Artificial intelligence and machine learning are relentlessly revolutionizing marketplaces and ushering in radical, disruptive changes that threaten incumbent companies with obsolescence. To maintain a competitive edge and gain entry into new business segments, many companies are racing to build and deploy AI applications.
Read Part 1 here > Data analytics is a complex process that demands time and effort from data scientists. From cleaning and prepping data to performing data analysis, data scientists go through an extensive procedure to uncover hidden patterns, identify trends, and find correlations in data to make informed business decisions. The task of integrating, cleaning, and organizing data assets often take up the bulk of the data scientist’s time.
With the speed of change in artificial intelligence (AI) and big data, podcasts are an excellent way to stay up-to-date on recent developments, new innovations, and gain exposure to experts’ personal opinions, regardless if they can be proven scientifically. Great examples of the thought-provoking topics that are perfect for a podcast’s longer-form, conversational format include the road to AGI, AI ethics and safety, and the technology’s overall impact on society.
Simplifying feature engineering for building real-time ML pipelines might just be the next holy grail of data science. It’s incredibly difficult and highly complex, but it’s also desperately needed for multiple use cases across dozens of industries. Currently, feature engineering is siloed between data scientists, who search for and create the features, and data engineers, who rewrite the code for a production environment.
When it comes to machine learning (ML) in the enterprise, there are many misconceptions about what it actually takes to effectively employ machine learning models and scale AI use cases. When many businesses start their journey into ML and AI, it’s common to place a lot of energy and focus on the coding and data science algorithms themselves.
The ability to discover insights from past events, transactions and interactions is how many customers currently utilize Qlik. Qlik’s unique approach to Business Intelligence (BI) using an in-memory engine and intuitive interface has democratized BI for typical business users, who usually have little to no technical savvy. But, for many years, organizations have only been able to analyze metrics or KPIs of “what has happened” (i.e., descriptive analytics).
Data is an essential asset for any business, with comprehensive efforts made to generate, source, and prepare it for analytical use. But just as important as collection and cleaning is ensuring its accessibility for users across the organization. This highlights the need for an organized data inventory—a directory that makes it possible to easily sort, search, and find the data assets required. In other words, you need a data catalog, a core component of master and meta data management.
