Revenue is trending down, the pipeline feels lighter, or your campaigns aren’t delivering the results you expected. You can see the numbers, but you need to understand what’s happening and whether this is a short-term fluctuation or an early signal of something bigger.

So you start digging. You move between dashboards, compare time periods, cross-reference metrics, and pull in context from different teams.
The answers are there, but they’re scattered. And by the time you’ve pieced them together into a clear explanation, you’ve burned hours you didn’t really have.

What if you could just ask your data what changed, and get a clear answer in seconds?

Meet Genie, your AI Analyst

Genie is an AI analyst built directly into Databox. It lets anyone on your team ask questions about performance in plain English and get clear, contextual answers in seconds.

Here’s what you can do:

Get instant answers about your performance data

Ask Genie a question like:

• “Why are leads down this week?”
• “How is Q2 tracking against forecast?”
• “What changed in paid performance month over month?”

Genie analyzes your metrics, identifies trends, and explains what’s happening in plain English. It’s the kind of analysis you’d expect from your best analyst, delivered in seconds.

Create metrics, Databoards, and Reports by describing what you need

Need to track something new? Describe what you want, and Genie builds it. No templates to navigate, no configuration to wrestle with. If you can say it, Genie can build it!

Share your chat with anyone

Send a link to your Genie conversation to anyone, even people outside of Databox. Whether it’s a performance explanation your team needs to see, or an insight you want to hand off to a client, sharing is one click away.

Match Genie’s thinking to what the question actually needs

Switch between Standard mode for everyday tasks and Extended mode for deep analysis on more complex questions. You stay in control of the speed, depth, and cost of every response — so you’re always getting what the moment actually calls for.

Train Genie on how your business defines success (Coming soon)

Soon, you will be able to upload strategy documents or add custom instructions so Genie understands how you measure success. The more context it has about your goals and definitions, the more precise and relevant its answers become.

Find anything in your account with natural language search

Stop hunting through menus. Ask Genie to find a specific metric, Databoard, or goal, and it surfaces it instantly. It works like a search bar that actually understands what you mean.

Access conversation history, and keep building on past analysis

Genie saves your previous conversations, so you can revisit past questions or reference earlier insights.

Use Genie from anywhere, on any device

Genie is available on mobile, so you can check performance, ask questions, and explore insights wherever you are — before a meeting, between calls, or on the go.

Built for teams who need answers

Whether you’re running the business or running the numbers, Genie works for you.

• VPs, department heads, and team leads: MQLs are down 20% week-over-week, and leadership wants answers by the end of the day. You ask Genie why. It ties the drop to a specific paid channel, compares it to the last 30 days, and highlights where to focus next, within a few minutes.
• CEOs and executives: It’s Monday morning. You want a quick pulse check before your leadership sync. Ask Genie how the business is tracking against Q2 goals, and it delivers a detailed summary that pulls in revenue, pipeline, and other key metrics — current, clear, and ready in seconds.
  Agency account managers and consultants: A client emails asking why conversion rate dropped last month. Instead of digging through multiple dashboards, you ask Genie so you can respond confidently within minutes.
• Data analysts and RevOps teams: You need a new metric for CAC by acquisition channel and a supporting Databoard. Describe it once. Genie builds it. You use the time you saved to tackle the attribution model that’s been sitting in your backlog.

From having data to understanding it

For a long time, getting clear answers from your performance data meant doing extra work. Even with dashboards built and integrations connected, understanding what changed and why still required digging through reports or relying on someone who knew how to pull the numbers.

Genie changes that. Instead of manually piecing together context, you can ask a question in plain English and get a clear explanation based on your metrics, goals, and recent trends. Whether you’re looking for a quick answer or trying to understand what’s driving performance, Genie helps every team move from numbers to decisions with confidence.

Your data has the answers. Now you can just ask. Try Genie free today!

The post New: Ask your data anything, and get clear answers in seconds appeared first on Databox.

Even simple questions often require jumping between dashboards, piecing together insights manually, or relying on a small group of data experts to dig in. The process can be slow, and it often leads to more questions than answers.

AI is starting to make performance analysis feel faster and more accessible, but most AI tools still lack the context teams need to trust the answers, like consistent metric definitions, historical performance, and the way your business actually measures success. Without that foundation, insights can feel difficult to act on with confidence.

That’s where Databox MCP comes in. 

It connects Databox to your AI tools, making your performance data available across your entire AI ecosystem. You can ask questions and get clear, contextual explanations about performance, combine insights with information from other tools in the same conversation, and use them to trigger workflows and take action automatically.

What is an MCP server?

An MCP (Model Context Protocol) server defines how AI tools and agents connect to your systems in a consistent, predictable way.

Most businesses rely on many systems, like analytics platforms, CRMs, and internal services, each with its own rules for how data is accessed and shared. An MCP translates those rules into a common structure, helping AI understand what data is available, what actions it can take, and how those actions should be used.

This means that when you ask AI a question, it can work with multiple systems at the same time to give you a more comprehensive answer.  It can see which systems it has access to, pull the right data, and use that information to explain what’s happening or trigger workflows and automations. 

