Business Intelligence

As a general-use technology, artificial intelligence (AI) is being likened to the steam engine and its effects on the economy in the 17th century. As a result, it is generally agreed that AI and its applications have the potential to significantly alter entire industries through the disruption of value creation and the impact of business models and organizations through a number of mechanisms, such as the redistribution of decision-making authority. Artificial intelligence is changing the nature of work and the workplace as a whole through its integration into processes and activities. Developing a more in-depth comprehension of the consequences and implications for future companies and professions is essential for a new technology like AI. AI is transforming the way we produce software in general, from enhancing code quality to shortening the time required for repetitive chores like debugging and testing. The many advantages brought to the table by artificial intelligence would help engineers become more productive and efficient, thereby revolutionizing the software development process. It is relevant and important to not only investigate how AI might improve process productivity and efficiency but also to take the first steps toward understanding how to adopt a technology that is here to stay. Some of the potential AIs tools we may consider are ChatGPT, Copilot, and TabNine. ChatGPT is a chatbot powered by a big language model. It makes use of an AI designed to have in-depth conversations with humans in order to provide solutions to complicated problems. Its capacity to have natural-sounding conversations and respond in ways that make it seem human is quite astonishing. Copilot or “AI pair programmer” is a language-agnostic AI that can provide coding ideas through natural language questions, having been trained on billions of lines of code. TabNine is an artificial intelligence code completion helper to get extended snippet ideas and focused line code completions. TabNine reuses an organization’s common coding patterns to reduce unnecessary developer toil.

After some research, I was taken back with so many machine learning applications already in use: weather forecasting, medical diagnoses, law enforcement, and self-driving vehicles. Also, I did not realized that it was the advancements of big data and faster computing that allowed the break-thru of AI in our daily lives. Most of us, I believe, think that artificial intelligence is still science fiction. Not so! We as business analysts need to pursue AI education and recognize the many business opportunities opening up to all of us.

With the vast array of data that organizations have access to, Customer Analytics is becoming a top priority so you can predict how customers will behave when they receive a catalog, enter a store, research and buy online, or interact with your organization in any other way. The more you know about customer and prospect preferences, the more successful you will be at creating relevant offers that resonate with them positively.

 Analytics drive key strategic decisions in major corporations every day. However most legacy tools and solutions that help companies make these critical strategic decisions, simply aren’t built to deal with the reality of today’s modern business environment. Below are some essential questions to ask as you assess the potential benefits and limitations of new strategic analytic platforms for your organization.
 

Predictive Analytics can play a massive role in the simplification of Big Data. The role it plays comes down to getting the most out of this Big Data, and it starts with what some people refer to this as the fourth “V” of Big Data (Velocity, Variety, Volume), and that is Value. Predictive Analytics takes all of the data available and can pull the most important elements of that data, helping to create a predictive model or create an ideal profile to help organizations in many different aspects of the business.

I learned this in a virtual meeting where about 10 stakeholders were invited to give input to a mock-up created by our project. They were all subject matter experts within the area, and had earlier provided some input on an individual basis. I walked through the whole thing, and what happened? There were no comments or suggestions. I couldn't believe it. I know that subject matter experts always have an opinion.

There’s an old fable about six blind men who encountered an elephant for the first time. Although they couldn’t see it, they wanted to learn what an elephant was like. Each of them touched a different part of the elephant.

I believe the Problem Pyramid™ provides the appropriate structure for guiding effective business analysis, both for initiating and carrying out projects, whether for what BABOK® v2 calls projects or for topics that truly fit within Enterprise Analysis.

It is no surprise that organizations spend over $15B annually on business intelligence and data mining technologies. But despite this focus on infrastructure technologies, there is little emphasis on the art of analysis.

Analysts are being asked to assimilate increasing amounts of data into meaningful information that can be acted upon quickly. This is a daunting task as the volume of data that comes into play is staggering and crippling to most analytic tools. This article discusses three innovations in data analysis that empower analysts to explore expansive data sets and gain actionable intelligence.