Resilient, Agile, and Futuristic Data Management Strategies is a Key for Modern Enterprises

As the business landscape globally is evolving at an unprecedented pace, modern enterprises need to design and implement a resilient, agile, and scalable data management strategy based on three distinct aspects – data, governance, and the customer

Modern business models today generate a large amount of data with a revolution in cloud computing, Artificial Intelligence (AI), and Machine learning (ML). According to a report by IDC titled “Data Age 2025,” the global data sphere will reach up to 175 Zettabytes of data by 2025.

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Irrespective of such tremendous amounts of data generated, enterprises are not able to make the most out of data gathered through various channels. A recent 2022 industry report by Intelligent Automation Network sponsored by Veritone titled “THE FUTURE OF UNSTRUCTURED DATA PROCESSING” suggests that only 10-20% of the data is structured data, readable by machines and is, while the majority of it is unstructured, which cannot be utilized.

“In order to harness the power of the vast and exponentially increasing volumes of data being created, organizations must have a clearly defined strategy for architecture and ownership of data,” says Colleen Tartow, Director of Engineering, Starburst.

CDOs need to craft effective data management strategies to make sense of the huge volumes of data gathered from all the channels, for long term use. It is crucial to implement effective DataOps workflows that produce and process high-quality, easily accessible data, throughout the organization without compromising on compliance and privacy. Traditional data management approaches have become obsolete because they are not capable of delivering excellence in a competitive environment.

CDOs can consider the following points to ingrain resiliency, agility, and future-ready data management strategy into their organization to enhance their capability to become a data-driven enterprise:

Data-driven decisions, engagement, and operations

The entire workforce leverages data to accomplish their daily operations and ensure accuracy in their work. Enterprises that aim to set resilient and agile data management practices need to consider data literacy as their top priority and set a work culture where all the resources know the true value of data. Ingraining an innovative data management approach that enables organizations to overcome challenges quickly rather than having a long-term road map is essential in today’s environment. DataOps teams need to strike a perfect balance between effective decision-making and automating repetitive daily operational decisions. It is one of the most efficient ways to free the workforce to focus more on innovation, collaboration, and communication.

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Organizations need to set a data-driven work culture that fosters business efficiency to deliver unique top-notch customer and employee experiences. CDOs can leverage AI-based data tools that enable the business to harness the true potential of the data gathered. It is crucial to reskill employees in how to use artificial intelligence to make strategic data-driven operational decisions and interactions. DataOps teams need to reimagine their entire data management work processes, journey, and functions to scale their business by being a data-driven enterprise.

“The key to a truly modern data tech stack is to focus on optionality – making sure your organization remains agile and avoids vendor lock-in, and shortening the path between data and the value it creates wherever possible. Whether this means embracing a decentralized architecture and ownership model (as in a Data Mesh), or targeting technologies that use familiar interfaces like SQL, it’s important to focus on the business value at all stages of the data stack evolution,” adds Colleen.

Design and implement a data management strategy that considers data as a product

Most of the data management processes lack ownership of responsibility which might inject the data lakes with irrelevant data sets. There is a possibility that duplicate and inaccurate data sets are stored across various environments and servers, making it challenging for data analysts to explore, access, and integrate data in real-time when they need it. CDOs should consider developing organized data management workflows that consider data as a product, irrespective of whether it is consumed internally or externally.

While considering data as a product, modern enterprises need to have dedicated teams that align the workflows with data security needs, generate new data sources, and deploy self-serve data analytics tools. CDOs should evolve their data management strategies with an agile and resilient approach to meet consumer needs by leveraging DataOps and continuous integration and delivery tools. DataOps can design and enforce a data governance policy that assures data quality and treats data like a product to modernize the data management capabilities of the entire enterprise. Treating data as a product throughout all channels will enable businesses to overcome most of the business challenges.

“To make data consumable and usable and to extract the maximum value from data itself, treating data as a product is key. Creating curated, well-described data products will allow everyone from analysts to executives to discover and take advantage of data as a strategic asset,” adds Colleen.

Data management strategy that prioritizes automation for privacy, security, and resiliency

With evolving laws like California Consumer Privacy Act (CCPA), Virginia Consumer Data Protection Act (VCDPA), General Data Protection Regulation (GDPR), and others, modern enterprises need to consider data security and privacy as one of their top priorities to stay compliant. Consumers are becoming more aware of their data rights, and on the other hand, malicious actors are exploring opportunities to successfully accomplish a data breach. Many organizations have very basic or insufficient data security posture rather than having a customized approach to each data set. It is crucial for organizations to offer the entire workforce seamless and secure access to data sets to authorized users whenever required.

CDOs can consider integrating self-serve provisioning systems to manage and process data provisioning automatically by leveraging predefined frameworks to offer secure data access to the users in real time. Moreover, DataOps teams need set frameworks to constantly take a backup to achieve data resilience and have quicker data recovery procedures to reduce the risks of technical glitches. CDOs can integrate AI-based data tools in their tech stack to effectively manage such large volumes of data. These AI-powered tools can automate the identification, correction, and remediation of data quality challenges.

Modern enterprises need to reimagine their data management strategies to develop better trust in how they gather, store and process data. Resilient, agile, and futuristic data management processes are the key to building a scalable business model in this competitive age.

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