Move faster, with trusted data 

Instead of AI operating in isolation, Databox MCP connects it to trusted performance data – making insights more accurate and easier to act on.

• A single place to connect the dots across tools: Databox MCP lets AI combine performance data with other platforms in the same conversation, giving you clearer explanations of what’s happening and why.
• Reliable answers, powered by trusted logic: Because AI is grounded in established metric definitions and business context, insights are more reliable and easier to act on with confidence.
• Insights available where work already happens: Teams can explore performance and ask questions directly from the AI tools and workflows they already use, reducing context switching and helping more people stay aligned.
• From insight to action, in seconds: Because insights stay connected to systems, they can flow into workflows, downstream tools, or ongoing processes, automatically. 

Connecting Databox to your AI tools

You can connect Databox to any AI tool and workflow platform that supports the Model Context Protocol, making performance data available wherever your team is already working. 

To help you get started, we’ve created simple guides that walk you through the setup:

• Claude 
• N8N 
• Cursor 
• ChatGPT
• Other MCP-compatible clients

What you can do with Databox MCP 

Databox MCP changes the role AI plays in analytics. Instead of building reports and searching for insights, AI makes it easier to explore performance and move work forward. 

Here are a few ways you can use Databox MCP:

1. Ask questions, get answers instantly 

When Databox is connected to your AI tools, you don’t have to start with dashboards or reports. You can start with questions. Ask AI things like: 

• “Which channels generated the most sales?” 
• “What is my marketing spend across channels this week? 
• “How much revenue was generated last week?”

The AI uses your Databox data to explain what’s happening in clear, natural language,  pulling trends, comparisons, and context automatically,  and replies in the language you ask in.

2. Turn messy data into reusable datasets

Instead of spending time fixing headers, formats, and inconsistencies by hand, you can upload raw exports and let AI standardize the data for you. Then, ask it to push the dataset to Databox, so it can be used in your reports. 

3. Merge data from multiple tools in the same conversation

Combine performance data with data from other tools in the same conversation. For example, you can compare ad spend from Databox with revenue from an external file or sales data from another system. 

4. Automate work 

Instead of checking dashboards and planning your next steps, you can let AI do that for you using live Databox data.

Connect Databox to your AI or automation tools and set up actions like:

• “Send me this report every Monday morning.”
• “Alert me if conversions drop last week’s average.”
• “Summarize this week’s performance and post it to Slack.”
• “Watch this metric daily and notify me when it’s trending up or down.”

5. Bring external data into Databox, on demand 

Ask AI to pull data from other sources and push it directly into Databox, where it can be visualized and reported alongside your existing metrics. This makes it easy to add new context, like benchmarks, supplemental data, or one-off datasets, without the manual effort.

Make AI work with your data

Teams are already using AI to think through problems and explore ideas. Databox MCP connects those conversations to trusted performance data, so answers are grounded in real metrics, consistent definitions, and historical context.

Try Databox MCP today and start using AI with data you trust.

The post New: Connect Databox to Claude, ChatGPT, N8N, and more!  appeared first on Databox.

We introduced powerful new capabilities that make it easier for anyone on your team to access the data they need, uncover insights, and share insights across the business —so decisions can happen faster and with more confidence.

Here are this year’s top highlights: 

Prepare your data for analysis, without code

 Create a reliable source of truth your team can use to explore performance and build metrics with confidence.

• Datasets: Build custom tables of raw data you can filter and standardize, then turn them into metrics your team can trust.
• Merged Datasets: Combine multiple datasets from different sources into one unified table, and use formulas to create new columns so you can build metrics with your exact business logic. 

Answer your data questions, faster

Explore your data from multiple angles and get to the details that matter, faster. 

• Drilldown to row-level data: Click into any metric built from a dataset to see the raw data behind it. 
• Advanced filters: Apply multiple filter conditions to your Databoards to explore performance across different segments and uncover insights more quickly.
• Color-coded charts: Enable color indicators for gauge and progress bars to quickly see whether you’re on track, at risk, or falling behind on your goals.
• Datablock Explorer: Expand a Datablock to explore a metric in more detail, so you can understand changes and identify trends.

Plan with more clarity and confidence

Build plans backed by data and keep your team focused and aligned, all year long.

• OKRs: Define objectives, break them down into measurable sub-goals, and keep your team aligned and focused on what matters most. 
• Forecast Modeling: Model different scenarios so you can test your assumptions before committing resources. 

Explain performance clearly and drive better decisions, together

Explain what happened, why it happened, and what to do next, so stakeholders can easily understand performance and align on next steps.

• Pageless formats: Create a scrollable pageless report, and switch formats anytime based on your audience’s needs.
• Presentation mode with speaker notes: Present performance confidently with full-screen slides and private notes that guide your discussion.
• Customization options: Add cover photos, tables, page dividers, backgrounds, and other styling elements to make your reports clearer, more engaging, and on-brand.
• Editable performance summaries: Edit your AI-generated summaries to add context, highlight key insights, and shape the narrative in your own words.

Keep your workspace organized and easy to manage as your team grows

Give your team a workspace that’s easier to navigate, maintain, and scale.

