How do you design and conduct descriptive analytics projects in a team?

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Descriptive analytics is the process of summarizing and visualizing data to understand what has happened in the past or present. It can help you identify patterns, trends, outliers, and insights that can inform your decision-making and actions. However, descriptive analytics projects can be challenging to design and conduct in a team, especially if you have different data sources, tools, and skills. In this article, you will learn how to plan, execute, and communicate descriptive analytics projects in a team effectively.

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1 Define the problem and scope

Before you start collecting and analyzing data, you need to define the problem and scope of your project. What are the questions you want to answer, the objectives you want to achieve, and the stakeholders you want to serve? How will you measure your success and impact? What are the constraints and assumptions you have to consider? You should align with your team and your clients on these aspects and document them clearly. This will help you avoid confusion, scope creep, and unrealistic expectations.

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Martha Emily K.
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Ask why. As part of defining the problem & scope, understand why they need this work. For example, if they asked you to measure the duration of a process, ask why. The why not only helps provide context but can aid in the development of your work (i.e. you could put a reference line for the average or the standard).

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2 Collect and prepare the data

Next, you need to collect and prepare the data for your analysis. Depending on your problem and scope, you may need to access different data sources, such as databases, files, APIs, or surveys. You should ensure that you have the right permissions, formats, and tools to access and manipulate the data. You should also perform data cleaning, validation, and transformation to ensure the quality and consistency of your data. You should document your data sources, methods, and assumptions for transparency and reproducibility.

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Martha Emily K.
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To start, determine if this is data that is already being collected. If not, determine whether there is a need to collect before analyzing and visualizing, whether there are proxy numbers you have access to, or whether there is data internally and whether it needs to be cleaned and prepared for reporting and analysis. I want to point out that whatever is done in the data preparation should be documented and can be easily communicated so that downstream users can convey that information as appropriate.

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3 Analyze and visualize the data

Once you have the data ready, you can start analyzing and visualizing it to answer your questions and achieve your objectives. You can use various techniques, such as descriptive statistics, charts, tables, dashboards, or reports, to summarize and present your data. You should choose the appropriate methods and tools that suit your data, audience, and purpose. You should also follow best practices for data analysis and visualization, such as avoiding bias, ensuring accuracy, and providing context and interpretation.

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Martha Emily K.
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If you love analysis and data visualization, this is the fun step. I think of analysis as exploring an unknown land, looking for treasure (the insights). As you start to explore and find an insight, ask why it happened (and keep asking why til the data doesn't have any more answers). This helps get to the root cause and provides insight into the findings, which is a key to data storytelling.

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4 Communicate and collaborate with your team

Throughout your project, you should communicate and collaborate with your team effectively. You should share your progress, findings, challenges, and feedback regularly and clearly. You should use tools and platforms that enable you to work together efficiently and securely, such as cloud-based storage, version control, or project management software. You should also respect and leverage the diverse skills, perspectives, and roles of your team members, such as data engineers, analysts, scientists, or visualizers.

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5 Deliver and present your results

Finally, you need to deliver and present your results to your clients and stakeholders. You should tailor your delivery and presentation format and style to your audience and purpose. You should highlight the key insights, recommendations, and implications of your analysis. You should also provide supporting evidence, documentation, and references for your analysis. You should be prepared to answer questions, address feedback, and demonstrate the value and impact of your descriptive analytics project.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Alireza Hamidi

Director of Analytics at SciPlay - Jackpot Party
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I would add another step to build a repeatable framework, if applicable, to make the analysis simpler and quicker in future, if similar situations occur. These frameworks can act as a blueprint of what to observe (e.g. particular metrics’ correlations, certain funnel, a specific dimension) and what to watch for (e.g. certain time period seasonality, data issues, external impact) for a frequent investigation.

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Rahul Setia

Data Lover from years | Senior Consultant @ PwC | Business Intelligence and Data Analytics
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"Collaboration fuels the heart of my descriptive analytics projects! ???? Designing and conducting projects as a team brings diverse perspectives to the table. We start by defining clear objectives and roles, ensuring everyone knows their part in the puzzle. We gather, clean, and explore data together, sharing insights and refining our approach. It's like weaving a tapestry of knowledge and creativity. Through open communication and knowledge sharing, we transform raw data into actionable insights that drive impactful decisions. Let's keep the data journey rolling together! ?????? #DescriptiveAnalytics #Teamwork #DataDriven"

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Sarah McRae

Data Scientist | Linguist | Founder of The Violin Seed Project
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Some stakeholders will only accept a “one size fits all” presentation of the data. Educating the stakeholders that sometimes the best data has been cleaned and compiled for them in Excel, other times PowerBI, Tableau or Metabase has the proper visualizations of their descriptive data. "If the only tool you have is a hammer, it is tempting to treat everything as if it were a nail,” Maslow. Teach your customers that there are many tools in the descriptive analytics toolkit.

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ADDIS M. DE LA FUENTE

Using data analytics and visualizations to provide insights for informed decisions.
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A project management timeline helps getting everything planned within team project and it is a very important piece to be on track. In my experience it comes very handy specially in complex projects and managing ETA expectations with top executives, as well as members’s tasks and contributions more efficiently and in a timely manner.

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Hilary Bowen

Data Project Manager | CPG & PE | AI & Data Strategy
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One challenge I've often encountered is engaging less analytical stakeholders who may struggle to grasp the significance of the findings and their implications. In such cases, incorporating dynamic prescriptive analytics (dynamic storyboarded data) into the process can be a game-changer. Users can be guided step by step through the data insights, making it easier for them to comprehend complex patterns and trends. The interactive nature of this approach fosters a sense of curiosity and empowerment, as stakeholders can explore "what-if" scenarios and witness real-time changes in outcomes and allows for repeatable, updated results.

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How do you design and conduct descriptive analytics projects in a team?

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1 Define the problem and scope

2 Collect and prepare the data

3 Analyze and visualize the data

4 Communicate and collaborate with your team

5 Deliver and present your results

6 Here’s what else to consider

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