Businesses are producing more data from more sources than ever before, as they rely on applications running in the cloud, data centres and edge devices, to deliver their digital services. However, with all this data, it can be difficult for enterprises to gather actionable insights which can be used to remove operational bottlenecks and improve efficiency, as the pure volume of data presents an overwhelming challenge.
Navigating through bundles of data
The issue stems from the practice of spreading data across multiple disconnected systems, which is all too often a feature of traditional approaches to analytics. This makes it difficult for end users to assess, due to each system consisting of varied data structures with multiple transactional properties. Developers try to overcome this by developing non-standard architectures for analytics. Invariably, this ends up having a knock-on effect on transactional applications, due to a lack of structure.
Meanwhile, organisations are finding themselves having to deal with a variety of non-shared systems across teams and workspaces. Not only does this exacerbate the problem, but it also causes higher costs, data leakage and governance problems.
To address this, there’s now an urgent need to combine operational and analytical workloads while processing real-time data. This will give enterprises a better understanding of user behaviour, while developers can examine that data with ease and efficiency, keeping costs and time to a minimum.
Real-time data analytics and the value of continuous actionable intelligence
Navigating past challenges
Traditional data systems and tools create one challenge in particular – they separate transactions from analytics. This causes a delay as analysis tools must then wait for data pipelines to change when the underlying structure of a database changes. More delays are caused by the processes that make data available for in-depth analytics when needed.
Not only do these processes incur high costs, but this multi-step approach makes management of data difficult, reducing agility and slowing down decision making. This is a result of the fact that many businesses need to make use of both transactional and analytical systems at the same time, which has its own challenges.
But real-time data is fast becoming essential for a wide range of businesses across industries, so services can be improved. The issues raised here make it extremely difficult for organisations to create advanced analytics and business intelligence; meaning it’s near impossible to apply trends such as machine learning or any other analytics in a timely manner.
The era of hybrid analytics
Enter hybrid analytics. The world of data management has been reimagined with analytics at the speed of transactions made possible, through simpler processes, and a single hybrid system breaking down the walls between transactions and analytics.
It’s possible through hybrid analytics to avoid the movement of information from databases to data warehouses and allow simple real-time data processing. This innovation enables enhanced customer experiences and a more data-driven approach to decision making thanks to the deeper business insights delivered through a hybrid system.
Hot topics and emerging trends in data science
Reaching the summit
Thanks to hybrid analytics, real-time allows a faster time to insight. It’s also possible for businesses to better understand their customers with no long, complex processes while the feedback loop is also made shorter for increased efficiency.
It’s this approach that delivers a data-driven competitive advantage for businesses. Both developers and database administrators can access and manage data far easier, only having to deal with one connected system with no database sprawl.
Additionally, a single hybrid system is far more affordable, needing less infrastructure and fewer copies of data. Better still, the separation of workloads between operational and analytics means resources are utilised more efficiently without the risk of impacting any transactional systems within the business.
Achieving new heights
It’s clear there’s a huge advantage to be had for businesses that utilise the power of hybrid analytics. Whether it be fintech and insurance companies using it to detect fraud and score risks in real-time across policies and claims, or IoT platforms detecting device issues in real-time, hybrid analytics allows organisations to be far more agile and data driven. Delivering invaluable business insights that matter, hybrid analytics allows real-time data analytics to be harnessed through operational data so that applications and services can be quickly improved to boost the customer and employee experience. This level of agility can only be a positive, offering greater efficiency, flexibility and simplicity in the management of data.