[ad_1]
In today’s data and AI-driven In today’s world, the challenge for Chief Data Officers is to build an enterprise data team that is trusted by all stakeholders across the organization and that delivers on business goals. Additionally, data leaders need to be able to clearly communicate to stakeholders who they should communicate with, as well as when, where and why. To build an adaptable and effective team, data leaders should focus on the functions required to achieve business outcomes rather than being bogged down by the boxes on the org chart.
Specifically, the process of developing an organizational chart can be tedious, but it becomes exciting when we shift our mindset and think of building a data team like assembling an all-star sports team. Just like in sports, a successful data team is determined not just by individual positions, but by the variety of skills, strengths, and unique life experiences that players bring to the table. By viewing data team building as a celebration of talent and collaboration, data leaders can create a dynamic and effective data organizational structure.
The myth of the unicorn all-round data expert
This is not necessarily a new idea. 2012 TED TalkInvestor and entrepreneur Ernesto Sirolli reminds us that no one person can be great at all aspects of running a business. Just like in sports, it’s unrealistic to expect one person to be the coach, goalkeeper, striker, defender, and ball handler. Similarly, in the data world, it’s unrealistic and counterproductive to expect professionals to have expertise in every aspect of the field—you’ll rarely find a great Python programmer who can tell a good data story and also tell a great story. By acknowledging this, data leaders can set more realistic expectations and build teams with complementary skills.
But data leaders still need to help others intuitively understand the intangible capabilities and value that data team members bring so that they can secure budget and gain buy-in from non-technical stakeholders to work with their data teams. Data leaders must be able to clearly articulate which professional groups others should talk to — who, when, where, and why.
Six functions of modern enterprise data organization
To build an effective data organization, avoid being seduced by fancy titles and false hopes for other organizational best practices. There is no one-size-fits-all approach when it comes to crafting an effective org chart for your data team; instead, look beyond the titles to find the inherent function and purpose of each role to help craft your use cases and ultimate ideal team. It’s critical to identify and embrace the six essential functions of a modern data team.
1. Designer
These individuals work closely with stakeholders and manufacturers to integrate business requirements into data solutions. They play a vital role in developing frameworks, services, products, data sets, reports, applications, and slides that align with business needs.
2. Maker
Makers build and implement data solutions, synthesizing data insights to drive actionable outcomes. Their activities range from developing machine learning models, building data pipelines, and creating data visualization dashboards.
3. Communicator
Successful data organizations prioritize the value of data fluency and relevant solutions. Communicators play a critical role in translating this value to drive awareness and adoption across the organization. By effectively communicating the benefits of a data-driven approach, they drive organizational buy-in.
4. Operators
These personnel configure and manage the systems that support data functions. They maintain production data applications and AI models, continuously monitor systems, perform regular maintenance, and optimize system performance. Operators ensure the smooth running of data operations.
5. Iterators
Iterators are responsible for driving the organization’s long-term data strategy and continuously refining and optimizing data priorities. They incorporate innovative knowledge from other areas into the data ecosystem, keeping the organization at the forefront of data-driven innovation.
6. Regulatory bodies
Data governance is essential to maintaining data security, access control, and ethical practices. Regulators develop and enforce data governance policies, oversee data security measures, and ensure compliance with sustainability and ethical standards.
High-Performing IT Teams Pave the Way to the Velocity of Curiosity and AI
As the C-suite drives AI adoption, data leaders must navigate organizational complexity to effectively enable employees Curiosity SpeedBy focusing on the six essential capabilities of a modern enterprise data organization, data leaders can build teams that align with business outcomes and help their organizations thrive in the age of AI. Leveraging the strengths and skills of many different data team members, rather than expecting data leaders to hire one or two unicorns, will lead to the creation of successful data teams that drive success in a data-driven world.
This article was written by Kim Herrington, a senior analyst in Forrester’s Business Insights practice with expertise in data leadership, organizations, and culture. Her research interests include data literacy, data storytelling, data leadership and culture, insights-driven businesspeople, insights-based organizational models, chief data officer research, and insights communication. Kim is a former data journalist and holds a master’s degree in healthcare administration from Deyouville University and a bachelor’s degree in biology from SUNY Oswego.
To learn more about the key elements of building an effective data and AI team, visit Forrester’s North American Technology and Innovation Summitwill be held digitally from September 9-12, 2024 in Austin, Texas.
[ad_2]
Source link
