The Sticking Points Of Big Data In Companies

Computing's Big Data report identifies some key business issues with data today


Big Data has become an integral cog in many companies today, but what we have seen is that it is not always used in the correct ways and that some companies are still struggling to deal with some of the issues thrown up by data.

Following the ‘Computing Big Data Review’ most of the key issues were identified and we have pinpointed three of the key findings below.

Information Is What Has Value

Although many commentators have claimed that data has a value, the truth is that it is the information that is garnered from this data that has the real value.

A local government CIO who was interviewed by Computing noted that he doesn't believe in “this claptrap that data is the new currency. It’s not. Data is the new raw material and information is the new currency…”.

The idea is that the information is the finished product of the data production line, whilst the data itself is what is needed to create it. Although it certainly has a value, unless it is used and moulded effectively it will not have as much as it potentially could.

The Movement Of Data Causes Issues

As data moves through an organization, companies are finding that the way it is being used and communicated is causing issues.

For instance, the movement from the BI or Data Science team, through to business analysts and then to departments who can action changes is one of the key aspects that businesses are struggling with.

The same CIO claimed “The hand-over of the information and understanding from the BI teams to business analysts is not as seamless as it could be, even though they work pretty much as one team,”. This shows that although the information that comes from this relationship is still useful, the speed at which is gets to key players is diminished.

Turning The Data Into Useable Insights Remains The Key Challenge

48% of those asked in the survey maintained that turning raw data into information is there biggest challenge within the process; something that only goes to show that the value of data comes after it has been processed.

This finding is not surprising, simply because the actual collection, and often even the collation, of data has become relatively simple and can often be automated. The dearth of data science talent that can bring this data into useable insights speaks to the fact that the creation of useable insights is the most difficult and valuable aspect of the process. 



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