Focus from Day 1 on establishing data and analytics as a key business value function.
- Gartner client? Log in for personalized search results.
Essentials for Your First 100 Days as a Chief Data and Analytics Officer (CDAO)
Be the CDAO that delivers concrete business value from data
Whether you have been promoted, transitioned from another role or have arrived from a different enterprise, use the early days of your tenure to set yourself up for long-term success.
Download this toolkit for:
- A playbook of foundational activities, from forging key relationships to assessing the maturity of your D&A team
- Sample objectives and initiatives for each
- BONUS: A quick-start diagnosis of your team
Key actions to achieve mission critical outcomes with data
New-to-role CDAOs who display executive behaviours are more successful than those who act like a team manager. Your approach during the first 100 days sets the right tone.
- 4 Priorities From Day 1
- Establish CDAO Leadership
- Focus on Business Value
- Build Skills and Culture
- Coordinate D&A Platforms
Focus from the outset on four proven priorities for data analytics leader success
There is an identifiable pattern of behaviours that lead to the best outcomes for the head data and analytics executive. Gartner defines CDAO success as “the consistent achievement of measurable business outcomes such as revenue growth, cost savings, risk mitigation, improved supply chain operations and increased customer value.”
Our conclusion after reviewing years of survey data is that the most successful CDAOs go on the offensive.
An offensive approach to data and analytics starts by framing the strategy around business outcomes, even if formalised data governance and data management practices are not in place. This approach requires CDAOs and their teams to have the discipline to plug any governance and management holes as they pursue their offensive strategy.
The offensive approach starts by building a data platform including the necessary data management and data governance. In comparison, CDAOs with a defensive approach focus only on use cases to generate business value after that platform is in place.
A defensive approach may be the right choice for organisations facing compliance issues, maintenance debt or risks from data governance and data management failures.
This is more common with financial services and public sector organisations. Yet even these organisations should work in parallel to achieve short-term business outcomes so that the D&A initiative can show value.
Start by pursuing the following four priorities within your first few months on the job:
Establish CDAO leadership by positioning the CDAO as a business leader and cultivate strong relationships with business peers.
Set priorities to focus on business outcomes such as revenue generation or contribution, customer experience improvement, process efficiency and other ROI opportunities.
Build the necessary skills and foster the right collaborative and data-driven culture.
Build a common data platform including data governance and data management.
Chief data officers should cultivate a reputation as executive leaders
Top-performing data analytics leaders operate as business executives who manage and control the portfolio of D&A initiatives. According to the Gartner Chief Data and Analytics Officer Agenda Survey for 2023, CDAOs who cultivate an executive presence are more successful than those who operate as team or programme leaders, yet only 30% of you are developing yourselves as executives. (Download more results from the Gartner CDAO Agenda Survey for 2023.)
One way that effective data analytics leaders cultivate an executive reputation is by collaborating with business peers to define data-enabled goals and work towards them. As a result, new CDAOs need to start by forging relationships. These relationships will help drive stakeholder buy-in to data and analytics initiatives.
In addition, CDAOs have to lead their own organisation, which retains the power to execute on D&A initiatives. The central data and analytics function also influences data and analytics activity across the organisation, making sure that local D&A teams collaborate and use consistent standards and ways of working. This model is often referred to as a hybrid distributed model or “hub and spoke” model.
Many CDAOs also take the lead on promoting the responsible use of data ethics and responsible artificial intelligence (AI). They not only consider the business impact of AI and data in terms of risk but also as technologies that can positively contribute to the digitalisation of society.
Create a self-financing D&A organisation by basing strategy on business outcomes
Modern D&A strategies focus on business outcomes such as revenue generation, customer experience improvement, process efficiency and other ROI opportunities. When achieved, these outcomes provide CDAOs with key proof points they can use to cultivate continued buy-in from executive stakeholders, particularly the CEO, CIO and CFO. This cycle of prioritising D&A use cases with high business value, delivering on those business outcomes and communicating success must be treated as a continuous cycle.
To identify use cases to prioritise in the data and analytics strategy, data analytics executives should focus on three criteria:
How much direct business impact will it generate? Will the costs of executing on the case exceed the value that the case delivers in measurable financial and non-financial terms?
Are important stakeholders committed to success? It may be better to prioritise a highly committed use case over one with a higher direct business impact.
Will the successful execution of a case provide leverage for other use cases? It may be better to have a slightly lower business impact but a high-leverage use case.
When securing funding, CDAOs should favour a business case approach over a budget approach. A business case approach creates a self-financing D&A function based on value generation. In contrast, a budget-funded D&A function must often field discussions about how to decrease cost.
Drive D&A success by building data skills in business professionals
Success as a chief data officer requires you to lead the way in changing organisational behaviour, both for existing D&A professionals and the broader business.
D&A professionals will see their job focus shift from delivering data results to managing and enabling D&A self-service for business professionals. They will not just roll out D&A platforms and tools. They will also become D&A advocates and teachers responsible for advancing data literacy. This will involve rolling out a common data methodology, as well as training programmes and standards. The goal of these efforts is to increase data literacy to overcome shortages of skills and staff, which is what 39% of CDAOs name in their top 3 roadblocks to success.
For their part, managers, analysts and knowledge workers in the business will need to respond by embracing the mandate to improve their data literacy skills. D&A needs to become a routine part of the way they work to achieve its full value for business processes and decision-making. Executives who once considered data stewardship a chore must now recognise it as a necessary responsibility of their business function. Making this shift requires the CDAO to lead a conscious, deliberate and sustained campaign to teach new skills, revise outdated belief systems and develop new data-driven behaviours.
The structure of the D&A organisation may evolve over time to include different professionals who reside in different parts of the organisation. (Download this research to learn: What Are the Essential Roles for Data & Analytics?) In more mature organisations, data scientists can reside in the Office of the CDAO and the various business units independent of central or distributed reporting. In a culture of collaboration, job rotation is the norm and data scientists routinely spend a significant part of their time working with colleagues in other business units.
Build common data platforms and processes to accelerate business value delivery
A common data platform speeds up the time to business outcomes and enables collaboration between people. Twenty per cent of respondents to the 2023 Gartner CDAO Agenda Survey named insufficient data sharing and lack of access as an impediment to success and 64% reported plans to invest more in data management. These facts point to the need for a shared and accessible data platform to address the convergence between data management, analytics and business intelligence, data science and AI and to provide the foundation for delivering business value.
To get started, learn about 3 Essentials for Starting and Supporting Master Data Management.
The data and analytics platform should support a bimodal delivery model:
Bimodal Mode 1 involves the development of sustainable, scalable data and analytics platform solutions and business applications for contexts with well understood and repeatable requirements and low volatility.
Bimodal Mode 2 involves an innovation lab that takes a research-oriented approach, performed in agile sprints of two to four weeks and including sandbox environments to test experimental solutions focussed on identifying new opportunities for value creation.
Opportunities that emerge from the Mode 2 approach and lead to sustainable, scalable solutions are retrofitted back into the Mode 1 environment.
Experience Data and Analytics conferences
Join your peers for the unveiling of the latest insights at the Gartner conferences.
