Big Data: Biting-off what you can Chew
Big Data is a big deal – if you can’t handle it, you won’t harness the value of it. Wherever the data comes from, whether created internally or sourced from third parties, you need to be able to use it to see any return on your investment.
The key problems with handling Big Data are often due to:
Size: The volume of data is too large to be processed using traditional tools
Quality: The inbound data is of dubious quality
Structure: The data often arrives in an unstructured form
Relevance: Users only need small parts of the wider datasets available to them
If we leave aside the challenge of storing Big Data, you’re left with the headache of processing it. The raw datasets often need lots of work to make them usable, but how do you do it?
Crank up the brain power: The default approach – throw more hardware and humans at the data. It’s a short-term fix that needs high IT involvement to keep you on track, let alone future-proof your operation. It also adds the risk of human error, giving more opportunities for bad data to hit your systems. Bad data means bad decisions. Bad decisions are expensive.
Invest in Big Data tools: Big Data Management Tools are another option. These purpose-built, costly tools need specialist knowledge to kick-off and keep running. This often leads to expensive maintenance contracts. Tools like these usually offer limited customisation, and can sometimes struggle to adapt as the data environment evolves.
Neither of the above options offer a sustainable solution. They both focus on using brute force to get the data into something your systems can work with. But there is an alternative approach; turn Big Data into Small Data.
Breaking it down… Use a small, flexible tool to break data into chunks that users and systems can digest. These low-cost tools are highly adaptive to change and can be managed internally. They can also be combined with cloud-based processing to be scaled up when large batch processing is needed.
Big Data doesn’t need Big Solutions.
We think it’s time to stop thinking of your data as one entity, and instead use tools to cut-out the smaller chunks an individual needs to work with. Only then can you harness the intelligence contained within your data and use it to drive real business value.