Organise For Success
Embarking on a data transformation journey towards deeper data maturity requires organisational change management and this begins with deciding how to embed the central data transformation team within your organisation structure. Common barriers to data transformation are business silos and a lack of collaboration, hence agencies should bear that in mind when deciding on where to embed your data transformation team.
There is no one-size-fits-all solution and organisations will have to evolve their team to complement their existing structure. It is common in the current landscape that most agencies either do not have a full-time CDO or the CDO is focused more on data governance and policies – rather than driving organisational awareness and vision for data-driven business decision-making and actively engaging business teams on data projects.
Commitment-level of the CDO, size of the data transformation team and the decision on where to site the team will vary for each agency, but we share with you here 3 broad functions that the entities driving the CDO office should be collectively responsible for.
Top Priorities For A CDO Office
1. Strategy & Policy - Define and shape the data analytics vision and strategy
Develop policy and plans to strengthen discoverability of data and align data analytics efforts to business outcome. Where such policies and guidelines are drafted by a central planning agency and disseminated for adherence, the important role here will be to adapt them for implementation within the organisation.
2. Achitecture and Governance – Govern data analytics assets, integrate and manage data
Maintain oversight of organisation-wide standards on data governance and security, harmonize metadata, and ensure high data quality of central data assets. Serve as the design authority for data platforms & data tools for regularisation
3. Data Exploitation and Transformation – Pursue data analytics use cases, develop organisational talent and organisation-wide data platforms and tools
Focus on data exploitation by partnering with divisions to drive data maturity through projects and implementation of organisational-wide tools and platforms. Executing data transformation efforts and uplifting organisation through training and campaigns.
A CDO office that aims to bring about impactful data transformation will have to actively drive and balance these 3 priorities. How the CDO office is going to be implemented at every organisation will largely depend on its current setup, bureaucracies and state of maturity.
While this Playbook focusses more on the 3rd function – Data Exploitation and Transformation, it should not be misconstrued that one function is significantly more important than the other. If anything, it reflects a more deliberate emphasis on the data exploitation piece due to the complexities of balancing technical work (e.g. data systems building and data science projects) versus non-technical aspects (e.g. culture building and training), which is more easily misunderstood.
Right-Site The Data Exploitation Function Within The Organisation
In the formative years, it is important to have top-down leadership to steer the data strategy as well as a central team to spearhead data exploitation efforts as part of the transformation journey. Strategies on how to embed the data science team can range from having very de-centralised teams where existing business functions choose to hire a data analytics team at their own discretion to having a centralized team that supports all the analytics efforts.
Both extremes have their pros and cons. By allowing business functions to hire their own teams, they can be focused on specific domain analytics. But at an organisational level, this may result in a duplication of effort where you see different divisions all trying to solve similar issues without coordination, or each business unit trying to adopt different tools and processes that lack synergy in the long run. On the other hand, having a centralized team helps to consolidate the above efforts but run the risk of a lack of cohesion between the data science team and the business functions.
More often than not, the ideal would be to find a middle ground where a central data transformation team can be set up with a strong mandate to drive projects and transformation. Even though the central data transformation team is not embedded as part of any business function, the team should actively lean out to build strong partnerships with them.
Capabilities-wise the central transformation team should comprise staff that will lead data transformation and staff that possess technical skills-sets such as data engineering and data science. Hiring a mix of data scientists, data engineers and specialists in creating dashboards will be essential. Collectively, this team will drive organisation-wide data solutions and infrastructure and support data related campaigns.
Sustaining Data Maturity Through Domain Analytics
As organisations look at the next bound, the challenge is for all divisions to prioritize how data can contribute to their strategic business objectives. It is paramount that standards are established at an organisational level to allow such collaborations to take place. At this stage, ensuring proper governance and having an oversight of data architecture and data models will become more important.
One strategy to drive deeper data maturity within divisions is to adopt domain analytics. Domain analytics aims to harmonize decentralized analytics efforts and helps prioritize projects that are aimed at delivering the highest value in advancing the domain’s capability. Gartner defines domain data analytics as the collective set of analytics applied across and within industry verticals, business process and functions. It is therefore important for leaders to increase their domain-specific competencies to succeed in the digital business. A hub-and-spoke model, where the central data analytics team works with the line businesses to identify key projects that are aimed at advancing domain capabilities can be adopted. This approach will require partnership from the central data transformation team and resources from the domain to serve as joint owners to champion data projects within to improve business processes and insights.
