Big data does not make a small business successful but clean and useful data does. Successful small business owners already know that and are bountifully reaping benefits by simply recognising the difference between big data and clean data.
Billions of pounds have been spent by businesses in the quest to join the big data wave. You hardly converse with any information technology professional for one hour without he or she saying something about how awesome big data is.
Many small and medium sized businesses have invested a fortune in their pursuit to become more trendy and efficient. And a lot think that big data will perform the magic
You may invest those millions or perhaps billions into gathering big data without knowing that what you are gathering are simply dirty data.
The weight of big data sometimes become too heavy for organizations to manage and this is all down to the fact that businesses spend far too many resources gathering dirty and useless data rather than focusing attention in building clean data.
Gathering and building clean data is the smart way of utilising the power of data analytics. Normal activities like going to work using train or tramp now generates data that can be used to make calculated and informed decision.
The only problem with the information superhighway that we now live in is that businesses especially the smaller ones struggle to grapple with the speed at which things are internet-ting thereby producing quantum data which are in most cases noisy and dirty.
Managers’ efforts should be channelled towards generating clean data that will indeed add value to the decision making process of an organization.
This article highlights characteristics of big data that turns it into clean and useful data which is what organizations need.
Characteristics of big data that turns it into clean data
Targeted: for big data to be clean, efforts towards its generation must be targeted from the source. There is no need wasting resources gathering and analysing data that is of no use to the company. An ice cream company for example will not benefit much trying to understand how their customers browse the web. This energy can be channelled towards gathering information on what flavour, fragrance and colour their customers love.
Cost effective: advancement in technology has made it possible for information to be cost effectively gathered and analysed. But big data analytical tools still costs businesses a lot, especially small businesses. What is the point spending your scarce resources buying both hardware and software required to make big data work when the benefits it brings to an organization is nothing compared to what it costs the company.
It requires minimal analytical work: an important thing to bear in mind when planning to roll out your big data acquisition strategy is the amount of time that is required to analyse the data. One thing that that turns big data into clean data is when you don’t spend disproportional time on it all in the name of analysis. The golden rule is not to invest in any big data acquisition exercise gathering data if its potential usefulness does not jump straight at you in the planning stage.
It does not violate laws: big data quickly become toxic if it lands the organization large legal bill. The public now take their privacy issues very seriously and would not hesitate to drag any company that violates it to court. A clean data is concise and manageable thereby reducing the likelihood of breaching data.
Multidimensional: a clean data must be multidimensional in all ramifications, shape and form. I read an article from the institute of information technology website and the writer was describing how a bug data software fed with image from CCTV camera can be used to analyse the demography of a business.
It provides competitive edge: gone are the days when the mere acquisition of gizmo gadgets gives competitive advantage. Nowadays, the gadget must provide real competitive advantage. One of the features of a big data that gives competitive advantage is its ability to provide real time information.
Easy to integrate into existing system: big data gathering infrastructure must seamlessly integrate with other business technologies used by a company. A big data with the ability to generate clean data must easily integrate with other infrastructures like the enterprise resource planning system.
Easy to interpret: the output of clean data process should not require the end user to do much interpreting the data. You don’t expect a manager to spend additional time struggling to make sense of the output of an analytical process. For big data exercise to produce clean data, the infrastructure used must have the ability to produce easy to interpret clean data.
It must be automatable: a clean data tool must have the ability to run on autopilot. The tool should have the ability to intelligently gather clean data without much involvement from the owners of the small business. Imagine babysitting your big data acquisition tool just to make sure that it is doing its job. Am sure this is not what the plans are when that huge investment was made in purchasing the big data technologies.
Extended features of big data that makes it a clean data
If you have been following big data since it became popular, you will come across the Vs of big date. Well, just for the record, the bulleted points below are lists of words starting with the letter V that a big data project must have.
You may have noticed that I have added more Vs to the original 3 or 4 that were invented by IBM, Microsoft and Oracle. The other Vs are the little things that adds extra value to the whole idea of clean data.
You are simply wasting money implementing big data project. The ideal thing to invest money on is clean big data. This article identified the features that turn dirty big data into clean, lean big data.
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