• Spaces: Organize Databoards, Reports, and metrics by team or function so everyone can quickly find what they need.
• Manage data sources & users from one screen: View and manage all data sources and users across your agency and every client account—from one place.
• Recover recently deleted Databoards and Reports: Find and restore Databoards, Loops, or Reports deleted in the past 30 days with one click. 
• Default visualization settings: Save your visual settings as the default for new Databoards and Reports, so everything stays consistent and polished, right from the start.

Bring in any data, from anywhere

Sync data from more tools, warehouses, and custom sources so your team can get a complete view of performance and find insights you would have otherwise missed. 

• New integrations: Connect Reddit Ads, Airtable, GoHighLevel, and more to visualize performance alongside your other KPIs. 
• Databases & cloud warehouses: Sync data directly from MongoDB, SAP HANA, Oracle, Amazon Aurora, Google Cloud, Amazon Seller Central, and other enterprise systems to make your backend data accessible to the teams who need it most.
• New Ingestion API: Send structured, raw data from any source directly into Databox, so you can centralize more of your business data and help your team make faster, more informed decisions.
• Partnership with Dataddo: Sync and prepare data from 350+ tools for analysis, including Amazon Advertising, Monday.com, Adroll, Asana, and many more!

Get the guidance and support you need to improve performance

Access on-demand education and a growing community that helps you use data more effectively and level up how your team operates.

• Predictable Scale: A free, self-paced course that teaches you how to build a clear strategy, turn it into a practical plan, align your team, and use data to forecast growth and consistently hit your goals.

• Databox Community: A growing space where users, partners, and product experts can connect, share best practices, and learn from each other. 

A look into 2026…

And this is just the beginning.  In 2026, we’re bringing AI to the center of the Databox experience to make working with your data faster, simpler, and more intuitive.

From Genie, your AI Analyst, to a completely reimagined data connectivity layer through Databox MCP, we’re building a future where insights are always within reach. 

Stay tuned! 

The post Product Highlights: 2025 Year in Review appeared first on Databox.

• Data lives in a less commonly used regional app
• Data lives stored in a custom internal tool
• Data lives in a database or format that requires technical preparation

While Databox already connects to 130+ of the most popular tools, some teams need access to a broader range of integrations and more flexibility to prepare their data for analysis.

That’s why we’ve partnered with Dataddo, a no-code data integration platform that now connects directly with Databox. 

With Dataddo, you can sync structured, row-level data from 350+ tools into Databox and prepare it for analysis, so you can track more of your data in one place, build metrics that actually match how your team operates, and uncover insights you couldn’t see before. 

What is Dataddo?

Dataddo is a data integration platform designed to make it easy for business teams, not just engineers, to move data between the tools they use. It works behind the scenes to pull data from a wide range of cloud services, applications, and databases, then delivers it in a structured format to the system of your choice. 

It connects to everything: CRMs, ERPs, ad platforms, finance tools, databases, cloud services, and even flat files, giving you the flexibility to bring in data from across your entire business, no matter where it lives.

What you can do with Dataddo & Databox 

• Multi-Channel Marketing Performance: Combine campaign data from platforms like Amazon Ads, Apple Search Ads, or Outbrain with Shopify or CRM data in Databox.
• Customer Support and Revenue Insights: Connect tools like Gorgias or Freshworks CRM and see how support activity impacts revenue metrics.
• Enterprise and Finance Reporting: Integrate systems like Odoo, NetSuite, or Microsoft Dynamics 365 and pair them with CRM or marketing data to get a unified view of financial and operational performance.
• Database and File-Based Sources: Bring in data from AWS RDS, S3, or flat files and blend it with customer and marketing insights in Databox.
• Sales Channels and Marketplaces: Unify revenue streams from Shopify, Mercado Libre, or Square and analyze them alongside your advertising spend.
• Lead Generation and Funnel Tracking: Feed top-of-funnel tools like Typeform, Sendinblue, or RD Station Marketing into Databox and follow prospects through the entire funnel.

Dataddo + Databox: Turn your data into insights your team can act on

Dataddo expands what’s possible in Databox, both in the tools you can bring in and the ways you can use that data once it’s there.

By combining Dataddo’s broad data coverage with Databox’s powerful reporting and analysis features, you can: 

• Track more of your business data in one place:  Bring in data from platforms you couldn’t access before, from regional tools and internal systems to cloud databases and ecommerce apps. And if you don’t see your tool listed, Dataddo can build a connector for you, so you’re never stuck or forced to work around missing data.
• Make more of your data analysis-ready: All Dataddo-synced sources are datasets compatible, so you can clean, merge, and transform data before it powers your reporting. That way, you can feel confident that your metrics are accurate and built on the right logic.
• Build custom metrics that reflect your business: Merge datasets and calculate columns so you can create custom metrics tailored to your team’s goals, processes, and definitions of success.
• Report faster and with more confidence: With more data structured and ready to go, you can spend less time wrangling spreadsheets and more time making decisions based on real-time, complete insights.
• Share performance data confidently: Easily share insights with stakeholders while keeping sensitive information protected. When syncing datasets from Dataddo, you can hash or exclude fields like names or emails before they reach Databox. Once inside Databox, built-in security features, like user permissions, flexible access controls, and password-protected links, ensure your reports show only what’s needed, and only to the right people.
• Give every stakeholder the full picture: Whether you’re blending data across departments or sharing reports with execs, your dashboards and reports will include everything that matters. 

