Why cloud-based analytics make your business data more productive

David Hanus Technical Director Data at Versent

David Hanus

Partner Data and Advisory, Versent

June 9, 2021

Most large enterprises are generating vast amounts of data from a wide range of sources. But that data isn’t always used effectively to yield competitive advantages.   

How do companies leverage big data to create opportunities for innovation and drive revenue growth?  

The increasing convergence of cloud computing and big data tools has produced a uniquely efficient mechanism for big data analytics. Cloud-based analytics platforms offer greater agility in a more productive, cost-effective delivery model. 

From storage to strategy

The quality and sophistication of data analytics tools are constantly evolving. Software as a Service (SaaS) platforms, based in the cloud, are much quicker to update to the latest methodologies and eliminate the concept of software obsolescence.   

How does a business go from passive data collection to a situation where data is used strategically to achieve goals?   

To think about this challenge constructively, we need to consider two vital questions. What business goals can be unlocked with cloud data analytics? And, what sort of data can the system manage?   

Thinking about data strategically

There are many reasons for large organisations to adopt cloud-based data analytics.   

Speed and agility are at the top of many company’s priority lists. Cloud infrastructure allows organisations to set up sophisticated analytics systems swiftly, compared to the lengthy process involved with legacy on-premises systems. Cloud systems also scale up and down smoothly and easily to accommodate fluctuations in user traffic.  

A cloud environment allows a large scale organisation to easily bring all its data assets into one place.  Rather than innovating on siloed parts of a business, cloud data platforms give an enterprise-level view of a corporation. This macro view enables business leaders to build a data war-chest; all their organisation’s available data from every various information source. This kind of big-picture view can help decision-makers be more responsive to changes in the customer landscape and get an edge on competitors.  

Cloud security is now the industry benchmark for secure data handling. Cloud providers like AWS implement multiple layers of security through data, network, authentication and authorisation strata. Cloud platforms are readily updated to meet changing regulatory standards, so no patching is required to guarantee compliance.  

Well-designed cloud platforms lend themselves to iterative development so data analysis processes can be built and refined over time with much lower up-front capital investment. Cloud-based data analytics systems also require much less time to set up and maintain, freeing up IT teams to focus on work that drives productivity. With the rich data insights modern cloud platforms provide, there’s an unprecedented ability to experiment with strategic planning and model outcomes with up-to-date information.  

The relatively low cost of running cloud data platforms is a compelling argument in their favour. Cloud data insights are faster and more adaptable, but the systems that provide them are also less expensive to build and maintain. This isn’t just an advantage in terms of capital expenditure; it also frees up IT teams to experiment with different analytic models and configurations without making expensive mistakes. 

What sort of data can a cloud system manage?  

A top-flight cloud platform like AWS can handle massive volumes of data, so there’s broad scope for complex integration of that information. From public data sources that provide contextual insight to transactional and behavioural information generated by a company, a cloud data system can encompass it all. Before the advent of powerful cloud tools, these massive data streams’ sheer volume and complexity precluded meaningful analysis, especially if that analysis was urgent.  

Some of the data sources cloud systems analyse include:  

  • data outputs from machinery, robots and IoT (Internet of Things) devices,  
  • visual data from cameras and streaming video sources,  
  • warehousing data from product logistics,  
  • social data from messaging and client interactions, &  
  • web-based databases.  

These are just a few common examples, but the versatility and scalability of cloud data mechanisms means these systems can integrate almost any kind of information.  

The revolutionary characteristic of cloud data management is its ability to ingest, sort and analyse all the inputs a business creates in almost real-time. Complex analytics reports that previously took teams of people days to create can be generated by a cloud system in mere minutes.    

Faster complex insights support better decision-making

Before faster, more flexible solutions were available, batch processing was the standard model for generating data insights. Batch processing entails a non-interactive workload that ingests a prescribed data set and uses it to produce defined outcomes in the form of a report. Once the system commences its work, new inputs don’t factor in the results, and because a large data set is being processed in one go, it’s a lengthy process. If new data becomes available, it has to wait until the next cycle to be implemented because the parameters of batch processing are set once and can’t be altered until the process is complete.  

Cloud-based analytics solutions enable near real-time data analysis. Every new query submitted to the system produces an up-to-the-minute report, and the speed of cloud platforms means that new data streaming into the system is accounted for in every query.   

Versatility of data between related cloud tools further expands the range of opportunities for data implementation. In a cloud environment, data can be deployed in advanced activities like machine learning and predictive analytics, all within the scope of existing, customisable apps.   

In a legacy on-premise system, a company’s data is only useful within the constraints of its own existing internal systems. Conversely, in a cloud data situation, when new tools come online the data can easily feed into those new processes. Data utility can also be extended to other enterprises like partners of clients.   

Cloud platforms promote safe integration with user-facing applications and digital experiences. Using a cloud platform, a business can put its data into the hands of external users or partners without compromising security. That means an ever-expanding range of data-driven tools and opportunities to leverage information into growth.  

The speed of cloud systems enables faster, more accurate reporting, but, as we discussed earlier, it also supports the ingestion and consideration of multiple, diverse data sources. This combination of greater speed, accuracy and volumetric superiority makes cloud data platforms the state-of-the-art solution for large enterprises. It’s no accident that disruptive companies like Uber and Netflix have big data cloud analytics at the core of their business models. The more a business knows about the activity of its users and the consumption of its products, the better equipped it is to meet customer needs and plan for the future.   

The new competitive sphere 

Scientia potentia est. Knowledge is power. This well-known Latin maxim has held true throughout history. Companies that strive to better understand market forces thrive, while those that ignore fickle commercial trends often fall by the wayside.   

Big data is the new frontier for corporate growth. In this information enabled era, companies with the best data insights win the race because their analytics tools make them more efficient and competitive. Getting your enterprise data into a cloud environment allows you to focus on innovations rather than operations.  

What proportion of enterprises are making the most of their data analysis opportunities? Versent’s research indicates that 57% of Australian corporate leaders are fast-tracking digital transformation activities. The cloud data insights era is a work in progress, not a foregone conclusion. Looking at the disruptive success of Silicon Valley’s data-driven companies, it’s not unreasonable to predict that those making cloud transformation investments now will be dictating the terms of engagement in the next decade.

Want to learn more about cloud-based data insights? Read about Versent’s automated data management systems that optimise business analytics.  


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