Top Strategic Trends that Businesses Should Know to Ace Data Management in 2021

Technology | 2 minutes to read

08th Jan, 2021

With a challenging and unexpected 2020 almost behind us, it is time for enterprises to look ahead. The last one year alone has ignited more digital transformation than the last decade. Enterprises scaled their IT ecosystems and database environments expanded in size and complexity in parallel. Simultaneously, organizations faced challenges in integrating, protecting, governing, and analysing data.

Enterprises vying for database management services should keep a tab on some emerging trends to make a strategically wise decision.

#1 AI will take over

According to Gartner, 75% of businesses will shift to operationalizing AI from piloting AI. This shift will drive a 5x increase in the data and analytics structure. AI techniques including ML will be leveraged by database service providers to provide vital insights about industry specifics. To support these techniques, organizations must invest in new chip architectures like neuromorphic hardware. These architectures can reduce the reliance on centralized systems that need high bandwidth, and accelerate computations.

AI and ML (Artificial Intelligence and Machine learning) techniques are used in augmented data management to improve and optimize operations. An augmented engine can optimize configurations, performance, and security by using the existing workload data and usage. Automated Data Management (ADM) can be deployed to examine massive samples of operational data including performance data, actual queries, and schemes. Organizations should deploy necessary practices to consolidate and simplify their architecture and increase automation in their redundant data management tasks.

#2 Decision Intelligence will be priortized

More than 33% of massive-scale organizations are likely to have analysts who practice decision intelligence and decision modelling. Decision intelligence will be responsible for combining a number of disciplines including decision support and decision management. It offers a framework to support B2B database and analytics leaders create, align, implement, tune, and monitor decision models. Businesses can also harness the power of whitespace discovery to solve the problem of redundant and unstructured data.

#3 Cloud will be the showrunner

Gartner says that by 2022, Public cloud services will be necessary for almost 90% of data and analytics innovation. With organizations moving to the cloud, data management experts will struggle to align the database services to their respective use. This will eventually lead to overhead integration cost and an unnecessary increase in database management services governance. Organizations that want to avoid this should prioritize workloads that can exploit the full potential of cloud capabilities. Industry leaders should focus on cost optimization as well as other benefits such as the evolving changes while migrating to the cloud.

#4 The Role of Blockchain in data and analytics will evolve

Blockchain in data management mainly addresses two challenges. First, it offers the full lineage of transactions and assets. Second, it offers full transparency for the intricate networks of participants.  Therefore, data and analytics leaders should identify the capability mismatch between Blockchain technologies and database management approach. This will make their Blockchain technology supplementary to their existing data management systems.

#5 The Galaxies of Data and Analytics will Collide

Till now data and analytics capabilities are considered as distinct capabilities and are handled accordingly. However, in the upcoming days, database management services vendors are more likely to offer end-to-end workflows powered by augmented analytics. This will diminish the distinction between the data and the analytics market. The collaboration of data and analytics will foster communication. Enterprises can leverage the combined data and analytics ecosystems to deliver coherent stacks of data. Tech savvy enterprises should combine Natural Language Processing (NLP) and data analytics to detect instances of aberrations and get a coherent view of their data.

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