Connect Dataddo Today

With Dataddo and Databox working together, you can bring more of your performance data into one place, prepare it for analysis, and turn it into insights your team can act on.

Whether you’re tracking performance across departments or building metrics tailored to your business, you’ll have the foundation you need to report with confidence.

Connect Dataddo today! 

The post New in Databox: Sync Data From 350+ Tools With Dataddo appeared first on Databox.

It’s annual planning season!

Across the company, leaders are setting targets, scoping new initiatives, and shaping the roadmap for the year ahead.

But if you’ve been through this before, you know how quickly plans lose relevance. Goals that looked clear on paper become harder to measure in practice, teams lose sight of the bigger picture, and the assumptions you made about resources and impact rarely hold up.  

And now, with markets changing overnight and resources stretched thin, annual planning is only getting harder. To succeed in 2026, companies will need a new approach – one that makes plans measurable, adaptive, and grounded in data.

That’s why we launched two new features to help you build annual plans that last:

• OKRs, so you can define clear objectives, set measurable goals, and give every team visibility into how their work contributes to company success. 
• Forecast Modeling, so you can test your assumptions and commit to targets with confidence. 

Together, they’ll give you a new system for connecting strategy to execution, and the flexibility to stay on track when things change.

Build annual plans that teams actually deliver

In most companies, the planning process is fragmented: strategy gets documented in slide decks, plans are built in spreadsheets, and progress is tracked in separate tools. 

With OKRs and Forecast Modeling in Databox, you can: 

• Align your entire company: Set your strategy, define company-wide objectives with connected goals and sub-goals, invite unlimited users, and make the plan visible to everyone. 
• Manage everything in one place: OKRs, forecasts, dashboards, and performance data all live in Databox, so strategy, planning, and tracking are finally connected.
• Track progress automatically: OKRs update in real time and send notifications when things change, so your team always knows whether you’re on track or need to adjust.
• Build plans based on reality: Convert forecasted values into measurable OKRs, so plans are grounded in data from the start. 
• Test assumptions before committing resources: Run scenarios to see the impact of different strategies, resources, or market conditions, and commit only to realistic ones. 
• Create shared accountability: Assign ownership and cascade OKRs down to teams and individuals so everyone understands their role in achieving company objectives, and stays accountable for results.

Start building your annual plan in Databox

Here’s how to build your 2026 plan from the ground up, using OKRs and Forecast Modeling:

1. Define your “north star”

Before you define objectives or set targets, your team needs to understand what they’re working toward, and why.

That’s why the first step in setting up OKRs in Databox is to use the Strategy Overview to document your company’s mission, vision, and long-term goals. This gives every team a shared place to guide planning and see how their daily work connects to the bigger company strategy.

2. Turn your strategy into clear objectives

Once your strategy is in place, it’s time to define your Objectives—the high-level priorities your company is committing to this year.

When creating a goal in Databox, you’ll see an option to create an Objective. Setting up an objective is simple: 

• Add a title to capture the high-level priority
• Assign an owner and accountable team so responsibility is clear from the start
• Set a timeframe to keep progress measurable and time-bound
• Choose privacy settings to control who can view and contribute

Once created, you can click into the objective to access the details page. Here, you’ll see everything related to that objective in one place, like progress history, owner, and the specific goals and sub-goals linked to it. 

3. Make your objectives measurable

Once you’ve set an objective, the next step is to define the Key Results—the measurable outcomes that show whether you’re making progress.

In Databox, Key Results are represented as Goals. 

From the objectives detail page, you can link existing goals or create new ones. Goals can be created from the metrics you’re already tracking, so progress is updated automatically. Or, you can set them manually by entering target values and updating progress yourself. Learn more about Databox Goals.  

For example, if our objective is to “Grow our recurring revenue base,” one Key Result could be to “Increase MRR by $100K this year.” In Databox, we’d create a new goal for MRR and link it directly to the “Grow recurring revenue” objective, making the connection between strategy and execution clear.

4. Test your assumptions before committing resources

Once you’ve defined your Key Results, the next step is to make sure they’re achievable and understand what it will take to get there.

That’s where Forecast Modeling comes in.

It lets you simulate different scenarios using historical data and the drivers that impact your main KPI. You can choose from five forecasting models at launch, with a sixth model planned to support additional needs. Add impacting metrics to see how they influence performance, and then model increases or decreases to test different assumptions.

The result? You’ll know whether your goals are realistic and which levers matter most, so you can commit resources with confidence.

For example, if our Key Result is to “Increase MRR by $100K this year,” we can forecast MRR and add impacting metrics like Net New MRR, Churn Rate, and Expansion MRR. From there, we can test different assumptions like: 

• What if Net New MRR grows 15%?
• What if churn improves by 2%?
• What if expansion increases 10%?

