IDG Contributor Network: 3 tips for a successful data strategy


When we witness the story—where Facebook’s user information was used to interpret and influence electoral votes in the US and other countries—and when we see targeted ads and product recommendations every time we browse the internet, one thing is clear: Data is power. Hopefully this power is in the right hands and the data is used in the right timing and context. Data can be a potent weapon if misused and a crucial asset if used strategically and mindfully.  

In fact, most modern businesses will survive only if they use data to acquire and retain customers, and if they use data to automate their supply chain or protect their valuable assets.

Here are the top three tips for the deployment of a successful data strategy.

1. Act on data before it loses its value

The diagram below of the time value of data demonstrates how data quickly loses its value, and how traditional batch processing (big data), BI, and data warehousing technologies are becoming irrelevant in today’s AI-driven and always-connected world.

 methodology of continuous development and integration. You partition your service to smaller microservices that can evolve, autoscale, or be upgraded independently and break development to short sprints with few simple and well-defined goals or features. The testing and staging of your solution is automated using one of the many CI/CD frameworks (such as Jenkins or Travis).

You can form sets of microservices based on existing open source and commercial tools to handle the four analytics steps: capture, contextualize, analyze, and act. For example:

  • API microservices to capture web transactions, sensor data, voice commands or chatbot data, preprocess or contextualize them, and push them into a stream.
  • Serverless functions, stream processing microservices (like Apache Spark), or AI inferencing logic (using tools like TensorFlow) that analyze the data.
  • Microservices that act immediately on the results by sending an alert, controlling a device, or interacting directly with the user.
  • Microservices for data visualization or data movement/transformation.
Yaron Haviv

Continuous analytics service architecture

This loosely coupled and agile architecture lets you add and modify functionality frequently. Microservices can be automatically deployed, scaled, and upgraded by using Kubernetes, a widely adopted orchestration framework that can be delivered as a service through all major cloud or edge providers. Another great way to quickly develop and productize microservices is to use serverless platforms (such as Amazon Lambda or Azure Functions) or cloud-independent open source alternatives like Nuclio, OpenFaaS, or OpenWhisk (these also have native integration with Kubernetes).