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We’ve all been aware for some years now about the data deluge facing businesses. What’s only now becoming apparent is just how diverse this data is and will become.
A recent global survey conducted by Oracle, in collaboration with WSJ Custom Studios showed that 96 percent of 742 executives in large enterprises surveyed have seen an increase in business information in the past two years, especially involving customer information, operations data, sales and marketing data. But this is just their usual business data; they’ve also had to contend with an enormous increase in data from numerous other sources.
Businesses now look beyond traditional data to information generated, for example, via social media and website interaction. In addition, data created by the latest mobile applications, particularly those in the geospatial area are providing new sources of data.
All this is before you take into account the impact that the Internet of Things (IoT) will have in the coming years. With billions of objects carrying sensors that produce data that businesses can draw value from, the range of sources and types of data going into businesses is going to explode.
The sheer variety of new data sources businesses have to deal with every day may seem overwhelming, but they need ways to deal with it if they’re to come up with insights that can lead to transformational customer services, products, business models and operational practices.
With billions of objects carrying sensors that produce data that businesses can draw value from, the range of sources and types of data going into businesses is going to explode.
One challenge facing businesses is that all these different sources of data come in different formats and from different places (both from within the business and from external sources). Using traditional approaches to integrate all these different types of data into an enterprise infrastructure so that it can be analyzed is virtually impossible. The time and cost associated with this would be huge and the task never-ending.
Engineered systems — in which hardware and software is integrated together — can help in this regard, as they’re designed, tested and built together, meaning everything is integrated and optimised for the task at hand. This approach also means businesses can get their big data capabilities up and running much more quickly and with fewer integration problems than building their own infrastructure.
A related issue is around future-proofing your capabilities, as by the time you have integrated the initial range of data sources you can bet your bottom dollar there will be many new ones also demanding integration. In addition, it may become necessary to scale capabilities to cope with the changing nature of data coming into organisations.
By the time you have integrated the initial range of data sources you can bet your bottom dollar there will be many new ones also demanding integration.
Again, engineered systems can help. They can be easily upgraded and scaled-up depending on how your business needs change and the technology evolves. As all the elements are engineered together, they can be patched simultaneously, reducing downtime and the resources needed for the task. And with the technology maintained by a single vendor, the process couldn’t be more straightforward. Businesses can benefit from a technology infrastructure that operates seamlessly, bringing greater efficiency, increasing speed to market, and lowering overall TCO.
The big data tools that can sit on Oracle Engineered Systems are evolved to cope with the demands created by the dizzying array of data types and sources. Big data management and analytics capabilities are key in this respect. Together, they ensure information is stored in the most relevant environment and can be readily searched and interrogated to develop insights in a process usually known as information discovery. Another huge benefit is that Oracle Engineered Systems are cloud-ready, making it possible to expand big data capabilities into the cloud and back to on-premise as necessary
Working with these new types of data is already paying off for online game provider Wargaming.net, By using Oracle’s Big Data Appliance to collect and analyse the 40TB of customer data each day the company has been able to generate insights on how to improve its online multi-player computer game. This approach has enabled the company to increase revenues by 62 percent through data-driven insights in one regional customer segmentation programme.
It is big data integration that is the key technology that will help organisations cope with the increasing diversity of data in their possession.
But it is big data integration that is the key technology that will help organisations cope with the increasing diversity of data in their possession. This is the technology that applies governance to ensure data is organised and categorised appropriately, and which enables analytics tools to locate and interrogate data points in a way that will generate insight.
Big data integration applies policies around the authentication, traceability and auditing of data. It also cleans the data up to ensure analytics tools can make sense of it. With a vast range of data arriving in different formats and forms – audio, video, text, social media and sensor information – integration makes meaningful analysis of this data possible by applying a structure to previously unstructured data, thus making it machine readable.
The creation of a big data lake that I alluded to in my previous blog breaks down the siloes that previously limited what businesses could do with their data. Without data integration capabilities, however, it would be very difficult to extract any kind of value from the mass of raw data that businesses face when they embark on their first big data projects.
The tools to deal with the diversifying nature of data already exist, so businesses should not feel overwhelmed by the vast range of data they will increasingly need to deal with. If they take the right approach with the appropriate technology, they can use these new data types to generate genuine and significant improvements to their businesses.
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