Businesses make operational decisions daily, from deciding which marketing campaigns to run to how much and which products to keep in inventory.
These decisions equally have far-reaching consequences. Gartner estimates that poor operational decisions cost businesses at least 3% of their profits.
All companies can leverage data analysis and reporting to make better operational decisions. That said, the data analysis process producing insights from data has to be precise, reliable, devoid of biases, and based on sound objectives.
The strategies you can embrace include defining clear objectives, utilizing automated tools for data analysis, developing standardized reporting templates, implementing data visualization techniques, and leveraging advanced analytics platforms for streamlined data analysis and reporting.
In this article, we deep dive into the above tips you can adopt to make your data analysis and reporting process more effective and efficient.
Tip 1. Define clear objectives and questions to guide data analysis
Before analyzing data, you must establish a clear objective for each analysis. You can only determine the right objective by asking questions. Lots of questions.
Examples of questions you can ask include:
What business problem is the company hoping to solve with this analysis?
How is this problem affecting the business?
What is the ideal outcome for this data analysis project or request?
These and many more are examples of questions that’ll provide more context. Asking questions during elicitations achieves three purposes:
Helps you identify the key stakeholders of the project
Stakeholder expectations matter for every data analysis project. Stakeholders are those directly or indirectly affected by the business problem.
For example, stakeholders in a website conversion analysis may include the marketing team, web design team, copywriters, and the sales department.
You can narrow the list of stakeholders to those with the greatest potential to affect business outcomes. These are the people responsible for that KPI.
That said, there’s always some ambiguity within companies on KPI ownership. This overlapping or conflicting KPI ownership can delay your project or lead to a wrong path.
Helps you identify independent variables to test
Independent variables are factors that stakeholders have suggested can affect the outcomes.
For example, the website design team can tell you that they suspect the call to action button is not leading to more conversions, while the marketing team may argue it’s the website visitor demographics.
You can adopt these factors as independent variables and test them during your analysis. Website conversion is the dependent variable or KPI.
Determine if stakeholders agree on the problem to be solved
You may find that each stakeholder is trying to solve different problems during elicitation. This will affect the value each stakeholder gets from your analysis.
Moreover, as you’ll see, each analysis must focus on a single problem. You can create separate analysis plans for the other challenges if they’re valid concerns.
It’s problems the stakeholders agree on that you synthesize into an objective.
What are clear objectives?
Clear objectives are S.M.A.R.T. objectives. They lead to a focused and streamlined analysis plan. They’re:
Specific: The object should be simple and focused on a single problem.
Measurable: Can be quantified and assessed.
Attainable and action-oriented: The objective should encourage change. This factor is mostly dependent on the available business data.
Relevant: The objective is significant to solving the business problem.
Time-bound: Limit the scope of the analysis to a specific period.
For example, “increase the number of returning visitors to the website” is not an objective that meets the above criteria.
A better version is: “In 2 months, analyze click-stream data for the last 24 months to determine website changes that will most efficiently increase revenues by 15% month-by-month.”
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Tip 2. Utilize automated data analysis tools for faster and more accurate insights
The five steps for data analysis include asking questions, data preparation, processing data, analyzing data, and sharing or visualizing data.
You can automate data extraction, preparation, processing, and analysis to save time. Data preparation and processing is time-consuming. Did you know that a data scientist spends, on average, 80% of their time on data cleaning and transformation?
Data analysts and scientists spend a lot of time cleaning to correct errors because data integrity is essential to actionable analyses.
Rather than manually cleaning large datasets every time you need to perform an analysis, you can set up a pipeline with a set of rules (removing duplicates, identifying outliers, replacing missing values, etc.) that automatically apply to every dataset you process.
For example, you can use the Pandas and Numpy libraries to automate data processing with Python. You can also achieve the same outcome with Power Query in Microsoft Excel.
Using Kippy with Zapier can also help you automate how KPIs are updated. For example, you can update KPIs by automatically extracting data from a spreadsheet as soon as they’re updated.
You may also like: 5 Quick Ways to Automate Your Business Processes
Benefits of automating data analysis processes
Many benefits of automating data preparation processes include:
Reduces human error, especially with the data entry process, which improves accuracy.
Ensures there’s a consistent approach to data processing.
Serves as a building block for future projects. You can simply modify existing pipelines to deal with new requirements or data types.
Saves time that you would have devoted to repetitive tasks.
