When people hear AI, they often think of artificial intelligence—a powerful technology transforming industries. But at Kubit, we view AI differently. For us, it stands for Analytics Intelligence, an approach that prioritizes data transparency over the complexities artificial intelligence can create when applied to misunderstood data.
Artificial Intelligence and Misunderstood Data: Adding to the Confusion
While artificial intelligence has immense potential, applying it to poorly understood or siloed data often worsens confusion. It’s similar to automating a flawed process—you’re simply accelerating the problem. When AI is layered on top of this kind of data, the results are typically conflicting insights, incomplete answers, and poor decision-making.
A 2023 report by Forrester found that 60% of decision-makers struggle to trust the data output from AI-driven systems. This is largely due to black-box AI systems, which offer no visibility into how data is processed. Business leaders are often left in the dark about how insights are generated, which breaks down trust and leads to inefficiencies. Moreover, relying on AI to run complex queries without understanding the data sources can be extremely expensive.
The solution? Embrace Analytics Intelligence and lean into data transparency.
Kubit’s Approach to Transparency: Analytics Intelligence in Action
At Kubit, we believe in empowering businesses with full visibility into their data, enabling them to understand and trust the information driving their decisions. Here’s how we make that happen:
1. Open Data Sources and Streamlined Systems
Traditional product analytics solutions, like Amplitude, operate within a black box, leaving users in the dark about how insights are generated. Kubit takes the opposite approach: we open up data sources so users can see exactly where their data comes from and how it’s processed. This transparency eliminates confusion and reduces errors caused by disconnected or misaligned systems.
2. SQL Visibility After Reports Are Built
A common frustration with AI-driven data solutions is the inability to understand how reports are created. At Kubit, we provide full SQL visibility after reports are built. Users can see the exact queries behind every report, giving them full transparency into the logic and calculations used.
This level of insight helps users:
Identify errors or inconsistencies in reports
Troubleshoot issues when data seems off
Refine reports and queries as needed, without guesswork
3. Self-Service Access to Data
Traditional AI systems often centralize data access, making business users reliant on data teams to pull reports or run queries. Kubit changes this dynamic by offering a self-service model, allowing users to access data independently, without needing a data team to mediate.
Benefits of self-service data include:
Faster decision-making
Less dependency on technical teams
Increased agility across departments
4. Removing Data Silos by Keeping All Data in the Warehouse
Data silos—isolated pockets of information stored in different systems—can cause discrepancies and confusion. Many AI systems only layer additional complexity on top of this fragmented data. Kubit solves this problem by keeping all data in the data warehouse, ensuring everyone operates from a single source of truth. This eliminates silos and allows teams to collaborate using consistent, reliable data.
A Future Built on Analytics Intelligence
As businesses grapple with increasing data complexity, relying solely on AI-driven black-box solutions can be risky. The better approach is Analytics Intelligence, which emphasizes transparency and accessibility.
Kubit’s focus on open data sources, SQL visibility, and self-service data access empowers companies to make smarter decisions, faster—without the confusion and inefficiencies artificial intelligence can sometimes create. By keeping all data in the warehouse, we ensure there are no silos, only clear, actionable insights that drive business success.
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When analyzing data, analysts often need to compare various measures simultaneously and break them down by different properties and segments. Introducing Kubit’s latest analysis chart, Data Tables, which allows for multi-measure, multi-dimensional analysis in a single view.
Tools like Excel and Sheets have provided this type of data visualization and it works! While you may still want to see data in funnels, lines, bars and pie graphs; it can sometimes be best to see it laid out in a table view.
Our Customers are using Data Tables to understand things like:
Cross Tab Analysis
How are User engagement metrics across different user segments and features?
Custom Measures and KPI Analysis
Compare custom-defined measures or KPIs across different dimensions
Segmented A/B Testing
Analyze user segments by control vs. variant groups
Impact of Marketing Campaigns
Show click through rate, conversion rate by user segments and Campaigns all in one report
Getting Started with Data Tables:
Navigate to Report → Data Table.
As you can see from the snapshot below, the end user can easily begin adding new events, saved measures, breakdowns and segments.
Highlighted below is an example of a user selecting 3 saved measures, building 2 measures on the fly and breaking it down by Country (United States, Canada, United Kingdom), Plan Type and Platform.
When executed, the below table will be displayed. Users have the ability to sort, search, adjust columns widths, export to CSV, and view the SQL behind the chart.
Take it for a Ride
Now that you have a high level overview of Kubit’s Data Table, click through the guide below and get a feel for it yourself. If you’re interested in learning more, please reach out to our team.
We all make mistakes, but the worst is when we don’t even realize it. How can you fix what you didn’t know was broken? For businesses, this is a problem when it comes to data-driven cultures and decision-making.
From a workplace culture perspective, companies have typically assigned data queries and reporting to their data scientists. But smarter technology and savvier user interfaces are making it possible to open up data dives to employees across departments. Doing so, however, is easier said than done. With advanced software as a vital tool, leadership still needs to ensure strong communication practices, maintain the quality of data sources, and understand the value of contextual data.
Check out the article here to get some great tips on how to make sure your business’ data analytics practices are the best they can be, and feel free to send us any questions at info@kubit.ai!
Why do 72% of data-driven projects fail? Let’s use a human-centered example. Say a person intent on gaining muscle commits to lifting weights at the gym regularly for a year. If they saw little to no results after that year, would you blame the weights–or would you bet the problem lay in their form?
