There’s nothing quite like the sinking feeling that accompanies looking at a report revealing missed KPIs ahead of a meeting with leadership the following day. Especially when it comes as a surprise. Diagnostic analytics can help you identify the origin of issues, speed up problem resolution, identify and address blockers to company growth, and eliminate unnecessary costs. And with an analytics tool that enables non-technical users to easily conduct diagnostic analyses, every business team — including sales, marketing, finance, and operations — can move quickly to realize these benefits on a daily basis.
What is Diagnostic Analytics?
Diagnostic analytics is a type of analytics that’s used to identify the origin of business issues and find appropriate solutions to prevent them from happening in the future. For this reason, diagnostic analytics is also called root cause analysis. It involves looking at internal company data and (often) pulling in external data, using a variety of techniques including data discovery, drill-down, data mining, and correlations.
In discovery, teams identify which data sources that will help them find their answers. Drill-down focuses on a certain angle of the data or specific dataset. Data mining involves automation to analyze large amounts of raw data. Looking at correlations helps to identify where possible causes lie.
Benefits of Diagnostic Analytics
High-level reports will tell you what has happened. But they don’t tell you why it happened. Before you can identify a solution, you must uncover the root cause. That’s where diagnostic analytics comes in. Understanding why a trend is developing or why a problem occurred will make your business intelligence actionable.
Diagnostic analytics helps companies to:
- Improve organizational agility and resilience
- Mitigate blockers to company growth
- Minimize unnecessary costs
- Increase operational efficiencies
- Maximize profitability
What does diagnostic analytics reveal?
Typically, there is more than one contributing factor to any given trend or problem event. Diagnostic analytics can reveal the full spectrum of causes, ensuring you see the complete picture. You can also see which factors are most impactful. You may also need to bring in outside datasets to more fully inform your analysis. Sigma makes this easy, especially when connected to the Snowflake Data Cloud.
Diagnostic Analytics for Every Team
Root cause analysis can benefit every team in an organization. For example:
- The sales team can identify shared characteristics and behaviors of profitable customer segments that may explain why their lifetime value is so much higher than other segments.
- The marketing team can look at the unique characteristics of high-performing emails compared to lower-performing ones to identify the reasons for performance differences.
- The finance team can compare the timing of key initiatives to month-over-month and year-over-year revenue growth or decline to help determine correlations.
- Operations can look at regional weather patterns to see if they’re contributing to demand for particular SKUs across geographic locations.
Diagnostic analytics in action
Let’s look at an illustration of how a manufacturing company uses root cause analysis to identify the origins of defects and design flaws. First, an initial report alerts the operations team to missed SLAs, leading to an inability to accept new business while they correct the problem and catch up on production.
Next, the team uses diagnostic analytics to combine and correlate millions of data points of historical measurement and manufacturing line data. They then filter and sort to identify patterns. During this deep dive, they discover that their machines are not being recalibrated as frequently as they were previously. After exploring other potential causes, they determine that this was the root cause that created the errors and begin recalibrating the machines on a more frequent schedule. As a result, they’re able to optimize performance, make better technical decisions, and increase revenue.
How To Empower Teams to Use Diagnostic Analytics for Daily Problem-Solving
In today’s complex world, many factors affect a company’s success. Problems arise and must be solved. Business-changing trends develop and must be addressed. For teams to effectively act, they must move quickly. It won’t do to wait on the overwhelmed BI team to deliver follow-up report after follow-up report as requests pile up. Business teams need to be able to conduct root cause analyses on their own, as needs arise. Today’s highly demanding and rapidly evolving markets require on-demand, data-driven decision-making at all levels and across all departments.
Many teams rely on static, siloed spreadsheets that they dump data downloads into. But if you’ve ever tried to conduct the deep-dive analysis of the massive amounts of data that diagnostic analytics demands, you know that traditional spreadsheets hit their limits quickly. (Not to mention that this process opens up serious security risks.) Today’s teams need a cloud-native solution that allows even non-technical users to dive into the data on their own, securely and easily. Every team member should be empowered to ask questions and get answers from their data.
How Sigma Re-Imagines Diagnostic Analytics
Sigma was built to sit on top of cloud data platforms and data warehouses that manage massive volumes of data from hundreds of sources in a centralized, secure, and fully governed manner. Whether a company uses the Snowflake Data Cloud, Amazon Redshift, Google Big Query, or another established solution, Sigma accelerates the ability to make use of data. (Additionally, Sigma supports data partners like Matillion, Fivetran, Alooma, and Stitch.)
Sigma gives teams direct, governed access to all of the live data inside the data platform or warehouse to take advantage of the near-unlimited speed, scale, and power of these solutions. The result is that users can perform root cause analyses on hundreds of billions of rows of data down to granular-level detail — all in a few seconds.
And unlike traditional business intelligence tools, Sigma doesn’t require coding skills to investigate data ad hoc or do complex, iterative analysis. Instead, it looks and feels like the familiar interface of a spreadsheet, and automatically translates familiar functions into SQL on the back-end.
Because Sigma is directly connected to your company’s cloud data platform, data is always fresh, and analyses are automatically kept up to date. They can be easily edited over time without having to start from scratch. Sigma also gives teams a better way to reuse analyses and work collaboratively by combining spreadsheets, charts, text, and images that can easily be built upon and shared with one another.