I recently read a post from a founder of a consulting company about customer excellence. He quoted a business leader who said, "Our clients know they will get all the attention and time from us that they need because we set aside enough time to serve them well."
The consulting company founder believed that this concept aligned perfectly with where he wanted to take his own consulting practice. He went on to ask himself, "How many clients can I serve at once without sacrificing excellence?"
This got me thinking about 'analytics excellence' and how much work a single analyst should take on before the customer service excellence gets sacrificed.
During an interview for a company I once worked for, I asked the Director, "How many Google Analytics analysts currently support your organization?" She responded, "One." I said, "Just one doing the reporting and analysis?" She responded, "No, one doing it all. Gathering KPIs. Implementations. Reporting & analysis. Meeting with stakeholders to review the analysis and make recommendations. Running trainings. Etc." I was shocked! A company this large, managing dozens of websites and microsites, with dozens of data stakeholders...had one analyst. Yet the company was trying to be a better "data-driven" company.
I had just come from a company where we built the digital analytics group to about 12 team members, by the time I left. And all 12 were very busy on a daily basis. I couldn't imagine the workload this single analyst must have been facing. How in the world could they provide analytics excellence when they were constantly having to shift from one task to another?
How is the analyst supposed to focus on developing top level reporting and analysis when they still have implementations to complete? Think about the possible impact on data quality and accuracy when implementations and reporting/analysis has to be rushed.
How can the analyst find time to meet with stakeholders to discuss goals and business objectives when they have brand new ad hoc data requests they have to prioritize and complete? Think about the possible impact on understanding the stakeholders business objectives. If they don't have the time to properly meet with the stakeholder, the analyst might struggle to align their analysis with the stakeholder's goals which makes it harder to provide actionable recommendations.
How does the analyst then find time to communicate complex insights to stakeholders, via trainings? Presenting insights in a clear and meaningful way can be a struggle. Especially for stakeholders who do not have a strong background in analytics. Translating technical terms and complex data into insights is very difficult. Analysts need time to effectively communicate the information so stakeholders can comprehend, appreciate, and trust the insights being provided.
Again, "How many clients can I serve at once without sacrificing excellence?" I would add, "How many tasks can I handle at once without sacrificing excellence?" Having a limited number of analysts impacts the depth and breadth of analysis that can be performed.
If you ask ChatGPT, it defines Analytics Excellence as, "the optimal use of data analysis processes, tools, and techniques to derive meaningful insights that drive informed decision-making, improve business performance, and achieve strategic objectives. It involves a holistic approach that encompasses high-quality data management, advanced analytical capabilities, a skilled workforce, a data-driven culture, and a commitment to ethical practices and continuous innovation. Key attributes of analytics excellence include accuracy, reliability, relevance, clarity, and actionable outcomes from data analysis efforts."
Note how it says, "a skilled workforce", not a single skilled worker. It takes a workforce to meet and/or exceed the needs/goals/business objectives of the stakeholder/client/customer AND it takes a workforce to achieve that excellence. Too many companies are not spending enough on building an analytics workforce. Yet, they want to achieve a data-driven culture. You cannot achieve one without the other!
If a company wants analytics excellence, then they need to invest in it. Stop trying to hire the one analyst that can do it all and instead work on building a workforce of specialists. Hire implementation managers, reporting/analysis managers, advanced analysts, data engineers, QA engineers, etc. Only then will stakeholders "...get all the attention and time from us that they need because we set aside enough time to serve them well."
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