Doing periodic reporting always feels like it's going to be easy, just make another copy of everything, change some dates, and run it again! But stakeholders have a way of looking at your hard work, thanking you for (if you're lucky) and asking for exactly the new view that was the hardest to implement. How do you build out a report that won't become tangled mess when all of these requests pile up? The first thing to understand is that you can't predict the future. You, an analyst, should understand this better than almost anyone in the organization.
Companies widely recognize the potential power of artificial intelligence (AI). They instinctively understand that it feels like we're on the cusp of something that will change our lives and our businesses in a profound way. Yet, many struggle with where to apply it. Executives can't shake the feeling that they should have use cases for AI and use it productively today, even recognizing that AI is not mature yet and will be far more powerful tomorrow and in the future. If you're looking for how and where your company should use AI, let me give you a perspective on a great application of AI today: your digital platforms.
Most businesses collect data but are unable to use it to generate business value or deliver insights in a timely fashion. Data volume and data types continue to grow, as do the different types of data consumers ranging from business users to data scientists. As a result, data management and delivery often become critical bottlenecks. DataOps comes for the rescue here. DataOps (data operations) refers to practices that bring speed and agility to the end-to-end data pipeline process, from collection to delivery.
There has been a lot of talk about making machine learning more explainable so that the stakeholders or the customers can shed the scepticism regarding the traditional black-box methodology. So, in order to find out how it is being implemented, a group of researchers conducted a survey. In the next section, we look at a few findings and practices for deploying as recommended by the researchers at Carnegie Mellon University, who published a work in collaboration with top institutes. During their survey, the researchers have come across some concerns such as model debugging, model monitoring and transparency among many others during the interviews that they have conducted with organisations as part of their work. The study found that most data scientists struggle with debugging poor model performance.