Data Analytics Stacks for Startups to Power Disruption
Sr. Content Marketing Manager, Sigma
While many startups understand the value of the information contained within their data, generating actionable insights from it is often easier said than done. Huge volumes of data are being generated from SaaS applications, social media pages, external sources, and other locations, in various formats.
Fortunately, emerging technologies are making analytics accessible to organizations of any size — so even small companies can leverage their data to level the playing field. This article explores the integrated set of tools and services that allows startups to unlock the full value of their data and enable the faster, smarter decisions necessary to compete and grow their business.
Why Traditional Analytics Stacks Don’t Work for Startups
There’s a reason that 56% of startups “rarely or infrequently” check their data. The traditional analytics stack doesn’t make it easy, especially for small companies with limited resources. Traditional analytics stacks are resource-intensive and require building and maintaining expensive infrastructure. But even large organizations encounter barriers to organization-wide data-driven decision-making for three reasons.
- Infrastructure Requirements. Most of today’s analytics tools were designed for on-premise warehouses and retrofitted for the cloud. They often require data to be extracted for preparation and heavily modeled by the BI team before it can be used by the business. This creates bottlenecks and leaves business teams waiting for answers.
- Access Limitations. Most analytics solutions are focused on reporting dashboards and require SQL or proprietary code to drill into data, preventing non-technical business users from quickly accessing the data they need. When engineers can’t get to a business user’s request in time, they’re forced to access the data the only way they know how: by extracting it to spreadsheets. This is problematic for many reasons, including stale data, data silos, scale limitations, and governance and security risks.
Surface-Level Dashboards. Domain experts are often limited to view-only metrics in surface-level, static dashboards, which prevent them from performing more in-depth analyses. If they have follow-up questions about the data, they must go back to their data or BI team. Growing startups must remain agile and cannot afford to wait days or weeks for insights.
Modern cloud data exploration enables startups to take advantage of the cloud’s speed, accessibility, and near-infinite scale. All in an easy-to-implement, low-cost, hosting-free environment that saves time and resources, allowing startups to more readily compete with the large enterprise corporations they are trying to disrupt.
The Cloud Data Analytics Stack for Startups
The cloud data analytics stack refers to the layered set of technologies, cloud-based services, and data management systems that collect, store, and analyze data. It’s an effective end-to-end solution that manages where data comes from, how it moves around, and how it’s prepared for analysis and consumption by end-users.
Layer 1: The Cloud Data Pipeline.
Data is valuable because it can provide a glimpse into business operations in real-time. But to get a real-time view of business processes, you need a pipeline that quickly ingests and transforms data across applications, databases, files, etc., into a centralized repository (a cloud data platform or warehouse).
Manually extracting and integrating data isn’t feasible for startups — it usually doesn’t make sense for large organizations either. And there’s no need to rely on a manual process. Modern SaaS solutions offer out-of-the-box connectivity to popular data sources, SaaS applications, and more, as well as normalize and transform disparate sources of data and move it around without having to write code.
Cloud data pipeline tools like Fivetran and Matillion can maintain integrations to a vast array of sources with structured, unstructured, and semi-structured data and deliver near zero-maintenance, ready-to-query schemas, flexible transformation, and basic modeling capabilities. They can also ensure data is kept fresh, in real-time, and automate the process.
Layer 2: The Cloud Data Platform
Siloed, stale, and constrained data does not deliver the insights you need as a startup. The second layer of the cloud analytics stack, the cloud data platform or warehouse, provides elastic infrastructure, unlimited scale, risk mitigation, and security management. Cloud data platforms like the Snowflake Data Cloud, as well as cloud data warehouses like Amazon Redshift and Google BigQuery, eliminate many of traditional on-premises warehouses’ upfront costs and support many analytic workloads for faster insights without sacrificing security, governance, or data compliance, making them well within reach of startups.
The cloud data platform is the hub at the center of your analytics stack, acting as the centralized, fully governed repository for all the data in a company. When selecting a cloud data platform, ensure that it can store all types of data, including structured, semi-structured, and unstructured data. You want to be able to take advantage of data coming in from all sources. Your cloud data platform should seamlessly scale to support thousands of users and hundreds of billions of rows of data, facilitating collaboration as your company grows over time.
These tools take away the burdens and costs associated with data warehousing and integration — and when partnered with a cloud-native analytics tool like Sigma — helps reduce the time to value for analyses.
Layer 3: The Cloud-native Analytics Solution
Startups need a solution that empowers their employees to freely and securely interact with data in real-time. But most analytics tools fail in this mission. They don’t allow users to ask follow-up or “What if?” questions of the data without knowledge of SQL or proprietary code. They’re limited to surface-level reporting based on a limited, predefined set of business metrics, preventing the ad hoc analyses that lead to novel insights. As a result, non-coding business users have to wait on busy BI and data teams for help or to get their questions answered.
This is problematic because BI teams don’t have business-level domain expertise. And because business users don’t always know the right questions to ask when they make the initial request, the reports don’t contain the novel insights needed to drive business decisions. Companies can’t afford to wait on the data needed to drive critical business decisions in today’s fast-paced competitive marketplace. They need a solution that allows everyone to explore data on-demand and use it to adjust their strategies and quickly pivot to take advantage of new opportunities as they present themselves.
Sigma: A Plug-and-Play Cloud Analytics and Data Exploration Platform
Sigma was designed for detailed data exploration and analysis, empowering teams to securely explore data at scale in a UI they know and love: the spreadsheet. As the final piece of the cloud data stack, Sigma enables startups to fully harness the power of their data and maximize the value of their investment through limitless cloud data exploration.
Sigma minimizes ad hoc reporting requests by empowering non-technical users to explore data and find answers to complex data questions independently. Users can join, slice, dice, filter, and calculate data using the same format, functions, and formulas as traditional spreadsheets because of Sigma’s spreadsheet-like interface.
As a cloud-native BI solution, Sigma operates on top of the cloud data platform, allowing anyone to explore and query live data directly from the repository in real-time, down to row-level detail — no copies or extracts required. And with Sigma, teams can take full advantage of the cloud’s speed, scale, and compute power while ensuring that data is safe, current, and complete. Sigma’s modern security frameworks help startups avoid the risk of a security breach or compliance violations and offer a centralized way to govern users and ensure proper access to data.
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