While the buzzword ‘big data’ has gone a long way to raising online awareness of the recent exponential growth in consumable data, the traditionally incremental analytic models employed by businesses mean that this surge of information is locked out, available to only IT professionals.
Over the past decade, the hardware used has come a long way in terms power and accessibility but it is the barriers of traditional software implementations that stand in the way of a fully accessible platform.
For too long, business intelligence has been restricted to IT departments, not business users. To harness the power of the huge amounts of data there needs to be not only improvements in analytical capability but in analytical availability.
This is the much-needed democratisation of business intelligence.
What does this mean for businesses?
When achieved, anyone within the organisation can view the data and make informed, strategic decisions without having to rely on a mediating IT department – in essence, true self-service analytics.
Knowledge transfer is key to achieving this ideal.
Formal training and hands-on migration of skill sets need to be ingrained in the planned personal development of staff to ensure a culture of data-driven decisions is nurtured in every link of the company chain. With this level of analysis, decision makers can confidently draw conclusions knowing the quantitative evidence stems from all areas of the organisation.
With this company-wide mantra in place, a highly scalable analytics solution capable of processing the immense volumes of structured and unstructured data in as near to real-time as possible is required.
The incremental updates to analytics ranging from the progressive, such as the shift from OLTP to OLAP, to the kitchen-sink approach of in-memory analytics, where the answer is simply to throw more computing power at outdated methodology, cannot be the basis for self-service analytics.
The ‘solution’ to many analytical issues over the past three decades has been to ‘bolt-on’ changes to the existing procedures, resulting in inefficient, inaccessible solutions that are reliant on costly specialists to draw conclusions.
By allowing staff to interrogate data on an ad hoc basis, the strain placed on an IT department is greatly reduced.
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Not only is reporting time slashed due to staff finding answers to their own data queries, valuable resources and man-hours are free to tackle the more value-generating and demanding tasks.
With the careful planning of time, the potential to further streamline the analytical process through an updated computer infrastructure, for example, will result in data analysis that is prepared for the further influx of data to analyse in the future.
This concept is all about ensuring it is the business users interrogating data rather than IT departments, finding answers to ad hoc queries themselves rather than relying on specialists to find answers.
If businesses are planning to utilise ‘big data’ rather than be burdened by it, the democratisation of business intelligence is key.
Sourced by Tom Jardine, content editor, Connexica