<p>Breaking down some of the problems I’ve seen in data collaboration and offering advice on how to make better, faster decisions with collaborative analytics.</p>
<p>We all know how analytics can help make businesses make better decisions. Being data-driven just simply isn&rsquo;t an option anymore. Startups are collecting more data than ever before and are increasingly using that data to&nbsp;<a class="mds-text-primary" rel="noopener" target="_blank"  href="https://a16z.com/2011/08/20/why-software-is-eating-the-world/">eat the world</a>. We&rsquo;ve seen the rise of&nbsp;<a class="mds-text-primary" rel="noopener" target="_blank"  href="https://www.whatmatters.com/articles/ted-talk">OKR goal-setting</a>, reporting tabs in nearly every SaaS tool, and metrics dashboards lining the walls in office hallways.<br /><br />Yet for all of this progress, the ways we share insights and make decisions together haven&rsquo;t evolved over the last decade. Too many companies spend too long collecting, preparing, and reporting on data to improve. In this post, I&rsquo;ll break down some of the problems I&rsquo;ve seen first-hand in data collaboration and offer advice on how you can create a better environment for creating, sharing, and measuring data together.</p>
<h2 id="the-problems">The problems</h2>
<h3 id="finding-a-source-of-truth"><br />Finding a Source of Truth</h3>
<p>Startups now have a plethora of dashboards in our tools to find data. But it&rsquo;s still too challenging to find the data you need. Legacy BI tools suffer from&nbsp;<a class="mds-text-primary" rel="noopener" target="_blank"  href="https://venturebeat.com/2022/04/10/7-easy-ways-to-fight-the-data-sprawl/">dashboard sprawl</a>&nbsp;and poor search experiences. It&rsquo;s challenging to agree upon and standardize metrics to get common questions answered in a scalable way.</p>
<p>To fix this, we&rsquo;ve tried creating data catalogs so operators can more easily answer FAQs and find the data they&rsquo;re looking for. We&rsquo;ve tried to provide definitions for data to help operators understand what tables entail, how metrics are calculated, and what a particular column means. These efforts result in incremental progress towards becoming data-driven, but more is needed.</p>
<h3 id="data-breadlines">Data Breadlines</h3>
<p>Too few employees have access to real-time data to make everyday decisions. When operators need to query the database or ask a question involving data from multiple sources, they&rsquo;re&nbsp;<a class="mds-text-primary" rel="noopener" target="_blank"  href="https://canvasapp.com/blog/why-were-building-canvas-a-data-tool-for-non-technical-teams/">forced to ask for help</a>&nbsp;in a shared analytics channel or submit a ticket to their data or engineering teams.</p>
<p>This process results in a painful waiting period until the technical team can prioritize the request. Even when the data or engineering team can prioritize a request, there&rsquo;s typically a frustrating back-and-forth of questions and answers across Slack, Email, and Zoom.</p>
<p>Operators shouldn&rsquo;t have to wait days or even weeks for the answers they need, while technical teams should be able to focus on strategic work without constant pings for help on&nbsp;<a class="mds-text-primary" rel="noopener" target="_blank"  href="https://canvasapp.com/blog/enabling-ad-hoc-analysis/">tactical, ad-hoc questions.</a></p>
<h3 id="siloed-broken-workflows">Siloed, Broken Workflows</h3>
<p>Stop me if you&rsquo;ve gone through this process before. You need to prepare for a presentation. You go to your apps, pull CSVs, build some metrics, screenshot some charts, and throw them into a deck. You present the information and get some live questions about the data. Unfortunately, because the chart is a static screenshot, you can&rsquo;t dive into the data live, so you promise to follow up afterward.</p>
<p>The hours-long workflow we endure to prepare presentations across a suite of apps is fundamentally broken. There&rsquo;s no connective tissue between the data sources, the analysis tools, the presentation, and the feedback on the data.</p>
<p>Because of these silos, we may get fewer eyeballs from peers before a presentation. We might forget to include a killer insight or make a potential error in our analyses. Because it's too difficult to answer questions together live during a presentation, we can get caught flat-footed and look unprepared. And because it's so difficult to measure and discuss progress on metrics together, we fail to monitor updates closely and act quickly and decisively as teams.</p>
