Implementation of data analytics to maximize profits have been present conceptually for a long time. In recent times, with the advent of newer technologies to store petabyte-scale data economically, data analytics has become more effective. More data, of course, means more robust algorithms to predict better business outcomes.
According to Gartner, 85% of the companies don’t realize the full potential of the data available with them as traditionally, it used to reside in siloes in data centers all across the globe. But with cloud computing, this is set to change.
Why Cloud-Based Data Analytics?
With data being generated every second in the form of social media, IoT, more attributes are available to the data scientists to analyze and unearth patterns and anomalies. However, it is more viable to store it on the cloud rather than on-premise.
The analytics on the cloud market is growing at an astonishing rate of 25% per year and will be as big as $ 25 billion by 2020. More and more companies are opening up to the concept of moving their data warehouses and lakes to the cloud which is essentially resulting in more and more data being ported to cloud.
Retail, as an industry, is leading the pack in adopting cloud-based analytics followed by utilities and public services. Cloud is enabling the organizations to run predictive analytics as well carry out business reporting on a cloud. Moreover, many public cloud service providers also provide native services like text analysis, machine learning, etc.
The Wider Adoption of Cloud-Based Data Analytics
Vanishing Security Concerns: Earlier security aspect acted as a hindrance in cloud adoption for the organizations. But as the security compliances have become more robust, and security on the cloud has become more or less foolproof, the propensity to store data on cloud has increased.
Data Exploration in the Cloud: Companies are storing data on the cloud in large numbers and are also leveraging the native features of cloud to automate data exploration process. Most organizations are not only migrating their application but also building the grounds of their application up for better cloud fitment.
Seeing the writing on the wall more and more open source software like python etc. sprung up in the market which was cloud-friendly and is making the market more democratic.
Offerings from Cloud Providers: Due to all this, public cloud providers are also expanding their solution offerings. They have made it possible to constantly store and stream data to the cloud. They have come up with various storage solutions for unstructured data, structured data, transactional and non-transactional data. Most of them have come up with services which can be leveraged for data massaging, de-duplication, and in general cleansing of data. The cloud providers’ capability to learn from the data stored and create robust algorithms is improving.
Cloud-Based Data Analytics – Challenges
Opportunities for more and more adoption lies in the barriers still prevalent in the market. There is a lot of complexity in the data and in the analytics environment. A lot of overhead is generated due to the available siloes.
Custom applications which are not cloud-ready and having a pipeline for data too are creating problems. These problems can be handled by using AI enabled DWs. AI ensures that the execution time becomes faster.
Re-architecting and rebuilding for the cloud have caught up as companies are waking up to the advantages of shifting the analytics workload to the cloud. Rather than migrating the workloads, it makes sense for the organizations to assess their applications cloud-readiness. On the basis of that, they decide upon the treatment plan or go for a born in the cloud route where in the building the app grounds up.
The Tools and Technology Arsenal
Leveraging open source tools for analytics has democratized software trends. Traditionally there were only a few proprietary analytics software which could have been used. But now with open source tools coming into the picture, even the SMBs are being able to unlock the potential of data. Python, Tensor flow, etc. are a few examples which show how popular these open source tools are becoming.
Public cloud offerings, as discussed, are creating serverless architecture. These ensure that the clients don’t have to worry about provisioning environments, data cleansing, and all the other song and dance routine which comes with it and can simply focus on the analytics portion. What the cloud service provider does in such a scenario is that it takes care of the monitoring, performance tuning, ensure optimal utilization, scaling, deploying and provisioning of resources. The companies then need to handle only the data analysis and insights gathering.
Some of the interesting use cases companies are exploring on the cloud are churn analysis, customer lifetime value, segmentation of customers and forecasting. The major driving factors are obviously the low costs, optimum resource utilization, and faster time to market.
Cloud data analytics is already here, and it is going to change the way businesses operate in the coming years. Are you prepared?