Blog.

Guest post from Barry Green, Interim Chief Data Officer at Allianz Partners.

In March 2019 our CDO Hub event topic was Tools and Technologies, with the discussion led by our friend and (one of the first!) CDO Hub member, Barry Green.

At the event Barry ran through the types of tooling functionality needed to implement a data management framework. This covered functional groupings and different implementation approaches. A broad framework was presented to provide context for the tooling functionality.

And he also put this together for us to share with you…

Data Management Tooling

1.Background

The use of data and information for competitive advantage requires that an organisation implement holistic data management capability. This capability is developed using the data management framework. The framework supports both data and information management. In addition, it should assist with identifying operational effectiveness, innovation and rapid change.

The three core components for success are:

• Culture Change: Cultural change happens over time through increased understanding and management of data and process. This needs collaboration, communication and education, backed by strong leadership support. By breaking down organisational silos, data supports a customer or product driven approach. This will require organisational change over time.
• Consistency: A consistent approach and way of working is provided by implementing the data management framework. Supporting tooling is used to ensure capture and reuse of institutional knowledge (business and technical metadata). This also provides support for ongoing education and engagement;
• Discovery: Through discovery, definition, understanding context, tagging and securing data, information is improved. The framework uses tools to store and reuse institutional knowledge. Process understanding drives efficiency and improved customer experience.

When combined these components increase the ability of an organisation to change more rapidly. The objective is rapid change, which is started from a base of knowledge captured when implementing the framework.

The tooling blocks supporting data management are defined and briefly explained in this blog. The relationship of these tools with the organisations existing systems and external data sources is shown below in Figure 1.

Figure 1 should be read from bottom to top. The layers one to four, highlight the dependencies when implementing tools supporting the framework. The highlighted areas represent key components – that are often missing in an organisations approach to managing data and information.

1.1 Layer One

2.Tooling

The next section provides a brief description of each layer. It is important to use a list of functional requirements to drive tooling choices. The functional list is a basis for understanding and assisting with the architecture roadmap. By doing a gap analysis of the “as is” state you can better prioritise what is implemented first in support of organisational priorities.

Consideration should also be given to how you decommission end user computing solutions and bespoke department tools. The objective is to bring together all contextually relevant information so it can be shared across the organisation. This assists in building the organisational window through collaboration and understanding.

It is also important to understand that some of the functionality is common. As such, your functional listing needs to be a single list for all tools described.

2.1 Systems (1a to 1c)

Information is provided through raw data acquired via the organisations systems, with enrichment adding new data points. Infrastructure can be mixed, so there is often complexity in obtaining the right data.

The assumption is there are legacy systems with core issues, some examples being:

• no product codes;
• limited fields for development;
• limits to customisation (i.e. field length);
• written in old programming languages with knowledge not documented;
• Poorly documented configuration.

Supplier data, with some issues, some examples being:

• Poor quality;
• No definitions or standards defined and applied;
• Incomplete records, i.e. minimum data requirements not met.

Potentially external data sources being semi or unstructured data. This data is assumed to be:

• Not tagged or tagged inconsistently;
• Collected and largely unused;
• Used by data scientists with minimal correction at source where cleaning and tagging is undertaken. This work is then duplicated multiple times.

By not having an approach to managing and understanding data, this causes issues both operationally and strategically. Key known issues are:

• Large number of reconciliations undertaken on an intra-month and monthly basis;
• Regular data cleaning exercises undertaken on an intra month and monthly basis;
• Inconsistent reporting;
• Data misused in analysis.

In order to manage data and provide more robust information, there is a need to manage data and process.

This is not done purely to have better data. The objective is the identification of new revenue opportunities, cost efficiencies and better customer service. Better data simply supports these objectives.

Implementing the data management framework provides structure, consistency and reuse of internal knowledge. The framework has six core components:

1. Operating model;
2. Policy and standards;
3. Mapping critical business processes and defining its critical data. In addition, map data to a conceptual model (physical attributes linked via tooling);
4. Assign ownership to critical process and data;
5. Apply data quality across a process;
6. Ongoing validation of process and data to ensure it is “fit for purpose” and documentation is current.

2.2 Data Management Tools