Big Data technology is a highly powerful tool that generates major benefits for companies. Most importantly, companies that capitalise on data analytics can execute better strategies, implement improvements to their data management practices and effectively compile data insights.
In this regard, most companies use data to achieve two goals: on the one hand, to make better decisions to increase productivity, reduce costs and improve efficiency. On the other hand, to improve the customer experience in order to secure greater loyalty.The use of Big Data undoubtedly provides an excellent chance to improve their results, identify new opportunities and optimise established business models. This is why understanding the Big data trends that will shape the future is so important.
The current Big Data context
After three years of accelerated digital transformation, companies have had to adapt to a new economic, business and social context and adjust to the technological advances. The above, coupled with the development of technologies such as 5G have placed data centre stage within this context, in which the management of data and the infrastructures linked to them are key to the success of any current-day company.
Companies are currently opting for tools such as existence in the cloud to optimise their ICT investments. There are also numerous storage, computing and application options that can help companies to incorporate replacement strategies to innovate in terms of data and analytics.
Over the coming months more and more companies will focus on identifying innovative ways of upgrading their technological infrastructures and incorporating Big Data trends into their working environments.
Five Big Data trends set to develop the fastest in 2023
– The importance of FinOps is on the rise
The incorporation of multi-cloud architecture is speeding up as companies move more and more data to the cloud. Similarly, many companies haven’t obtained the benefits they expected from their cloud investments.
This need has given rise to FinOps or Financial Operations, financial management systems based on Big Data into which all the teams working in the cloud are integrated. These programmes control the expenses generated by cloud infrastructures in a more responsible manner, thus optimising their costs by involving different teams such as IT and finance.
A FinOps roadmap is key to achieving several objectives. Firstly, the company’s digital transformation, as it is explained in detail what it consists of and how it will be carried out in order to achieve the goals. Next, control over spending and assessment to prevent any excessive or unnecessary use of resources. Finally, the optimisation of the available resources, as any superfluous cloud costs can be significantly reduced.
– Data Fabric and Data Mesh
Data management has gone through flows of centralisation and decentralisation. In recent years it has been shown that data are more decentralised than centralised within most organisations. Data devices are generated as a result, leading to an absence of equalised data governance. In 2022 we witnessed the acceleration of the integration of two data architecture approaches: Data Fabric and Data Mesh, to manage and improve exchanges of data that come from different sources.
The two structural data models display significant differences. Data Fabric is a modular set of technologies that can process the large amounts of data generated within a company, while Data Mesh is a process-oriented approach to the different data management teams, as deemed appropriate by the company.
Both solutions can prove to be of great value to companies, as they also make it easier to access, control, integrate, manage and securely deliver the data.
– Cybersecurity
It is precisely the increase in the amount of data that has led to the need to protect them more effectively. For this reason, being able to rely on specialised cybersecurity systems in the cloud is more necessary than ever before.
In this respect, the increasingly intensive use of cloud solutions, for example, requires data analytics in order to detect and prevent cyberattacks in time. Thanks to the speed of the analysis and the quality of the data it provides, it’s possible to study dangerous behaviours and locate patterns to perform advanced threat detection.
– Predictive analytics
Predictive analytics is one of the priorities of companies’ ICT departments. Now that data are their most valuable asset, organisations are striving to increase the use of predictive analytics platforms, which permit advanced management of their customers’ data and anticipate their needs.
Owing to the use of Big Data, AI and machine learning, predictive analytics has great potential, particularly in the Industry 4.0 sector, as it can improve productivity and efficient use of resources.
– Blockchain
Although Blockchain is often associated with cryptocurrencies, it’s true to say that its applications are much broader. This technology is used as a way of monitoring and tracing medicines and clinical trials, given that it’s able to control the origin of certain food products and exchange information between different public institutions. In the coming months it will be implemented to improve the management of users’ digital identities and drive the development of Industry 4.0.
