Investing in Data-Driven Business Initiatives — Technical Approach

Big Data and AI have been around the block for quite some time now. Many of us have heard it over and over again the past few years. However many companies are yet to invest in adoption of the emerging technologies to become data-driven businesses.

In this age of data, it is very essential for companies to invest in data-driven business initiatives. Targeted investments in emerging analytical tools and in the underlying IT infrastructure has proven that many companies have reaped the benefits from data sense.

A research by Forbes and Cisco says..

1. Companies with strong analytical maturity have proven to be competitive in the market by utilizing the historical and real-time data , analyzing the patterns of customer behavior, across industries.
2. It has become possible to identify new business opportunities and optimize the existing processes by tapping into large historical data sets
3. Real-time analytics have made it feasible to detect early warnings in production and service line which reduces the overall down time
4. And more importantly companies investing in Analytics solutions have shown to increase the revenue by more than 7% (Ref:)

While the benefits of investing in emerging analytical tools are quite evident, the challenges in implementing such business strategy and the underlying infrastructure is unequivocally complex.

Technical Infrastructure:

For any data-driven business the essential part of the overall architecture is the data lake - “ The one source from where all the required data can be retrieved, processed and transformed”. With cloud adoption at its peak, there are several architectural frameworks and patterns that can be implemented to drive data analytics solutions.

Irrespective of the cloud platform here are the key components of any data analytics solution.

Data Engineering:

1. Extract Load and Transform
2. Feature Engineering
3. Feature Market

The core element of the Data Engineering is to build a Data Lake and create a Feature Market that can be readily used by either the Machine Learning models or the reporting tools.

Building data pipelines to extract, load and transform the data from several structured & unstructured data sources takes the lion share of the effort in ensuring data availability in its right form.

Creating Features that drive the cause of the initiative through several engineering techniques is a desired deliverable from this stage( For ex: Customer DNA, Product Sales, Region wise transactions)

Data Science:

1. Model creation
2. Train, Test and Validation
3. Production model
4. Scores and Metrics

For any data driven business that is oriented towards profit, focuses on either the top line or bottom line processes to either increase new sales or to optimize the cost of existing production.

Several Machine Learning models like Demand forecasting, Propensity detection, conjoint analysis and many more help companies stay competitive in the market.

While the major portion of the effort in building this stage can be tagged to Data Science, optimizing the process of model creation, capturing metrics and scores to evaluate the model performance, and seamless updates to a production model are attributed to engineering machine learning.

Reporting , Analytics and Application access:

1. Predictions
2. Analytics
3. Applications

And, finally the piece of the puzzle that enables to use the predictions from models, reports generated from either the Feature market or from the data lake make it easy to visualize and comprehend the results.

While this article explains the general architecture framework and the key components, the next part of this series dives deep into the implementation of this framework on leading cloud platforms : Azure, AWS & GCP and the role of open source tools like Airflow, MLflow.