Databox will then generate three projections side-by-side (Optimistic, Realistic, and Pessimistic), along with correlation scores that show which drivers have the greatest impact on our main KPI. 

Instead of guessing, we can quickly validate if $100K is achievable and see exactly which areas we need to prioritize to get us there. 

5. Turn forecasts into trackable goals

You can keep tweaking your model until the forecast aligns with your target. Once you’re confident in the scenario, you can use forecasted values to set measurable goals.

While this won’t be available in the initial launch, it’s already planned as an improvement, so you can validate your assumptions and turn them into trackable, accountable goals. 

Then, link the goal directly to the Key Result or Objective it supports using the details page. That way, your forecast becomes a live goal your team is accountable for. 

For example, let’s say our model showed that hitting $100K in new MRR depends on reducing churn. We can convert that churn prediction into a goal, then add it as a sub-goal under our MRR Key Result.  

6. See how your plan comes together

By repeating these steps, you can build out your full annual plan, documenting your objectives, linking measurable goals, and validating them with forecasts.

Once your plan is in place, OKRs will make it easy to see how everything connects. With the Strategy Map view, you can visualize the relationships between objectives, goals, and sub-goals at a glance. Or, switch to a List View for a more detailed breakdown of your progress.

7. Align your entire company around what matters most

With unlimited users in Databox, you can invite your entire company to participate, so everyone has visibility into the strategy and understands how their work contributes to the bigger picture.

Each team member will be able to see the objectives, goals, and sub-goals they’re responsible for, while automated notifications will keep everyone accountable. If something starts to fall off track, teams will be alerted right away so they can adjust before small issues turn into bigger problems. Leaders will get transparency across departments, and individual contributors will get clarity on how their daily work drives company success.

A better way to plan and execute in 2026

For too long, annual plans have lived in slide decks and spreadsheets, quickly losing relevance as assumptions change and teams lose focus. 

With OKRs and Forecast Modeling in Databox, you can align your company around clear objectives, validate your plans with data, and keep every team accountable as the year unfolds.

Start building your 2026 plan in Databox today!

And to help you make the most of these tools, learn the framework behind them in Predictable Scale, our free, self-paced course. You’ll learn how to set a focused strategy, turn it into a clear plan, and keep teams aligned as you execute.

The post New in Databox: Build Annual Plans That Actually Work With OKRs and Forecast Modeling appeared first on Databox.

When I first started working on the OKRs feature at Databox, I interviewed customers to understand how they apply OKRs and similar frameworks in their companies today.

Again and again, I heard the same story: it’s hard enough to set the strategy and write the OKRs, but it’s even harder to align teams around them, keep them visible, and avoid the endless cycle of manual updates.

Companies invest considerable time and energy in defining objectives and key results, only for them to be forgotten by Q2. Updates quickly feel like admin work. Tools are disconnected from performance, making it hard to track progress. And what was meant to drive focus often ends up as just another “management exercise.”

That’s when it became clear: the problem isn’t with the idea of OKRs, it’s with how they’re implemented and managed. And that’s the gap we set out to close.

In the rest of this post, I’ll walk through what we learned and how those insights help shape OKRs in Databox.

The problem with OKRs today

Through interviews with business executives, team leads, and individual contributors, I’ve found that the biggest challenges with OKRs lie in how they’re put into practice.

Here are the four common themes I’ve heard in those conversations:  

1. OKRs fade after the kickoff

Teams set OKRs at the start of the quarter, but without regular visibility, they slip out of focus. By the next review, most feel outdated or disconnected from day-to-day work.

2. Manual updates kill momentum

Most teams still track OKRs in spreadsheets or slide decks. The result? Updates get skipped, data goes stale, and OKRs lose the ability to keep teams accountable.

3. Tools are disconnected from execution

Too often, OKRs live in HR systems or dedicated planning apps, while the actual performance metrics live somewhere else. This forces constant context-switching and makes it hard to connect strategy with execution. Goals exist on paper, but teams can’t see if their daily work is truly moving the needle. 

4. Complexity slows teams down

Ironically, some OKR software makes things harder instead of easier. Heavy structures, jargon-filled workflows, and added admin tasks bog teams down. Instead of helping teams focus, OKRs become another layer of process that gets in the way of execution.

Our goal: make OKRs stick

We didn’t want OKRs in Databox to become just another place where objectives get written down and forgotten. From the start, we set out to design them in a way that would actually help teams use OKRs to stay aligned, focused, and accountable.

That started with a few guiding principles: 

1. Keep it simple. Teams should be able to start small and build confidence without unnecessary overhead.
2. Connect every level. OKRs should link company strategy to team and individual goals, so alignment is always clear.
3. Automate progress. Tracking should never feel like busywork. Progress must update automatically from live performance data.
4. Grow with you. Once the basics are working, teams should be able to expand into more advanced frameworks without friction.

Or simply put: to design OKRs that truly turn strategy into execution.

Introducing OKRs in Databox

Based on what we learned and our guiding principles, here’s how we approached OKRs: 

Define strategy and align across levels
Start with your company’s vision and mission, then cascade objectives and goals down to teams and individuals, so everyone can see how their work connects to the bigger picture.

