What is Data Science in 2023?

A decade ago, Doug Laney defined the three dimensions to data growth as increasing volume, velocity, and variety.

In 2016, IBM defined the “Four V’s of Big Data” adding veracity to the mix, stating that both data quality and availability were key to business analytics. At the same time finding one in three business leaders “don’t trust the information they use to make decisions”.

Veracity is an issue today with over 50% of business leaders saying they don’t fully trust their data assets according to the 2021 Experian study. This increase in mistrust over five years is likely a reflection on the complexity of the data lake today and perhaps a lack of data governance oversight overall, not data science itself.

The art of Business Intelligence and its core support areas of Data Engineering, Data Analytics and Data Science has grown tremendously over the past decade, with exponential growth during the pandemic as more businesses rely on the collection of big data to make important decisions at this time.

Bi-surveys.com recent study reported gains for big data usage with an “8% increase in revenues and a 10% reduction in costs”.

Dedicating time to each area within the job description while balancing time for one’s further learning of these specialties is a struggle for many.

Staying on top of new technological advancements in the data science field means online learning programs are the most effective and wise way to improving knowledge in this area quickly.

Enroll in a Data Science Course today.

Additionally, being adept in these areas entails writing customized code to get the job done, resulting in expanded training for languages like Python and it’s supporting libraries, like Pandas, Numpy, Scipy, Scikit-learn and Notebooks.

DataOps and MLOps are moving further into the mainstream in 2022 with new technologies like Data Fabric and Data Mesh emerging to help speed up the processing and management of data. Interest in cloud technology one stop solutions like DataBrick’s Lakehouse, Snowflake’s SnowGrid and Explorieum’s augmented data discovery tools is helping businesses manage costs and meet security and regulatory requirements effectively.

The slippery slope of privacy will continue to be at the forefront of the data scientists mind going forward and companies need to ensure compliance using proper master data management and data governance programs.

Growth in computer vision tools due to the pandemic like DarwinAI, used in medical diagnoses for Covid-19, has many companies implementing best practices for Artificial Intelligence (AI) around automating the analysis. AI uses specialized computer-assisted solutions knowledge in other areas too that benefit businesses like cybersecurity, web search, e-commerce, advertising, smart homes, and infrastructure.

“It seems as though every week companies are finding new uses for algorithms that adapt as they encounter new data”. The application of ML, a subset of AI, that makes predictions on new datasets has skyrocketed covering everything from speech recognition, fraud detection, spam and malware filtering, image recognition and even self-driving vehicles.

Anaconda’s 2021 State of Data Science Survey Results showed the biggest problem to tackle in the AI/ML area today was the social impacts caused by bias in data and models. In the same study, 55% of respondents hope to see more automation and AutoML in data science.

2022 sees ML expanding beyond tabular table data sources and simplified training models to the utilization of off the shelf prebuilt models from various sources. AWS Marketplace for SageMaker was one of the first out of the gate with this approach and it has been highly successful for data scientists with one testimonial from OneCup AI saying it “reduced our training time from several hours at best and days at worst down to 15 minutes, giving us a massive competitive advantage.”