Data-sharing and cloud: A big data match made in heaven

In this guest post, Thibaut Ceyrolle, vice president for Europe, Middle-East and Africa (EMEA) at data warehousing startup Snowflake Computing, makes the business case for using cloud to boost data-sharing within enterprises and beyond.

The phrase, ‘data is the new oil’, continues to hold true for many organisations, as the promise of big data gives way to reality, paving the way for companies to glean valuable insights about their products, services, and customers.

According to Wikibon, the global big data market will grow from $18.3bn in 2014 to an incredible $92.2bn by 2026, as the data generated by the Internet of Things (IoT), social media and the web continues to grow.

Traditional data warehousing architectures and big data platforms, such as Hadoop, which grew out of the desire to handle large datasets are now beginning to show signs of age as the velocity, variety and volume of data rapidly inclines.

To cope with this new demand, the evolution and the innovation in cloud technologies have steadily simmered alongside the growth in data. Cloud has been a key enabler in addressing a vital customer pain-point of big data: how do we minimise the latency of data insight? Data-sharing is the answer.

The business of big data

By channelling big data through purpose-built, cloud-based data warehousing platforms, it can be shared within and between organisations, in real-time, with greater ease and help them better respond to the market.

Through cloud, organisations are able to share their data in a governed and secure manner across the enterprise, ending the segregation and latency of data insights among both internal departments and companies external third-parties.

Previously, analysing data sets was limited to the IT or business intelligence teams that sat within an organisation. But as data continues to grow and become an important ‘oil’ for enterprises, it has caused a shift in business requirements.

For data-driven companies, data is now being democratised across the entire workforce. Different sections within a business such as sales, marketing or legal will all require access to data, quickly and easily, but old big data architectures prevented this.

Instead, the advent of cloud has effectively supported concurrent users, meaning everyone from junior to board level employees can gain holistic insight to boost their understanding of both the business and its customers.

On-premise legacy solutions also struggle to process large datasets, taking days or even weeks to extract and process the data at a standard ready for organisations to view. This data would then need to be shifted to an FTP, an Amazon Web Services S3 bucket or even an email.

But the cloud has become a referenceable space, much like how the internet operates. We are easily able to gain insights by visiting a webpage, and in the same way, the cloud can also be used as a hub to quickly access this data.

Data-sharing enables collaboration

Data-sharing not only improves the speed of data insights, but can also help strengthen cross-company collaboration and communication protocols too.

Take a multinational company, for example, they are often divided into many subsections or have employees based across the globe. Yet – through cloud – data sharing can help bring these separate divisions together, without having to replicate data insights for different regions.

As data volumes increase year-on-year, we also see data sharing evolve in the process. The insatiable desire for data will result in organisations tapping into the benefits of machine learning to help sift through the mountains of information they receive.

Organisations who capitalise on machine learning will also be better positioned to extrapolate the variety of data sources available and glue it together to serve as an interconnected data network.

Most of the data pulled is from cloud-based sources and as a result, organisations can use machine learning to get 360 degree visibility of customer behaviours. This enables organisations to better tailor specific products or services to them based on their preferences. Without the cloud, this simply wouldn’t have been possible.

Walmart, the largest retailer in the world, has grown its empire by capitalising on big data and using machine learning to maximise customer experiences. By using predictive analytics, Walmart stores can anticipate demand at certain hours and determine how many employees are needed at the checkout, helping improve instore experiences.

Other retailers, such as Tesco are similarly using machine learning to better understand the buying behaviours of their customers before products are purchased to provide a more seamless e-commerce experience.

As the data-sharing economy grows, data’s reputation as the new oil will continue to surge in the years ahead. Cloud-based technologies are working hand-in-hand with big data to not only offer key insights, but also serve as a foundation for collaboration and communication between all parties within the business ecosystem.

With more and more organisations drawing on cloud and machine learning, it will only fuel the business decisions made through data, offering unparalleled opportunities to respond to customer demands faster than ever before.

Data Center
Data Management