How can collaboration with other departments make your data analytics projects more effective?

From my experience especially working with customers who now start their data analytics journey, the data that they already capture from other departments is not necessarily the data that will bring value in the end and help the department achieve its goals. Depending on the project's scope, you might need to collaborate with different departments (e.g., marketing, sales, operations) to gather the right data and then clean, transform, and segment them in a way that aligns with each department's business context.

In my experience, I have found the CRISP-DM Data Science Methodology to be quite useful in data analytics projects. The process is; 1. Business understanding: this is basically problem understanding and consultation with key stakeholders. 2. Data understanding: know your datatype, volume, value and velocity. 3. Data preparation: clean your data, transform your data, perform some statistical tests. 4. Modelling: feed your data into your model, create correct relationships. If inadequate go back to step (3), create tables, transform data etc. 5. Evaluation: this step involves model performance evaluation. If performance is poor revert to either steps 2, 3 and 4 until the model is fine tuned. 6. Deployment: distribute your solution.

understanding the business context is the cornerstone of successful analytics endeavors. It's not just about crunching numbers; it's about comprehending the broader picture. By immersing yourself in the business context, you gain a holistic view of the goals, challenges, and opportunities your organization faces. To truly elevate your data analytics game, collaboration becomes paramount. Engaging with stakeholders from various departments ensures that your insights align seamlessly with the organization's strategy and vision. It's not merely a data project; it's a collective effort to drive the company forward. After all, successful analytics projects are not isolated endeavors – they're woven into the fabric of the entire organization.

I strongly believe other departments collaboration will significantly add value to data analytics projects by adding relavant knowledge and perspectives which will ensure to arrive at insights faster, and other departments will also ensures that the data is available to the team which is need..

My recent experience in creating a dashboard for our organization was a vivid illustration of this. It was not just about aggregating data; it was about understanding the goal, the stakeholders, and how this tool fits into our broader organizational strategy. Before delving into the technicalities of dashboard creation, I took time to understand our organizational goal, the problems we aimed to solve, and the opportunities we hoped to capture. I conducted multiple training sessions. These were crucial in helping users understand how to interact with the dashboard effectively. To keep the stakeholders engaged and informed, I set up email reminders highlighting updates from the dashboard.

Apart from defining data requirements from the data, don't structure it data backwards. As in if you have dataset A and B, don't plan outcomes out of it. Post defining outcome and understanding the scope, check the requirements for new data/instrumentations. This is more relevant in case of platform data where not all info may be getting passed for analytical consumption.

In a recent project, I navigated the challenge of integrating data from various departmental spreadsheets into a cohesive dashboard. Collaborating closely with IT and other teams, I focused on ensuring data quality and security within our Google Workspace. The process of streamlining this data into Looker Studio was both challenging and enlightening, transforming simple spreadsheets into a powerful tool for organizational insights. This experience underscored the value of collaboration and meticulous data management in achieving impactful outcomes.

Clearly delineate the business case before you start compiling data. Once you begin data collection, clearly define the requirements in terms of dashboard & chart population, timeliness, and storage. Create the data model you will need for the visualizations you intend to create. Once you have that model in place, separate the dimensions from the facts (even if you intend to denormalize later). Now, you are ready to assess the available data against what you will need to create your visualization.

In my journey, I've found that involving IT early on is a game-changer. They're the architects of the data infrastructure, wielding the expertise to address questions on data sources, formats, and security. Together, we establish a robust framework for accessing, collecting, storing, and processing data – a foundation for a successful analytics venture. Defining data requirements isn't just a technical checklist; it's a strategic dance that ensures we're not just gathering data but the right data. Collaboration helps in steering clear of potential pitfalls like silos, gaps, or errors that might compromise the integrity of our analysis.

By working closely with other departments, data analysts can accurately identify and prioritize the data requirements for analytics projects. Collaborative discussions enable stakeholders to articulate their specific data needs, ensuring that the right data sources are identified and accessed for analysis.

In our project, Looker Studio proved to be an invaluable tool for disseminating data insights. Its capability to send automated emails at preferred intervals and frequencies greatly streamlined our communication process.

