Consuming critical data and getting to grips with large-scale datasets have become vital requirements within enterprises today. The emergence of ‘big data’ has seen businesses of all sizes realize the potential they have lurking beneath the surface of the organization. Now they all want to bring it out of the depths and into the light. But how can organizations not only leverage the information available to them, but also ensure they create an effective ecosystem to support the gathering, analysis and development of this data?
The key is to understand what data needs to be gathered, how to go about gathering this data in real time and using it to act quickly to affect change. This change is at the heart of big data and, without the desire for change and improvement, there is no requirement for big data analysis. It’s important to add that creating a big data ecosystem should not be approached blindly. It should not be done because competitors are doing it. Rather, there must be clear deliverables on how this ecosystem will improve anything from internal business processes to customer satisfaction. But it must improve something.
A common query we have seen cropping up within enterprises is: ‘How do I simplify the way I connect to these sources of data in a way that is compatible with my tools and do so in real-time to avoid dealing with stale data?’. Businesses use many different applications and it’s worth noting that every business requires unique applications – no two applications are the same anymore. The need to access and analyse this data is extremely critical to their business.
Getting to grips with large-scale datasets is no mean feat and another challenge businesses must address before implementing a big data ecosystem. Not only does the data being gathered need to be agreed, but how various datasets relate to one another needs to be agreed also, and this is the hard part. Relating the strands of information together and organizing what information needs to be held against other pieces of information is a hurdle that many often find too high. Understanding what needs to be compared and how is half the complication. There is no right or wrong answer to this and it must be understood that it may take a trial and error situation before data gathered and compared is meaningful. After figuring this out, elements such as sourcing space to store this information, is then the easy part!
However, although there are many components to organize internally, externally there are experts on hand to help with the data processing function. Technologies such as Hadoop and NoSQL provide platforms to enable scalable, flexible, cost effective, rapid and resilient to failure solutions. Many organizations will have a combination of both structured and unstructured data and platforms such as these, offer the space to host and handle these differing datasets.
Organizations such as these allow businesses to cope with large amounts of disparate data. They also help in creating a real-time environment for your data. If real-time or near-real-time analytics are required, it’s important to source an organization that can actually make this a reality. You will need a flexible and robust architecture that can handle new data types as and when.
There is an endless amount of data within organizations to be processed, with not only present analysis, but also a backlog of historical information to provide new insights into customer behaviours for example. Organizations must consider all of the elements that come together to create a big data ecosystem and ensure that they all play in together to create a robust and fit for purpose ecosystem.
I'll be talking more about this in my session on at Cloud World Forum on Tuesday, June 17 at 11:00 BST, titled, "Creating a big data ecosystem in your organization," so if you're at the conference, don't miss it! You can also tweet me directly at @JeffReserNC. Hope to see you there!
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