Five Enterprise Data Trends to Watch in 2021

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Click to learn more about author Adi Paz.

Digital transformation has taken on heightened importance in the wake of the coronavirus pandemic. Enterprises throughout the globe are striving to glean real-time operational insights from big data to boost profitability, provide superior customer experiences, and adhere to regulations.  However, ingesting and analyzing rapidly growing data volumes at the speed of business, from diverse data sources, is presenting a huge challenge, especially to enterprises with legacy core infrastructure. 

Next year, enterprises will compete to launch new digital services and provide the best user experience by better leveraging their enterprise data along with automating and optimizing data operations to meet the new business realities. 

Here are five trends we see on the horizon:

1. AIOps Will Be a Must

AIOps will become a given this year, as machine learning algorithms trained on more data will generate more accurate actionable and proactive insights to make data operations efficient and less costly. Suitable for optimizing the complex operations typical of large enterprises, AI will be used to spot trends and patterns to identify and isolate problems, such as capacity issues. AI-driven autonomous scaling of resources for transactional and analytical workloads will save on overprovisioning and enable scaling exactly when needed to handle peak loads for all environments.  Data will need to be managed and moved efficiently across different data tiers to meet demanding SLAs, reserving the high cost of RAM for the highest-priority data that needs the fastest access.

2. Data and Analytics Will Merge

Co-locating analytics and operational data results in faster data processing to accelerate actionable insights and response times for time-sensitive applications such as dynamic pricing, hyper-personalized recommendations, real-time fraud and risk analysis, business process optimization, predictive maintenance, and more.

To successfully deploy analytics and ML in production, a more efficient Data Architecture will be deployed, combining OLTP (CRM, ERP, billing, etc.) with OLAP (data lake, data warehouse, BI, etc.) systems with the ability to build the feature vector more quickly, and with more data for accurate, timely results.

3. IT Modernization Will Accelerate

The current crisis will be an additional trigger for upgrading infrastructure to support and launch more digital services in a more agile manner. The concept of Digital Integration Hubs, a modern, smart ODS will help organizations to offload and decouple from legacy systems of record and databases to rapidly introduce new digital applications and provide the scale and availability required for always-on services. The ability to seamlessly integrate with existing infrastructure and offload the data to a cloud-native, high-performance, compute-and-storage tier will enable fast go-to-market and low-risk, continuous migration to the cloud without the need to completely divest from existing mission-critical systems.

4. Enterprises Will Compete on Speed

Enterprises will compete to deliver new low-latency digital applications and services to meet the growing demands of the new online economy. In order to provide a superior customer experience, enterprises will use technologies that automatically scale to handle vast amounts of data and can provide faster access to data and accelerated analytics to reduce latency. In-memory computing is an example of one technology that will be deployed to achieve extreme performance, speed, and scale by providing distributed in-memory speed to process high-performance, transactional, and analytical processing.

5. Cloud Complexity Will Continue

By 2022, public cloud services will be essential for 90 percent of data and analytics innovation. Enterprises will be more likely to avoid cloud vendor lock-in and will embrace a multi-cloud approach to meet their application, data locality, and cost requirements.  Frictionless hybrid and multi-cloud services will provide consistency across on-premises and cloud environments, helping enterprises leverage the optimal environment to power their services. There will be more technologies deployed to provide consistency across on-premises and cloud environments to make the transition to the cloud smoother.

Improving the speed, accuracy, and costs of data operations will be critical this coming year for companies to efficiently leverage their data to optimize business processes and digitize their business to successfully compete. The rate of digital transformation will continue to accelerate, driving innovation forward, while enabling enterprises to develop new data-driven business strategies. 

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