specialized techniques to unearth the hidden patterns which govern a certain aspect of a business, process or event. This is done by what is known as mining data in a warehouse and extracting these possible patterns which may exist already.

The focus here is to find out if these patterns are likely to repeat in the future and see how they are built into a prediction model and utilized for making a confident prediction.

Big Data is tightly coupled with data science as it provides the necessary infrastructure to implement it and help predict the future. Big data is a field dedicated to the analysis and process and storage of large collections of data that come from varied sources. For data to be classified a BIG there are 5 characteristics which are found in it.

Part, Velocity, Variety, Veracity, and Value. Part is the huge size of data. Velocity is the speed at which data is generated, such as Facebook, twitter, google searches that generate terabytes of data every second. Variety pertains to the types of sources of data. Data can reside in multiple sources such as text files, RDBMS, JSON files etc. Next is the Veracity of data which really is the feature which brings authenticity to data. Data provenance