Ultimate Guide to Cloud-Based Data Analytics
The concept of using data analytics to maximise profits has been around for a long time. Data analytics has become more effective in recent years, thanks to the introduction of newer technologies that allow for the economical storage of petabyte-scale data. Of course, more data means more robust algorithms for predicting business outcomes.
Why Cloud-Based Data Analytics?
According to Gartner, 85% of companies do not realise the full potential of their data because it has traditionally been stored in silos in data centres all over the world. But, thanks to cloud computing, this is about to change.
The cloud data analytics market is growing at an astonishing 25% per year and will be worth $ 25 billion by 2020. More and more businesses are opening up to the idea of moving their data warehouses and lakes to the cloud, resulting in more and more data being ported to the cloud.
Retail is the first industry to adopt cloud-based data analytics, followed by utilities and public services. Cloud computing enables organisations to run predictive analytics as well as business reporting. Furthermore, many public cloud service providers provide native services such as text analysis, machine learning, and so on.
The Spread of Cloud-Based Data Analytics
Security Concerns Disappear: Previously, security was a barrier to cloud adoption for organisations. However, as security compliances have become more stringent and cloud security has become more or less foolproof, the proclivity to store data on the cloud has grown.
Exploration of Data in the Cloud: Companies are storing a large amount of data in the cloud and using cloud native features to automate the data exploration process. Most organisations are not only migrating their applications but also laying the groundwork for better cloud compatibility.
Seeing the writing on the wall, more and more open source software, such as Python, sprung up in the market, which was cloud-friendly and is making the market more democratic.
Cloud Service Providers’ Products: As a result of all of this, public cloud providers are expanding their solution offerings. They have enabled continuous data storage and streaming to the cloud. They have developed a variety of storage solutions for unstructured data, structured data, transactional data, and non-transactional data. Most of them have developed services that can be used for data massaging, de-duplication, and general data cleansing. The ability of cloud providers to learn from stored data and create robust algorithms is improving.
The Challenges of Cloud-Based Data Analytics
The remaining market barriers present opportunities for increased adoption. The data and analytics environments are extremely complex. The available silos generate a lot of overhead.
Custom applications that are not cloud-ready and do not have a data pipeline also cause issues. AI-enabled DWs can handle these issues. AI ensures that execution time is reduced.
Companies are realising the benefits of shifting analytics workloads to the cloud, so they are re-architecting and rebuilding for the cloud. Rather than migrating workloads, organisations should assess their applications’ cloud readiness. Based on that, they decide on a treatment plan or go for a birth in the cloud route, where the app is installed in the building.
Tools and Technology
The use of open source analytics tools has democratised software trends. Historically, there were only a few proprietary analytics software options. However, with the introduction of open source tools, even small and medium-sized businesses can now realise the full value of data. Python, TensorFlow, and other open source tools are examples of how popular they are.
As previously stated, public cloud offerings are enabling serverless architecture. These ensure that clients don’t have to worry about provisioning environments, data cleansing, and all the other nonsense that comes with it, and can instead concentrate on the analytics portion. In such a case, the cloud service provider is responsible for monitoring, performance tuning, ensuring optimal utilisation, scaling, deploying, and provisioning of resources. The companies are then only responsible for data analysis and insight gathering.
Some of the interesting use cases that businesses are investigating on the cloud include churn analysis, customer lifetime value, customer segmentation, and forecasting. The primary motivators are undoubtedly lower costs, optimal resource utilisation, and faster time to market.
Cloud data analytics is already a reality, and it will transform how businesses operate in the coming years. Are you ready? Nuvento is here to help you.
