The increasing value of data and information demonstrates the need to know the lineage of the data and its metadata to understand its relevance to operations and decisions
The data landscape, and the management of data, are changing faster than before. Big data, legacy data, external data, internal data, data of data and much more; organizations want to use all possible types of data where they can because there is value in doing so.
Luckily technologies make data exchange relatively easier and hence, it is possible to ingest any data from literally any source. But it also creates challenges to manage different types of data from various sources. It becomes hard to get a holistic view of the data ecosystem and also to get a detailed understanding of the same at any given point in time.
The challenge can be described by some diagrams. Here are two different data landscapes.
Former Data Landscape
Figure 1: Former Data Landscape
Current Data Landscape
Figure 2: Current Data Landscape
As seen from these diagrams, an organization’s data landscape has become complex. Data engineers, data scientists, and the data analyst community want to use every piece of data. Business seeks to make all decisions based on their business’ actual data from every source.
However, there are several areas in which most organizations encounter challenges:
- the visibility of data
- the definition of data
- the traceability of data
- the common definition of data
Implementing a metadata management tool is the way to get a full visibility on data landscape. The metadata management application can:
- Give semantic support for information assets classification
- Show lineage for visibility through complex data pipelines
- Be the enterprise metadata management for aligned data governance
Here are some of the key benefits of using a Metadata Management tool:
1. Cheaper and faster project execution – Projects aiming to deliver a data related solution tend to spend ~20% of their time in understanding data, mapping data, analyzing data etc. A metadata and lineage tool can accelerate this activity. Having this tool also simplifies the transition into operational activities (“business as usual”).
2. Regulatory Compliance – Data is essential for a regulated entity to achieve its business objectives. Furthermore, reliance on data has increased as a result of process automation and greater reliance on analytics and business intelligence to support decision making. In the financial industry, regulators want to ensure that organizations have an adequate data risk management framework in place. This kind of framework ensures that the overall business objective of the regulated entity can be met at any point.
3. Operational efficiencies – Faster response to change, better data sharing across the organization, increased visibility of data movement, reduced data related work.
Moreover, it is possible to automate the metadata and lineage management.
Thanks to Apple and Google; they have simplified our lives with extraordinary automation. And even though using a mobile phone is quite different from using any other technology in the organization, it is a human tendency to compare both worlds; we have started expecting simplification and automation in every aspect. We don’t tend to understand and adopt things fast enough if they are not simplified or automated.
A simple definition of metadata is “data about data”. In this rapidly changing data landscape, it becomes incredibly important to simplify the data management activities by keeping a track of data and its lineage, i.e. from where is a particular set of data is coming and where it is going.
If the metadata and lineage are not automated, then it becomes challenging for the organizations to keep a track of their data assets. At any given point an enterprise can struggle to demonstrate a full traceability of its own data. Without this traceability, the true and complete value of the data cannot be demonstrated for operational or decision-making purposes. This inability reduces the value of the data assets greatly. It is essential to be able to automate metadata management and lineage tracing to provide value to data for operations and decision-making.
As organizations start treating data as an enterprise asset and investing in data related projects, it is inevitable they will want to know the meaning and lineage of their data. Therefore, they will need to use a tool that will automate metadata management and data lineage.