What To Do And What Not To Do With Big Data
Big Data is getting bigger in 2017; cliched though as a statement, but industry figures prove that it is happening at a seriously rapid pace. With industries being aggressive with their acceptance of Big Data into the enterprise operations, it presents a strong case for market expansion, as more decisions get taken based on data, more specifically data analytics tools.
However, all the benefits of big data will only be borne, when the adoption and core function of data management, is done in a structured manner. Since the world by large is still witnessing the early stages of big data development and use cases, some guiding factors could certainly make a difference.
What to do with big data
- Aggregation of data from business units: If an enterprise has multiple divisions or separate business units, it would be be advisable to aggregate the data from all the sources, which could give valuable inputs on sales, customer experiences, and other business matrix, etc. Isolation of data beyond a point, could actually yield incomplete or wrong results.
- Make use of the cloud: When the name is ‘Big Data’, it perhaps is easy to guess that it requires a large enough storage space, and the volume of data is very large. This could work out new challenges when it comes to storage facilities for the volume of data generated, and hopefully cloud technology is there to save the day. Judiciously, a cloud connected interface could help manage the large data volumes effectively, and store them safely.
- Trust old data sources: Big data is not always an enemy of older data sources, technically. Older or traditionally used data sources could actually add to the big data processes, by having more enriched sources which to derive results out of. Once there are more enriched results, better and definitely more accurate decisions can thus be taken.
What to avoid doing with big data
- Using a singular approach: Big Data is obviously huge, often running to Petabytes of volume. But to make sense and compute such volumes, it would be better if only a singular approach is avoided. It would be more advisable to actually ‘shop around’ for the right technology and computation method, which would control the entire big data ecosystem in the enterprise.
- Too hurried an approach to big data: Though big data has its multiple benefits, but it would not be prudent for enterprises to adopt big data or a technology associated with it, if it doesn’t actually suit the enterprise business needs. There needs to be be time and other resources diverted towards adoption of big data, which should ideally pick and choose what works best for the enterprise. In fact, if the approach is too hurried, it might even end up causing further damage than just using a singular approach.
- Keeping security aside: Though selecting the right data source, and using the appropriate data computing methods are paramount, the other infrastructural aspect, other than cloud technology, is that security has to be every step of the way, a part of the design framework. With hacking and data theft becoming equally innovative and efficient in their functions, all steps of the growing big data framework, needs to be protected against such data breaches and manipulation, which could either yield wrong results, or bring down the entire enterprise management systems.
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