Why The Future Of Data Analytics Is Prescriptive Analytics

Analytics is probably the most important tool a company has today to gain customer insights. This is why the Big Data space is set to reach over $273 Billion by 2023 and companies like Microsoft, Amazon and Google among so many others are so heavily invested in not only collecting data, but enabling data for the enterprise. As AI and machine learning continue to develop, the way we use analytics also continues to grow and change. While in the past, businesses focused on harvesting descriptive data about their customers and products, more and more, they’re about pulling both predictive and prescriptive learnings from the information they gather. So—what is the difference between descriptive, predictive analytics and prescriptive analytics? And do you need the latter in your company? If you’re new to the data analytics field, let’s do a quick overview: ●     Descriptive analytics: data that provides information about what has happened in your company. Think about a monthly sales report…

Redefining Work - Leveraging Human Capabilities in a Future of Expanding Automation

How will labor markets evolve in our 21st-century digital economy?  What’s the likely future of jobs, given that our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans?  How will AI, robotics and other advanced technologies transform the very nature of work? Over the past few years, a number of papersreports, and books have addressed these very important questions.  They generally conclude that AI will have a major impact on jobs and the very nature of work.  For the most part,  they view AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspects of the job that most require human attention.  Overall, few jobs will be entirely automated, but automation will likely transform the vast majority of occupations.  A recent M…

DAX Cheat Sheet

ProblemCalculation ExpressionTotal Sales CalculationCalculated measure using SUM to aggregate a column. Total Sales = SUM('TableName'[SalesAmount]) Total Cost CalculationCalculated measure using SUM to aggregate a column. Total Cost = SUM('TableName'[Cost]) Profit CalculationCalculated measure using two previously created calculated measures to determine profit. Profit = [Total Sales] - [Total Cost] Profit MarginCalculated measure using two previously created calculated measures to determine profit margin, the DIVIDE function is used to perform the division. Profit Margin = DIVIDE( [Profit], [Total Sales]) Transaction CountCalculated measure that returns a count of all rows in a table, ultimately, many times this simple calculation is used to return transaction counts.

Business Intelligence Treasure Chest

It happened so fast …. With one foot in the trap, it looked like he had utterly failed in his mission. … It all started nineteen years earlier when ….

Everyone likes a good story. Especially marketing teams in today’s leading businesses. They know that effective storytelling enhances brand and knocks down barriers to sales.

Similarly, it’s becoming a powerful way to distribute data and information in business intelligence initiatives. Several business intelligence vendors even promote storytelling as a needed component of data discovery.
So, with the participants in one of my recent Friday #BIWisdom tweetchats, we explored what’s happening today with BI storytelling. I started the discussion by stating that I think it’s about applying context to BI-derived content and that I see storytelling as an integral part of a broader collaborative capability.
Several agreed that storytelling is “sharing” and thus part of collaboration to bring people “through a data-driven journey” or bring the “re…

Five predictions for AI and process automation in 2020

The development of artificial intelligence (AI) and robotic process automation (RPA) rose rapidly during 2019. In the coming year, this pace will climb even further. Enticed by the potential for the technologies to streamline workflows and improve customer service, organisations will be undertaking deployments in ever-increasing numbers. At the same time, the capabilities of the technologies will continue to grow at a breakneck pace. Where initially they were limited to assisting with very structured activities, they will increasingly be put to work in areas that until now have required human intervention. During 2020, five key trends will shape the field of AI and RPA. They are: The rise of the RPA robot Current projects involving the deployment of RPA robots have tended to focus on replicating existing tasks that have traditionally been completed by humans. The robots have been able to learn a repetitive task and then complete it much more quickly. While their AI capabilities allow…

The Power of Visualization

A picture really does say a thousand words. “Visualization is really about external cognition, that is, how resources outside the mind can be used to boost the cognitive capabilities of the mind.” — Stuart Card The importance of visualization is a topic taught to almost every data scientist in an entry-level course at university but is mastered by very few individuals. It is often regarded as obvious or unimportant due to its inherently subjective nature. In this article, I hope to dispel some of those thoughts and show you that visualization is incredibly important, not just in the field of data science, but for communicating any form of information.
Visualization Goals Essentially, there are three goals to visualization: Data Exploration — find the unknownData Analysis — check hypothesesPresentation — communicate and disseminateThat is essentially it. However, these terms are pretty vague, and it is thus quite easy to understand why it is so difficult for individuals to master the ar…