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Data governance is the process of managing and protecting the availability, integrity, and security of an organization’s data. It is a critical component of any data management strategy and can help organizations to avoid costly data breaches, ensure compliance with regulations, and improve the overall quality and value of their data. In this post, we […]

Artificial Intelligence is seen by many as a great transformative technology. However, as with any transformative technology the impacts on society can be tremendous. As such,if your organization is going to be using AI and ML technologies, it makes sense to spend time considering what you want these systems to do and make sure to

Picking a data preparation solution is a crucial step in any big data analytics, ML or AI project, and unfortunately it’s one that is often overlooked,  rushed, or not seen as crucial. As a result, this can lead to mistakes that can cost you time, money, and valuable insights. In this post, we are going

Starting a machine learning project can be both exciting and daunting. However, with a bit of planning, preparation, and following best practices you can set yourself and your project up for success. In this blog post, we’ll go through the top 10 things to consider when starting a Machine Learning project. These steps are applicable

Big data is a term used to describe the large volume of structured and unstructured data that businesses collect on a daily basis. With the advent of new technologies such as the internet of things and social media, the amount of data available to businesses has grown exponentially. This data can be used to gain

Robotic Process Automation (RPA) is a technology solution that has taken the business world by storm in recent years, and for good reason. RPA can automate repetitive and mundane tasks, freeing up employees to focus on more important and strategic work. In this post, we’ll take a look at 10 examples of RPA applications across

As a business owner or manager, you know that data is a critical component of making informed decisions and driving growth. There are many approaches to building systems for collecting, storing, and analyzing data at scale. With so many data engineering solutions available on the market, it can be overwhelming to select the right one

Natural Language Processing (NLP) is a rapidly growing field that is revolutionizing the way we interact with technology. As part of the conversational pattern of AI, NLP technology allows computers to understand and process human language, making it possible for businesses to automate tasks and gain valuable insights from large amounts of unstructured data. In

Big data is a hot topic in today’s business world, and for good reason. With the right big data platform, companies can gain valuable insights into their operations and customers, leading to improved decision-making, increased revenue, and improved customer satisfaction. However, implementing a big data platform is not a decision that should be taken lightly.

Creating ethical AI requires a combination of technical expertise and a strong understanding of the potential ethical issues that may arise. By taking proper steps including understanding the potential biases in your data, creating a diverse team, developing and following a clear ethical framework, and continuously educating yourself and your team you can help ensure

Machine learning has the potential to unlock insights, automate processes, and drive revenue growth. However, with so many different machine learning tools available, it can be overwhelming for those looking to take advantage of machine learning to know where to start.  In this post, we’ll explore 10 considerations for ML tool selection.   Purpose: Make sure

Data preparation is one of the most important steps in any artificial intelligence (AI) project. Unfortunately, it’s also one of the most overlooked. Many organizations dive headfirst into building models without properly preparing their data, only to hit roadblocks and delays later on. In this post, we’ll cover 10 data preparation Issues that can sideline

Picking a natural language processing (NLP) solution for your business or project can be a daunting task, especially if you’re not familiar with the technology. However, with the right approach and mindset, you can avoid common mistakes and find the perfect solution for your needs. In this post, we’ll discuss the top 10 mistakes people

If you’ve worked at an organization for any length of time, or read any of our posts, or listened to any of our AI Today podcasts,  you know that data is the key to making informed decisions and growing your business. But, where do you even start when it comes to implementing advanced analytics in

Robotic Process Automation (RPA) has become an increasingly popular way for businesses to automate repetitive and time-consuming tasks, allowing employees to focus on more valuable work. However, with so many RPA tools on the market, it can be overwhelming to know which one to choose for your organization. In this post, we will share 10

In today’s fast-paced digital landscape, it’s no secret that artificial intelligence (AI) is rapidly becoming a crucial component for many businesses and industries. However, as AI continues to evolve and advance, it’s important to consider the ethical implications that can derail even the most carefully planned AI projects. This article covers 10 Ethical AI Issues

In order for your AI and ML projects to work, the data needs to be prepared in a way that is useful. After all, garbage in is definitely garbage out when it comes to AI and ML projects. We’ve put together a quick checklist that helps you ensure that your data is clean, organized, and

Machine learning (ML) is a powerful tool that is being used to revolutionize a wide range of industries. For businesses, the ability to apply machine learning to various applications at their organization can bring tremendous value including cost savings, efficiencies, competitive advantage, and improved customer and employee satisfaction. With several decades of implementation experience, machine

Machine learning (ML) projects can be complex and time-consuming, and it’s easy to make mistakes along the way. In this article, we’ll go over 10 common mistakes people make with ML projects and how to avoid them. Not clearly defining the problem for ML Projects  Before starting any ML project (really any project for that