Big data is fast becoming the catchphrase in all IT conferences and seminars and in most places; the word is floating around everywhere. Everyone wants to do something with big data and not everyone is sure of what to do. Whatever be the case, most people want to participate in this exodus that’s for sure. Given the situation, it is essential to ponder about its possible future since future as you may know has many potentials and it is always a single one that is often realised.
As it happens with future, the big and powerful often have the maximum to say. In that case, it is important to listen to what the big names in analytics and big data are talking about and which big enterprises are thinking about big data. It is because both the developers and the clientele need to think in a specific direction for big data to have a concrete future.
What is being predicted?
There are volumes written by many people on the future and they can be summarized before proceeding. People are talking about areas like Data governance, data visualization, data fabrics where data is managed, sourced and projected in the most integrated and cohesive manner. More and more power to business partners instead of data scientists to make processes and decisions quicker is another concern for such articles. Another interesting aspect would be the validation of Moore’s law once again given the steep curve of development for big data.
Many people have gone on to hail big data as the universal currency in becoming and they are confident that big data will force data tools become prevalent everywhere even if some enterprise is not participating in the big data network. Another aspect that will develop in time in multiple sources for one data for cleaner data, many reckon. In short, it’s going to be a challenge for enterprises in the coming years with data explosion everywhere.
The challenges in brief
There are four specific challenges that IBM recently pointed out while tackling big data. They call it the four Vs and they more or less function as the aegis of all the possible problems that an enterprise may face. The first problem is the problem of Volume because big data talks about petabytes and zetabytes. One needs to think in terms of storage problems, data management and new methods of databases to really counter this issue of big data.
The second problem is Variety, where data comes in all sorts of formats from any possible source in the most unstructured manner. Tackling and structuring them would be a serious concern. The third challenge would be Velocity, because the data arriving needs to be processed in real-time and results must be delivered too to ensure that data does not go to waste. The final issue is Veracity, because the data needs to be crosschecked and preferably, come from multiple sources.
To have insights about data
Another direction of big data that is taking shape recently and will be part of a big data future is the process by which insights are gathered about big data. To begin with the processes, one need to identify a number of sources scattered across the paradigm and then connect them together through some process of data integration. Then, you need to clean the data of redundancy and spurious information. Once the master and reference data is segregated, try to see through the data and create a visual representation.
However, there are certain advancements going in the field of data science and these will have direct impact on the data analytics scenario. Developments such as multiple platform data visualization, Self service BI, Agile warehousing concepts, new capabilities for data enrichment will lead to empowered users having a futuristic user experience by directly using big data for their purpose. Self-service will increase with data visibility increasing manifold for all strata of users. Also, development of analytics on unstructured data now enables insanely innovative insights.
Management of enterprise information and more
The distilling of insights from data through various processes forms the crux of big data. But, to do this in an optimal manner, metadata management needs to pervade every form of data processing with more control over master data. Real-time decision making must be achieved in a smoother manner with data governance existing across the whole process of insight extraction from data.
Apart from the obvious business needs, there are certain aspects that matter in the long run so that you know you are doing it right. For example, data source can be a source of major headache if there is little affirmation regarding the data incoming. Also, too much redundancy can in turn affect the storage as it may lead to an unnecessary overflow. So, the future is only taking shape.
