Perspectives

Data & Analytics – The Year Ahead

Arvind Purushothaman
Data & Analytics practice at Virtusa
Article

Data is the backbone that keeps enterprises performing at the optimal. This fact isn’t going to change any time soon. If anything, companies will continue to bolster their investments to ensure clean and timely data to all parts of the enterprise. Here are some of the big strides we expect in data and analytics when it comes to 2019.

From lethargic legacy systems to super-speed systems

2019 will see a lot of focus in migrating away from legacy data management hardware and software systems to flexible, scalable and cost-effective platforms that can work with all varied data types, and deliver high performance computing. Data systems will be expected to support in-memory computing, real-time analytics, graph analytics and high-performance machine learning. These systems will need to be geared to handle large volumes of data being generated from numerous sources. And that’s where cloud will play a key role when investing in new systems, especially when it comes to Big Data storage and Analytical processing.

Automation and machine learning for data quality and governance

One of the biggest challenges that enterprises will continue to grapple with, is the scale of data and the challenges of governance and quality that comes along with it. 2019 will see enterprises leveraging automation to help boost the efficiency of data for regulatory & compliance requirements. As data quality emerges as a key differentiator, organizations are expected to implement a robust automated data quality framework with appropriate control mechanisms to define, measure, and monitor data quality at enterprise scale.

Statistical and machine learning techniques will play a prominent role in identifying and correcting large volumes of data across the enterprise. These techniques will also be used to ensure continuous improvement of data quality rules. 2019 will also see numerous data quality initiatives come to the forefront. Initiatives focused on data Lineage, data self-service, data quality management, metadata management security, as MDM/RDM, audit and access control will be given priority.

AI/ML initiatives move to production

The dynamic market conditions and data volumes are forcing enterprises to accelerate moving AI/ML initiatives to production. The use cases for AI/ML will continue to grow and become more complex and broad based. Enterprises will have to ramp up their understanding of larger data sets in 2019, and will need to collaborate across teams to make sense of data.

Data protection is paramount

With regulations like GDPR coming into place in the EU in 2018, the way organizations collect, store and use data has had to undergo significant change. While this lays the groundwork, with more such regulations to kick in over the next few years, enterprises worldwide will have to look at a more holistic approach to data protection.

Data science goes mainstream

In terms of innovation, technology is expected to help democratize data science. We’re already seeing instances of how data science is not restricted to the ‘just the niche’ data scientist community, but can now be used by all data enthusiasts. To ensure mainstream adoption, enterprises are already working on bringing an element of standardization at an enterprise level by leveraging well-defined processes, platforms and playbooks for data science programs. This combined with DataOps will help eliminate several inefficiencies in the path of innovation. 2019 will also see a rise in for data monetization opportunities. Enterprises are expected to leverage data across internal and external systems to build analytical products to help explore different options including data products & partnerships, data as a service (DaaS) and data APIs

2019 is going to be another exciting year for data and analytics. With technology opening avenues we couldn’t imagine a few years ago, the key priority for all enterprises will be to ensure their data systems deliver on promises of speed, scale, safety and efficiency all at affordable costs. Open source and cloud, coupled with advanced analytics will push data and analytics into new realms of possibility.

Arvind Purushothaman Data & Analytics practice, Virtusa Arvind Purushothaman leads the Data & Analytics practice at Virtusa. He has over 22 years of experience in this space, and focuses on Data consulting, Data Engineering, Analytics and AI/ML.