Planning the visualizations should be a collaborative effort between the data analyst and the stakeholders. It's usually best to start with some basics: line charts for trends, numeric display for important KPIs, segmented bar charts for amounts over time, even pie charts where relative contribution is important. Use these initial charts to ensure that your metrics are clean, correct, and relevant to the stakeholders. Heat maps, distributions, and other visualizations are useful but only when the 'base' of the dashboard is in place, in use, and in vogue with the stakeholders.

Crafting a persuasive data story involves more than just charts and graphs; it requires a strategic partnership with the marketing team. Together, we can determine the most impactful way to present and visualize the insights, ensuring they resonate with our audience. Marketing's expertise in communication is invaluable when it comes to explaining data methods, assumptions, and limitations in a clear and concise manner. Highlighting key findings, recommendations, and actions isn't just about conveying information; it's about inspiring action. By leveraging the storytelling prowess of the marketing department, we can turn data insights into actionable intelligence.

Collaboration facilitates the sharing of data insights and analytical findings with relevant stakeholders across different departments. By disseminating actionable insights to decision-makers, data analysts empower teams to make informed decisions and drive business outcomes based on data-driven evidence.

The collaboration with finance is invaluable when it comes to quantifying and justifying the return on investment (ROI) of the project. Their expertise helps us in measuring outcomes, addressing questions, concerns, and objections with a solid financial perspective. By understanding the financial impact, we can align our analytics efforts with broader organizational goals and priorities. Collecting and evaluating feedback isn't just a one-time task; it's an ongoing process that ensures our insights remain relevant and impactful. This iterative approach, with the support of finance and other departments, allows us to adapt, measure, and monitor the outcomes continuously.

Collaborative tools are great for getting quick feedback, especially in a prototype stage. However, no feedback cycle will provide better results than meeting real-time with your clients and reviewing the visualizations you've created. Set up meetings with stakeholders, execs, and front-line workers. Review the visualizations and provide a tutorial for navigation. Take down the immediate feedback and provide a forum or chat capability for users to keep playing with the prototype and leaving feedback. Some people will come up with new ideas after the initial meetings. Some users might be shy. Make sure you allow feedback from everyone.

Engaging in collaborative discussions allows data analysts to solicit feedback from subject matter experts and end-users regarding the relevance, accuracy, and usability of analytics outputs. Incorporating feedback from diverse perspectives helps refine analytical models, improve data visualizations, and enhance the overall quality of insights.

Working closely with operations is a game-changer. Their insights into coordination, integration, and workflow optimization are invaluable in streamlining our analytics processes. By aligning our efforts with their expertise, we not only enhance efficiency but also foster a culture of mutual support. Collaboration isn't just about getting the job done; it's about infusing creativity into the process. By leveraging diverse perspectives from various departments, we can approach challenges with innovative solutions, leading to more robust and impactful outcomes.

Fostering strong relationships between departments promotes effective collaboration, reduces redundancies (a serious productivity dilemma spurred from excessive siloing), and helps to drive greater synergy. Your fellow colleagues in other departments are closer to the subject matter and may reveal the presence of sources or solutions previously unbeknownst to your own department. This greatly streamlines projects and positions all stakeholders to launch further and faster at their objectives. Start with the individual(s) managing reporting - these experts are knowledgeable of where the data is coming from, what's being done to it, and the final products available and being utilized by the organization.

Build relationships with every department you touch in the organization. Learn the business side, and sit with the business stakeholders. Learn the data process, and sit with the engineers. Ask to join the management meetings for the department in question so that you can hear the questions being asked. Over time, you might be asked to present your work at these meetings. It will be easier if you've already established these relationships.

Building relationships effectively, creatively and respectfully become challenging when some teammates understand the idea but others don’t. If this situation happens no one should take it personally and keep moving forward seeking positive outcomes.

Building relationships effectively, creatively and respectfully become challenging when some teammates understand the idea but others don’t. If this situation happens no one should take it personally and keep moving forward seeking positive outcomes.

In my experience, you should always celebrate the success with your project. Not only you demonstrate your expertise , but also this is a good chance to thanks all the partners who work with you along the project. Basic Framework can go as following: What's the Goal of project Did you acheive the goal ? and more importantly how and why ? What are the learning that you can taken into next project Who do you thankful for their support and work ? Following these questions would help to showcase a slide of great work with your project.

Collaboration with other departments reduces work load hence accelerating time to insights. When teams bring knowledge and perspectives, they arrive at the insights faster.