<h2 id="how-collaborative-analytics-helps">How Collaborative Analytics Helps</h2>
<p>To help teams move quickly to get the answers they need, you should consider investing in collaborative processes and tools. See below for some key components you should consider.</p>
<h3 id="the-warehouse-as-a-source-of-truth">The Warehouse as a Source of Truth</h3>
<p>Having a single source of truth that&rsquo;s accurate and up-to-date ensures your teams can all work off the correct data independently and confidently. Best-in-class companies typically use a data warehouse and ETL tools to centralize data from multiple sources in one place. If you don&rsquo;t know where to start, check out my guide on navigating the tools in the modern data stack.</p>
<p>Suppose you don&rsquo;t have the bandwidth or technical experts to implement a data stack correctly. In that case (time for a quick plug),&nbsp;<a class="mds-text-primary" rel="noopener" target="_blank"  href="https://canvasapp.com/blog/no-warehouse-no-problem/">Canvas provides a managed data stack</a>&nbsp;for centralizing your apps and modeling your data in one place.</p>
<h3 id="reusable-datasets-for-fast-discovery">Reusable Datasets for Fast Discovery</h3>
<p>With your data in one place, you&rsquo;ll need to organize it, provide context, and make it easy to find. Key metrics and common areas should be searchable and social proof should help teams quickly identify the right data to answer their questions.</p>
<p>You should have clear owners for building and maintaining dashboards on the company and team levels. They should define and document data clearly and ensure a clear process for getting help on questions. You should be aware of which dashboards are frequently used and invest accordingly, while continually improving or pruning dashboards that are ghost towns.</p>
<h3 id="collaborative-workspaces-for-exploration">Collaborative Workspaces for Exploration</h3>
<p>Shared, collaborative workspaces are critical in breaking down silos and helping teams share and discuss insights with others.</p>
<p>Yes, having dashboards are a must. But as I pointed out, your teams will still have everyday questions that dashboards don&rsquo;t answer. Instead of a ticketing system, you should have a way for your operators to answer these questions without knowing SQL.</p>
<p>No-code and spreadsheet-like interfaces help your operators gain self-serve access without going days or weeks for technical teams to answer strategic and tactical questions. And when they have a question, operators and technical teams can collaborate and comment right where the data lives. This way, you can ditch the screenshots, snippets, and Slack channels and start making decisions together from anywhere.</p>
<h2 id="canvas-the-collaborative-analytics-platform">Canvas, the Collaborative Analytics Platform</h2>
<p>At&nbsp;<a class="mds-text-primary" rel="noopener" target="_blank"  href="https://canvasapp.com/">Canvas</a>, we&rsquo;re thrilled to be working on the future of data collaboration. Unlike legacy BI tools, Canvas has:</p>
<ul>
<li>Integrations to over a hundred apps so you can centralize and explore your data without a warehouse</li>
<li>A spreadsheet-like interface for your teams to pivot, write formulas, join, and create charts without SQL</li>
<li>Figma-like collaboration lets your teams communicate where the data lives</li>
<li>Out-of-the-box usage metrics inform data teams of who is using specific models so they can invest in popular areas or fix problems affecting less helpful models</li>
<li>Role-based access control so you can make sure the right people have access to the right data</li>
<li>A native dbt integration so your data models and documentation about the tables and columns are automatically synced and surfaced directly to operators</li>
</ul>

How to Make Better Decisions Together with Collaborative Analytics

<p><em>The modern data stack is on the rise. Many companies use raw data from their SaaS analytics tools as input for their data warehouse, but this introduces problems downstream. Are there better ways?</em></p>
