Data Management software is essential to providing organizations with critical insights about their customer’s behavior. Robotic process automation (RPA) is a process in which software programs perform repetitive Data Management tasks, such as data validation, email responses, normalization, and metadata organization. Put another way, RPA automates the mundane. It does this by observing and imitating […]
Messy Data Shouldn’t Stop Machine Learning in Its Tracks
Click to learn more about author Jon Reilly. Businesses are creating data at an incredible pace that will only accelerate. In fact, data storage company Seagate predicts it will pass a yearly rate of “163 zettabytes (ZB) by 2025. That’s ten times the amount of data produced in 2017.” Moore’s Law – the principle that […]
Is Lakehouse Architecture a Grand Unification in Data Analytics?
Click to learn more about author Arsalan Farooq. I do not think it is an exaggeration to say data analytics has come into its own over the past decade or so. What started out as an attempt to extract business insights from transactional data in the ’90s and early 2000s has now transformed into an […]
Closing the Data Science Skills Gap at Your Organization
Click to learn more about author Itamar Ben Hamo. Data scientists are some of the most in-demand professionals on the market. A LinkedIn Workforce Report in 2018 found 151,000 unfilled data scientist jobs across the United States, with “acute” shortages in San Francisco, Los Angeles, and New York City. And the demand for data scientists […]
Scaling Machine Learning Applications
When the number of users for a predictive model grows, it is expected (albeit often wrongly) that the machine learning powered systems will automatically scale to keep up with this growth. If the system fails to scale, processing requirements may outpace performance. Using an example from a LinkedIn article, a sample recommender system fails to […]
So You Want to be a Machine Learning Engineer?
Ideally, a machine learning engineer would have both the skills of a software engineer and the experience of a data scientist and data engineer. However, data scientists and software engineers usually come from very different backgrounds, and data scientists should not be expected to be great programmers, nor should software engineers be expected to provide […]
The Quick (and Ultimate) Guide to Regularization
Click to learn more about author Ram Tavva. May it be in statistics or mathematics or finance – particularly in machine learning and inverse problems – regularization is any modification that one makes in a learning algorithm that is intended to not reduce its training error but its generalization error. In layman’s language, we can […]
The Future of Augmented Analytics: Adding the “Why” into Your Business Reports
Click to learn more about author Neerav Parekh. Data is the new oil, and unlike oil, we are never going to run out of data. With more and more data being produced each minute, businesses have massive, complex datasets that are difficult to deal with. Digging deeper into the data to uncover valuable insights is […]
Maximizing Your Data Fabric’s ROI via Entity Data Modeling
Click to learn more about author Dr. Jans Aasman. Data fabrics are emerging as the most effective means of integrating data throughout the enterprise. They deliver a single access point for all data regardless of location — whether it’s at rest or in motion. Experts agree that data fabrics are the future of data analytics and […]
Tapping the Value of Unstructured Data: Challenges and Tools to Help Navigate
Click to learn more about author Daniel Martin. The amount of data generated in the digital world is increasing by the minute! This massive amount of data is termed “big data.” We may classify the data as structured, unstructured, or semi-structured. Data that is structured or semi-structured is relatively easy to store, process, and analyze. […]