Data Locum Diary

Week One.

Week One had an exciting start. I found a business strategy document and artefacts that explained business goals and values. Strategic customer journey visuals helped me to understand business ambitions. A proposal paper for a business transformation was a real treat, because these are not normally done at the enterprise level.
As per usual, my first week was the time when I needed the most support from business stakeholders. I still had questions about current business and technology capabilities in the data domain. As I reviewed the strategic artefacts, I clarified those elements, which I thought could be the most relevant to the data strategy. I am happy to say that I was able to map these to the critical data capabilities in order to support the business strategy.
Fortunately, there was a nominated “go to” person for my generic business model questions. He helped me to improve my understanding of the business structure and operations, especially after they invited me to a detailed business overview session with a few business SMEs. It was a super-useful and efficient way to view various aspects of the business. By end of the week I felt confident that I would be able to infuse the business goals with my data strategy, and vice versa.
I am looking forward to absorbing more next week and improving my understanding of where the organisation wants to be as well as the current state of data architecture.

The deliverables of my first week were:

1. A simple visual representation of the business subject areas. Despite its simplicity, it represents main data domains and doing this builds a good foundation for the future data modelling work. This simple model will be my tool to trigger effective conversations with data stakeholders, which in turn will define the quality of my future deliverables;
2. A high level mapping between data domains and business functions along with some supporting business applications. We were able to identify the business owners for two data domains.

For the next week, I requested and planned a conversation about the authoring systems for each data domain.

1-Oct-2021

Week Two.

This week I took time to read, mull over last week’s conversations and think. I sought help from a business SME to answer my specific questions about the types of information, current information management challenges, the use of unstructured content, and any new data-intensive business initiatives in the pipeline. The process of using this collected information is more of a creative process. Not all questions could be answered, and that’s normal. These can be parked and returned to later. Sometimes, the more ambiguous questions become redundant over the first few weeks. The valuable discovery of the week was obtaining information about all core business applications and the main Secondary Systems, which are usually data warehouses, data lakes, data hubs or data-marts. For everything that I could find, we would mark it with a trust score, to be considered as a first draft and to be updated later. I also attempted to discover the current level of maturity in the information security and data protection domain. These capabilities are very important for an enterprise data strategy. A random conversation led to a high level of understanding of the current records management and content services capabilities.

It was a successful week for me, as I was able to:

1. Formulate the main Focus Areas of the future data strategy. That was the main deliverable of this week;
2. Create a high level Conceptual Business Data Model that uses those business subject areas defined in the week one. It is a good testing communication tool for the scope of the future data strategy.

8-Oct-2021

Week Three.

I immersed myself in work this week three. I facilitated a few workshops with the business SMEs and users of the enterprise data. In one of the workshops, I aimed to complete a current challenges exercise, so I was able to test out my understanding with a wider audience. Their  collective knowledge covered the whole enterprise. The goal for another workshops was to walk through the first draft of the Conceptual Data Model. I thought we achieved a very good outcome as I was able to make more iterations to the model, and now my confidence in the Conceptual Data Model after this week’s analysis is high. All participants of the workshop were in an agreement with the overall representation of the business in the model’s data language. As a result of these interactions, I should have had a good understanding of the organisation’s enterprise data quality and data governance maturity. However, I feel there are still gaps. The positive result was a good understanding of analytics from the business appetite and capacity viewpoint. Now I am eager to formulate and confirm the main focus areas of the future data strategy. An overall Information Lifecycle Management (ILM) process is relevant to all types of data, including operational (primary) and secondary, master and transactional, structured and unstructured. I will aim to understand the information lifecycle management processes maturity next week.

The deliverables for this week:

1. The working version of Conceptual Data Model;
2. Current Challenges artefact to steer the data strategy work.

I am in a position now to have a clear understanding of my operating model over the next few weeks.

15-Oct-2021

Week Four.