Connect objectives with goals — powered by live data
Progress updates automatically from the metrics you already track in Databox. No manual updates, no busywork.

Visualize alignment at a glance
Expand or collapse your hierarchy of objectives, goals, and sub-goals in List View, or switch to Strategy Map for a visual snapshot that’s perfect for leadership reviews and all-hands.

Stay on track together
Each objective and goal includes clear statuses, regular updates, and automatic notifications. You’ll know when progress is off track, so you can respond before it’s too late.

A familiar experience, extended
If you already use Goals in Databox, OKRs will feel like a natural extension. You can connect existing goals as sub-goals under higher-level objectives, building a complete strategy framework without starting from scratch.

What are users most excited about

When we shared the OKRs prototype with our users, the response was immediate and enthusiastic. Here’s just a glimpse of the excitement we heard:

Making OKRs work at every level of the company

Databox OKRs are designed for the people who need them to actually work, not as another management exercise, but as a daily driver for focus and accountability. 

• Executives get instant visibility into priorities and progress across the company.
• Analysts track progress against OKRs, ensure data accuracy, and surface insights that keep teams on track, so leaders can make decisions with confidence and contributors stay aligned.
• Team leaders align team goals with strategy and keep execution front and center.

Individuals see exactly how their work contributes to the bigger picture.

Going forward

This is just the beginning. OKRs are the foundation of a broader Strategy & Planning pillar in Databox, one that connects long-term vision with daily execution and performance data. 

We’re rolling out early access soon. If you’d like to be among the first to try OKRs in Databox and share your feedback:

Join the waitlist:

Want a sneak peek in the meantime?

Watch the prototype walkthrough video:

The post The Story Behind OKRs: Why We’re Building Them in Databox appeared first on Databox.

When we first introduced Teams in Databox, the goal was to give every team a “home base”, a shared space where they could collaborate around the metrics, Databoards, and Reports that mattered most.  

Executives could monitor company-wide performance at a glance. Team leaders could use it to organize dashboards around key initiatives and keep their teams aligned. And individual contributors could quickly access the data most relevant to their role.  

For many of you, that was the case. But as we continued collecting feedback and studying how you were actually using the feature, we realized something important: 

Data organization isn’t always tied to teams. It’s tied to the work itself.

You weren’t just looking to create spaces for departments; you wanted to create them for projects, regions, campaigns, and more. 

This insight led us to a small but meaningful change: renaming Teams to Spaces. It’s a name that better reflects how you’re already working and gives you more flexibility to organize your data around the way your company actually operates.

While this is just a name change for now, it’s the foundation for more improvements to come! 

Spaces, at a glance

Spaces still includes everything you know and love about Teams. Here’s a quick look at what you can do:

• Group related insights in one place: Add metrics, Databoards, and Reports to a Space and create a single source of truth for your team or project.
• Manage visibility: Choose who can view or join a Space by selecting from three privacy settings (Open, Closed, or Private )
• Keep things organized with Sections: Structure each Space by department, workflow, or focus area to make navigation easy.
• Add the right collaborators: Invite users to the Spaces that matter to them, so they can get fast access to the data they need.
• Explore other Spaces: Jump into other Spaces to view related data, without bottlenecks.

What could your Space look like?

During our interviews and research, we saw countless examples of how companies were using Spaces (formerly known as Teams) to organize their work. Here are just a few:

• A global company managing marketing across 10 countries created regional Spaces, like “Europe” and “North America”, to give each region a clear, dedicated place to track performance.
• A startup running multiple growth initiatives used Spaces to organize Databoards and metrics by project, bringing cross-functional teams together in one view.
• A leadership team overseeing company-wide performance set up an Executive Space to centralize reports and KPIs.

What’s next: Smarter organization, simpler access control as you scale

As your team and data grow, staying organized and controlling who has access to what becomes increasingly important. That’s why we’re making improvements to help you manage both, without adding complexity.

Soon, you’ll be able to add even more resources to each Space, including Goals, Forecasts, and Benchmarks, making it easier to centralize everything related to a specific project, department, or region. You’ll also be able to use Spaces as filters across the app, so you can quickly find the data you need, without searching through long lists.

Looking ahead, we’re also working on new ways to manage tackle access at scale with Groups.

Groups will give you a simpler way to manage access, so you can grant access to multiple assets at once, and automatically keep it up to date as your team changes. 

• Create user Groups based on functions or teams, like Marketing, Sales, Leadership, and more. 
• Give a Group access to specific Databoards, Metrics, and Goals in just one click
• Automatically grant access to new members when they’re added to a Group
• Eliminate repetitive permission work as your team grows

So… Spaces will help you organize your data. And Groups will help you control who can access it. Together, they’ll make it easier to scale Databox across your organization!

A small change that opens up bigger possibilities

Renaming Teams to Spaces is a simple change, but one that better reflects how you’re already organizing your work in Databox. 

Everything you could do before is still here, and soon, you’ll be able to do even more. With upcoming support for additional data types and easier access management through Groups, we’re laying the foundation for a more scalable, flexible way to manage performance across your organization.

The post Product Update: “Teams” Are Now Called “Spaces” appeared first on Databox.

