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Skill 29 · Knowledge Base For Startups
Subchapter 29.214
references/learn/general/prove-whats-possible-data-startups-greatest-asset.mdMarkdown8 KBView on GitHub
Data is a critical asset for founders, and the more you can gather, the better you can understand your startup’s performance and future potential.
Data is the lifeblood of modern startups (opens in a new tab). It helps describe the dynamics of your market, create profiles of your customers, and record the history of your transactions.
Data comes in many forms, from the structured data of transactions, to the unstructured data of customer feedback (opens in a new tab). When used correctly, data can tell you everything you need to know about your startup’s past, their present – and, sometimes, their future.
Data is a critical asset for founders (opens in a new tab), and the more you can gather, the better you can understand your startup’s performance and future potential.
But making the most out of data requires careful planning, right from Day 1—something that can be overlooked in the rush to bring a great new idea to market.
Many startups are born from a founder’s insights, but insights can only carry a startup so far. The sooner you can start gathering data, such as through your idea in the real world, the sooner you can begin to understand crucial factors such as the number of potential customers you can reach, and the value those customers place on your products or services.
Data is a source of continuous insight from a startup’s earliest days. For example, transaction data such as website visitors and their behavior can be critical to determining the strength of an offer, while website dwell time can provide insights into the stickiness of an idea. There are countless variables which can provide a window into a startup’s performance and possible future, and when used effectively, customer data can help point the best way forward (opens in a new tab) through the many choices a founder faces each day.
But making the most out of data requires having a strong foundation in collecting, understanding – and most importantly – analyzing data.
To translate data into actionable insights, every startup needs a data strategy.
The more data a startup collects at the beginning, the more data it has to work with over time, because you can’t analyze what you haven’t collected. This is one of the many reasons why the cloud is the perfect place to build a startup, because you can scale your data storage as needed.
As many founders soon realize however, storing all cloud data in the same way can quickly become expensive, which is another reason a data strategy is critical.
Amazon Simple Storage Service (Amazon S3) (opens in a new tab) provides a range of cloud storage options with pricing that varies depending on the speed at which you need to access your data.
As mentioned, data is only valuable when it you use it, and we offer a number of services to help you maximize the value of your data (opens in a new tab), including:
Amazon Textract (opens in a new tab) is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents, going beyond simple character recognition to identify, understand, and extract knowledge from forms and tables.
Amazon Transcribe (opens in a new tab) is an automatic speech recognition service that uses ML models to convert audio to text. You can use it as a standalone transcription service or to add speech-to-text capabilities to any application.
Amazon Redshift (opens in a new tab) is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools.
Amazon Athena (opens in a new tab) is an interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to set up or manage, and you pay only for the queries you run. Athena scales automatically—running queries in parallel—so results are fast, even with large datasets and complex queries.
Amazon SageMaker (opens in a new tab) is a fully managed ML service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment.
These tools enable you to paint the clearest possible picture of your startup’s performance based on your data. When combined successfully, they make it possible to develop predictive analytics solutions which use yesterday and today’s data to peer into your possible future.
All data has value, and many startup investors are keenly aware that much of the overall value of their startup can be determined by the data it holds and how that data can be used.
The fact that data has value means that we must appropriately protect it (opens in a new tab). And given that data is often collected from customers, it is critical to ensure we use data in ways that are respectful of privacy and considerations of fair use, to both retain customers’ trust, and to avoid falling foul of government regulations.
The value of data can only truly be realized if that value is understood early in a startup’s life. So, while data might not seem like the most exciting aspect of building a startup, it can often become the most important.