Evolve your Modern Data Stack for Impact

The journey from raw data to valuable, actionable insights is a long one, and not for the faint of heart. Each step of the journey requires time and effort, and more often than not, different tools.

The first step – data collection – for example, can already be a daunting uphill battle as companies have data scattered across their various source systems and files. The integration of fragmented data into a centralized data warehouse relies on an ELT or ETL process, for which you may need several providers to connect all your sources. The following steps – storage, transformation, visualization, and analysis – all involve data being relocated or transformed into different formats and structures. Needless to say, it’s no easy feat.

Brent Dykes, in his Forbes article, refers to this process as a Data Analytics Marathon. The grueling nature of this “marathon” is why the data world has come up with so many evolving concepts, philosophies, and tools, to efficiently take on this multi-stage process with reduced effort and hassle. The Modern Data Stack is a set of cloud-native tools that are designed to streamline each step of the way.

Here’s a snapshot of the Data Analytics Marathon:

As Dykes argues in his article, however, and as with any marathon, there’s not nearly enough emphasis on the “last mile.” Modern companies seem to be powering through and losing steam, rather than keeping a steady pace to gear up for a strong finish. If you think about it, the last mile is really the only part where it all counts – in data, it’s when your company can finally take action on the insights generated from data. If you don’t finish the marathon, did the rest of the journey even matter? If data is visualized, but no insights are taken from it, the rest of the process isn’t worth the investment.

To ensure that companies are getting value out of their data investments, the Modern Data Stack is evolving to favor the “last mile” – consisting of BI (business intelligence), DI (decision intelligence), and the human-led tasks of communicating insights across the organization and taking strategic action. Business intelligence allows data to be illustrated and visualized in a way that reveals trends and information. Decision intelligence takes it a step further by helping companies automate manual analysis and diagnose the drivers of the changes within their business performance. Using the insights uncovered and re-allocating the time gained, teams can take actions that will bring business value.

Let’s expand further on what’s behind BI & DI, and how to finish strong.

Business Intelligence

a) Data Modeling

Your data is replicated from your data warehouse to your BI platform, and is ready to go. Ideally, your BI platform will have a modeling layer, as well as visualization capabilities. BI platforms like Whaly are becoming more robust, offering more than just the visualization component.

The modeling layer is where you begin to make your data comprehensible to all, since your end business users will need to leverage it for their business decisions. In analytics, modeling is the process of reshaping data into a format that’s easy to understand and to query for your non-technical users. Models are designed to make sense in the context of company definitions and business logic, written through SQL or low-code options. In many cases, models will be the foundation of your Explorations.

In general, there’s a higher level of data-savviness required in this step, which means it’s usually handled by data analysts or data engineers. The data team is also responsible for creating a semantic layer by defining metrics and dimensions across the company.

b) Self-service data exploration and visualization

Now that you have your data models and the semantic layer in your BI platform, it’s time to explore and visualize the data in dashboards and charts. This will shed light on what’s going on in your business.

The tricky part here is that the end users who need to consume the data to drive their business decisions generally don’t come from technical backgrounds. As a result, data teams get treated like a support team, fielding requests for dashboards all day, and end up being a bottleneck for reporting.

How do data teams and business teams collaborate better? The answer is a self-service BI platform. Within the platform:

• Data teams can model the data, create the semantic layer, and ensure that the data is reliable and well-governed.
• After the data teams “tee up” the business teams for success by preparing the data, the business teams should be empowered to answer their own questions and instantly get a picture of what’s happening in their business.

A strong self-service layer means that business teams can create their own visualizations and dashboards, based on the metrics & dimensions that the data teams have assigned. It should be customizable, with a range of charting options, so that the “data story” can be told in the most effective way that can be easily grasped, and minimizes the time to insight.

When self-service really works, and business teams can autonomously run their own queries, this will boost data adoption and foster company-wide trust in data.

Decision Intelligence

So, you have the dashboards and charts in front of you. You can see things like team performance against objectives, trends over the past few quarters, the upward tick in MRR. That’s great, but what do you do with this information? Perhaps there are a few quick analyses you can run to infer if things are looking up or down, but that’s not going deep enough. These days, you can also leave diagnosing why metrics are changing to machines.

Decision intelligence platforms like Kausa, test millions of factors and combinations of factors that might be driving the change in your metrics and point your attention to the most significant ones so that you can direct your attention to areas that matter the most. And they can do it about ten times faster compared to manual slicing and dicing.

Till recently, this category has been overlooked by most of the teams, creating a diagnostic analytics gap. Not having the necessary tools at hand, most teams have been searching for why metrics change by drilling down on dashboards. While dashboards are very informative in terms of providing a high-level overview of what is happening, doing this analysis on dashboards requires a significant amount of repetitive manual work. Teams in slower-moving industries or dealing with smaller, less complicated data sets can make this work. But for a fast-moving business with complex data, this analysis can take hours or days, leading to a lot of missed opportunities. The next step in BI is to accelerate the speed to actionable insights, and this is the exact focus of Decision Intelligence platforms.

Depending on the way your teams are structured, decision intelligence platforms can be used directly by user acquisition and marketing teams, or to power data teams to become trusted advisors for the business teams they are working with. It aims to initiate a proactive way of working with data, looking into performance drivers regularly and continuously acting on every opportunity to improve business performance. This gives the teams the opportunity to closely align data-initiatives with business goals as opposed to the reactive approach of chasing the answers to questions that are randomly rising due to the fluctuations in KPIs.

Similar to dashboards, decision intelligence platforms integrate directly into various data warehouses and enable teams to analyze all the available data using an already existing SQL query. This way instead of just testing for 2-3 scenarios you already suspect, you can uncover unexpected drivers hidden in your data and account for counter-factors, canceling out one another and appearing flat in the BI tools. By augmenting diagnostic analytics using machine learning, decision intelligence puts an end to the trade between speed and comprehensiveness. As a result, it unlocks the last mile of analytics by surfacing actionable insights within minutes and frees teams to focus on communicating and acting on these insights.

At this point, you’ve made it all the way through the “last mile,” starting to deliver real value from your data. Congrats on finishing the marathon!

In summary, make sure your company has the right mindset and tools in place to effectively get you through Business Intelligence and Decision Intelligence – which is where it all counts. By streamlining the BI & DI steps, you’ll not only get a picture of what’s going on in your data, you’ll also quickly learn why it’s happening, so you can take quick action and grow your business.

This piece is a joint collaboration between Whaly & Kausa.
For BI-related inquiries, please reach out to Anna Lorentz.
For DI-related inquiries, please contact Yagmur Anis.
We’d love to hear from you!

About Kausa
Kausa can analyze all your data using machine learning, diagnose why metrics are changing, and provide actionable insights to improve business performance. With Kausa teams can save countless hours on data analysis and get ahead of competition by finding opportunities to unlock hidden value.

About Whaly
Whaly is a self-service business intelligence platform that empowers data teams to grow data adoption among business users. Whaly allows companies to make better decisions and drive revenue by reducing the amount of time from raw data to insight, and by removing data teams as a bottleneck for ongoing analytics.