Tip 3. Develop standardized reporting templates for consistent analysis and reporting
Imagine getting the same report in separate months, but the data and presentation in both reports are so different that it’s hard to identify trends or make any comparisons.
Designing and instituting standardized report and dashboard templates can improve clarity, consistency, and accuracy and ultimately help make informed decisions.
Anyone preparing the report knows which key performance indicators (KPIs) to prioritize and doesn’t have to bother asking what to include in the report.
This saves time and allows employees to focus on other things, like providing well-thought-out recommendations.
Key elements of a template
Every template should include the results and KPIs and how to present them.
Do you want certain data points provided as a continuous (0-100%) variable or as a discrete (low, average, high) variable? Should they use bar charts or line graphs?
Having templates also keeps report generation concise. You’ll only receive reports with the information you need.
You can decide if you want benchmarks against other companies, drivers of certain variables, and much more. The level of detail is up to you.
Where to get templates?
Data visualization tools like Tableau and Microsoft Power BI offer countless dashboard and report templates you can choose from. However, you’ll most likely need to tailor these templates to your needs.
You can also get templates from industry organizations or companies in your niche.
But one note of caution: just because a metric is popular in your industry doesn’t mean it adds valuable insights to your analysis.
You may also like: How to Measure Performance Metrics like a PRO
Tip 4. Implement data visualization techniques for easy interpretation of data
Visualizations combine lines, shapes, color, space, and movement to tell data stories. They blend one or multiple principles of design to achieve the desired impact.
Examples of design principles include balance, emphasis, repetition, variety, pattern, proportion, etc.
Below, we’ll discuss key themes that can help when creating data presentations.
Elements of effective visualizations
Effective visualizations have these characteristics:
Clear meaning: They communicate their intended insight without any extra explanation
Highlight essential data: They use contrast to highlight the most essential data from the rest through visual contexts.
Refined execution: Simple yet functional designs, like picking a color your audience expects (red for loss and green for profit), and the proper use of annotations, labels, subtitles, and headlines. This is especially important for building interactive dashboards that may have multiple layers.
Design thinking and visualizations
Design thinking recommends solving problems in a user-centric way. The same should apply to creating data visualizations. Always put yourself in your audience’s shoes.
The five phases of design thinking include:
Empathize: Understand your audience’s needs and emotions.
Define: Define the needs and your insights.
Ideate: Generate data visualization ideas, including identifying the right tools for your design and the best chart or graph for each insight.
Prototype: Put your graphs and charts together
Test: Test your viz by showing others like your colleague or friend. See how they engage with the viz and if they find it hard to understand details.
Make visualization accessible
Part of design thinking is considering multiple end-users, including those with disabilities or impairments. You can make your visualizations more accessible by:
Adding data labels
Providing alternative texts
Using bright colors
Beyond colors, also using texture and shapes to show contrast
Simplifying the visualization by avoiding too much information, texts, or insight into one visualization
Tip 5. Leverage advanced analytics platforms for streamlined data analysis and reporting
According to Gartner, "Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations."
In a survey by Transforming Data With Intelligence (TDWI), 21% of respondents say they "are still using spreadsheets for analytics." TDWI estimates that adoption of advanced analytics has only increased by 10-20% in the last 15 years.
However, tools like spreadsheets can't deal with unstructured data like clickstream and geospatial data. Also, while they can process structured data, their performance begins to lag with large volumes of data.
That's why businesses must adopt these advanced platforms to be able to analyze all kinds of data for business growth.
Advanced analytics platforms like Altair, Alteryx, Databricks, Dataiku, Google Cloud AI, and IBM Watson Studio have multiple use cases, such as AI lifecycle management, natural language processing (NLP), building and deploying models, and automated machine learning.
Takeaway: Data analysis is only valuable with actionable insights
Data analysis without actionable insights is just an activity and a hog of resources.
A substantial part of the equation is operationalizing analytics by adopting frameworks and setting up processes based on best practices like the tips above.
You can make your data analysis and reporting process more effective and efficient by defining SMART objectives, asking probing questions, and utilizing automated data analysis tools.
Other strategies include developing standardized reporting templates for consistent analysis, implementing data visualization techniques, and leveraging advanced analytics platforms for a more comprehensive analytics toolkit.
Kippy, a strategic performance management tool, can recommend KPIs. Objectives and Key Results (OKRs) with artificial intelligence in seconds from your strategic objectives.
You can then monitor these KPIs with live, interactive dashboards. Find out how Kippy can further help your data analysis and reporting by requesting a live demo.