Although routinely meeting new people felt normal this week,  it was still exciting. I also needed to team up with a local information leader, because we are looking out for the data champions across the whole enterprise together. There is no formal data governance function established in the organisation, which may complicate the ownership and implementation of the data strategy. Then I might need to adjust the stakeholders’ expectations.

Before getting into the details of disparate information management processes, my first step now is to introduce a Data Domain Owner concept. The business subject areas, which we defined in the first two weeks, helped us with this task. I translated those business subject areas into the Master data domains. The Conceptual Data Model helped me to formulate the most important entities relevant to master data on a very high level.  We are in the process of finding potential business owners for the Master data domains and I look forward to working with the them next week. In addition to the Master data domains we analysed the Conceptual Data Model to formulate business events and transactions, usually these are represented with the links to the subject areas. Next week will be a successful week if we formulate other data domains based on these events and transactions and create a complete list of all Data Domains critical to business and that need to be governed.

This week:

1. I also drafted a Data Governance framework that I considered suitable for the environment and current data governance practice.
2. I got to know enterprise architects and was able to retrieve available arterfacts that could help me to understand the current technology and data architecture landscape and continue identifying the authoring systems for the master data entities.

22-Oct-2021

Week Five.

By the start of this week I was very ready to meet the technology owners. I needed to extend my knowledge to the business applications and their lifecycle status. An authoring system was mapped to most master data entities, which allowed me to start investigating the ILM processes for these data sets. Some of the master data is authored in multiple systems, and that will be looked into later.

In addition to the technologists, I was introduced to the PMO processes and activities, including current and in the pipeline projects. I will need to temporarily assist one of the largest projects, and I am familiarising with the documentation for the technology selection, solution architecture and design, data migration, data cleansing, application and/or data integration, data governance, information security, and reporting requirements. This knowledge is crucial for developing the aligned Technical Reference Architecture, which will help to create a strategic data roadmap based on the gap analysis.

The deliverables of this week are important, however they are not ready for the reviews:

1. Defined and agreed set of arterfacts to visualise each of the focus areas for the future data strategy, including a few ready drafts.
2. An initial draft Technical Reference Architecture for the enterprise information management.
3. Target State ILM process.

30-Oct-2021

Week Six.

Equipped with the technology landscape knowledge, during this week I finished the first draft of the Technical Reference Architecture (TRA) from last week. It was great to socialise it with the technology stakeholders as I watched them enjoy and examine the reference architecture whilst mapping it to the existing capabilities. This also allowed me to have further discussions, work on iterations and make improvements. As we could map most capabilities on the reference architecture to the current or strategic approved technology, I consider this Week Six to be a very successful week. Next week I can produce another visualisation as a Mapped TRA, which will represent the identified gaps very clearly.
The other deliverable for the week six was a Glossary to accompany the TRA to help streamlined understanding of the terminologies. It may also help non-technical stakeholders to become familiar with the reference architecture when needed.
While working within a technology community, the Information Lifecycle Management (ILM) process, which was drafted last week, was also discussed there. In particular, the critical roles that are required at each information lifecycle stage were identified. Their gaps will be covered in the corresponding business cases later. New architecture artefacts will be required to support the ILM process, hence they will need to be thought through and included in the Data Strategy. Another artefact was shared, it was a Data Governance Framework drafted during Week Four. Although the Data Governance should be driven from Business domains, there are many IT functions that are enablers for the Data Governance. We need to get IT on board with our thinking as early as possible.

5-Oct-2021

Week Seven.

Identified and non-identified gaps in the TRA capabilities define the direction for the next few weeks. I had to consider the choice of storage for the data migration as part of a project that I reviewed during week five. This approach will be important to the organisation’s data literacy as it allows the organisation to remain metadata and knowledge of data relationships and data quality post-implementation. From a data architecture viewpoint, storing the data and processes on a strategic platform will be the initiation of the enterprise Data Hub. Next, I started to think about the Operating Model of the future organisation’s data functions that to be integrated into one ecosystem with projects governance, business innovation, information security, and regulatory compliance.The deliverables for this week are:

1. An initial draft of the Primary Data Architecture, which includes all the main business applications that were reviewed within the master data authoring systems activity during week five. How this data is collected, stored, integrated and used should be depicted in this artefact.
2. A set of proposed Business Cases for the prioritised and approved capabilities as mapped on the TRA, which also may include a technology selection in the scope.

