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Author's profile photo Miles Hanley

Personal perspective: Why technology leaders need a Data Modernization strategy, and how to not miss

If you are a technology leader, you know Data Modernization is top of mind in the C-suite. Having been fortunate to support some of the world’s largest enterprises as an expert in Cloud technologies I have found this is because data-driven organizations make smarter decisions faster.

Data Modernization is the process of updating data management practices and technologies. This is a significant organizational maturation process that may include moving to the Cloud, using more efficient databases, and adopting new analytics tools. Executives that I’ve spoken with are concerned with data modernization because it underpins essential capabilities to remain competitive and meet the ever changing demands of the digital age:

  • Insightful decision-making: Accurate, meaningful and timely data precedes decisions to mitigate risks, identify new opportunities and find efficiencies
  • Improve operational efficiency: Through automation and AI/ML,  reducing redundant processes that were previously not possible as a result of inflexible legacy systems, save time and money
  • Increase business flexibility: Adopt new business models and innovation with open and scalable technologies to better serve customers in a rapidly evolving digital landscape
  • Enhance customer experiences: Connected understanding of customers, their needs and preferences provide better customer experiences, personalize services, and improve customer retention
  • Harden security: Modern data technologies with built-in security features and regulation compliance along with updated data governance policies better protect sensitive data which is important to avoid embarrassing breaches and regulatory fines
  • Increase scalability: Scale operations quickly with the tools and resources required to meet the needs of the business

The reality in many boardrooms is that it’s not “if” data modernization, but how to hit the mark on this investment. Exploring the challenges, steps, and best practices when adopting and executing an Enterprise Data Modernization strategy organizations are better positioned to execute.

Virtually all organizations today have data collection capabilities that far outpace their means to connect, interpret and act on data. Swaths of data are readily available if not for non cross compatible tools and technologies. Thus it is imperative to become a data driven business and that long term IT investments and architecture are built on open, secure and scalable technologies. So how does this translate into meaningful action for technology leaders? 

Assess the current state and set the vision

  • A detailed mapping of existing data infrastructure, applications, and processes highlights gaps and inefficiencies. The stated vision is less concerned with how these are to be addressed, but rather establishes the why and the overarching business objectives, data needs, and technology requirements that will support the organization’s future state

Rely on deep technical expertise to develop a roadmap

  • From the current state assessment, organizations are better equipped to curate a roadmap outlining the steps needed to fulfill the vision. The big blocks on the roadmap include implementing new data technologies, upgrading legacy systems, and enhancing data governance practices
  • Legacy systems can prove to be a major hurdle on the path to data modernization. Incompatibility of legacy systems with new technologies require significant resources to update or migrate and may take a phased approach to minimize business disruption

Build for the needs of the business

  • Think big. Start small. Start with a manageable data project core to your business and do not let fear of having “perfect” data stop you. Identify the sources of data required to support the business need which may include multiple systems and sources
  • Use data to make informed decisions, and not fall into the trap of paralysis by analysis. Define metrics that matter and are centric to your business. There is no need to start from zero. Accelerators and prepackaged reports are commonplace among modern data technologies

Implement and upgrade data technologies with security in mind

  • Modern data technologies including cloud based data lakes, data warehouses, and data management tools should be evaluated based on their ability to support the organization’s data needs, foster innovation and align with the overall stated vision
  • Data governance practices ensure that data is managed effectively and securely. This may involve establishing data quality standards, implementing data security measures, and ensuring compliance with data privacy regulations

Lead with conviction 

  • Technological change proceeds organizational cultural change. Build a data-driven culture by emphasizing data to inform decisions and training to do so. Often ask “What does the data show?” and challenge conclusions in this way. Investments in data access, analytical systems and dashboards empower employees. Increasingly over the data modernization journey accurate, meaningful and timely data will answer this question.
  • The C-Suite must model this. Leaders should welcome the use of data to challenge decisions and bust confirmation bias. Leadership here helps the organization adopt the data driven mindset

Modernizing a data strategy is a comprehensive and structured methodology that involves defining the vision, assessing the current state, developing a roadmap, updating systems and implementing new technologies, establishing data governance, and building a data-driven culture. Iterative reflection of these steps throughout the process is required for organizations to better compete and meet the expectations of the digital age, and ensure the execution of the strategy hits the mark.

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      Author's profile photo Peter Baumann
      Peter Baumann

      Hi Miles Hanley!

      Thank you for your perspective here. I'm with you considering these points about leveraging current technologies, care about data governance, foster data culture and data leadership and establish a vision to follow.

      Let me add one point. Sometimes I see most of these points in place and nevertheless no value with data is created. Then the organizational side is often neglected. To establish a target operating model has sometimes not much to do with technology but is possibly essential to drive your strategy. Check collaboration model, organizational structure, service definition, sourcing strategy and so on to make it happen.