The Future of Enterprise Data Management: Trends to Help You Win in 2023 and Beyond

AI Consulting

Date : 03/08/2023

AI Consulting

Date : 03/08/2023

The Future of Enterprise Data Management: Trends to Help You Win in 2023 and Beyond

Enterprise data management trends for 2023

Richard Williams

AUTHOR - FOLLOW
Richard Williams
VP – Data Engineering

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The Future of Enterprise Data Management: Trends to Help You Win in 2023 and Beyond

Table of contents

The Future of Enterprise Data Management: Trends to Help You Win in 2023 and Beyond

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Data will double every 12 hours by 2025 as its power and potential continue to grow exponentially. Hence, what we do with that data now matters more than ever. Interestingly, data only accounts for 49% of enterprise decisions, while insights account for 10%; there is clearly room for growth in both categories. An organization’s data management strategy will ultimately become its survival card in the new digital era. The smartest businesses realize that data can help them make better decisions and propel a new era of innovation based on facts. After helping many organizations across the globe, manage their data challenges and opportunities, we have accumulated a wealth of information, insights and intelligence when it comes to managing large amounts of data effectively and proactively. In 2023, data will continue to take center stage and impact the success of companies of all sizes.

Here are a few data management lessons that will guide us through the next year.

  1. Less is more: We can often accomplish more with less. With the contextual understanding of fictional challenges, with right tools and processes in place, organizations can scale models, streamline data management operations and use their resources more efficiently. 
  2. Ensuring good governance is essential: By establishing a robust governance structure and policy, an organization can determine what data is relevant, where it should be stored, and how to best use it. In addition, a good governance policy will ensure that data is used appropriately and contextually throughout the organization.
  3. Companies must maintain enterprise data catalogs:  A data catalog provides centralized access to all data. They also protect sensitive data from unauthorized access. Thus, enterprise data catalogs have gained traction and can help organizations better manage and understand their data.
  4. Build trust in data: Establishing trust in enterprise data is crucial. To make informed decisions, organizations must ensure their data is accurate, reliable, and trustworthy.

All of these lessons can help organizations make key data governance and management decisions as they develop their post-pandemic data management strategies and goals. A thoughtful data strategy that brings together the best of governance, streamlining, centralized access, and trust will set organizations on a better path to the growth and success they aim to achieve.

Likewise, enterprises are beginning to realize that investing in smart technology, designing more efficient processes, and hiring the right talent will give them the competitive edge to build enduring, thriving businesses. However, it still needs to be determined just what exactly these technologies and processes entail. 

Several key factors are shaping the data management space today, and it is imperative for organizations to understand these trends as they formulate and execute their enterprise strategies:

  1. Shift from CAPEX to OPEX models: Next year, we expect to see a shift from the CAPEX to the OPEX model. This is partly due to cloud, which has caused businesses and organizations to switch from traditional infrastructure and on-premises offerings to pay-as-you-go plans. This change will also impact how data governance and management operate.
  2. Data democratization: Data democratization is another trend that will significantly impact data-driven enterprises. The emergence of self-service analytics tools has facilitated business users’ control over their data. As a result, companies need to provide easy access and user-friendly workflows to all employees using these technologies to see how they can improve both individually and company-wide.
  3. DataOps and Machine Learning Models (MLOps): DataOps and MLOps are two essential components of data management that help businesses extract maximum value from their data assets. DataOps is focused on improving the speed, quality, and agility of data processing, while MLOps is focused on streamlining the delivery of machine learning models. Keeping these two processes aligned with business goals will be essential for enterprises in 2023 and beyond.

The next generation of data-management capabilities may be enabled by the evolution of data tools & technologies sophisticated models, and data governance practices simplifying administrative hassles that companies face today. Therefore, getting the interplay right is imperative to identifying the relevant opportunities, driving visibility, building reliability, and achieving scalability in an organization. Hence Chief Data and Analytics Officers must balance organizational needs and strategies to deftly manage the proliferation of data now available.

Richard Williams

AUTHOR - FOLLOW
Richard Williams
VP – Data Engineering

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