I attempted to start investigating what reporting is done across the whole organisation and to group them into Reporting Categories. It is rare now when an organisation doesn’t have any kind of performance reporting, or regulatory and compliance reporting, which are types of the reporting from the Standard Reporting category. Standard reporting is usually regular and it requires the data of the highest quality possible in an organisation. Analytics have too many variations to list them all here, and they are all unique for each organisation. The most common categories are:

• Operational Analytics – for near real time decision making
• Data Discovery – for the new and unknown data sources
• Advanced Analytics – for supporting business decisions or testing the hypotheses for the formulated business problem statements with predictive and prescriptive analytics  (ML).

As a result of this analysis I am planning to create the Data Architecture for Data Management and secondary usage of the business data, which I will call Secondary Data Architecture.

12-Nov-2021

Week Eight.

• Data Strategy Focus Areas (week two)
• Conceptual Data Model (week three)
• Data Governance (DG) Framework (week four and six)
• Refence Architecture with glossary and mapping  (week five and six)
• Information Lifecycle process (week five and six)
• Primary Data Architecture (week seven)
• Secondary Data Architecture (week seven and eight)

20-Nov-2021

Week Nine.

This week is a very short week for me, as I had to take some time off. Still, a few things have been achieved:

1. I had a conversation with a Head of Risk and Compliance. It is looking good for my thinking on the governance operating model, where its major part  will be on managing risks. We will have an escalation path now.
2. I had good discussions with the Information Management (IM) team. That made clear to me there is some disconnection between IT Risks and organisational risk management functions. I hope DG risk management will be a good bridge for that.
3. While working with the IM team, I got a better understanding of data source systems, so I updated my Primary Data Architecture.

Even while being offline, I couldn’t stop thinking about the Data Quality practice that can be the most effective for this organisation. I mentally drafted an artefact to visualise a data quality practice approach, which I will propose for discussions next week. Yes, the next week will be very busy catching up on the Reporting and Analytics work!

28-Nov-2021

Week Ten.

• Data Quality (DQ) is going to be the first focus for this group, and on accepting the Data Quality Practice model all data domain owners were assigned an action to work on with their teams. They should then be able to suggest one or two DQ metrics that could be monitored.
• Risk management was another point of discussion, and my proposed operating model was well received. Risks related to data protection, information management processes and regulatory reporting will be managed through this DG organisation, however, any escalation will be to the Risk and Compliance Committee.

5-Dec-2021

Week Eleven.

1. Hold fortnightly meetings.
2. Add two more ‘domains’ for the Reporting/Analytics and Data Strategy.
3. Each domain owner has a permanent membership, and other SMEs will be invited.
4. Each domain owner will be responsible for activation of the working group, should relevant issues be delegated for resolution.
5. Implement escalation paths to the Risk and Compliance Committee and to the COO.

10-Dec-2021

Week Twelve.

1. The need to develop a more detailed Content Services Platform strategy with technology selection and business processes automation focus.
2. The need to develop a more detailed plan for organisational cultural transformation to galvanise an Analytics Community where Business, Technology and Data are all part of the community.
3. The need to refine the EIM operating model presented as a 3-year roadmap for the harmonious journey of transformation.

1. Linking EIM with Business strategic objectives.
2. Current State represented by business challenges, current information management technology landscape  and data function teams.
3. Strategic data architecture.
4. Three focus areas: Information Governance,  Data Management and Analytics,  and Business Capabilities.
5. Expanding each focus area separately,  however basing each one on a single EIM framework.
6. Top EIM priorities and business outcomes.
7. Roadmap
8. Next steps.

17-Dec-2021