With the enormous amount of big data that we have for running businesses efficiently in our everyday lives, the cloud comes in as a very efficient solution. But moving data analytics on the cloud has its own set of difficulties. Here are some of the parameters to keep in mind while using data analytics on the cloud. In this article, we will take a look at the migration process as well as the advantages of using cloud analytics.\n\nFigure out where to host the data modelling:\n\nWhile adopting cloud technology, a common question of where to host the data modelling is an important one to consider. There model hosting should provide good performance and functionality gains and flexibility benefits. It becomes necessary to identify where to host data modelling.\n\nStudy the challenges thoroughly:\n\nIn any migration, many companies focus a ton on the project\u2019s challenges but don\u2019t spend a lot of time learning the shortcuts, resources, and tools to help them out. It is important to understand migration challenges thoroughly to help companies ease the transition to the cloud. Not every tool is for everyone, but by becoming much more familiar with these accelerators, you can potentially save time and money. By having someone focus on understanding this landscape and bringing them to the project team there will be a start on resources that can add value.\n\nLearn from the employees:\n\nThere would be people already in your organisation that must have downloaded some sort of cloud-based analytics and have even begun to use them. Instead of banning them, learn from them and adopt in the entire organisation. It is necessary to need to keep informed about the solutions readily available. It is also important to learn what tools they refer to and what solutions are being provided and how they win against the traditional business intelligence tools of the organisation. \n\nUnderstand the extent to which you rely on Universes: \n\nThere a unique way to handle data models for every BI environment. For a large organisation, it is likely that there are different front-end reporting tools and data sources. This has to be taken into consideration while moving data analytics in the cloud. \n\nBuild the first frame:\n\nThe task of transforming the data, building and testing the model, creating visualisations and then turning the output into action causes an analytical block. Building a basic data frame is important for cloud services. Companies should build a basic data frame on a relatively manageable and familiar dataset, process statistics against it and create analysis out of it.\n\nThen start to layer in new data by adding analytics, eventually bringing out new visualisations. The aim is to keep it simple, flexible, and understandable. This helps build consensus internally, increase buy-in from lines of business, and get a faster time to value. \n\nAvoid shortcut of a point solution:\n\nIt is always better to have a start-small approach. The cloud platform is yet to acquire certain capabilities to make it useful. Using shortcuts might in the realm of cloud analytics starting short and having a long way is always better than going for short cuts for a point solution, which may not help.\n\nAdoption Of Cloud Analytics\n\nCloud analytics is important for organisations belonging to different sectors to adopt because of the following advantages:\n\nImprove product availability: Study buying behaviour to improve product availability and delivery.\n\nStudy genetic diseases: Test genomic data to better understand the genetic disease and how to offer cures. It can also be used to keep track of a lot of data and identify patterns of disease reporting to improve the availability of medicine and vaccines\n\nImprove customer service: Identify patterns in speech, images and videos in order to improve customer satisfaction and improve customer service.\n\nOptimise IT costs: Cloud analytics can be used to analyse hybrid cloud infrastructures to improve application performance and optimise the IT costs.