To change that, we made several improvements based on user feedback. We added support for seasonality and holidays. Introduced a confidence score to help teams understand how reliable their projections were. And made it possible to save forecasts for future comparison.

Each update made the feature more powerful, but even with all those changes, adoption barely moved. 

So, we had to ask ourselves: Were we solving the right problem in the first place?

To find out, we conducted a round of Jobs To Be Done (JTBD) interviews with people across different roles, like executives, RevOps leaders, marketers, finance managers, analysts, and agencies. 

Our goal was to understand what forecasting looked like in their day-to-day work. 

What were they trying to figure out when they built a forecast? What decisions did they need to make? And what was stopping them from using forecasting tools like ours?

In the rest of this post, I’ll share what we learned and how it led us to rebuild forecasts into a tool teams actually want to use for planning.

The problem with forecasting as we knew it

Through those interviews, we started seeing the same patterns come up again and again. No matter the role or company size, the struggles were surprisingly consistent:  

Forecasts stop at prediction

Most forecasting tools, including ours, only show what feature results might look like if past trends continue.  They don’t explain why results are trending in a certain direction or what could be done to change the outcome.

No way to test assumptions 

Teams wanted to add correlated metrics to their forecasts so they could test assumptions, explore outcomes, and see which path would get them to their goals.

Limited forecasting methods

Some teams needed advanced models with regressors. Others preferred simple trend-based forecasts. Many wanted to override the system with their own numbers. But most tools only offered one method, which didn’t always fit their data.

Assumptions were hidden

For annual planning, quarterly reviews, or campaign targets, most teams were still building scenarios in spreadsheets. While flexible, this approach buried important assumptions (like correlation factors) inside formulas.

The goal: forecasting that drives planning

We didn’t want forecasting in Databox to stop at predictions. 

We wanted to build something that actually helps teams plan: test ideas, explore different scenarios, uncover dependencies, set goals, and make more informed decisions on what to prioritize next.

Introducing Forecast Modeling.

Here’s what’s new: 

From prediction to explanation
Add impacting metrics to your forecasts and see how they correlate, so you can understand what’s driving performance and what you can do to improve it.

From static forecasts to what-if modeling
Adjust inputs and run scenarios to compare outcomes instantly, without playing with formulas in spreadsheets.

From one-size-fits-all to flexible methods
Choose the forecasting method that fits your data best. Use Databox’s advanced model, regression, smoothing, or full manual control.

From hidden assumptions to transparent modeling
See every forecasted value and correlation factor in one table. With every input and assumption visible in one place, it’s easier to explain, adjust, and keep your team on the same page.

Built for people who actually plan

Right now, most planning still happens in spreadsheets. Teams export data from their tools, move it into a spreadsheet, and build formulas from scratch before they can even start modeling outcomes. 

Forecast Modeling was built to change that. 

It’s designed for the people who actually drive planning in their orgs: marketing leaders, finance managers, RevOps teams, and executives who need to move fast, adjust often, and back decisions with data. 

It gives them the flexibility and power of spreadsheets, without the friction. Forecasts are easy to build, quick to adjust, and directly connected to the performance metrics they already track in Databox. 

This isn’t a tool for data analysts or data scientists who want to build complex models from scratch. It’s designed for people who need simplicity, but also the power and flexibility to plan with confidence, without writing code or maintaining massive spreadsheets.

What are users excited about

During validation interviews, users described exactly how they plan to use Forecast Modeling to simplify their planning. Here’s what they said: 

Finding what really drives results
A Digital Marketing Manager told me that lead generation is her primary goal. In the past, she forecasted leads based on clicks from different platforms and rough assumptions about conversion rates. With Forecast Modeling, she can connect those inputs directly, test different assumptions, and quickly see which platform has the biggest impact.

Planning with confidence across different periods
A RevOps Director shared how his team sets monthly and quarterly targets by comparing results to both the previous month and the same period last year. Until now, that meant digging through spreadsheets and building formulas for every new cycle. With Forecast Modeling, he could run those comparisons automatically, adjust assumptions, and spend more time planning instead of number-crunching.

Replacing spreadsheets with scenarios
A Sales Manager at a B2B company said he always prepares pessimistic scenarios to account for risks like rising fuel costs or staff shortages. Previously, that meant tweaking Excel formulas manually. Now, with Forecast Modeling, he can build and save those scenarios directly in Databox, making them easier to share with his team.

Turning forecasts into goals and OKRs
That same sales manager explained how frustrating it is that forecasts and goals live in separate places. His team would forecast revenue in Excel, but track goals in Databox, making it hard to stay aligned. With Forecast Modeling, he can turn any forecast into a goal, track progress automatically, and set plans that are grounded in data. 

Looking ahead

Forecast Modeling is just the beginning. It lays the foundation for a more flexible, dynamic approach to planning in Databox.

As a product manager, I know a feature only succeeds if it solves real problems. That is why we started with research, validated with a prototype, and shaped the experience around direct feedback. The first reactions have been very positive. Users are excited to try it! 

Still, nothing replaces real-world usage. I’m excited to get this into your hands, and even more excited to hear your feedback!  

