There has been a significant growth in the use ofin recent years. There was a time not that long ago when the technology was only available and usable only by major enterprises and tech-savvy start-ups. However, things have moved on. is now a fact of life for many different companies from all sectors who want to use information and insight to drive business growth.
However, there has been a problem in implementinganalytics strategies for many companies. There are all sorts of reasons for this naturally, but it’s now unquestionable that one of the major reasons is a failure to make the best use of available resources, particularly resources like the cloud. Wired contributor, Jake Gardner, recently wrote about this failure, and stressed that the cloud should and must play an essential role in enterprise big data analytics strategies.
Research has shown that less than 50 percent of the big data analytics projects initiated by companies ever see completion or benefit the firm in the long term. According to Gardner in almost 60 percent of these cases, organisations ultimately encounter problems because they failed to properly account for the size and scope of the project in the early stages. Consequently they often discover that the data storage and analytics tools they invested in are simply insufficient because of the size of the raw data deployed. Having already spent most of their budgets initiating the project, there are no more resources to see the project through to a successful and beneficial conclusion.
This is where the cloud can play a critical role. Unlike legacy solutions, the cloud is scalable, allowing firms to increase their storage and process resources as and when required. This is especially important because the amount of available big data is increasing at an exponential rate. However much big data a firm has already amassed, that amount will almost certainly be dwarfed by what be producing in the future. Enterprises that under-invest in resources for managing big data will face problems, while those that over-invest will simply be wasting money. A cloud-based big data solution is the perfect solution to this dilemma.
For most companies, particularly larger ones, it is crucial that multiple workers can access big data sets simultaneously in order to perform analytics and generate reports and strategies. However, to be useful the data accessed has to be as up-to-date as possible. Legacy solutions cannot accommodate the enormity of big data sets, making migration and sharing difficult and inefficient. Storing big data in the cloud, on the other hand, overcomes this issue. In a cloud-based deployment, every employee will access the same collection of data for analytics purposes. This ensures consistency throughout the company, improving the quality and the usefulness of the insight produced.
However, storing big data in the cloud presents its own challenges. Enterprises will have to move their data into the cloud, but unfortunately the majority of big data is produced on-site. Legacy solutions are too slow and inefficient when moving data to a cloud environment. So enterprises will have to invest in tools which have been specifically designed to move data to and from cloud environments.
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