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Big data and business analytics are set for double digit growth, with the global market forecasted to grow to more than $203 billion in 2020, up from $130.1 billion in 2016, according to IDC. Moreover, small and medium businesses (SMBs) in particular are predicted to continue to increasingly invest in big data and business analytics, with nearly a quarter of worldwide big data and business analytics revenues coming from companies with fewer than 500 employees.

Data analytics is considered an essential function for business of all sizes as it can help organisations identify new ways to optimise existing processes and thus ultimately save costs. However, with masses of big data available to sift through, big data analysis can also pose great challenges that must be overcome before businesses can realise the benefits.

For instance, datasets no longer consist of one type of data. Information can be gathered from numerous sources, including social media platforms and mobile devices, which all offer a different view of the same picture. Moreover, traditional on-premise analytics packages simply cannot handle all these differing data types, limiting the perspective they can provide on an issue.

It’s estimated that over 2.5 trillion gigabytes of new information is being generated daily and organisations need to be able to handle terabytes or sometimes even petabytes of data with ease. Traditional analytics packages rely on the business’s hardware infrastructure, which may limit the speed at which these high volumes of data can be processed, meaning that by the time the key decision-makers get the information they need, it’s most likely out of date.

Data changes occur rapidly and analytics software needs to be able to handle these fast changes and present an accurate picture from moment to moment. Traditional analytics packages can provide snapshots, but if they can’t keep up due to lack of processing power or infrastructure, these snapshots provide an outdated and most likely incomplete picture.

Cloud on the other hand, offers businesses the opportunity to run their business analytics tools from a third-party location. Big data as a Service (BDaaS) analytics tools have many features that overcome some of the challenges that big data presents and can offer a very inviting alternative to traditional on-premise business analytics tools.

So how can cloud enhance big data analytics?

1. Using Software as a Service (SaaS)

With SaaS, the infrastructure needed to run an analytics package and conduct the analysis is done on the host’s side, thus reducing the pressure on businesses to invest and maintain expensive computing infrastructure. In this way, SMBs can focus on their staff and processes, thus allowing companies of all sizes to compete in the same space. When using a business analytics package in the cloud, companies can be sure that the software is functioning optimally and that any information it provides is both up-to-date and relevant at the time.

2. Anytime, anywhere access to data

Cloud provides unparalleled access to analytics tools. With on-premise tools, the software was installed on one, or a small number of computers. Cloud allows any computer to connect to the software at any time, removing the need to return to the office to get the latest information, providing employees and decision-makers with more mobility and freedom than before. The information is accessible at all times, which can be incredibly useful for businesses that operate in a number of different time zones.

This communal access also provides employees and decision-makers with opportunities to collaborate and discuss decisions based on current information, resulting in rapid decision-making and quicker responses to changing market conditions.

3. Social media analysis

Social media is an incredibly valuable source of information regarding customers and their views on a business. Before cloud, it was functionally impossible to get the data from various social media servers and process the activity across these sites. Cloud automates the data collection portion, providing the analytics tools with information that can be quantified and rapidly analysed.

4. Data without borders

Cloud allows information to be collected regardless of physical location. This means that records can be kept across various operating sites. Orders can be tracked globally, and inventories can be managed in real time without the need to wait for inventory reports. This data aggregation allows businesses to function more efficiently and to lessen the burden of miscommunication between various sites.

Not only does cloud reduce the up-front infrastructure costs associated with on-premise business analytics tools, but it promotes efficiency and communication throughout the business, regardless of physical limitations. The business analytics tools market is growing rapidly, and numerous organisations offer tools and packages that can handle the demands of big data.

Information and data have become the key to competitive business advantage, and cloud computing can provide a company with the right platform to tap into it.

About the authors

Prabhjot Sodhi is currently leading the Big Data practice in NSW at SMS Management and Technology. He has over 16 years of experience in a range of industry sectors including Banking & Finance, Manufacturing, Energy, Telecom and Aviation.

 Melody Yang is a senior Big Data Solution Designer and Cloud Solution Architect at SMS Management and Technology, with 14 years of experience in various industries. At SMS, she is responsible in leading projects that leverage Big data in the Cloud.

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Prabhjot Sodhi and Melody Yang

Prabhjot Sodhi and Melody Yang

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