If you want a sneak peek, you can watch the video here. And if you would like to be notified as soon as Forecast Modeling is available, you can join the waitlist below.

Join the waitlist

The post The Story Behind Forecasts: Why We’re Rebuilding It (and What We’re Learning) appeared first on Databox.

MongoDB is one of the most popular databases for modern applications. Its flexible, document-oriented model makes it easy for developers to store and query everything from user activity logs and product usage events to application telemetry.

But while MongoDB is great for storing and organizing data, it’s not always easy for business teams to use it to answer questions or track key metrics. The data often lives in a raw format that requires technical skills to interpret. This means non-technical teammates must rely on engineers or analysts for reports—slowing down decisions and keeping valuable insights siloed. 

That’s where Databox comes in. Our MongoDB integration lets you connect your database and instantly turn collections into clear, actionable dashboards anyone can use. Track KPIs in real time, set up automated reports and alerts, and give every team the insights they need to make better decisions, faster.

Why Use MongoDB with Databox?

Instead of keeping valuable insights siloed in a database only engineers can query, Databox makes your backend data accessible and actionable for your entire team.

• Access backend data without writing code: Turn raw MongoDB collection’s data into metrics and dashboards that teammates can explore on their own—no queries or BI tools required.
• Build metrics your entire team can trust: Pull exactly the data you need, apply filters, format columns, and merge it with other sources. Then create custom metrics from a single, reliable source of truth.
• Get real-time visibility into performance: Dashboards and reports update automatically as your MongoDB data changes, giving every team an up-to-date view of KPIs and trends.
• Create a unified view of performance: Combine MongoDB metrics with data from your CRM, marketing, finance, and other tools to see your business performance in one place.

MongoDB Integration with Databox Overview

In just a few steps, you can securely connect your MongoDB data to Databox, prepare it for analysis, build trusted metrics, visualize trends, and automate reporting.

Connect MongoDB

Whether you use MongoDB Atlas (cloud) or host it yourself, connecting is straightforward:

1. Go to Available Integrations in Databox.
2. Select MongoDB and enter:
   • Mongo URI (connection string
   • Read-only authentication credentials (recommended)
   • Database name
3. Click Connect and select the collections you want to pull in

For more information, have a look at this article: Connect Databox to MongoDB

Prepare Your Data for Analysis

Once connected, you’ll create a dataset—a curated table of raw data optimized for analysis.

Select the collections and fields you want to work with, then:

• Apply filters to remove irrelevant records and focus on the data that matters most
• Format columns as currency, dates, or other types to ensure consistency
• Use formulas and functions to create calculated columns that reflect your unique business logic
• Merge multiple datasets from different sources to provide full context in one place. 

For example, for a MongoDB collection like:

JSON

{

  "user_id": "a1b2c3",

  "event_type": "signup",

  "timestamp": "2025-07-01T09:42:00Z",

  "plan": "Pro"

}

You might want to build a dataset that pulls: 

• timestamp as a date field
• event_type as a dimension
• And create a calculated column that counts the number of “signup” events per day

With your dataset in place, your data is clean, structured, and ready for reliable reporting.

Build Metrics Your Team Can Trust

From your prepared dataset, create the KPIs and metrics that matter most to your business, whether that’s daily signups, feature adoption rates, or revenue by plan type. 

Every metric comes from a single, curated source of truth, so your team is always working with accurate numbers.

Visualize & Analyze Trends

Once you have your metrics, use Databox’s no-code metric builder to visualize them and uncover insights: 

• Combine MongoDB data with CRM, marketing, finance, or operational metrics.
• Drill into row-level data directly from charts or tables.
• Filter dashboards by dimensions (e.g., region, campaign, product line).
• Compare performance across time periods.

Build Reports & Automatically Deliver Performance Updates

You can also create comprehensive reports that keep your team and stakeholders informed. Add existing dashboards and metrics to provide full context, and use AI-generated summaries to explain performance in plain language. 

Then, schedule reports to be delivered automatically, daily, weekly, or monthly, so the latest insights are always in the right hands at the right time.

Use Case: Visualizing Product Usage Events 

Let’s say you have a MongoDB collection tracking every interaction with your product:

Collection: product_events

Right now, those records are just raw data. But with Databox, you can turn them into a live, interactive view of product adoption that your product managers, marketers, and execs can explore anytime—without writing a single query.

In minutes, you can:

1. Connect your product_events collection to Databox with read-only access—keeping your data secure.
2. Prepare your dataset by selecting timestamp and feature as fields, formatting them, and filtering out anything irrelevant.
   Create your “Daily Feature Usage” metric to automatically count interactions for each feature by day.
3. Bring it to life in a dashboard:
   • A line chart showing usage trends over time to spot adoption patterns.
   • A table highlighting the week’s most popular features.
   • Automated alerts whenever there’s a spike in usage after a new release.

Instead of sifting through JSON logs or running ad-hoc queries, your team gets a clear, visual story of how users are engaging with your product, helping you double down on what’s working and fix what’s not.

The post New Integration: Turn MongoDB Data into Beautiful, Actionable Dashboards with Databox appeared first on Databox.