Are data analysts more effective sitting with IT or sitting with the Business?
It’s an age-old question asked by technical and business leadership alike: “where should my data analysts sit to be most effective?
Should they sit with the business or should they sit with IT?” As data becomes more critical to business needs and analytics integrates with daily decision-making, this question is increasingly common.
To be competitively effective, an organization must deeply understand the role of the data analyst and deploy these analysts to the areas of the organization where their skills and talents are best leveraged to drive true insights and actionable recommendations.
The Case for Sitting with IT:
An effective analyst needs to know both data and business. A deep understanding of data and data architecture is critical to creating sustainable, secure, and scalable data platforms.
A good data analyst will consider data quality and verification into a solution.
An exceptional data analyst will consider the aforementioned in addition to a scalable design that is flexible to accommodate future data needs, especially when considering upcoming market trends within the organization’s competitive industry landscape.
These types of design conversations and technical reviews are quicker and more thorough when conducted with a variety of other technical professionals that typically sit with IT.
Sitting with IT also typically gives data analysts access to technical training on industry trends and shifting technology platforms.
An understanding and ability to apply the tools and standards available in the rapidly changing technical environment are key to maintaining data security and compliance and lowering an organization’s exposure to risk (which, if improperly implemented, can cause a great deal of negative public attention as well as significant financial penalties and fines).
The Case for Sitting with the Business:
However, this technical aptitude is only as good as the underlying business knowledge needed to design a solution that is truly useful to the business.
When data analysts sit with the business, they’re closer to the actual processes, decisions, and understanding of customer needs.
Data analysts that are close to the business are more likely to understand the daily pains of the business users and tend to develop more understanding for the environment in which business users operate.
In my experience, when data analysts have genuine empathy for the business user, they are more likely to translate this to comprehensive requirements and solution design, which will greatly affect the success of a project.
This typically leads to a better working relationship between IT and the business that is based on trust and understanding, which in turn creates more thorough requirements (making it easier for IT to create a usable solution) and flexible designs (that users actually want to use).
Business users are also more likely to reach out to those with whom they work closely and have relationships when an issue arises, which can create a more open environment of collaboration and problem-solving.
When business issues are resolved quickly, the organization has the potential to both minimize costs as well as increase revenue.
Effective Hybrid Approach:
Organizations with advanced analytics programs typically do not delineate between “IT” and “Business” lest they risk a higher propensity to silo the two units or create more opportunities for missed expectations by both parties.
Hence, the most effective data analysts must possess the skills needed to co-exist within both areas. Data analysts must bridge the age-old gap between business users and IT developers.
Data analysts can help steer a project away from conflict and ensure better working relationships, priorities, and most importantly, goals.
This is most effectively done when data analysts are allowed regular access to IT and technical resources to grow and enhance technical knowledge, yet still remain deeply ingrained in the daily business operations and user experiences.
Leadership’s Role in this Structure:
Despite where the data analyst sits, leadership plays an important role in the success of any data analytics program or solution.
Leaders of both IT and varying business units must make the example of cooperation and communication by reaching across the aisle and opening doors.
Too often, leaders get focused on goals specific to the group or handling daily issues that remembering to be an example of cross-organizational progress becomes an after-thought.
It is a leader’s responsibility to actively invite other organizational units to participate in solution conversations by attending appropriate group meetings, soliciting feedback through email chains, and ensuring that stakeholders from all impacted areas are encouraged to provide feedback that is seriously considered.
To go even further, leaders can formally document and share this feedback as a way to encourage more participation and ensure that all team members, both Business and IT, understand the importance of each group’s contributions.
Leaders can inspire the inclusion of both IT and the Business through their own deliberate actions: business leaders should encourage data analysts to sit with their business units to learn from their team and IT leaders should offer data analysts a place on their teams to learn from their specialists.
Attitude Over Location:
Ultimately, the decision of where a data analyst should physically sit is not one of logistics as much as a decision of cultural inclusion and organizational attitude.
When teams are encouraged to work together, communicate, and value the inputs of every contributor, the physical barriers of location are overcome.
In these situations where a data-centered attitude is adopted, a data analyst will function highly effectively in either or both locations.
Rachel Stuve is a technical and strategic leader with 15+ years of experience in data-driven decision making and innovation across multiple industries and organizational size. Her skills include development, management, and guidance with a focus on analytics-based programs.