Today, data underpins the decision-making process for organisations of every kind. It helps governments model pandemic strategies, scientists forecast the impact of climate change and enterprises discover opportunities and predict changes in the market. Its immense value means companies are collecting far more data and analysing it more often. To cope with such increases, many conduct their big data analytics in the cloud. Here, we explain why the public cloud is the best place to store and process big data.
Challenges of in-house analytics
For most businesses, experimentation with big data begins in-house; however, the results are often limited due to issues with data management and the restrictions of on-site infrastructure and applications.
In-house, data is often held in departmental silos, and in different formats, making it difficult for it to be brought together for full analysis. Indeed, 50% of businesses don’t have centrally stored, unified data and many have significant quantities of high-value customer data left unused on their CRMs. On average, 80% of the data that companies hold is unstructured and cannot be analysed for insights without advanced applications like AI, something that is difficult to deploy in-house.
Even if data management issues could be resolved in-house, the storage and processing capacity of on-site hardware is often insufficient, with much of it used to run the critical applications needed on a day-to-day basis. Scaling up to provide additional capacity can require the purchase of expensive new hardware and associated ongoing expenses. As a result of all these challenges, today’s data-rich enterprises find themselves unable to analyse data effectively or quickly enough on-site to inform decision making.
How the public cloud benefits big data
The public cloud makes it far easier and cost-effective to benefit from big data analytics. From an infrastructure perspective, cloud-based servers offer several advantages. Firstly, there is no need to invest in expensive hardware as this is provided and managed by the vendor. Instead of significant capital expenditure, all the storage and processing capacity needed is paid for as a more manageable monthly fee. The public cloud also enables enterprises to scale up or down on demand and compute resources are only charged for on a pay as you go basis, thus keeping costs to a minimum.
Another key advantage is that data can be managed far better in the cloud. All an enterprise’s data, including that previously held in silos or left idle on CRMs, can be held centrally, unified and analysed. Open-source AI solutions can be deployed in minutes in cloud environments, allowing unstructured data to be mined, while the distributed file system of Hadoop, Apache’s open-source software framework, improves the management and processing speed of large-scale data analytics.
The result of this is that, in the public cloud, enterprises can analyse more data, glean deeper and more beneficial insights and obtain them far quicker than in-house. End to end customer journeys can be mapped and mined across all touchpoints, operational data can be analysed across multiple sites, communications managed across departments and so forth. And with the plethora of open-source apps available in the cloud, it also means that analytic processes can make use of technologies like AI and machine learning so staff can quickly obtain more accurate predictions and make more informed decisions.
With this happening in the cloud, employees can also access this data over the internet whenever they need it and wherever they are, with the results being available via dashboards with user-friendly graphical interfaces. Held centrally, access management protocols can be implemented to restrict access and minimise risk, while the data itself can be encrypted. It can also be backed up regularly and securely, as can the company’s servers, providing swift disaster recovery should the need arise.
Additionally, when migrating data analytics to the cloud, enterprises also benefit from the cloud’s high availability, 24/7 technical support and the vendor’s managed services.
Conclusion
Migrating big data analysis to the public cloud provides enterprises with a range of important benefits. Compared to in-house analytics, it is more cost-effective and offers highly scalable processing capacity; it removes company silos and delivers improved data management and security; and it enables more data to be analysed using advanced tools like AI and ML. Partnering with an experienced and trusted managed IT solutions partner, like Hyperslice, ensures enterprises have the right solutions, expertise and support in place, providing a smooth migration to the cloud and enabling the effective analyses of big data to make better decisions and drive their business forward.
For more information about our cloud-based IT solutions visit Hyperslice.com.
