A company meets various challenges that drive it to develop advanced data management capabilities. I investigated multiple sources. Below, you will find a summary of business drivers for each capability.

Metadata management (MM)

Key drivers for a company to start its metadata management initiative are:

  • Increase confidence in data by providing context
    Data by itself has no meaning. Currently, many companies focus on getting value from data. Improving decision-making is one of the ways to get value. Machine learning and artificial intelligence are techniques that allow discovering deep relationships between data, understanding complex business situations, and using data analytics outcomes for decision-making.
  • Integrate data
    A modern company uses data from multiple internal and external sources. The company can obtain value from data after its integration. Metadata describes data and assists in data integration.
  • Improve efficiency by identifying and removing redundant and duplicated data
    Medium- and large-size companies have dozens and hundreds of IT assets like applications, databases, integration tools, etc. Not all data stored in these IT assets are being used. Data is also duplicated. Redundant and duplicated data blows up IT operations and maintenance costs. Proper metadata management assists in improving the efficiency of data.

Knowledge graph (KG)

The following business drivers lead to a KG initiative:

  • Link data with its meaning
    Data is linked to its meaning by adding metadata to data. As we discussed, metadata creates a context in which data gets a particular meaning. As we demonstrated, some graphs contain basic data facts. Others contain higher-level semantic information.
  • Integrate data from multiple unconnected sources to provide a unified view
    This driver is similar to the metadata management driver discussed above.
  • Ease data modeling
    Data modeling describes, models, classifies, and designs data. Semantic modeling techniques form the basis of knowledge graphs, assist in developing graph databases, and building IT solutions.

Data lineage (DL)

Various factors motivate a company to document data lineage. There are several key groups of factors:

  • Compliance with regulations
    For all companies, compliance with regulations in personal data protection is the most significant stick to starting a data lineage initiative. Another example of legislation that requires data lineage is SOX regulations.
    For companies in the financial and insurance industries, regulations like BSBC 239 (The Basel Committee on Banking Supervision’s standard “Principles for effective risk data aggregation and risk reporting”) and IFRS 17 require data lineage implementation.
  • Business change
    Multiple companies have started such initiatives as digital transformation and customer 360 view. These initiatives should improve business efficiency and generate additional revenues.  Nowadays, many companies perform various development projects like building microservices, migrating to clouds, and establishing new data architecture using data mesh and data fabric approaches.
  • Data management-related projects
    Many companies perform data management-related projects. They aim to reduce IT maintenance costs and optimize data chains. Data quality, master and reference data, and data integration initiatives are examples.
  • Audit requirements
    Financial professionals regularly face the necessity to explain financial results and meet other audit requirements. Data lineage is a tool that can assist in performing these tasks effectively.

Conclusions

  • The common business drivers for MM, KG, and DL initiatives are the need for data integration and an increase in data management efficiency by reducing IT costs.
  • Legislative requirements regarding data traceability and transparency are often the main reason for a DL initiative.