data ecosystem modernization challenges

Data Ecosystem Modernization Challenges and How to Avoid Them

/in , /by

Data ecosystem modernization is the fundamental component of a successful digital transformation strategy. The key for successful real-world Big Data applications is to understand the multifaceted nature of the digital transformation journey – one that’s focused on the plan, process, tooling, architecture, and culture associated with your Big Data ecosystem. This article will discuss the top data ecosystem modernization challenges facing organizations at different maturity phases of their modernization journey.

Before we delve deeper into modernizing the data ecosystem from a systems perspective, it’s essential to understand the characteristics of current data assets and capabilities. Big Data workloads are evolving continuously – increasing in volume, velocity, variety, and veracity – and a rethink of perspective is required across the following fronts:

  • Every data source brings its own sets of characteristics and challenges that must be addressed accordingly.
  • Driving business value with IT modernization efforts requires a close analysis of fine-grained technical requirements.
  • Data security and privacy require a complex and exhaustive access management process.
  • Data dependencies are complex, non-linear, and sensitive to the dynamic changes in a scalable IT environment.
  • Data governance’s behavioral, organizational, and cultural aspects make modernization processes obscure and complicated for most organizations.

With this understanding of Big Data in general, let’s review the data ecosystem modernization process from a systems-wide and business perspective, drawing upon relevant Big Data problem focus areas:

Data Ecosystem Modernization Challenges: For New Organizations

These are the organizations new to the data ecosystem modernization journey and in the process of considering pilot projects such as partially migrating analytics to the cloud or developing strategies for a multi-cloud data infrastructure environment. The end-goals of digital transformation primarily drive them:

  • Scalable business
  • Technology compatibility with a business vision
  • A reduced Total Cost of Ownership (TCO) for scalable Big Data business operations
  1. Understanding Technical and Business Requirements: Defining vendor selection, scope, and timing requirements is a challenge, especially since ecosystem modernization tasks can quickly translate into technical debt and business disruptions.
  2. Lack of Vision and Support: Inadequate view or vision of the future state of the data platform can make it harder to reach the associated business goals. IT may lack executive sponsorship across multiple siloed transformation projects that fail to converge or integrate for cohesive business enablement without this vision.
  3. New Big Data Characteristics: Volume, velocity, and veracity of Big Data streams determine the requirements on platform build and design patterns. Data insights on complex relationships across data sources should be considered, along with the higher costs and technical requirements on real-time processing within a secure and compliant data ecosystem.

Data Ecosystem Modernization Challenges: For Growing Organizations

These organizations have developed a solid foundation for modern data architecture and are experiencing economies of scale with Big Data use cases. They foresee significant business value in scaling their Big Data capabilities through data ecosystem modernization projects designed for specific application use cases.

  1. Operational Readiness to Scale: A new data platform solution may not be operation-ready, especially for scalable Big Data applications. Lack of standardization, failure to mitigate data quality issues and redundancies, and a high learning curve can prevent organization-wide adoption of newly developed solutions.
  2. Uncontrollable Data Proliferation: Big Data capacity proliferation and sourcing encourages users to take advantage of significant data assets from widely available and diverse data sources. However, users tend to introduce manual processes to control and analyze large aggregated data pools. Inadequate automation, data management, and lack of oversight into these processes prevent scalable platform use and add to the cost of operating high-capacity data lakes. Issues arising from manual processes further tend to propagate across the data ecosystem.
  3. Solving the Cultural and Expertise Challenge: Technical solutions to these challenges are associated with the cultural and behavioral alignment of the user base. People who champion the specific operational use cases of different data sources and understand how to deliver business value should naturally focus significantly on data governance and quality.

Data Ecosystem Modernization Challenges: For Mature Organizations

These organizations have developed a successful data modernization strategy and are in the process of improving data capabilities within a well-established and modernized data ecosystem. The improvements are driven by the need to improve the speed of insight, adaptability of the technology, deploying tooling to meet new technical requirements, and standardization of big data operational processes.

  1. A State of Stagnant Evolution: Once a solution gains momentum, exceeding value requires evolving the solution itself. As part of the evolution, many users copy and replicate the process and data into their own IT environments. This leads to siloed data deployments and prevents an enterprise-wide view of the data ecosystem. Creating business-driven solutions in these circumstances becomes an added challenge, despite the mature and modern data architecture in place.
  2. Overzealous Bureaucracy: Once the data architecture reaches maturity, IT executives tend to discourage changes to the current standardized processes. As a result, the data ecosystem does not evolve at the pace of changing business dynamics. Consensus building to approve changes for predetermined patterns and techniques can take weeks and months, which bottlenecks operational agility once the data ecosystem is considered mature.
  3. Inadequate Automation: Data discovery, security, and compliance can transform into a complicated and time-consuming endeavor for organizations handling vast volumes of data, even with modern data architecture. Some tasks are operated manually, making the complete security and compliance process prone to errors and bottleneck operational performance. As organizations mature in their data ecosystem modernization efforts, they should automate these processes by carefully evaluating data observability, timeliness, and quality characteristics.

Key Takeaways:

Organizations face different challenges and pitfalls at different phases of their Big Data architecture and ecosystem modernization projects. A systematic and organization-specific solution may be required to address these challenges. Still, it can potentially follow a common theme for continual improvements of their digital transformation and data architecture modernization efforts:

  • Data ecosystem modernization is a continually evolving process for organizations at all stages of the maturity curve.
  • The value derived from data also changes as organizations transition across the modernization maturity spectrum.
  • Continue to leverage tools, expertise, and experiences when and where needed to realize a model of continuous improvement in your Big Data and Digital Transformation capabilities.

As your modern architecture matures, re-evaluating its purpose is vital to continued success