Analytics/Big Data

The DBizInstitute.org has launched a new course—Digital Transformation 101! 

This is a foundation course, taught by Dr. Setrag Khoshafian, for those embarking upon cultural and digital transformation journeys leveraging Hyperautomation, No Code, AI, Process Mining, Design Thinking, IoT, and Blockchain with best practices and real-world examples.

Editors Note: DBizInstitute is excited to share this article, written by Dr. Setrag Khoshafian, with our community and in advance of his new book release. Keep an eye on our website as we share additional articles in the coming months written by Setrag, as well as a pending Meet the Author webcast to discuss his new book 'How to Alleviate Digital Transformation Debt’ expected to air Fall 2021. This article was originally published on CognitiveWorld.com on June 26, 2020. 

Editors Note: DBizInstitute is excited to share this article, written by Dr. Setrag Khoshafian, with our community and in advance of his new book release. Keep an eye on our website as we share additional articles in the coming months written by Setrag, as well as a pending Meet the Author webcast to discuss his new book 'How to Alleviate Digital Transformation Debt' expected to air Fall 2021. This article was originally published on CognitiveWorld.com on December 17, 2019. 

We are in a perfect storm for making great decisions and nothing less. There are converging forces that put a premium on better decisions in that organizations are being asked for more in a changing world. At the same time the number of assists that are available to boost better decision making are also emerging quickly. What are these forces and boosts to increase an organization's ability to make better at the minimum and great decisions at a maximum? The coming decision wars will be at the forefront of success going forward for organizations and individuals.

Forces Affecting Decisions:

Business Contexts Shifting Faster

Businesses from across the globe - both large enterprises and small and medium businesses - want to improve their operations and advance. This is where technology is crucial. Data science and analytics is changing the way we do marketing and has given us many new opportunities for profit, growth and so on. 

Big data analytics is enabling enterprises to do many tasks in real time and with ease. This rapid digitalization has given companies many new opportunities and choices. 

Global revenues in big data and business analytics has increased from $130 billion to more than $203 billion in the future. 

Big data science and analytics include statistics, math and many other mechanisms which enable companies to analyze data and get valuable information from it. The amount of data is increasing with each day. 

Practitioners in our field have long been evangelizing on the critical link between decision management and predictive analytics. As James Taylor accurately and succinctly stated “Decision Management operationalizes predictive analytics. Traditional approaches to analytics are hard to scale and hard to use in the real-time environment required in modern enterprise architectures.”

On cue I noted with great interest several writers predicting analytics trends for 2016. These included:

Decisions inject Analytics smartness into Processes, making the processes smarter. 

Now that we have automated most routine processing using some programming logic and some basic rules, the next competitive frontier is making these processes adapt dynamically to changing conditions and unforeseen situations - and learning from each such situation. Processes with such sophisticated dynamic behavior are smart processes.

Smart behavior cannot be programmed into the processes without causing unmanageable complexity and catastrophic brittleness. In any case, such ‘programmed’ processes cannot ‘learn’ by themselves. Additional knowledge has to be physically programmed into the process.

So instead of trying to make the process smarter, the focus should be on making the embedded decision smarter - through automation using decision management technologies available today.

Smart Processes are really Smart Decisions

Big data and related topics like the IOT (Internet of Things) are always big topics of interest in large forums. So it was the case in CeBIT 2016, Hannover which I was fortunate enough to attend.  There were several talks on Big Data - Digital Disruption was the theme, and there was general consensus that Big data was here to stay and grow phenomenally. People were moving from basic reporting and visualization of data (Data discovery and understanding) to a phase where predictive analytics would rule. This was because of the huge strides made in the fields of Machine Learning and artificial Intelligence. One of the keynote speakers in CeBIT was Professor Nick Bostrom founding Director of the Future of Humanity Institute at Oxford. An extremely impressive speaker and futurist, I was blown away with the vision he had for the future. He spoke passionately that the time for Artificial Intelligence has finally arrived and what it means for society at large.

This is the first of a two-part look at a “Day in the Life” of a Business Analyst (BA). It’s broken into hours as a simple way to illustrate the myriad duties and skills that a BA needs to have, but in real life the process described below would take weeks, months, or possibly years, depending on the size of the effort. And, as every BA knows, there’s no such thing as a typical day – or even a standard job description. We’ll look here at the phases that every effort has, and tools that every BA needs. 

When you hear the terms “big data” or “analytics” what comes to mind?

Do you think of technical experts pushing exabytes of data through an algorithm? Perhaps you think of marketing experts attempting to get answers about your company’s customers.

No matter which scenario you think of, it is important to recognize that big data and analytics are most useful when their associated processes are in place and observed.

 In fact, big data and analytics are useless without process.

Why? Because quite simply, Data without process is meaningless!