Edition 5 - Leveraging data in your organization

The correct data lets you know what subset of users need your products or services and when. Over the years, , I’ve come across multiple digital strategies that involve the collection of large volumes of data. Still, very few can leverage that into actionable insights for their product, sales, and growth teams.

Wouldn’t it be fantastic if you knew the playbook on how to do this? What are the steps to follow to get actionable insights?

In this edition of LeadReads, I’ll give you the steps that have worked for me.

DATA TALES

Netflix's recommendation algorithm is a data-driven powerhouse. Its special system uses your viewing history, ratings, and searches to predict what you want to watch next. The algorithm is so effective that it is estimated to account for over 75% of all hours watched on Netflix

How to leverage data in your organization

Step 0: Access and Roles

Identify the access patterns and requirements for various personas. Craft roles basis those. Employ a zero-trust policy. A single data breach is enough for a loss of reputation.

Step 1: Data Sources & Propagation

Know where your data comes from and how it's coming in—streaming or batched.

Step 2: Use Cases & Schema Design

Identify the use cases for which you will need to query your data and infer the best schema design pattern, given your use case. Consider applicable compliances and adhere to them in transit, at rest, and during access.

Step 3: Destination

Basis your use case, identify the ideal target. The destination could range from files in s3 to a Data Warehouse. This vastly impacts the cost. Pay close attention to this.

Step 4: ETL Pipeline

Set up your ETL pipeline(s). Create derived data points and perform feature engineering to ensure that your data warehouse is queryable and can provide the insights that you’re looking for.

Step 5: Visualization

Connect your data warehouse to a tool that turns your data into charts and graphs that make sense.

Step 6: Monitoring

Monitor metrics like data quality, access, resource utilization costs, etc.

Automation needs to be woven into each of these steps. Use Infrastructure as Code (IaC) for set-up, a solid Continuous Integration (CI) pipeline for quality checks, and a Continuous Deployment (CD) pipeline for hassle-free rollouts.

Getting your pipeline into production is like your app going live. It's not the end; it's a new beginning. You need ongoing checks for data quality, performance tweaks, and constant monitoring for security vulnerabilities.

Something to Chew On

Data isn't a set-and-forget deal. It’s like a garden; it needs constant care. Your data operations must be as iterative and agile as your software development.

Are you merely collecting data, or are you cultivating it?

~Mac✌️