This is the fifth in a seven-blog series on generating data insights to drive business decisions. In our first blog, we discussed why generating data insights is important. We followed that with Step 1 (data ingestion), Step 2 (data transformation), and Step 3 (data modeling). In this blog, we examine the elements of Step 4—data visualization and analysis.
After going through the processes to ingest, transform and model your data, visualization and analysis are where you begin to bring data to life to solve business challenges and to seize opportunities.
In most cases, whereas visualization looks back and is descriptive and diagnostic (what happened and why), analysis is forward-looking—predictive and prescriptive (what will happen and what to do about it). Of course, you can build a forward-looking visualization and analyze historical data to find patterns and trends.
Two key aspects of visualization and analysis are speed and accuracy. Delivering data in real time or immediately after the close of a time period (day, week, month) allows business leaders to make decisions when there’s still time to adjust. Data that comes in a week or a month too late does no good.
It’s also critical that the data is consistently accurate. Only then will users trust they can rely on the data to make decisions.
Visualizations: More Than Colorful Charts
Data visualization is not just about producing colorful charts. The graphics must tell a story. They also should be meaningful to the managers and executives who read them so they can get the specific insights they need to make business decisions.
Businesses sometimes take the approach of presenting all the data that’s available in charts and graphs. Providing decision-makers with a lot of data may be good, but only to the extent that it builds a clear picture of what has happened within the business and why.
If your dashboards are filled with too many visualizations, it’s difficult to see the main story and what the data is trying to tell you. Cramming all the data about a particular business activity into one chart may clutter the message. If more information is needed, you can always expand. But begin small with a story that’s easy to digest.
To generate helpful visualizations, it's important to work with users to determine the necessary data points and start with the smallest set of data points to tell a concise story. For example, a financial analyst wants to see data in tables—rows and columns; they need details at the transaction level. But an executive doesn't need that level of detail. A different visual, such as a bar chart, column chart, or waterfall chart is more relevant—they convey the bigger picture that drives their decisions.
Going Deeper with AI-Driven Analysis
Analysis goes deeper than visualizations. This is where you enhance reports to expose insights and identify patterns and trends to make it easier for business leaders to predict outcomes.
Analyzing relies heavily on artificial intelligence (AI). In the past, you needed to hire data engineers to help with AI, but today it's easier because Microsoft Power BI capabilities automate machine learning. This makes forecasting and detecting patterns easier.
For example, you can run <Top N> analysis reports of any business activity that interests you, such as the top 10 salespeople by gross sales in a region, or the top 25 clients based on net profit. You can also explore statistical summaries.
Another feature in Power BI is the Quick Insights tool, which you can activate to generate advanced findings your end-users might miss. For example, they can conduct time-series analysis, which is especially helpful for financial purposes and identifies outliers, or they can use groupings and bins for regression analysis.
Also check out the <key influencers> feature. When data changes and you don't know why, such as which factors are driving down revenue, this feature detects the entities (data attributes) that contributed to that trend. It could be the weather, inflation, or any other element you capture. Power BI helps you determine which elements played a role in the outcome.
Relying on AI to Augment Human Observations
Sometimes, humans can’t see the nuances in the details of a visual, and they may miss important insights. That's where AI capabilities such as Quick Insights built into Power BI can help.
For instance, Quick Insights can alert you if there is a steep decrease in revenue in a particular region, which you might miss if scanning multiple regions. Once Quick Insights alerts you, you can then decide if that finding is a relevant insight or perhaps a temporary glitch that doesn't warrant attention.
Relying on AI to augment human observations of visualizations and analysis helps managers and executives solve business problems. The output tells a more concise story.
Designing Visuals for Accessibility
As you design the visuals, think about the audience who will access the reports. What’s the right color scheme? What’s the best font type and size? The visuals not only impact how easy it is for the audience to read the reports, but also the accessibility of the data.
Also consider the most appropriate visualization format, such as line or stacked column charts for trends, or matrices and tables for financial reporting. Matrices are especially useful for subtotaling and breaking rows into categories and subcategories on multiple levels.
If your audience wants to spot outliers, you could use a scatter chart. Waterfall charts are useful not only for trends, but also for identifying changes year-over-year and month-over-month. Readers can determine quickly if changes are positive or negative over a specified period.
In addition to the charts built into tools, you can import custom visuals, but make sure they're trusted and safe. And remember, it’s more storytelling than design. You don't have to be artistic, but you do have to be able to tell a story. That requires understanding what the stakeholders want, speaking their language, and telling stories they understand.
Self-Service Capabilities for End-Users
Your business users will discover that some reports are easy to generate as Power BI offers a self-serve capability similar to Excel. Just like many people can build table charts with rows and columns in Excel, Power BI extends that feature to visualizations. When business experts build their own visualizations and analysis, that approach usually works best—they understand the business insights they need.
But when things get complicated, and you need a specific formula to calculate certain fields, your users may need to collaborate with a data analyst. You may also need to reach out to an external partner like Western Computer that can help with in-depth visualization and analysis.
About the Author
More Content by Rasvan Grigorescu
