Your clients demand data visualization miracles. How do you navigate their high expectations and limitations?
Clients often expect data visual magic, even when faced with limitations. Here are savvy strategies to manage those expectations:
- Clarify the scope upfront. Define what's possible within the given constraints.
- Educate on complexities. Help clients understand what goes into creating effective visualizations.
- Offer alternative solutions. When demands can't be met, propose creative compromises.
How do you balance client expectations with practical limitations in your work?
Your clients demand data visualization miracles. How do you navigate their high expectations and limitations?
Clients often expect data visual magic, even when faced with limitations. Here are savvy strategies to manage those expectations:
- Clarify the scope upfront. Define what's possible within the given constraints.
- Educate on complexities. Help clients understand what goes into creating effective visualizations.
- Offer alternative solutions. When demands can't be met, propose creative compromises.
How do you balance client expectations with practical limitations in your work?
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Drill down on the intent of the miracles your clients are asking for. From there, come up with innovative way that could push the boundary of the envelop. For things that aren't quite possible, clearly explain tradeoffs and as well as offer alternatives that would still fulfill the intent.
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Navigating high client expectations should involve blending creativity with practical solutions. Using advanced data visualisation tools like Power BI or Tableau, along with AI-driven insights for real-time decision-making, can assist address these needs. Applying skills such as Python for automation and machine learning for predictive analytics strikes a balance between constraints and creativity. Customising Power BI visualisations with Python scripts, for example, can exceed customer expectations while remaining feasible. However, it is also important to inform client the “realistic” output they can expect instead of expecting whims & fantasies - because our mind works faster than our abilities 😎
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First, you need a clear and proper understanding of requirements and great communication. Then prepare a rough plan and route of work. Keep lots of options to drill down and up for the user to navigate. Select proper charts and icons for easy visualization and prepare the dashboard like a storytelling tool.
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Luca Meloni
Digital Analytics Consultant | Adobe Analytics, Google Analytics & Looker Studio Expert
(edited)I think there are three main points here: 1. understand clients expectations: what they want to achieve and the message they want to convey; 2. be realistic: given the tools, data and time, discuss any limitations upfront to prevent misunderstandings later; 3. educate the client: this is the most important. They may have unrealistic ideas due to a lack of technical knowledge. So you should gently educate them about best practices
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clients aren't aware of the complexities involved in data visualization or analytics work. I'll take the time to explain the process behind effective visualizations—why certain visuals are better suited for specific data, or why a particular request might not be feasible due to performance issues (like real-time dashboards with large datasets). Helping them understand the "why" behind design choices often fosters trust.Throughout the project, keeping clients in the loop with progress updates, limitations, and the next steps is essential. I frequently share prototypes or early versions of visualizations to gather feedback early, avoiding major surprises at the end.
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Adeola Juliet Odumeru
Data Quality Assistant @ National Population Commission | Power Bi Certificate
To my own experience , there is no shortcut to a result, The integrity of the outcome or our decision-making regarding our expectations will be compromised if we demand a miracle in data visualization. It should be clear to the client that creating a good data dashboard is not the answer. However, analyzing and comprehending the data's structure, reducing redundancy, using datatypes, and creating visual graphs that correspond with the datasets' questions. What is the indicator of the dataset. What the kpi of the datasets. What is the metrics or measures. What is the question to be answer. The answers to the aforementioned questions will provide clear, genuine data visualizations with improved outcomes.
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We can achieve miracles via data with these following steps: 1. Ask Questions: Asking necessary questions will help understand the end to end requirement completely. 2.Simple and Effective: Keep the dashboard simple with necessary KPIs and other simple visuals as per the requirement. The Key point is we should not be over providing with too much information that might confuse the end business users. 3.Storytelling: This is where the miracle happens. The visuals and other components has to be placed in certain way that should convey a story relevant to the requirement.
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Pra começar é sempre bom entender quais os indicadores e qual a necessidade atual do cliente. Nem sempre é possível fazer tudo com os dados que temos disponíveis, porém entendendo a necessidade e fontes disponíveis, podemos sugerir modelos de visualização similares para atender as necessidades sem aumentar muito a complexidade da entrega final. Além da complexidade do negócio individual de cada cliente, também temos outro desafio que é fazer com que toda essa informação seja disponibilizada de forma harmônica no dashboard direcionando do macro ao micro. Minha sugestão é, envolva o time relacionado a visualização e dados, quanto mais cedo avisados mais tempo pra criar e avaliar opções e soluções para sua necessidade.
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This is an issue we often deal with. Our approach is as follows: 1. Discuss the scope, drilldown and confirm the output with all details 2. Prepare a "mock" interface to show them how the data will appear, and with that outline the data complexities that will pose challenges with more complex visualizations 3. Get a sign off on the UI / present alternatives if possible 4. Keep the client involved throughout the development process so that they know what they will be getting finally.
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People often have an image of what they want to achieve, but lack the capability to efficiently communicate the image to you. As the storyteller of the data, it is up to you to build the story in a way that achieves their vision, even if they don’t know it yet, and then build the trust with client that what you have done captures their requirements. I do this by constantly asking for feedback and working with them to build the visualisations/reports, having conversations with them about the who, why, what of their requirements. As well as showcasing what is possible within the constraints of their system (and the creative ways to push them) with simple proof of concept demos.
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