By Brynne Henn - December 22, 2020
The rapid rise of standardized data pipelines and transforms is eliminating a classic analytics bottleneck. However, while data may be more accessible than ever before, the analytics tools most data organizations use to make sense of that data are not. In many organizations, this can lead to a chronic underutilization of data — recent research shows that enterprises generally leverage less than 70% of their data for analysis. Unfortunately, this means that most data teams are blocked from showing the full return on their data investment.
To unlock the full value of their data, data teams must augment their analysis by focusing observation, going beyond the dashboard, and driving decisions with actionable insights.
As the price of data warehousing continues to fall, companies can afford to track almost every business metric at an increasingly fine granularity. And along with these metrics, companies are able to capture more useful context about each individual transaction, customer, and product usage.
In theory, this data should enable teams to perform comprehensive analysis on what actually drives loyal, satisfied customers, what new products are driving engagement, and what campaigns should be optimized. But as the scale and complexity of data rises, it’s increasingly untenable to efficiently check every possible driver of change. Great analysts take a thoughtful, hypothesis-driven approach. But, this puts the responsibility squarely on their shoulders to know what to ask and where to look in the data to find an answer. Even for the best analysts, this manual, time-consuming process cannot scale, and as the complexity and volume of data continues to grow, the amount of unused data will grow without better ways to augment this approach.
To use all the wide, rich data available to them, analysts need to shift how they ask questions. By leveraging automated root-cause analysis to find the populations in the data that matter and then focusing hypothesis testing there, analysts can more quickly answer what changed and provide insight into why.
Sisu’s approach to Augmented Analytics makes this possible. Starting from core metrics, like MRR, CLV, or AOV, Sisu quickly tests every slice of your data to identify the factors and populations impacting a metric — allowing analysts to quickly identify changes and focus on where to investigate further.
While businesses have used dashboards and reports to understand what’s happening with key metrics for decades, relying solely on a static dashboard to understand and communicate changes in your complex data makes it difficult to use all of your data for analysis.
Every dashboard is a visualization of a single hypothesis, not the intricacy of a metric. It shows what’s changing in a metric but puts the onus on the user to decide the factors or combination of factors to monitor. This approach exposes a fundamental imbalance between a visualization that only shows a predetermined subset of features, excluding the increasingly dimensional data a company collects in the warehouse.
In fact, dashboards were the most cited complaint in our own research with data executives on the most painful parts of their analytics workflow. They found it easy to become desensitized to the proliferation of dashboards, leading to missed insights, unseen opportunities to better diagnose seemingly stable metrics, and eventually, forgetting to monitor a dashboard altogether as priorities change.
When they do present on a dashboard to communicate changes to the business, the executives we interviewed complained follow-up questions are often not covered in the original dashboard they created. Since analysts often build dashboards on simplified, aggregated views of the data, they usually must re-edit the query to answer a follow-up question, join new tables, or even create an entirely new dashboard.
Rather than relying on dashboards to monitor and communicate changes data, Sisu’s Augmented Analytics platform can explore billions of combinations to quickly identify the most impactful insights for the business. It moves beyond the limitations of point-and-click dashboards, and proactively stack-ranks observations by relevance and impact to the metric in question. With this approach, businesses are not only able to get insights faster based on all their data, but explains precisely why specific results are returned.
With easy to understand insights focused around the metrics that matter, not just individual hypotheses, Augmented Analytics enables audiences from across the business to collaborate and act on the data. This understanding is the key that will allow your organization to finally see a positive ROI on your data investments and uncover every opportunity in your data.
Want to see how Augmented Analytics can help you get more out of your data investments first hand? Get in touch and our team will diagnose your first metric for free.