Although almost every company in the world recognizes the power of data, most struggle to unlock its full potential. Companies such as Google, Amazon, and Uber that primarily deal with data are among the most valuable in the world, in terms of market capitalization, business performance, and innovation. One of the key reasons for their […]
Harnessing Data: From Resource to Asset to Product
Companies that are data-driven demonstrate improved business performance. McKinsey says that data and analytics can provide EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25% [1]. According to MIT, digitally mature firms are 26% more profitable than their peers [2]. Forrester research found that organizations using data are three times more […]
Enhancing the Reliability of Predictive Analytics Models
Predictive analytics is a branch of analytics that identifies the likelihood of future outcomes based on historical data. The goal is to provide the best assessment of what will happen in the future. Basically, predictive analytics answers the question “What will happen?” The value of predictive analytics lies in enabling business enterprises to proactively anticipate […]
12 Key AI Patterns for Improving Data Quality (DQ)
AI is the simulation of human intelligence in machines that are programmed to think, learn, and make decisions. A typical AI system has five key building blocks [1]. 1. Data: Data is number, characters, images, audio, video, symbols, or any digital repository on which operations can be performed by a computer. 2. Algorithm: An algorithm […]
Demystifying AI: What Is AI and What Is Not AI?
In recent months, particularly following the release of ChatGPT, there has been an unprecedented surge in interest surrounding artificial intelligence (AI). This heightened attention spans across a multitude of sectors, including business enterprises, technology companies, venture capital firms, universities, governments, media outlets, and more. As the interest in AI is intensifying, some companies have even […]
Understanding Linear Regression Intercepts in Plain Language
I am often asked about the role of intercepts in linear regression models – especially the negative intercepts. Here is my blog post on that topic in simple words with minimal statistical terms. Regression models are used to make predictions. The coefficients in the equation define the relationship between each independent variable and the dependent variable. […]
Demystifying Data Analytics Models
In today’s global landscape, organizations worldwide are increasingly turning to data analytics to enhance their business performance. Research conducted by McKinsey Consulting revealed that data-driven companies not only experience above-market growth but also witness EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) increases of up to 25% [1]. Additionally, Forrester’s findings indicate that organizations utilizing […]
The FAAR Framework for Consuming Insights from Data and Analytics
Faced with overwhelming amounts of data, organizations across the world are looking at leveraging data and analytics (D&A) to derive insights to increase revenue, reduce costs, and mitigate risks. McKinsey found that insight-driven companies report EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25% [1]. According to Forrester, organizations that use data and insights […]
Three Key Commandments of Effective Dashboards
A dashboard is a visual snapshot of business performance using KPIs (key performance indicators) to help users make smarter, data-driven decisions. An effective dashboard simplifies the visual representation of complex data and helps stakeholders understand, analyze, and present key insights at a glance. At the core, the objective of a dashboard is to make complex […]
Managing Missing Data in Analytics
Today, corporate boards and executives understand the importance of data and analytics for improved business performance. However, most of the data in enterprises is of poor quality, hence the majority of the data and analytics fail. To improve the quality of data, more than 80% of the work in data analytics projects is on data […]