by Daphne Chung, Research Director, Cloud Services and Software, IDC Asia/Pacific

Data, analytics and cloud: The new frontier

Jun 17, 2017
AnalyticsCloud Computing

big data little girl binary analytics sunglasses
Credit: Thinkstock

As digital transformation (DX) continues to drive technology adoption and change around the world, IDC expects information DX to be one of the biggest investment areas as organizations strive to become more information-based companies. In fact, IDC predicts that by end-2019, revenue growth from information-based products will be double that of the rest of the product/service portfolio for one third of A1000 companies. And with over 75% of enterprises expected to commit to multicloud architectures by 2018, IDC also expects that by 2019, the cloud will be a preferred delivery mechanism for analytics.

In the digital economy, the ability to leverage data assets for a competitive necessity is a must. With the proliferation of endpoints and new data sources, the digital universe is expected to increase multifold over the next five years. Organizations are expecting to leverage a multitude of data types and sources both internal and external from third party sources through data as a service as well. IDC expects that public data consumption will grow 150% driven in part by the development of new industry applications.

Without doubt, the value of data is rising as organizations seek to translate it into intelligence to drive strategic decision making and launch more personalized services to the market. However, organizations are struggling to translate the growing data asset into actual intelligence and business value, and will also need to address new capabilities from machine learning and cognitive computing.

Context is a critical element in data analytics as without it, the data is meaningless and irrelevant. The ability to draw contextual correlations from internal enterprise applications and external communications datasets is key to driving real value for intelligence and business value outcomes. Big data and analytics (BDA) can derive the underlying correlation and knowledge about the large volume of structured and unstructured datasets, both internal and external to the organization. However, to turn business intelligence into actionable insights that can deliver real business value outcome, they must be correlated to the little data like key performance indicators of the organization.

The majority of enterprises in the Asia/Pacific region are still in the earlier stages of BDA and have yet to make the transition to more mature stages of BDA. Besides the greater volume and variety of big data sources, there are also other sources of little data located both on-premises and in the cloud from the Internet of Things (IoT) devices and sensors deployed to track the performance of processes and people in the organization. Leveraging on these little data as part of the organization’s big data strategy is necessary to move up the maturity stage of BDA deployment. Thus, organizations need consistent data collection and governance, monitoring, security practices and integration processes to be in place across the enterprise to manage these silos of information effectively. With the adoption of multicloud architectures, organizations will have the ability to manage these datasets on and off-premises across the varieties of cloud, software as a service (SaaS) and platform-as-aservice (PaaS) platforms, which is crucial for BDA deployment in the cloud.

A large amount of a data scientist’s precious time is spent on data preparation, merging of files from a variety of sources, dealing with inconsistencies, transforming raw data and so on. Organizations are looking toward machine learning to tackle the task that traditional processes struggle with; that is, to deliver valuable intelligence from the vast amounts of data. Organizations should also consider the newer hardware capabilities such as embedded hardware systems to handle machine learning, computer vision and deep learning faster than traditional systems. Considerations toward SaaS apps, integration of data and intelligent applications should also be considered as organizations transition to a multicloud environment.

Organizations are learning to monetize their information by using advanced analytics techniques and processes, and data is emerging as a valued form of “currency” at the core. Most organizations positioning themselves to benefit from this new data economy need to adhere to an architecture governed by a central architecture board, drive collaboration, break down silos and integrate data, and ensure timely access to information across the multicloud environment.