The applicability of big data analytics has expanded greatly in recent years. Not that long ago, this technology was seen as usable only by major enterprises and tech-savvy startups. Now, big data analytics is leveraged by a huge range of firms from all sectors. Virtually every company can potentially benefit from these resources.
However, in many cases, organizations' big data analytics strategies come up short. There are a number of reasons why this may be the case, but undoubtedly one of the most significant is a failure to adequately leverage available resources. Of these, the cloud is among the most critical. As Wired contributor Jake Gardner recently highlighted, in many ways, the cloud can and should play an essential role in big data analytics strategies.
Big data to scale
As Gardner noted, research has shown that fewer than half of the big data analytics projects initiated by companies are ultimately completed and prove a boon to the firm. In almost 60 percent of these cases, he noted, organizations experience problems as a result of failing to properly account for the scope of the project in the early stages. As a result, they often find that the data storage and analytics tools they invested in are simply insufficient, considering the sheer size of the raw data in such deployments. Yet these companies will have already used up their budgets for these endeavors, resulting in an inability to reach a satisfying state for the big data analytics project as a whole.
The cloud can play a critical role in avoiding this scenario, as Gardner highlighted. Unlike legacy solutions, the cloud is scalable, allowing firms to increase their storage and process resources as needed. This is particularly important because the amount of available big data is increasing at an exponential rate. However much big data a firm interacts with currently, that amount will almost certainly be dwarfed by its needs in the future.
Firms that under-invest in resources for managing big data will face the issues described above, while those that over-invest will have wasted money. A cloud-based big data solution circumvents this dilemma.
Information access is of perhaps equal significance for firms hoping to leverage big data analytics. For most companies, particularly large ones, it is imperative that multiple workers can access big data sets simultaneously in order to perform analytics and generate reports and strategies. For the work produced to be useful, though, the data accessed must be as up-to-date as possible.
With legacy solutions, though, this is a difficult state to achieve. These systems cannot accommodate the sheer size of big data sets, making migration and sharing difficult and inefficient.
Storing big data in the cloud overcomes this issue. In such a deployment, every employee will access the same collection of data for analytics purposes. This ensures consistency throughout the company, improving the quality of the insight produced.
However, achieving this arrangement is easier said than done, as several challenges must be overcome. Most notably, firms face the challenge of moving their big data into the cloud in the first place. After all, the vast majority of big data is generated on-premise, rather than in the cloud. Yet as with general data movement, legacy systems will face speed and efficiency issues when moving data to cloud environments.
That is why companies pursuing a cloud-based big data analytics solution should invest in tools specifically designed to move data to and from cloud environments. With these resources, firms can avoid problems that would otherwise greatly undercut the utility of big data systems.