At Product World this week, our goal was to start a conversation around the importance of using the right methods to turn data into actionable insights–in other words, how product companies are lifting their data weights to build out their products. And I was glad to find that many other product companies in attendance are starting to have this conversation in earnest.
In my 20 years working in product analytics, I’ve seen the advent of commercially accessible machine learning, bigger data funnels, and AI. As a result of these new technologies, most product companies today would describe themselves as “data-driven”–when much of the time and unbeknownst to them, they aren’t fully there yet.
Data-Driven Dubiousness
Any app company will call themselves data-driven; user data is the key to its success, and so they make sure to prioritize tactics like A/B testing. What does that look like when it’s not truly data-driven? Imagine a scenario in which an app company is A/B testing two color schemes–pink and blue–to learn more about what appeals to their users. After spending lots of time and money, the results show that the pink color scheme performs the best. So they do the work to design the app accordingly, only to watch user engagement drop dramatically. Everyone finds themselves scrambling to answer “why?”; departments from business development to engineering will pull charts and run queries, and share them in a group email that eventually becomes a days-long thread cluttered with disparate pieces of information. A meeting is called, and a few people have minutes to make a decision on what to do next.
During this whole process, nobody thought to look further. Turns out, at the same time this app company was running tests, the marketing department had launched a huge campaign to attract female users between the ages of 12 to 18.
This anecdote depicts what’s missing in today’s understanding of “data-driven.” Just like gaining muscle takes so much more than showing up at the gym to lift, being data-driven requires more sophisticated, purposeful effort. At Kubit, we believe the foundations of a truly data-driven culture lie in contextual data, data quality, and wholly collaborative decision-making.
If there’s one thing we took away from Product World this year, it’s that the desire for better data-based moves is there, but there’s a lack of clear direction as to how to make those foundations a reality within a business. (TechCrunch’s Ron Miller touched on this in a recent article.)
I addressed these three foundations at the event this year and wanted to share them here.
Look at Data in Context
In the days before data, business decisions were largely intuitive. Today, data drives the agile race to market dominance–yet many of these agile businesses are driving the data bus with only partial sight.
When data became more readily available, businesses participated in “data-dredging,” which the newspaper headline above represents. This practice presents data as statistically significant in order to create false positives–but correlation does not equal causation. That’s why data in context is so important.
It’s an easy pattern to fall into in today’s ecosystem of fast-paced mobile and consumer product development. Businesses are consumed with looking at all the data they have, making it difficult to accurately navigate product shifts.
To really be “data-driven,” businesses should focus on looking at the right data for their product. That means shifting priority to the KPIs that matter most for each product, and looking at data in broader historical contexts–I call this benchmark analytics.
Additionally, being data-driven requires more than just leveraging data to identify a problem; companies must start applying smart data analytics–which we at Kubit call augmented analytics–to answer the “Now what?”, and push the data to do more, and take teams further into next steps.
Determine Data Quality
Imagine a CEO sends out a furious email saying a recent update has broken the company’s top-performing app, and she or he wants a solution–fast. This triggers a mad dash for data to figure out what’s going on–much like the A/B testing scenario I described earlier. After lots of double-work and siloed analysis, it’s decision-making time.
It’s tempting to cherry-pick the data that justifies the assessment of the highest-paid person in the room (and subsequently ignoring data that undermines it). I call this “data bullying,” and it forces businesses to ignore the very point of using data to drive intelligent business decisions: objective, “truthful” data. Once data quality is compromised, teams are working with half-truths, and that can lead to expensive mistakes.
To become a business that’s actually data-driven, make sure people are asking the right questions to ensure the data was handled correctly. Using only trustworthy data is something we learned in school (Wikipedia is not a reliable source), and it applies to analytics, as well. Data must be clean and make sense to be worthy of basing decisions off of it.
The irony here is that the more you build on a data-driven culture, the bigger and more successful your business can become–making data quality and control maintenance more of a challenge.
That’s why Kubit designed workflow processes around organization and transparency. Instead of wading through several versions of reports using various sources of data, Kubit’s Investigation Board guides the data-driven process in a way that puts everyone on the same page, and everything can be traced to a single source of truth.
Widen the Decision-Making Circle
It’s not entirely accurate to describe a company culture as “data-driven” if the information gathering and decision making processes are in the hands of only a few people. (Outside of data analysis, this concept is growing in popularity as more research ties it to employee turnover and engagement.)
I’ve seen firsthand the benefits that come from providing more employees with access to data so they can proactively discover insights and come up with the next big idea. Crucial to this shift is creating a portal that’s usable for everyone–which is why Kubit has made self-serve data analytics possible through its user-friendly Query Builder (while also focusing on the issue of data quality, addressed previously).
Communication is a major part of building a data-driven culture, so with things like Slack and email, why is that still an issue? Because conversations on those platforms take place outside of the data and are often mixed in with conversations about other issues. Centralizing communications in the same place as the data will help teams avoid the common pitfall of doing parallel or redundant work, and makes searchable, organized input easy. This need for aligned, data-based ideation is why Kubit made its collaborative communication tool a part of its platform–because getting lost in email threads is not collaborative communication.
I’ll end this post with one final observation coming out of Product World: there is no shortage of project management, data, and AI software out there. But just like finding the right data for each product, every company must find the right software for them. That doesn’t mean more bells and whistles–it means finding the software that will help teams close the gaps between each other, between themselves and their product users, and between an ever-growing set of data sources.
Despite the competition, I feel we’re on the right side of history; Kubit’s team has brought their decades of collective experience working in product analytics to address the pain points that so many companies are still experiencing with data–and I look forward to seeing the competitive heights our users will soar to.
