It’s tempting to try and find a data unicorn. Someone who will advocate for data at an executive level, lead others in adoption and literacy, develop machine learning models, be a caretaker for data and automate as they go. The reality behind this is that these do not exist in one person. Even if they did, there’s only so much that can be accomplished in a day, even a year, and significant risk if you’re pinning everything on a single hire. If you find your organization hunting for a unicorn, you’ve likely got an expectations issue on your hands. If you find a candidate telling you they are one, chances are you should run in the other direction.
Instead, take a good, hard look at how your organization works, what skills your team has gaps in, the state of your data and the level of follow on investment you’re likely to make. Then, make the difficult decision of what role will serve your attraction best, given the outcomes you’re looking for. You’ll need to make a decision on leadership level, technical orientation and skill focus. If there’s a mismatch between what you need to achieve versus the budget you have, look at other alternatives to hiring a new role without fit, such as investing in upskilling your existing team, extending your existing solutions or outsourcing.
If you have Dexibit, though you don’t need any specialist data roles, if budget is available we recommend starting out with a Head of Insights, closely followed by a Data Analyst. Reverse that order if your wider technical team is small, or your data problematic.
A c-suite role is an appropriate hire when you expect significant follow on investment in data as a core strategy beyond this hire alone, including building out a sizable data team and portfolio of data solutions. An executive level hire is best when your primary goal is to represent the insights agenda at the board and executive level, usually to attract, execute and demonstrate returns on a sizable investment of data as a core strategy. Their core focus is likely to be in developing and executing on a data vision, strategy and roadmap, scaling the data team and solutions and leading data culture organization wide. Additionally, you’ll expect this role to be a key leader in data privacy, security and compliance efforts and be the public face of your data strategy to investors or constituents. This role should report directly into the Chief Executive or Executive Director. Resist the temptation to add in other portfolios, such as IT (unless you’re instead really hiring a Chief Technology Officer and simply making data a part of their responsibilities, rather than hiring a dedicated data leader). Hire first for strategy and communication skills and leave the technical work to a partner or team. Expect to pay serious coin for an experienced candidate with executive experience.
A senior manager role can operate in a variety of modes: with a small data team, leading a community of practice for distributed data roles (such as a financial, marketing or digital analyst) and data champions or as a solo operator. This role is best appointed when your leadership team needs support to adopt a data informed decision and communication leadership style, as their work will likely focus on discovering business problems, uncovering connected insights and helping to develop data stories. This work may also involve an overhaul of organization wide Key Performance Indicators (KPIs) and the reporting framework. They’re also a prime candidate to develop and maintain a data solution, leading adoption and usage through organizational change management efforts. If an executive data leader is not in place, this role will likely report through to the Chief Technology Officer (CTO) or Chief Financial Officer (CFO). Hire first for people skills, not technical expertise. This is a great stretch role for a senior visitor evaluation specialist with business acumen, technical confidence and leadership potential.
The term data scientist is often thrown around to become a catch all data role, or to make a data analyst position look more enticing. A true data scientist’s work is focused on researching and experimenting with algorithms, training and evaluating models, often of a predictive nature. Once research is ready to be operationalized for everyday use, data scientists often need assistance from a Machine Learning Engineer or solutions provider to build, maintain and monitor a robust automated solution. However, data scientists should stay heavily involved in maintenance mode to monitor and continuously improve accuracy or depth – one only interested in researching the next best thing may lose interest in the realities of realizing its return. All data scientists should be prepared to do their own legwork in terms of cleaning and preparing data, but if you’re really wanting someone to answer queries and prepare reports, you’re hiring a data analyst, not a scientist. This role should report into a people manager leading the data agenda. Hire for curiosity balanced with pragmatism and a degree of technical expertise and don’t underrate communication skills – particularly the ability to bring clarity and simplicity to complexity.
Common technologies and skills include R, Python, Julia, SQL, TensorFlow, PyTorch and experience with regression and machine learning models.Â
Though only necessary if supporting inhouse custom data solutions, the data engineering function is often an overlooked one in data teams. If your data strategy involves the burden of building and maintaining your own integrations, pipelines and a data warehouse, this is the skill set you’ll require – and more than one, in order to provide for adequate peer review, knowledge management and support coverage. When hiring, have an emphasis on operations, maintenance and support – this should not be exclusively a build only role, as much of the total cost of ownership will fall following the initial solution. This role should report into a technical lead overseeing the data solution. Hire for core software engineering experience with specialist data engineering expertise. There are significant labor shortages in this role for most markets and a wide gap between entry level and specialist engineers. Underhiring will seriously impact the long term trajectory of your solution and undermine your investment.Â
Common technologies and skills include Python, Java, Scala, Apache Hadoop/Spark, SQL and ETL.
A data analyst is responsible for responding to ad hoc complex data requests including querying and visualizing information, plus managing business rules and configurations to support the data extract and transform function. Ideally, this role will work towards an automated solution – otherwise the time to insight for the business will drag out, disempowering others and ad hoc requests compete with regular data operations. They should also be charged with data quality management, including conducting periodic data integrity audits on both systems of record and intelligence and troubleshooting data integrity gaps. This role should report into either the data solution lead or can be embedded in a business function such as finance, marketing or digital. It’s a great stretch role for a mathematically minded, up and coming Systems Administrator or Systems Analyst with an eye for detail, who has been advancing their data skills. Hire for statistical excellence in someone with a business brain and passion for problem solving.
Common technologies include SQL, ETL and visualization tools, in addition to being a spreadsheet whiz.
This is a relatively uncommon position best suited for cultural institutions who are publishing data publicly, such as a science museum incorporating data into an exhibition or education program. Data journalists specialize in researching, validating and powering highly compelling data stories with consumer interactive visualizations and a heavy focus on data experience design, usually targeted towards the general public.
Common technologies include SQL, D3 and visualization tools.
Though not ideal, in most visitor attractions, product management is usually worn as a hat by a business lead, or a temporary position held by a roaming resource, rather than a product management professional. The data product manager’s responsibility is to facilitate the data product team in translating the business objectives and desired outcomes through the data strategy into iteratively defined problems and validated solutions which are feasible, viable, desirable and which work for the business. This role may report through to another part of the organization, so long as they lead a cross functional data team.
For more on data best practices at your visitor attraction, download our actionable workbook below:Â Â
Data audits don’t have to be hard or expensive. Bring your stakeholder together to discuss their data vision and find passionate champions for informed insight.
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As the global leader in big data analytics for visitor attractions, Dexibit takes privacy seriously. Read more about how we treat your privacy.
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As the global leader in big data analytics for visitor attractions, Dexibit takes privacy seriously. Read more about how we treat your privacy.
As the global leader in big data analytics for visitor attractions, Dexibit takes privacy seriously. Read more about how we treat your privacy.
