Continuous delivery of Analytics

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I am biased towards Analytics not only because it is my bread and butter but also my passion. But seriously, Analytics is the most important factor that helps drive businesses forward by providing insights into sales, revenue generation means, operations, competitors and customer satisfaction.

wud-slovakia-2015-datadriven-design-jozef-okay-8-638Analytics being paramount to businesses, the placement of it is still a matter of dispute. The organisations that get it right and are using data to drive their businesses, understand fully well that Analytics is neither a part of IT nor a part of business. It is somewhere in between, an entity in itself.

The insights generated from Analytics is all about business drivers:

  • Performance of the product (Product Analytics)
  • How well is the product perceived by customers (Customer Experience)
  • Can the business generate larger margins without increasing the price of the product (Cost Optimisation)
  • What is the bounce rate and what causes bounce (Funnel Analytics)
  • Getting to know the target audience better (Customer Analytics)

While the above insights are business related and require a deep understanding of the product, online marketing knowledge, data stickiness mastery and product management skills, there is a huge IT infrastructure behind the scenes to be able gather the data required and generate the insights.

To be able to generate the business insights required to drive online and offline traffic or increase sales, organisations need to understand their targeted customer base better. Understanding customer behaviour or product performance entails quite a number of technical tasks in the background:

  • Logging events on the website or app such as registration, add to cart, add to wish list, proceed to payment etc. (Data Pipelines)
  • Having in place a scalable data storage and fast computing infrastructure, which requires knowledge about the various layers of tech stack
  • Utilising machine learning and AI to implement Predictive Analytics and recommendations
  • Implementing data visualisation tools to distribute data easily throughout the organisation to facilitate data driven decision making and spread data literacy

As is the case, Analytics cannot be boxed into either Tech or Business. It is a conjoined effort of both business and tech to understand the business requirements and translate the same into technically implementable steps. Many organisations make the mistake of involving Analytics at the end stage of product or concept development, which is almost a sure shot fiasco. Analytics needs to be involved at every step of a product development or customer experience or UX design or data infrastructure to make sure that the events, the data points that lead to insights, are in place from the beginning.

Delivering Analytics solutions is a collaborative effort that involves DevOps, data engineers, UX designers, online marketeers, social media strategists, IT strategists, Business Analysts, IT/Data architects and data scientists. A close co-operation between tech and business leads to continuous delivery of smarter and faster automations, enhanced customer experience and business insights.

Build. Measure. Evaluate. Optimise. Reevaluate.

 

 

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