Navigating the Data Landscape: Finding Your Way Through the Maze

Can you provide a comprehensive list of all the sources that contribute to your data? Do you know who utilizes this data and the amount of data stored in each source? Would you be able to answer these questions instantly if asked right now?

It’s likely that, similar to many organizations, you have mainly concentrated on the flow of data from your crucial sources to your analytics platform. While this is important, it’s becoming more common to synchronize vital data directly with the analytics platform for decision-making purposes. Therefore, it’s crucial to grasp the broader perspective of how data flows in and out of your organization, beyond just analytics, as there are both risks and opportunities involved.

What is a Data Landscape:

A data landscape refers to the graphical depiction of an organization’s data ecosystem, its utilization, and its integration within and outside the system. It encompasses the tangible components across various data layers and formats, including data sources, data ingestion/integration, data transformation (ETL/ELT, streaming, etc.), data modeling, transactional databases, analytical databases, data analysis tools, programming languages, schedulers, and data orchestrators.

What Does the Data Landscape Give You

Organizations that depend on data find great value in visualizing their data landscape. By doing so, they gain a deeper understanding of how data is created within the organization and can identify the specific business circumstances that dictate the significance of certain data. This visualization also allows for the categorization of different data types, which is crucial for effective analytics. Furthermore, it enables the establishment of protective measures to prevent unauthorized access. Moreover, visualizing the data landscape provides valuable insights into the scale, diversity, and velocity of data, as well as the costs associated with it. This, in turn, helps organizations develop optimal strategies for managing their data efficiently.

Why Do You Need to Understand Your Data Landscape?

To successfully transition into a data-driven organization, it is crucial to recognize the diverse significance of various data sets. Data acts as a mirror, reflecting the characteristics of individuals, processes, and factual information, and different users may have distinct expectations regarding its importance. As a user, it is imperative to delve into the origins and upkeep of data sources, as this understanding can significantly influence your approach, user contentment, operational effectiveness, and compliance with regulations.

How to Create Your First Data Landscape

Developing a comprehensive data landscape requires an iterative approach. Start by gathering the information you already have and gradually fill in any gaps.

Consider the following general steps:

• Identify the known data sources.
• Assign ownership to each source using the EIIG framework.
• Take into account business considerations, such as the types of data generated and used, as well as the associated line of business.
• Decide whether to include all connected systems related to each source.
• While starting with your data analytics platform, remember to also consider other critical data sources for your business.

Your initial map may resemble the following:

As you delve into understanding the current state of your landscape, it becomes crucial to envision the potential future of your landscape. In this regard, it is important to identify the data points that you wish you had access to. By determining the missing data points, you can gain valuable insights and make informed decisions based on a comprehensive understanding of your landscape.

It is worth considering whether data can transition from being non-analytical to analytical, or vice versa, and under what circumstances this transformation occurs. Understanding the conditions that prompt such changes can help you effectively utilize and interpret the data at hand. By recognizing the factors that influence the transition, you can adapt your analytical approach accordingly.

Integrating New Technologies

With the constant evolution of technology, it is essential to assess whether new technologies should be integrated into your landscape. By evaluating the potential benefits and challenges associated with these technologies, you can make informed decisions about their integration. Additionally, it is important to develop a strategy for effectively managing and incorporating these new technologies into your existing landscape.

It is crucial to determine whether your current technology stack enables you to automatically collect the required information. Most tools are designed to provide metadata, so it is advisable to conduct research on how these tools expose this metadata and in what format. By understanding the capabilities of your technology stack, you can ensure efficient data collection and analysis processes.

It is important to remember that your data landscape is constantly evolving and serves as a powerful tool for data-driven decision making. While it is essential to pay attention to the details, it is equally important to avoid getting overwhelmed by the intricacies at the beginning of this journey. Starting small and gradually expanding your understanding and capabilities will allow you to build a strong foundation for effective decision making based on data.

Finding the Right Tool

Navigating the data wilderness requires more than just a map; it demands a dynamic, comprehensive guide that adapts to the ever-changing terrain of data management. The journey through understanding and optimizing your data landscape is intricate, involving a deep dive into the origins, utilization, and governance of data.

The Enterprise Information Intelligence Graph (EIIG) by Orion Governance emerges as an indispensable tool in this journey, providing a structured, iterative approach to visualizing and managing your data ecosystem. By leveraging EIIG, organizations can not only chart their current data sources and flows but also envision potential futures, identify gaps, and integrate new technologies seamlessly.

This proactive approach to data landscape management ensures that data transitions from being a static asset to a dynamic, analytical powerhouse, driving informed decisions, enhancing operational efficiency, and ensuring compliance. As we embrace the complexities of our data landscapes, Orion Governance’s EIIG stands out as the compass that guides organizations through the maze, enabling them to harness the true potential of their data in the quest for digital supremacy.

Ready to see EIIG in action? Schedule a free demo today.

About the Author: Ram Pratti is the Chief Evangelist at Orion Governance, Inc. Connect with Ram on LinkedIn.