The jury is out and there is no denying that data drives organizational success. Almost 2.5 quintillion bytes of data are created every day, allowing enterprises to collect and use this data to drive their organizational outcomes. However, these rising data volumes need effective data management to make sure that data delivers on its promise.
CTOs and data officers need to navigate certain data management challenges to exploit data to its fullest potential. Some of the key data management challenges that enterprises need to address are:
AI advancements
The role of data management continues to increase with the advancements in technologies such as AI. Technologies such as Generative AI have seen rapid and massive adoption giving enterprises the capacity to leverage data to create more opportunities for innovation, improvements, and revenues.
However, technologies such as Generative AI work with large volumes of data. Generative AI, for example, can produce impressive results since it can sift through and leverage large volumes of ‘unstructured’ data and glean impressive insights from these.
Data leaders, as such, have to evaluate their data management capabilities and existing strategies to assess how to create a powerful ecosystem that can handle staggering volumes of data.
Increasing enterprise complexity and data management sources
Enterprises run their organization on a combination of old and new technologies. Along with this, they also use multiple clouds to host the data. However, data management agility is becoming strategically important as enterprises become more diverse, distributed, and complex.
Reducing human error and overall data management costs as well as improving data management are now essential for enterprise success. CTOs need to navigate challenges like high-cost and low-value data integration cycles in data management, ensure frequent maintenance of earlier integrations, and fulfill the rising demand for real-time and event-driven data.
Gaining a single view of all the data taken from different sources like the public cloud, private cloud, on-premise, or IoT devices is also becoming increasingly important.
Concepts such as Data Fabric that leverage machine learning to automate data classification, discovery, and governance are emerging as a response to these challenges. The data fabric can be used to manage data more effectively by increasing scalability, gaining deeper insights, streamlining data management, and eliminating data silos.
The rise of self-service analytics
Enterprises are now focused on enabling the workforce with the right tools to help them make prompt and correct decisions. Decentralizing and democratizing data access assumes paramount importance here.
Self-service analytics has been consistently gaining traction over the past decade as enterprises look at improving decision-making and making it faster, more reliable, and more accurate.
Self-service analytics, as such, is steadily gaining a greater foothold across enterprises for improved data access. It also enables more employees to actively participate in analytics and employ data-driven decision-making.
Data management strategies, as such, must ensure easier data availability and accessibility. Creating a central data repository, such as cloud warehouses, and data management platform modernization become essential considerations for proper data storage and access. Organizing and tagging data appropriately is another consideration to drive transformational outcomes.
The increasing importance of real-time and the cloud challenge
Real-time applications are becoming popular in the enterprise narrative. These applications often support business-impacting functions and operate within an immediate timeframe. Recalibrating data management strategies is a key priority for CTOs and data officers as the focus on real-time data increases.
Enterprises are also realizing the benefits delivered by real-time data where there is no delay in the timeliness of the information provided. Industries such as healthcare, finance, manufacturing, automotive, etc. are all looking at real-time data to improve their organizational outcomes.
Real-time data needs a robust data architecture and smart data management to collect, analyze, and store large volumes of data quickly and securely. Leveraging the right and most appropriate cloud platforms is critical for enterprises to enable real-time data management, deliver scalability, and create a cohesive and centralized data management ecosystem.
The efficiency challenge
Enterprises are evaluating all aspects of their organization to identify opportunities for improvement. The objective now is to become more responsive to change, navigate constant disruption more efficiently, drive business continuity, and improve operational and business outcomes. As all aspects of the enterprise come under the scanner of efficiency, can data management be left behind?
Data management platforms have to deliver increased agility, scalability, and security and become enablers of organizational success. Comprehensive and robust data management platforms have to be built for a multi-cloud environment and designed to meet the specific enterprise needs.
Enterprises, need to urgently look at their existing data management platforms, assess their current data environment, and ensure that their data management capabilities align with their business objectives for smarter, impactful, and actionable insights.
Data governance challenge
The data governance landscape is also constantly evolving as business agility and responsiveness become essential for success. Data governance strategies have to ensure that the governance structures are dynamic to cater to evolving business needs. This is also becoming important since market conditions and technological innovations are evolving at an unprecedented pace.
Along with that an ever-evolving regulatory landscape also demands greater data traceability and security. Data management practices as such have to ensure continuous assessment and adaptation of data governance practices in an industry-specific manner. Data management strategies also have to ensure that they can adopt a proactive stance towards data governance, leverage data to anticipate future trends, and prepare to respond accordingly.
This also involves paying close attention to the commercialization, standardization, and governance of cloud computing applications. Enterprises also need to ensure data governance across cloud infrastructures and create robust governance frameworks for the cloud and cloud applications to deliver exceptional customer experiences.
Growing security concerns
Most enterprises now use the cloud for data management and spread their data assets across different clouds. However, the rising sophistication of cyberattacks and hackers needs data management across clouds to become even more secure and airtight.
Enterprises need highly secure and scalable multi-could environments that have a comprehensive data strategy, data governance programs, and policies and employ robust data stewardship rules and workflows.
360-degree data management
The growing volume, velocity, and variety of data and the rising data security concerns, demand 360-degree data management for enterprises. For this, enterprises need to unify, simplify, and optimize data, and create a single view for all stakeholders to drive business-aligned analytics and deliver improved compliance in a rapidly evolving digital ecosystem.
360-degree data management delivers quick wins, enhances business outcomes, and allows businesses to assess, monitor, and manage data for improved customer insights. It also enables them to go to market faster with new products, accelerate business growth, and provide proactive customer service.
In Conclusion
Data management is emerging as an enabler of great business outcomes. However, enterprises need to evaluate their current capabilities and assess if they can shoulder the expectations of security, reliability, availability, and compliance apart from cost-effectiveness. Well-designed, business-aligned data management strategies deliver greater responsiveness to market and business needs and offer on-demand flexibility and scalability and will be essential.
Connect with us to see how you can ramp up your data management capabilities and align them same with your business strategy to drive transformational outcomes in 2024.