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Seven trends that will impact your data management strategy

By Julianna Delua posted 21 days ago

  

In a world of emerging technology, data management is often underestimated. Yet, data management is the engine driving better outcomes for business, generating better returns on your investments in AI, hybrid cloud and advanced analytics. 

 

IBM has been the pioneer in driving the technological advances in data management for decades. To name a few:

 

 

 

 

Our DNA of pioneering the data management systems continues to help the organizations differentiate in their respective markets today. IBM Institute for Business Value found that 700% increase in hybrid cloud operations by CIOs since 2019. In addition, technology adopters with systems for moving and accessing data have 20% higher revenue growth rate premium, and those with end-to-end reinvention through cloud create 13X greater revenue growth impact potential. Examples include:

 

 

To achieve better outcomes, organizations are prioritizing and addressing the key aspects of data management as shown. Relevant IBM offerings and solutions and IBM client studies are provided below for your reference.

 

  1. Highly resilient transactional data: Maintain highly performant, transactional integrity at scale for online transactional processing (OLTP) with IBM Db2 for z/OS,  IBM Db2 and Informix
  • Always-on: ensure continuity, security and performance to keep applications and daily operations running smoothly
  • Cost-efficiency and manageability: enable investment in new digital services
  • Better productivity and allocations: free staffing time for value-added activities
  • Resiliency: reliably process rapidly changing, diverse and unpredictable workloads

 

Puma: “Microservices are part of going Forever Faster. Using them, we can add functionality to the applications much faster than before”

 

  1. Intelligent operational data: Empower real-time decision making and actions by providing data and analytics about current conditions with IBM Db2.
  • 24/7 real-time intelligence: spot evolving patterns and trends from customer, support, sales, product, and other data
  • Data-driven apps: embed insights into your apps and improve customer experience and operations
  • Reduced cost and risk: arm with on-the ground intelligence to streamline operations and mitigate risk sand cost from threats and anomalies
  • Operational agility: stay ahead of dynamic business conditions and make changes on the fly

 

Norfolk Southern: “24/7 insight into shipment status boosts user and customer satisfaction”

 

  1. Unified analytical data: Make data and analytics simple, trustworthy and secure across deployments, workloads and use cases with IBM Netezza and IBM Db2 Warehouse. both of which can extend to operational use cases.
  • Faster time to insights: reduce delays and slowdowns at peak times. Support low latency high concurrent data access
  • Improved analytic efficiency: spot patterns and trends and perform investigative tasks on years of data
  • Reduced fraud and compliance risk: perform ad-hoc analysis and drill-downs with any structured or unstructured data
  • Accelerated delivery of new services and offerings: unify exploratory and production environments

 

BIC Camera: “Batch processing times for inventory valuation and tabulation tasks fell substantially, from 210 minutes to just 15 minutes — a 93% reduction."

 

  1. Multimodal multicloud data ecosystems: Streamline and speed mission-critical data delivery by simplifying your data ecosystems with IBM, including Cloudera, MongoDB, EDB, DataStax and SingleStore.
  • Simplify and unify: access and integrate a growing variety of data sets across multiple vendors
  • Hybrid multicloud data: access data and data management capabilities anywhere
  • Lifecycle management: manage data, analytics and AI lifecycles across deployments with IBM and IBM partners
  • Accelerate innovation without lock-ins: take advantage of open-source communities and mitigate being tied to specific vendors

 

Active Vision International: “Generated $80M USD in estimated new business opportunities in year one. 100s hours saved for business development.”

  1. DataOps for AI Engineering: Make data ready for AI by adopting AI engineering with DevOps, DataOps and ModelOps. Deliver business-ready data with IBM DataOps.
  • Improve customer experience and product innovations at scale: synchronize DataOps with DevOps and ModelOps
  • Predict and optimize AI outcomes: by integrating multiple use reusing enterprise data across deployments, domains and data types
  • Improved yields and efficiency: get more from your data and Ai investment
  • Save cost and time: speed iterations with automated data and AI monitoring
  • Mitigate risk: through increased data-driven AI explainability

 

Better incorporate said data into new projects and initiatives, ultimately helping to jumpstart innovation.

 

  1. AI-powered self service: Manage data built for AI and with AI by automated development and built-in intelligence
  • Democratize smart data access: empower all to keep pace with new patterns and insights at scale
  • Spend more time on business-driven, high value tasks: speed analytics and AI use with no or minimum support
  • Automate and govern data prep and integration tasks: capture and implement routine analysis while remaining compliant using AI/ML
  • Increase productivity for business and IT: cut manual interventions, waste, escalations and overall support costs and risks

 

The Health Collaborative: Unified data helps speed preventative and urgent care

 

  1. Data-driven governance and security: Improve governance, compliance and risk posture with data visibility, auditability and transparency with
  • Reduce time and cost for governance, risk and compliance: automatically capture repeatable tasks to speed audit and analysis
  • Improve insight and processes to manage sensitive data: protect personal information (PI) and spot usage anomalies
  • Mitigate security risk and exposure through oversight to data access and usage: maintain resiliency, starting small and scale to enterprise deployment
  • Avoid penalties and mitigate reputational risks: move toward  enterprise, sustainability and governance (ESG) with continuous data auditability and traceability

 

ING: Solved the nagging challenge providing high-quality, governed, business- and regulatory audit-ready data across the entire enterprise, including the multiple locations.

 

IBM data management empowers businesses to improve outcomes using any data for analytics or applications across any cloud including on-premises, public and private.  You can also visit IBM database management, data lake and data warehousing to learn more.

 

To get started, you can book a complimentary meeting with an IBM expert.

 

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