A dataset of images, used for computer vision tasks, could be the key to success or failure. A clean dataset could lead the way to a great algorithm, model and ultimately system, while no matter how good the model or algorithm is, junk in - junk out. So, how do we know what we have? How do we make sure the data is clean, high quality, contains what we really need? If the set was very little, we could check it manually.

Manually sifting through 1,000 images is tricky, but what about 1,000,000? What if we get a new batch of 10,000 images every day? Many who work on various computer vision tasks face a similar challenge. Akridata Data Explorer is a unique and powerful tool for data curation. Curating and exploring your database is the first step to having a clean database, which can be used for model training and testing.

In simple terms, Open Data means the kind of data which is open for anyone and everyone for access, modification, reuse, and sharing. Open Data derives its base from various "open movements" such as open source, open hardware, open government, open science etc. Governments, independent organizations, and agencies have come forward to open the floodgates of data to create more and more open data for free and easy access. Open data is important because the world has grown increasingly data-driven. But if there are restrictions on the access and use of data, the idea of data-driven business and governance will not be materialized. Therefore, open data has its own unique place. It can allow a fuller understanding of the global problems and universal issues. It can give a big boost to businesses. It can be a great impetus for machine learning.

Modern businesses run on information, but we're drowning in data from line-of-business systems, company databases, the data generated by industrial IoT systems, and all manner of external data. So how can we stay on top of the data we have, in order to get the business insights we need, when good data scientists are rare and expensive to employ? Modern data analysis tools like Tableau and Power BI go a long way to solving some of these problems, with graphical tools that simplify building queries and displaying results. Building on the analytical tooling delivered in Excel, Microsoft's Power BI can work across data sets, constructing and testing queries, and comes with a customisable set of visualisations. Microsoft's Steve Guggenheimer, in a blog post on the subject of what he calls real-world or'pragmatic AI', suggests that data is the foundation for AI, and without data there is no AI.