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ecky Parisotto is the Vice President, Digital Programs at Orium, bringing over 13 years of experience in eCommerce client services and program management to some of the biggest client engagements. With a focus on in-store technology, loyalty programs and customer data activation, Becky’s work supports the future of unified commerce. Orium is focused on large-scale digital composable commerce transformations for the retail space, bringing omni-channel technologies together. Key accounts that Becky works with are Harry Rosen and Princess Auto in Canada, and SiteOne Landscape, Shamrock Foods, in

In this competitive environment, effective data governance software is the need of the hour that guarantees the business’s safety and availability of data. Data governance creates internal data standards and policies that can help data professionals have access to data, ensure the data is used properly, and serve real business value. In simple terms, by implementing data governance tools, you can build a strong foundation of data accuracy, reliability, and security. However, if you are curious to know more about the best data governance tools in the market, we have put together a list of the

With data-driven decision-making now the best competitive advantage a company can have, business leaders will increasingly demand to get the information they need at a faster, more consumable clip. Because of this, we’ll continue to see calls for AI to become a business-consumer-friendly product rather than one that only technically savvy data scientists and engineers can wield. As AI technology advances, understanding the processes behind its results can be challenging. This “black box” nature can lead to distrust and hinder AI adoption among non-technical business users.

Are you overloaded with chores that are trivial and take a huge amount of time in the functioning of your business? Well, this is where hyperautomation comes into play and allows handling such extended and complicated business rules. This only translates to the next level of automation, or, in other words, a set of technologies undergoing revolution to revolutionize aspects of efficient working. Picture intelligent robots working together with data analysis and machine learning to be able to orchestrate complex processes. The ability is to make all of this a reality through platforms of hyper

Leslie Kanthan, CEO and Founder at TurinTech AI - AI-Tech Interview. Dr Leslie Kanthan is CEO and co-founder of TurinTech, a leading AI Optimisation company that empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle. Before TurinTech, Leslie worked for financial institutions and was frustrated by the manual machine learning developing process and manual code optimising process. He and the team therefore built an end-to-end optimisation platform – EvoML – for building and scaling AI.

Programming is the backbone of modern software development, as this aids in driving creation when creating innovative applications and systems that eventually power the digital world. However, the coding process can be complicated and challenging, but thankfully, with the development of modern software, software developers (DEV) can easily navigate intricate syntax, troubleshoot errors, and manage large codebases. Therefore, with the introduction of AI, coding assistants have emerged as valuable partners that can change the programming game forever and enhance the coding experience for DEV.

AI bias has the potential to cause significant damage to cybersecurity, especially when it is not controlled effectively. It is important to incorporate human intelligence alongside digital technologies to protect digital infrastructures from causing severe issues. AI technology has significantly evolved over the past few years, showing a relatively nuanced nature within cybersecurity. By tapping into vast amounts of information, artificial intelligence can quickly retrieve details and make decisions based on the data it was trained to use.

Data warehouses have an older design, which becomes stifling in a world where information and data escalate at an exponential pace. Just try to picture hundreds of hours dedicated to managing infrastructure, fine-tuning the clusters to address the workload variance, and dealing with significant upfront costs before you get a chance to analyze the data. Unfortunately, this is the best that one can expect out of traditional data warehousing methodologies. For data architects, engineers, and scientists, these burdens become a thorn in their side, reducing innovation by 30% and slowing the proces