Data can be a startup’s greatest asset

Data is the lifeblood of modern startups. 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. 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, 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.

Building on a foundation of data

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 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.

From data to value

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) provides a range of cloud storage options with pricing that varies depending on the speed at which you need to access your data.

AWS services that help startups create value from structured 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, including:

• Amazon Textract 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 table
  Learn how Travizory Leverages GitOps and AI to Help Countries Unlock Safe Travel in Just 4 Weeks with the help of Amazon Textract.
  The Automate Document Processing with Amazon Textract eBook will show you how to extract text without configuration, training, or custom code.
•  Amazon Transcribe 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.
  Check out how PromoMii (now Nova A.I.) uses Amazon Transcribe to gain insights from video content for their Video Ads Powered by AWS Machine Learning.
• Amazon Redshift 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.
  Hubble uses a data warehousing solution through Amazon Redshift to monitor employee burnout, knowledge decay, and product defects.
  Vincere Health built a personalized smoking cessation platform on AWS and uses Amazon Redshift as their central data warehouse and Amazon S3 as a scalable data lake for understanding participants better and tailor the platform for their needs.
• Amazon Athena 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.
  AWS Glue and Athena were instrumental in optimizing Nimbus’ overall cost and upscaling its value. “By implementing file conversion Glue jobs, Nimbus saw improved Athena query performance from 15 to 30 minutes down to 20 seconds, while providing a significant reduction in Athena costs and consistent and predictable costs with AWS Glue.” – Mark Laczynski, Senior Cloud Architect, Nimbus/Timehop.
• Amazon SageMaker 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.
  super.AI uses SageMaker, along with other AWS ML services, to help their customers expand their scope of automation by extracting actionable information from unstructured data – images, videos, audio, documents, and text.
  Our eBook, Reduce the Total Cost of Ownership with Amazon SageMaker will show you how leading companies use Amazon SageMaker to improve efficiency, boost productivity, and lower costs.

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.

Data as the new currency

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. 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.