In 2010, while still in grad school finishing up my degree, I launched a small consulting company selling my data analytics skills. The motto on my website was "Data Driven." Original, I know, but I was attempting to capture a trend coming out of the '90s. Back in the '90s and early 2000s, the idea that businesses should be more "data driven" was catching like wildfire. As evidence mounted that creating a culture of data driven decision making was helping businesses to outperform their less data savvy competition, more and more enterprises began to require their leaders to be data literate. Gartner and Forrester started to develop metrics evaluating just how "data driven" companies were.

Data is key for the success of any business, and this is more relevant than ever before in the current crisis that industry and mankind are facing. Data insights will be a key driver in dealing with the situation of COVID-19 and it will be instrumental in finding the cure as well. Data insights are also important for the financial industry to read the current and upcoming market trends as events unfold every day. After spending two decades of my career in the financial industry, I have realized that most firms lag in data maturity, and this crisis is revealing many loopholes in their governance process. As I start my journey into retail and transportation with my recent client, I am realizing that never before was data so important for the retail sector, and especially for grocers as it is now.

Data science and machine learning professionals have driven adoption of the Python programming language, but data science and machine learning are still lacking key tools in business and has room to grow before becoming essential for decision-making, according to Anaconda, the maker of a data science distribution of Python. Python could soon be the most popular programming language, battling it out for top spot with JavaScript, Java and C, depending on which language ranking you look at. But while Python adoption is booming, the fields that are driving it -- data science and machine learning -- are still in their infancy. Most respondents (63%) said they used Python frequently or always while 71% of educators said they're teaching machine learning and data science with Python, which has become popular because of its ease of use and easy learning curve. An impressive 88% of students said they were being taught Python in preparation to enter the data science/machine learning field.

All the sessions from Transform 2021 are available on-demand now. A global survey published today finds nearly a third (31%) of respondents consider the social impact of bias in models to be AI's biggest challenge. This is followed by concerns about the impact AI is likely to have on data privacy (21%). More troubling, only 10% of respondents said their organization has addressed bias in AI, with another 30% planning to do so sometime in the next 12 months. Conducted by Anaconda, whose platform provides access to curated instances of open source tools for building AI models, the survey of 4,299 individuals includes IT and business professionals, alongside students and academics.

Artificial intelligence (AI) has disrupted numerous industries in recent years, but for the technology to work effectively, the technology needs to be used right. For the healthcare sector, one of the end goals is to provide better patient outcomes, minimise human errors and alleviate some of the physical and mental burnout felt by healthcare practitioners as a result of the volume of admin work required. A study in the US found that for every hour that physicians spend providing direct clinical facetime to patients, almost two additional hours are spent on desk work. By utilising AI and analytics, this can be reduced, and by extension, so too will the rates of mental illness. For this to happen, however, the industry must first get ready for the AI era by building up skills in reading, working with, analysing and arguing with data – also known as data literacy. Data is the lifeblood of AI; which is what makes AI and analytics the ideal combination.