Enterprise analytics refers to the collective process of acquiring, inspecting, and leveraging data across an organization to drive crucial business decisions and strategies. The practice uses advanced techniques and tools to analyze large datasets from multiple sources within the enterprise, such as marketing, sales, operations, finance, and human resources, to derive insights and improve overall business performance.

Are you a decision-maker at a financial institution looking forward to employing ML models? Here you go! Below are some successful benefits of predictive analytics in the finance sector.

Banking and financial institutions have pioneered experimenting, failing, and adapting quickly to innovative technologies, leading to early adopters of generative AI technology.

In this article, you will gain a deeper understanding of and structure of a data science team, key models, and roles that you should consider while structuring a data-driven organization team.

Migrating your data can be both simple and complex process. It depends on users, their requirements, structure of data and environment they are migrating to. Data migration have limitations, requirements and as well as good practices.

This is a concise guide to help you solve the problem of data labeling pain. It introduces several tools and practical approaches that you need to know to streamline your process.

Data cleaning is a very important first step of building a data analytics strategy. Knowing how to clean your data can save you countless hours and even prevent you from making serious mistakes by selecting the wrong data to prepare your analysis, or worse, drawing the wrong conclusions.

Data scientists are the unsung heroes of modern business. Data science can add value to any company, big or small. But why and what should you focus on that makes you stand out from your competition? This article explains it all.

Curious about how data science can help the healthcare industry? This blog explains all about data science technology with 13 use cases of practical data science applications for the healthcare industry.

The finance industry is undergoing a transformation that involves AI, data, and deep learning. This blog will give you an overview of what it is all about. And what AI holds in the future for the banking and financial industry.

Data analytics is an increasingly important aspect of business, and it's also one of the most misunderstood. I hope that this blog can provide some helpful information about how data analytics is used in business.

A list of top 25 tools used in prominent data science companies to enable users to build Machine learning models, develop complex statistical algorithms and perform other advanced data science tasks.

Learn what intelligent automation is, how machine learning powers it, and who can use this technology to automate their business processes.

This is a blog about the most popular MLOps tools which are in the use of our company.

Data Modeling is one of the most important parts of information modeling. A good data model, tightly integrated with its applications or systems is easy to understand, maintain and change. In this post, we will discuss top 15 data modeling tips and best practices.

This is a comprehensive list of practices to be followed in order to avoid common pitfalls when working with machine learning. The objective is to give you an understanding of best practices for each area within the landscape of machine learning.

Machine learning is one of the widely adopted technology in 2021. And it is going to be the same for 2022. Check out the Top 8 Machine Learning Trends for 2022.

In this article, learn how to help accelerate your financial services business growth through operational excellence with fast, scalable, and measurable efficiencies delivered through MLOps technology.

A blog about Top 10 Data Science Trends for 2024 with new and exciting developments around the world in Data Science.

AI development is now maturing and showing a lot of promise for businesses of all sizes. This blog covers key AI trends for business innovations, expert predictions about the future of AI.

Machine Learning (ML) is one of the hottest and most discussed topics in the Big Data space. But what is MLOps? What are the benefits of MLOps? And how to get started with it? We have covered it all.

You are investing in ML like never before and hiring more data scientists and machine learning engineers. However, there is a lack of clarity on the role of machine learning and its place in the life cycle of a data science project. Here's an attempt to resolve this uncertainty.

In this article, we'll cover the basics of data modeling, why it's important to leverage, and the different kinds of data models you can create for your business to stand out over your competitors.

This blog is an attempt to shed light on the best way businesses use enterprise data effectively using machine learning and artificial intelligence. Implement these business use cases and make your organization smarter, more efficient, and more profitable.

Correlation matrix is a statistical tool used to display the correlations between multiple variables in a dataset. It arranges these correlations in a table format, showcasing how each variable relates to every other variable in the dataset.

Do you feel that your company is not making the best out of its data? If yes, this blog will guide you in setting up the data analytics engine for your enterprise data.

If you're building ML systems for your business, you should check out these 5 common mistakes companies usually make while scaling their ML projects.

Learn why machine learning is a magical tool in data sciences and for operations managers. How machine learning is related to operations management and what kind of problems operation managers should also consider in order to better use machine learning technology.

If you are interested in what the future of AI and Machine Learning will shape the future of the banking and finance sector, this blog is for you.

Why does an Omnichannel solution make sense for banks? When to consider Omnichannel Banking for your financial institution? How to select & measure an Omnichannel Banking Solution?

While many organizations are paying attention to data analytics projects, very few are fully aware of what should organizations consider when building a data analytics roadmap or strategy? This blog will answer all your questions.

Uber uses data science for price optimization. AirBnB keeps its customers away from fraud with the help of data science. You get to ‘Netflix and Chill’ because its recommendation engine suggests movies and shows that are closest to your liking- it saves them more than $1 billion every year.

There is no other industry where everyone obsesses about improving efficiency and cutting costs as much as the manufacturing industry does.

B2B businesses love efficiency. They throw their hat at anything that makes them better, smarter, and more efficient.

This infographic unlocks these three jargons of AI, ML, and DL in the most simple language.

The pandemic has pushed everyone to the edge with new challenges and repercussions with each passing day. With

Read our full story to understand rule-based systems, machine learning, or self-learning systems and their advantages, limitations, and the business needs to apply them.

Data Quality is a challenge. In fact, both data quantity and data quality are equally important for Artificial Intelligence systems. Read the full story to overcome the data problems.

With increased competition customer churn is an increasingly important battleground for financial institutions. After all, customer experience drives customer churn.

From discussing core grounds to debunking common misconceptions and myths, the webinar provided actionable insights on how to implement AI for the betterment of Financial Institutions.

Two types of AI sellers today, one who consider AI to be the "silver bullet" for all problems and two "who don't think AI can solve all problems". Identify an "AI Charmer" versus an "AI Realist".

Having built data sciences solutions for financial institutions, traders and capital markets clients, one of the challenges we have experienced during the past 12 months is extracting the tacit knowledge that quant teams possess and applying it from the technology side.

As we were demonstrating a data anomaly solution for our client using “R” and “Python” today, my thoughts went back to a recent article that I read titled, “India’s mess of complexity is just what AI needs” written by Varun Aggarwal, co-founder of Aspiring Minds in the MIT Technology magazine during June 2018.

A survey carried out by NRF to locate current market trends in retail reveal interesting data of increasing number of customers engaging online for purchases.

Previously, I have discussed OCR and its functionalities and promised to share the examinations we carried out for finding best android OCR apps.

A major problem that many businesses face today is the inability to retrieve data which is trapped inside scanned documents and images. There are two ways of data extraction:

In a recent conversation with a prospect, I was asked about how Artificial Intelligence (AI) would impact software quality engineering and if Zuci is all set to handle the challenges.