Data program checklist

In the past 10 years, I had the privilege to assess, define and operationalize data programs in multiple top-tier global financial services institutions. This is my take on the success factors that matter the most.

Executive support

No large program of any kind can be successful without executive support. That support should be reflected through three stages. Stage one is to support the “data is an asset” philosophy. Stage two is to get support for the execution of a formally defined data strategy (and data program as an operational component of the data strategy) which has to be incorporated into the overall organization’s business strategy. Stage three is to get executive support for ongoing data program execution.

Data “boss”: Chief Data Officer

Accountability is one of the key success drivers for every effort. Given the complexity of the data program, like any other business line of corporate function, the data program must have a “boss” who will “own” the data program and be accountable for data program definition and operationalization. In addition to Chief Data Officer Role, his team (Chief Data Office) will be instrumental to define and operationalize data program capabilities across the business and functions at the initial stages, and later facilitating and observing the ongoing execution of the data program.

Data strategy and roadmap

A data strategy is a plan that defines the technology, processes, and people, required to manage an organization’s information assets. It should be formalized, and forward-looking and must unambiguously define the role of data in supporting overall business strategy and objectives. The roadmap defines the path with priorities and a phased approach to how the data strategy will be executed.

Data capabilities: data policy, data standards, and operating models

A sustainable data program requires the definition of standard and repeatable processes to execute day-to-day operations. A data program is complex and consists of a set of data capabilities addressing different aspects of data management, including but not limited to data governance, metadata management, data quality, data architecture, data integration, master and reference data management, data privacy, and security and data analytics.

Data policy is usually starting document in the definition of data capabilities. Data policy is like a constitution for the country, it defines how the organization understands and treats data and is a baseline for all other formal documents. The next step is to define data governance capability with the definition of forums (i.e., steering committee, data governance council, data working groups) and individual roles and responsibilities (i.e., data domain owners, data stewards, data custodians). Furthermore, for each capability, there should be defined data standards (i.e, what kind of metadata will be collected, what are the data quality dimensions which will be tracked) and an operating model with detail-level processes on how each of the capabilities will be executed on daily basis.

Data Architecture

Although considered one of the data capabilities, I would like to address data architecture separately as this capability is the subject of major modernization efforts in the last 20 years. Data warehouse, big data, data lake, data lakehouse, cloud migration, data fabric, and data mesh are the most common evolutive approaches to data architecture. The data landscape is becoming more complex every day in the terms of data volume, velocity, and variety. To address that complexity and be able to scale, data programs should use automation (i.e., metadata collection, data quality autogenerated rules, observability) and decentralization (domain-based data architectures), along with establishing a semantic layer over the data landscape to ensure enterprise view of data assets. The current trend, largely driven by the popular data mesh approach is about the decentralization of data architecture and domain-based data product exchange between data producers and data consumers.

Data culture and citizenship

Defining the data policy and standards and establishing data forums, roles and responsibilities remain steppingstones for data management operationalization. However, the secret sauce for success is to establish a data culture where every individual in the organization puts data-related activities on top of its agenda in daily operations — in other words, an individual becomes a data citizen with the right to access data, but also with the responsibility to contribute to data management. A pragmatic approach to make that happen is to define and operationalize metrics that will measure the impact of data on business outcomes, but also incorporate data responsibilities into individual performance goals for data citizens. The essential part of that culture must be to ensure data citizens are equipped with the skills they need to effectively manage and use the data — through the comprehensive and effective training program. I wrote more about that matter in my recent article addressing the need for data management approach modernization. By establishing a data culture and implementing data citizenship every organization will become a data-driven organization.

Metrics

Like any other activity, data programs require thoughtfully defined metrics with KPIs to track the data program performance and ensure the data program is designed and operationalized in alignment with data strategy and roadmap.

Agility and innovation

Data is changing on daily basis, as well as the organization. The data program has to be constantly observed, and evaluated against the latest data trends and adapt to changes in each area (i.e., strategy, roadmap, capabilities) to stay “up to date”. The agility and adoption of innovative methods, approaches, and technologies will keep the data program current and significantly contribute to the achievement of business objectives by driving positive business outcomes.

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions or positions of any entities author represents.