Like most data professionals, you probably have a good handle on your analytics workflow. You typically collect data from various sources, clean and prepare it, and then run analyses to generate insights. But there's one crucial step that needs to occur in this process: data communication.
You need to do more than just find valuable insights in your data. You also have to communicate what you found to the people who will use it to make decisions. This means creating clear, concise reports that explain what your data says and why it matters. It also means working with your end users to understand their project and how your data will be used. By understanding the who, what, when, where, and why of your data sources, you can share information, uncover business needs or root causes, and create better project outputs. Finally, give your business analysts the context they need for the data they are analyzing so they can understand it better. This takes a lot of preparation but it is necessary if you want your data professionals to be successful.
There are two main reasons communication is an essential deliverable in data analytics. First, data is meaningless without context. Insights that seem obvious to you might not be so apparent to someone with a different level of data expertise. So it's essential to explain your findings in a way anyone with any ability can visualize and understand. No easy task. And if you want to avoid answering the same questions repeatedly, you’ll need systems to house and update this documentation for your clients to see.
Second, business decision-makers are often bombarded with information from a variety of sources. They don't have time to sift through mountains of data feeds to find the nuggets of wisdom hidden within. So it's up to you to highlight the most important data takeaways from your project analyses and present them in a way that's easy for busy decision-makers to digest.
As far as presentation goes, as you streamline your workflow process and create more data assets, you’ll want a single place/analytics hub to organize your growing dashboard collection. This system should help you curate the 20% of high-value data assets and govern/clean up the 80% that are ineffective. Plus, having one system where your team can access, manage, monitor, and find these dashboards means you can show the correct data analysis to the right person at the right time.
The good news is that communicating your understanding is easier than it may seem. Here are a few tips to get you started:
Before you start, take some time to think about who will be reading your data analysis. What level of expertise do they have? What are their goals? What information do they need to make decisions? Is there any business context you need to understand in order to impart the correct details? Answering these questions will help you tailor your report to hit the mark with its intended audience.
When in doubt, err on the side of simplicity. Use clear language and avoid jargon whenever possible. If data engineers are presenting to business stakeholders, do not assume they speak the same language as you do. Also, people don't read; they skim. Your chart titles should tell a story if someone only read the high-level analysis. Be sure to provide easy-to-use visuals—they can be beneficial in conveying complex concepts quickly and easily.
Decision-makers are busy, so they appreciate brevity. Get straight to the point and make sure every example serves a purpose. If something isn't essential, leave it out.
Support your claims with evidence from your data mining. This will help decision-makers feel confident in the conclusions you're drawing and more likely to act on your recommendations.
Once you've created your report or dashboard, put it away for a day or two and then come back and edit with fresh eyes. This will help you catch any typos or grammatical errors, as well as any sections that are unclear or could be improved upon. Then, ask a team member to review it for feedback before sending it off to decision-makers.
As a data professional, communication should be an integral part of your analytics workflows—not an afterthought. Take the time to craft clear, concise reports; you can ensure that your data insights are understood and used by the teams who need them most. Talk at length with stakeholders to get a business understanding; it will enhance your workflow, processes, capabilities, and efficiency. Follow these tips, and you'll be well on your way to becoming an effective communicator of data-driven insights.
The data analytics workflow is a sequence of tasks that make up a data analytic production process. This explains how a data scientist can complete their work. The workflow is a guide that tells you and your team what to do and how to do it.
There's no one-size-fits-all answer to this question, as the best way to implement a data workflow analysis will vary depending on the specific project you're working on. However, here are a few preparation tips to get you started:
1. Define the scope of your project: What are you trying to achieve? What are the needs of the team consuming these outputs? What deliverables are typically expected from your audience? Ask for example data that has worked in the past.
2. Curate what you have: What data do you need to collect and analyze? Look at the depth and breadth of the data you have available to you and decide what are the must-have pieces. Consider what data has the highest impact, which cause frustration, and if any are expensive.
3. Identify the tools and technologies you'll need: What software and hardware will you need to complete your data project?
There are many ways to optimize a workflow. Some standard methods include:
1. Automate repetitive tasks
2. Streamline processes
3. Eliminate unnecessary steps
4. Use software to your advantage
5. Delegate and build a team
6. Simplify and standardize
Workstream.io's CEO knows analytics workflows. He has seen it all and warns that, left unchecked, dashboard sprawl can wreak havoc on business processes, internal systems, and your own workflow. Communication and productivity are covered here as helping to uncover business-relevant data patterns.
WATCH: Rein in Dashboard Sprawl @ Get Stuff Done with Data 2022 w/ Nick Freund
We would be remiss if we didn't mention helpful decision-making frameworks for varied applications. OODA, which stands for observe, orient, decide, and act, is an efficient evaluation framework that can provide insight and perspective in your analysis.
READ: Why the Best Analysts Are Developers
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