We’ve all been aware for some years of the data deluge facing businesses. What’s only now becoming apparent is just how diverse this data is and will increasingly become.
A recent global survey conducted by Oracle, in collaboration with Wall Street Journal Custom Studios showed that 96 per cent executives in large enterprises have seen an increase in business information in the past two years, especially data on customer information, operations, and sales and marketing trends. But this is just day-to-day business data; companies have also had to contend with an explosion of data from a number of other, less traditional sources.
Looking beyond traditional data
Businesses now look beyond traditional data to information generated, via social media or through website interactions, for example. Likewise, data created by the latest mobile applications, particularly those with geospatial features, has also become an interesting source of insight.
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 significant value from, the range of sources and types of data going into businesses is going to explode.
The sheer variety of data sources businesses have to deal with every day may seem overwhelming. However, companies must establish ways to manage these as effectively as possible if they are to come up with insights that can help them develop transformational customer services, innovative products, profitable business models and more efficient operational practices.
Different formats, different places
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 externally). As a result, it is virtually impossible to use traditional approaches to integrate this huge variety of information into an enterprise infrastructure so that it can then be effectively analysed. The time and cost associated with this activity alone would be huge and the task almost never-ending.
Engineered systems can help in this regard. They are specifically designed, tested and built for the software and hardware within to work together, meaning everything is integrated and optimised for the task of quickly analysing and extracting value from data. This approach also means businesses can get their big data capabilities up and running much more quickly and with fewer integration problems than if they were to build their own infrastructure from the ground up.
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. For this reason, it may become necessary to scale capabilities to cope with the changing nature of data coming into organisations.
Again, engineered systems can also play a part in overcoming this roadblock. 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 across the organisation, as it brings greater efficiency, improves speed to market, and lowers overall Total Cost of Ownership.
Integrating with big data
But it is big data integration that is the key technology which 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 in the right way, and which enables analytics tools to locate and interrogate data points in a way that will generate valuable 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 and ultimately making it machine readable.
The creation of a big data lake breaks down the silos 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 are faced with when they embark on their first big data projects.
We have come to a point where the tools needed to deal with the diversifying nature of data already exist, so businesses should not feel overwhelmed by the vast range of information 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.
Xavier Verhaeghe, Vice President, Technology Solutions EMEA, Oracle
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