The cloud increasingly dominates virtually every aspect of IT deployment, yet few companies have successfully embraced cloud-based analytics. The problem is not merely technical – although scalability concerns combined with the cost of accessing cloud-based data can rapidly derail an analytics project; the real issue is that companies are investing heavily in analytics platforms only to discover the data lacks the depth required to deliver real business value.
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It is time to go back to basics and take a data-first approach – and the first step is to leverage low-cost analytics platforms to verify the value of existing data quickly.
Business Value
The financial model adopted by cloud providers means that while it is easy – and relatively cheap – to put data into the cloud; it is difficult and extremely expensive to get it out again. In fact, the problem goes far deeper than the sheer cost of typical big data analytics in the cloud models. While many companies may have vast quantities of data – both structured and unstructured – there is no guarantee that data can deliver business value, however, clever the analytics. Data collection, categorisation and storage processes are inherently complex – and from data quality to ease of access, organisations can spend thousands of pounds only to discover that the data is simply not available to answer the critical business questions.
What happens when the retailer discovers transactions are stored at a basket level rather than individual item level as believed? Or the insurance company with policy data that is so complex and fragmented it is impossible to reconstruct a live system? Or the management team learning that instead of retaining customer history for five years, it is stored for just three months to reduce IT costs? Data issues can fundamentally compromise analytics activity.
Test & Evaluate
Rather than undertaking an expensive and time-consuming process of building up hardware, software and analytics expertise only to discover the gaps in data history, the new realm of cloud analytics providers now offer the capability and technology to undertake this essential data discovery step incredibly quickly. Within a matter of weeks, a cloud-based service can look at a company’s data to determine whether there is any business value and whether that data can be used to meet key objectives.
It is only once the quality of the data has been confirmed that organisations should embark upon an analytics project. At this point, the priority is to work in close collaboration with cloud-based analytics providers to identify business outcomes. In the world of big data it is possible to search for any number of patterns or trends, but without a clear business case, the vast majority of such activity will deliver little or no value.
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Analytics on Demand
Attitudes towards analytics need to change if businesses are to gain real value. Just delivering analytics capabilities will not deliver transformative business insight: a broad analytics capability alone is not a fast track to boosting profit or gaining new markets. Organisations need a business case to drive the analytics activity – and none of this can be achieved if the initial data evaluation step has not been undertaken up front.
Big, expensive, time consuming and resource intensive analytics projects are outdated and unproven. They are failing to enable organisations to gain the benefits of either the cloud or analytics. Analytics as a service model eradicates all the issues currently acting as a barrier to adoption from cost to control, security to confidence. It is once organisations have the chance to iteratively and interactively exploit analytics in the cloud to meet specific, tightly defined business objectives quickly and at a low cost, that the next stage of business innovation will be truly enabled.
Peter Ruffley is founder and chairman of Zizo, oneof the UK’s leading analytics as a service companies.
With more than 40 years’ broad experience in the IT industry, including working with some of the biggest data technologies such as Oracle, IBM and Ingres, Peter understood the move to cloud analytics was underway, and assembled a team to create a new type of technology, suited to deliver big data analytics and pattern database services at scale in the cloud.