In such a disruptive atmosphere, it becomes quite baffling for enterprises to decide which tool will earn them more profit and yield potential results in the coming years. With advancement in data, a volume of choices is available for professionals to delve deep into.
With the involvement of Big Data, AI/ML in the world of advanced analytics, novel mechanisms provide a unified platform for next-gen innovative disruption. In this competitive and fast environment, it becomes crucial for businesses to end up with appropriate decisions for better navigation of their services in accelerating complex data planet.
ML and other technologies related developments form O’Reilly Media’s Ben Lorica’s end foresees some of the data trends which are expected to be on every business’s radar in the near future.
Cloud Data Services
With the advancement of cloud data infrastructure, seems like data trend is in the air with growing efficiency. Recently a research report revealed that around 85 percent of respondents said that they already have some data infrastructure in the cloud whereas other IT executives disclosed that many of them are planning to enhance their funding in SaaS and cloud tools.
Data trends also depict that cloud migration will be powered by new cloud technologies options by next year. The new technologies will include serverless and containerization.
A major proportion of enterprises dealing in machine learning consider that the future build-up of the technology would involve simpler analytics applications which precede ML, already need data arrangements to be in place. The ever-rising popularity of Machine Learning will push companies towards investing in foundational data technologies which is important for scalability of ML initiatives. The foundational data technology involves items such as data ingestion/integration, storage and data processing, and data preparation/cleaning.
Looking at the past events of data and privacy breaches at digital platforms raises the severe issue of privacy and data preservation in recent times. To curb these events, we can expect the industry to divert more focus on analytics tools creation for privacy preserving. As the tools will come in to use, it will become convenient for organizations to identify and manage risks and threats.
Surviving with the advent of Machine Learning
Reportedly, many companies are zooming into pushing themselves into machine learning projects especially using applications designed to improvise their extremely critical projects. Most of the finance-centric companies are converging machine learning into risk analysis, while certain telecom companies are using a wide platform of ML to service operations. Also, automotive companies are focusing ML in manufacturing. This can be seen as the reflection of the emergence of certain data-centric tools specific to ML. These tools include data science platforms, data lineage, metadata management and analysis, data governance, and model lifecycle management.
Industry Data Culture
As automation and machine learning technologies are emerging, the skill gap becomes one of the major challenges for the industry to deal with. This can also become an obstacle in the complete sustainability of ML tools. There is an increasing pressure on tech leaders of any organization to retain and upskill the current workforce to be on track. It is important for the prevailing labor force to learn and upgrade their skills that can influence the current nature of the job.
Therefore, it is not enough for organizations to invest in technologies, they also need to set up a data culture at the workstation to create excellence.
Internet of Things (IoT)
IoT is no more a theoretical chapter to stay on the drawing board rather it has come into practical existence in the smart world with the emergence of cloud platforms, cheap sensors, and machine learning. Moreover, IoT enables automation with ML making split-second decisions based on real-time data. IoT in recent times will not only give smart factories but also voluminous data to back decision making in product management/engineering.
The high geared market of machine learning and analytics tools can be possible with certain tools which would enable data scientists to handle raging problems and maintain the system. This particular drive to machine-managed tasks will lead to more process automation. This range of automation will include data preparation, feature engineering, model selection, and hyperparameter tuning and data engineering/operations.
Some early applications of ML exist as for now, which focus on partial automation of tasks in data science, software development, and technology operations.
The above trends might seem diverse but have one unified platform, comprising of data culture, data skills, and data processes, for their strategic success as a whole. Organizations investing in these trending technologies definitely have shining and efficient future ahead.