Breaking the Data Barriers to Digital Transformation

Around the world, business leaders understand the need to harness digital transformation (DX) to jump-start growth, accelerate time to market, and foster innovation. This is more vital than ever as businesses seek to not only endure against current challenges and intensifying competition, but to forge ahead and succeed in the months and years ahead.

Yet businesses are stymied by a range of barriers such as inherited data silos, poor data governance, drawn-out data science cycles, and lack of a collaborative environment. And unlocking the value of data assets is also easier said than done, even as data continues growing at an unprecedented rate.

According to projections from IDC, 80% of worldwide data will be unstructured by 2025, including a growing volume of unstructured and semi-structured data such as streaming audio and video, social media, clickstream, sensor, and log data.

Unshackle your digital transformation

To meet the demands of today and stay competitive tomorrow, the right information architecture (IA) must be set in place to manage data. Such an architecture will need to be efficient and sufficiently agile to facilitate collaborative workflows and meet existing data analytics requirements, as well as to enable the deployment of cutting-edge AI tools.

Successful DX will necessitate the integration of data and processes across organizational boundaries, legacy systems, and cloud platforms, and bridging it with scalable AI deployments to achieve growth objectives. Instead of a hodgepodge of disparate tools and systems, an integrated platform with prebuilt support for AI and industry-centric applications is required to advance automation, streamline management, and drive innovation with automated AI and data science lifecycles.

Far from being a luxury, a modern Enterprise Information Platform (EIP) is a vital tool to help enterprises digitally reinvent and futureproof themselves. Deployed and leveraged correctly, it can enable savvy enterprises to significantly accelerate their DX initiatives and fully harness the power of data and AI.

Key capabilities to consider

While pain points differ across organizations, some key capabilities to look out for in a modern enterprise information platform will include:

  • An integrated platform: To manage data and the associated challenges around analysis and data science workflows, go with a tool that can collect, organize, and analyze data within the same integrated platform. Optimally, it should be deployable across either private or public cloud for maximum scalability and cost-effectiveness.
  • Eliminate data silos: A common platform and data virtualization can go a long way towards eliminating the complexities of integrating data of different types and structures, offering cost savings in storage and resources – while allowing enterprises to achieve the speed and scalability they need for tomorrow’s workloads.
  • Automated governance: Automated governance is essential to ensure that data can be trusted and enables self-service access to help data scientists quickly access the right data. Clean, trusted data from structured and unstructured sources that can easily be found, accessed, managed, and protected.
  • Unified tools for AI and data science lifecycles: The inclusion of AI-infused solutions and tools will mean that enterprises can immediately leverage their expertise and experience to deliver data-driven insights faster and with better reproducibility.
  • Collaborative user experience: Team members should be able to collaborate within a shared experience to break talent silos. Moreover, a consistent UI for different tools within the same platform helps with talent and training, driving time to value.

Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].​

Image credit: iStockphoto/Akintevs