      <h3 id="popularization-of-the-all-in-one-saas-analytics-tool" class="anchor anchorWithStickyNavbar_mojV">Popularization of the all-in-one SaaS analytics tool<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#popularization-of-the-all-in-one-saas-analytics-tool">​</a></h3>
      <p>When Google Analytics launched nearly 20 years ago, it took the market by storm. Its success created the mold for SaaS-based analytics offerings that included everything in one box: data collection, storage, analysis &amp; visualization.</p>
      <p>Ease-of-use and low barrier of entry were two of its main drivers. Instrumentation typically required little in terms of engineering resources and the included analyses, although somewhat limited, were ready to be used by a broad audience.</p>
      <div><img style="width: 100%"  class="blogImage_Nvn_ blogImageLarge_RuN5" src="https://objectiv.io/img/blog/urchin.jpeg" alt="Urchin Analytics, to become Google Analytics after acquisition." />
      <p class="imageCaption___5l">Urchin Analytics, to become Google Analytics after acquisition.</p>
      </div>
      <h3 id="the-big-shift-towards-data-warehousing" class="anchor anchorWithStickyNavbar_mojV">The big shift towards data warehousing<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#the-big-shift-towards-data-warehousing">​</a></h3>
      <p>While SaaS analytics tools are still very popular and widely used, their wild growth has left the data analysis landscape highly fragmented. Most companies have numerous tools collecting data, and each of them have a different way of analyzing &amp; consuming it. As a result, it&rsquo;s very hard to get a holistic view of what all that combined data is telling you.</p>
      <p>We&rsquo;re seeing a major shift where companies export data from their SaaS analytics tools to use as input for their data warehouse. Not only to create a single source of truth for all data consumers, but also to unlock the potential of raw data. By combining full raw data sets, you can go way beyond the scope of your SaaS analytics tools. You&rsquo;re technically only limited by the quality and completeness of the data.</p>
      <h3 id="the-rise-of-the-modern-data-stack" class="anchor anchorWithStickyNavbar_mojV">The rise of the modern data stack<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#the-rise-of-the-modern-data-stack">​</a></h3>
      <p>The big shift towards data warehousing is represented by the upsurge of companies that operate in this space and how fast they&rsquo;re growing.&nbsp;<a target="_blank" rel="noopener" class="mds-text-primary"  href="https://snowplowanalytics.com/" target="_blank" rel="noopener noreferrer">Over 10.000 companies now use Snowplow</a>&nbsp;to process events at scale and send them straight into the DWH. dbt now has over&nbsp;<a target="_blank" rel="noopener" class="mds-text-primary"  href="https://www.getdbt.com/blog/next-layer-of-the-modern-data-stack/" target="_blank" rel="noopener noreferrer">9.000 weekly active data transformation projects</a>.&nbsp;<a target="_blank" rel="noopener" class="mds-text-primary"  href="https://www.rudderstack.com/" target="_blank" rel="noopener noreferrer">13.000+ sites and apps are running RudderStack</a>&nbsp;to pull data from SaaS tools and send it into data warehouses. The list goes on.</p>
      <div><img style="width: 100%"  class="blogImage_Nvn_ blogImageLarge_RuN5" src="https://objectiv.io/img/blog/dbt-cloud.png" alt="dbt Cloud in action" />
      <p style="text-align: center;" class="imageCaption___5l">dbt Cloud in action</p>
      </div>
      <h3 id="-but-saas-analytics-tools-arent-great-input-for-your-dwh" class="anchor anchorWithStickyNavbar_mojV">... but SaaS analytics tools aren&rsquo;t great input for your DWH<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#-but-saas-analytics-tools-arent-great-input-for-your-dwh">​</a></h3>
      <p>Most data warehouses are fed with data that&rsquo;s collected by all-in-one-box SaaS analytics tools. While historically explainable, this often causes problems downstream.</p>
      <h3 id="the-data-they-collect-is-not-designed-for-advanced-modeling" class="anchor anchorWithStickyNavbar_mojV">The data they collect is not designed for advanced modeling<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#the-data-they-collect-is-not-designed-for-advanced-modeling">​</a></h3>
