Many organisations want to be data-driven and improve their data and analytics (D&A) practices, writes Frank Buytendijk, Distinguished VP Analyst at Gartner.
Chief data and analytics officers (CDAOs) can learn from their peers that are already advanced in their practices to improve how they lead, organise, retain, and enhance skills to set up delivery models and ensure business success.
Despite the different journeys that D&A takes, the offensive journey outperforms the defensive journey in terms of achieving measurable business outcomes. We recommend that the CDAO goes on the offensive and leads with value business generation. You can focus on five priorities to achieve this.
1. Establish Executive Relationships
A CDAO cannot operate alone, work together with your business peers. The baseline of success in every CDAO journey is in forging relationships with all relevant stakeholders.
Top-performing CDAOs cannot view themselves as internal service providers, delivering systems and insights based on requirements from ‘the business’. Instead, they must see themselves as business executives who manage and control the portfolio of D&A initiatives. They cannot achieve those results unilaterally and must continuously work with their business peers to obtain the right levels of stakeholder commitment.
While you have your own organisation, which is needed to control some power to execute, your role must be an influencer across your organisation, making sure that your local D&A teams collaborate and work in a similar manner.
2. Maintain focus on business outcomes and experiment with new techniques
The business outcome must always come first, even if there are deficiencies in data governance and data management. This is the very essence of the offensive approach that uses a value focus to determine the focus for supporting activities, rather than a focus on supporting activities with the hope for value.
The most successful CDAOs have a relentless focus on creating quantifiable business value and outcomes. If they want their organisations to be more data-driven, their own function must be data-driven. While most CDAOs have a bias and priority for action, they must also have the discipline to fix data governance and data management issues later.
3. Create a culture of collaboration
The CDAO journey to success is an exercise in change management in terms of organisational behavioural change, both for the existing D&A professionals and for the rest of the business.
It is notoriously hard to get the right skills onboard, particularly in the data science space. Similarly, Gartner routinely hears that CDAOs are finding it hard to retain specialist skills once they are hired. The most important reason that people leave is that they are unchallenged. They are bored because of being asked to do the same thing repeatedly.
To combat this, regularly rotate data scientists across business domains to keep them challenged and get the maximum value out of their capabilities. Remember that people want to feel appreciated, so ensure appreciation and interest for the specific skills of the individual shown.
If you are on the offensive it will encourage a culture of collaboration in which job rotation is the norm, and where data scientists routinely spend a significant part of their time collaborating with colleagues in other business units.
4. Refine your data management capabilities
The role of the common data platform is to speed up the time to business outcomes and to enable collaboration between people. According to the 2022 Gartner CDO Survey, 80% of respondents consider data management capabilities the most important foundational programme.
However, there is often a lack of clarity on the elements of analytics and business intelligence, data science, machine learning, and AI, and how they interact with one another. In modern data and analytics environments, capabilities for data management, analytics and business intelligence, data science, and AI converge and overlap. This convergence creates an opportunity to transform the D&A ecosystem, as well as the approach to orchestrating the analytics lifecycle, which CDAOs must take.
5. Optimise and innovate
The prioritisation of requirements can be a challenge, particularly given that in most environments talented resources and budgets are finite. Be experts in evaluating the potential for innovative solutions to deliver value. Additionally, be adept at negotiating (and sometimes arbitrating) between the competing priorities of differing lines of business stakeholders while also establishing a critical mass of capabilities that can support the delivery of future, as-yet-unidentified requirements.
Innovation requires an initiative-taking approach. Look to drive an aggressive agenda that advances analytical capabilities, and if needed, increases your risk tolerance for innovation. To combat this, introduce new use cases that leverage diverse types of predictive and prescriptive analytics. Move towards real-time analytics. Expand on machine learning. Introduce new data types and sources of external data, which are crucial for understanding how to sell products and impact customers (for example, geospatial data, satellite imaging, transport sensors data and all kinds of external databases).
Gartner event
Gartner analysts are exploring future trends in data and analysis at this week’s Gartner IT Symposium/Xpo 2022, taking place 7-10 November, in Barcelona, Spain.
