Why Enterprise Data Planning Is Crucial for Faster Outcomes

Are you planning on strategically using data to improve the efficiencies of your value chains? You are not alone and every organization in this league is looking at ways to augment these customer journeys. This can happen with artificial intelligence models that can make a journey interesting for a customer.

If an organization is able to standardize their data planning, all the involved people working on a change will be aware of the capabilities that will be fueled by data. You must hear this often – the acronym POSMAD signifies the six phases of data lifecycle – Plan, Obtain, Store, Share, Maintain, Apply, Dispose. The PMO can be tasked with having to marry the data lifecycle with the project lifecycle – be it waterfall, agile, or scrum. I have detailed this in my book “Data Management and Governance Services – Simple and Effective Approaches.”

1. Institutionalize data requirements management into your project/program/transformation lifecycle

a. Establish a documented way to elicit, analyze, and document your data requirements during planning for a change.
One can call this a data requirement document or embed in into your BRD.

2. Scope high-level domains and datasets that you are planning to store, distribute, and apply

a. It’s as simple as using data from a customer, marketing, or finance domain and further analyzing the lower-level data as you go through the process.

b. Against the dataset, the planned activity like a store, distribute, and apply can be identified.

3. Start with semantic modeling, progress onto logical, and then physical modeling of database

a. Semantics refer to the adoption of precise, shared ,and consistent business meaning of data across enterprise.

b. Conceptual level describes data at its highest level, identifying the critical data objects needed to satisfy a business objective while also defining their relationships to one another.

c. Logical level is a fully attributed conceptual model that has been abstracted from the physical implementation.

d. Physical level is the instantiation of the meaning, relationships, and attributes of data into a physical implementation. The physical models are usually attributed to the technology architecture.

4. Identify an enterprise data modeling and catalog stack to enable your data planning

a. Many tools for cataloging data, managing data changes, and modeling for data exist.  

b. A collection of Data Modeling tools.

5. Plan for archival, disposal of data while planning for data

a. Elicit the standard time, for which you want to have master, transactional data active on your databases while defining policies for archival and disposal.

b. These aspects can be recorded in the the catalog as well.

6. Understand relationships between data being planned and document them.

a. Let us further understand the lineage and relationships between various levels of data in an enterprise.

b. Below are some classifications of data along with their description.

7. Architect for data if the change is strategic and involves implementation of physical architecture

a. Data Architecture activities should be embedded organically, with ease, into the project lifecycle processes of the organization while also including the governance committee in the strategy analysis, requirement, and design phases.

b. There is a need for a consistent representation of the current technology architecture to aid decisions associated with defining the strategy and the future roadmaps that come with new business capabilities.

c. Business, application, and technology layers and the various data components in co-existence have to be articulated clearly for a change to be deployed. It’s also important to define the relationship of data assets in all layers and the way the components interact. The components marked in green are data components. 

Author: Tejasvi Addagada.