      <p>There is a big difference between what data teams want their raw data to look like and what it actually looks like when it comes from SaaS analytics tools. The data sets they collect were never designed for advanced modeling on the raw data. It is often incomplete or overcomplete, unstructured and ambiguous. Significant grunt work is typically required before it can be used for modeling.</p>
      <h3 id="data-duplication-frustrates-adoption-of-a-single-source-of-truth" class="anchor anchorWithStickyNavbar_mojV">Data duplication frustrates adoption of a single source of truth<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#data-duplication-frustrates-adoption-of-a-single-source-of-truth">​</a></h3>
      <p>By extracting data from SaaS tools and loading it into a data warehouse, you&rsquo;re effectively duplicating a source.</p>
      <p>SaaS analytics tools all offer their own way to consume the collected data, and chances are many of your company&rsquo;s data consumers still rely on the dashboards of these tools to inform their daily decisions. Either out of habit, or simply because it offers an easy &amp; fast way to get data.</p>
      <p>In practice, this often leads to discussions about why numbers of different teams don&rsquo;t seem to be adding up, as the data team and data consumers both use different versions of the same source of data as input.</p>
      <div><img style="width: 100%"  class="blogImage_Nvn_ blogImageLarge_RuN5" src="https://objectiv.io/img/blog/before-saas.png" alt="" />
      <p class="imageCaption___5l">&nbsp;</p>
      </div>
      <h3 id="they-operate-as-black-boxes" class="anchor anchorWithStickyNavbar_mojV">They operate as black boxes<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#they-operate-as-black-boxes">​</a></h3>
      <p>SaaS analytics tools typically don&rsquo;t disclose the actual models behind an analysis. As a result, rebuilding an analysis to work with the data set in your data warehouse requires significant reverse engineering effort, and troubleshooting &amp; debugging can be painful when anything is off.</p>
      <hr />
      <h3 id="unbundling-saas-analytics-tools" class="anchor anchorWithStickyNavbar_mojV">Unbundling SaaS analytics tools<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#unbundling-saas-analytics-tools">​</a></h3>
      <p>The way data is used has evolved, and the all-in-one-box model that SaaS analytics tools use no longer fits. In order to effectively combine and analyze data from multiple sources and truly serve all data consumers from a single source of truth, we need to unbundle the black box and separate data collection, storage, modeling and visualization.</p>
      <h3 id="moving-all-data-consumption-downstream" class="anchor anchorWithStickyNavbar_mojV">Moving all data consumption downstream<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#moving-all-data-consumption-downstream">​</a></h3>
      <p>By separating data collection, all collected data can be sent straight into the data warehouse without stopovers. This eliminates source duplication and moves data consumption downstream. It enables&nbsp;<em>all</em>&nbsp;analysis &amp; visualization to occur on a single source of truth: the data warehouse.</p>
      <div><img style="width: 100%"  class="blogImage_Nvn_ blogImageLarge_RuN5" src="https://objectiv.io/img/blog/after-saas.png" alt="" />
      <p class="imageCaption___5l">&nbsp;</p>
      </div>
      <h3 id="shifting-from-vendor-locked-tools-to-raw-data-and-code" class="anchor anchorWithStickyNavbar_mojV">Shifting from vendor-locked tools to raw data and code<a target="_blank" rel="noopener" class="mds-text-primary"  class="hash-link" title="Direct link to heading" href="https://objectiv.io/blog/unbundling-saas-analytics/#shifting-from-vendor-locked-tools-to-raw-data-and-code">​</a></h3>
      <p>Unbundling the black boxes also provides a big opportunity for the adoption of open standards. Shifting from vendor-locked tools to raw data and code enables data teams to adopt generic ways of structuring data to share and reuse each others&rsquo; tools, models and analyses.</p>
      <p>We think the future of analytics is open and data warehouse-native, and there are clear signals we&rsquo;re already on the way. It&rsquo;s time to enable meaningful collaboration and to unbundle SaaS analytics.</p>

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How to Make Better Decisions Together with Collaborative Analytics

The Unbundling of SaaS Analytics

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