INSIGHTS & PERSPECTIVES

One of the biggest outcomes of the ChatGPT trend is that every company is now exploring its AI strategy. If you haven’t started playing with ChatGPT and thinking about new use cases, your competitors already have...

Here are our top picks for data management strategies that will help you be most effective with data and analytics as you move forward in 2023. The data landscape continues to evolve at an increasingly rapid pac...

What’s more agile than a business-ready app that can be built in a day or a week? Business teams favor low-code/no-code options for the ability to deliver value quickly and get immediate feedback...

In the rapidly evolving field of data and analytics, technologies that were theoretical even last year are finding their way into the mainstream work of data scientists today. This webinar will illuminate some of the newer trends...

The world of data and analytics keeps adding concepts and jargon. It is hard to keep up with these concepts, let alone decide if it’s worth pursuing them. Join us for a webinar to demystify the latest terminology...

Much research is dedicated to novel deep learning architectures and complex feature engineering approaches to improve performance in deep learning models for NER (named entity recognition) tasks...

Today, a strong Data and Analytics (D&A) function is central to any organization. Yet many organizations have defined and are executing their D&A strategy, platforms, and processes based on ...

If your IT team ingests multiple data sources, most likely you’ve built a data pipeline or two. These pipelines routinely and automatically transfer your data from its source to the data lake where it is stored...

Stability and flexibility are seemingly conflicting and divergent requirements. So how do you balance stability and flexibility in data analytics? If you work in the data industry ...

“Time Kills All Deals” may be an old sales adage, but it’s equally applicable when it comes to your data. What good is your data if it arrives after a decision has to be made?...

How do you make sure that your AI/ML project doesn’t land in the heap of failures? The best way is to properly align your AI initiatives with the business’s strategic goals from the get-go....

How do you get started with machine learning? A good first step is choosing the right use cases for machine learning (ML). Given that some 87% of machine learning projects fail ...

Data scientists are experts at answering business questions with data. They may use tools such as machine learning, deep learning, artificial intelligence, and natural language processing...

The nation’s largest grocery chain knows it must build relevance, loyalty, and authority with its customers in order to command customer preference...

No matter how creative you are, you can’t possibly proactively identify every possible data validation. You have to accept that some, perhaps even most, of the possible data problems will be found...

What’s the cost of having bad data quality? Gartner estimates that organizations lose $15 million per year from bad data. IBM puts the cost to U.S. companies at $3 trillion per year...

What makes an organization successful at being data champions? A recent survey sponsored by Tableau and conducted by IDC...

Previously, we wrote about the benefits of using machine learning (ML) to replace costly algorithms and adapt more fluidly to changing data. In this blog, we give you a step-by-step primer on...