Creating A Big Data Ecosystem In The Enterprise

Big Data Ecosystem

Consuming critical data and coming to grips with large-scale datasets have become vital requirements in the enterprise. The emergence of ‘big data’ has seen small businesses and large corporations alike realise the potential they have lurking beneath the surface. Now they want to bring those insights out of the depths and into the light. But how can organisations not only leverage the information available to them, while also ensuring 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 affect change quickly. This change is at the heart of big data and, without the desire for improvement, there is no requirement for big data analysis. However, creating a big data ecosystem should not be approached blindly, nor should it 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.

A common query 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 – no two applications are the same any more. The need to access and analyse this data is extremely critical.

Coming to grips with large-scale datasets is no mean feat, but it’s something businesses must address before implementing a big data ecosystem. Not only does the data being gathered need to be agreed upon, but how various datasets relate to one another needs to be agreed to also – this is often the hard part. Understanding what needs to be compared and how is half the battle. There is no right or wrong answer.

Though there are many components to organise 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 solutions. Many organisations will have a combination of both structured and unstructured data and these platforms offer the space to host and handle differing datasets.

They also allow businesses to cope with large amounts of disparate data, which in turn also creates a real-time environment. If real-time or near-real-time analytics are required, it’s important to source an organisation that can actually make this a reality. You will need a flexible and robust architecture that can handle new data types as and when.

Let’s face it, there is a seemingly endless amount of data within organisations to be processed: from present analysis to a backlog of historical information. If integrated correctly, this data can provide new insights into customer behaviours. Organisations must consider all of the elements that create a big data ecosystem and ensure they work together to create a robust and purposeful ecosystem.

Jeff Reser

Jeff Reser is the Senior Manager, Technical Marketing at Progress Software. Before that, he was responsible for Business Process Management Solutions Marketing. Prior to Progress, he spent more than 25 years at IBM where he held a number of technical and management positions and was instrumental in developing and product managing IBM’s WebSphere Application Server – from its inception to an expanding and very successful portfolio of products. With over 30 years of experience in software technologies, product management, and product marketing, Jeff’s areas of expertise include Big Data & Cloud connectivity, Business Process Management, Business Rules Management, Web application serving, and mobile computing.