It comes down to this: The easier it is to access your data, the easier is to transform your business. A modern data structure allows you to create insights to enhance business applications and enable new digital services.

Here’s an example.

Let’s say you wanted to know more about customers who complained about your brand on social media.

At a fundamental level, you can gather insights about the positive and negative sentiment of the social media posts for your brand. But, what if you wanted to go deeper? What if you wanted to tie these complaints to actual customers and understand the impact of your customer service team across social and telephone?

You will want to know the answers to questions like this:

  1. What specific incident triggers customers to complain the most on social media?
  2. Are the customers who complain on social media more likely to cancel the service than the baseline customer? (Social Media Feed + Billing Data)
  3. What regions/service locations complained the most on social media?
  4. What types of customers complained the most?
  5. Do the customers who complained on social media try to resolve their issues on the website first before going to customer support?
  6. How many channels did the customers use before complaining on social media?

This data empowers businesses to understand the most profitable customer cohort, the cause of customer churn, and the promotions or rewards that will increase loyalty.

This above scenario is very difficult to solve if you don’t have a modern data structure. And that’s what a data-first architecture solves.

Building a data-first solution architecture with Data Pipelines and Data Lakes

A modern data architecture in the cloud has 3 components:

1)The ability to collect and ingest data from multiple data sources (structured and unstructured)

IT teams have usually been restricted by the ability to only store structured data. This has now changed with the creation of Data Lakes and Data Pipelines in the cloud. Data lakes allow you to collect any type of data, store it in a single, accessible location (at scale). Financially, cloud platforms have made this type of service a no-brainer.

Now, instead of having to only store and utilize structured data from specific systems, you can collect unstructured data from ERP Systems, Social Media, Network Traffic Logs and CRMs with buyer data, as an example.  Cloud native architecture doesn’t discriminate against the type of data.

2) The ability to natively process and move structured and unstructured data between compute and storage services.

You’ll often hear this referred to as a data pipeline. All of the major cloud providers provide cloud native services that allow you to store and process data. For example, when you have your data stored on Amazon S3, you’re able to utilize cloud-native serverless services that can access and manipulate data with zero configuration.

AWS Glue is a fully-managed, pay-as-you-go, extract, transform, and load (ETL) service that automates the time-consuming steps of data preparation for analytics.  An ETL utility that is native to the cloud and integrates with several other services is a very powerful tool to transform and process data. A game-changer for developers in many ways.

3) The ability to easily produce analytics and insights

In cloud-native environments, data is not locked into and by default is easily accessible internally as well as externally. In many instances, you have the ability to integrate with leading external big data analytical tools like Tableau and Looker.

Now that your data is all in one location, you can start with producing analytics and explore how you can feed this data into Machine Learning applications.

AWS Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL.

Athena helps query data from S3 files without loading them into a costly storage like Data Warehouse or Relational Databases.This cuts out many other IT processes needed by data analysts to get the data they want.

The actual speed of data access using S3 & Athena is slower than a traditional cloud database, but engineers can achieve results much quicker since they don’t need to set up the databases. Slower speed, but much faster for business goals.

If you want to learn more about how companies are building cloud-native data platforms, make sure to read our white paper on how billion dollar startups are building data platforms in the cloud.

Set up a conversation with one of our data architects to unearth the low-hanging fruit in your organization and get on the fast track to ringing-up your first cloud win.