the tug of war over enterprise data analytics

Today data is everywhere, and more than ever businesses are relying on insights gleaned from data to build better products and services, make smarter business decisions, and compete in their respective industries.

Businesses are collecting an incredible amount of data, but are often struggling to make sense of it all.There are such high rewards for making the right data-driven decisions, but such high consequences for data breaches and for making decisions based off inaccurate or incomplete data.

This has created a tension in the enterprise between business and IT departments which often leads to a tug of war over ownership of corporate data sets and analytics tools.

"Only 4% of companies said they have the right resources to draw meaningful insights from data   and to act on them"

— Bain & Company

Business departments priority is to leverage corporate data to make better business decisions and uncover opportunities for the organisation. IT departments priority is to keep the data secure, governed, centralised, and ensure compliance with applicable legislation.

These two priorities are often competing with one another — leading to data silos, a bottleneck on IT for access to data with a long backlog, and missed opportunities to make timely data-driven business decisions and uncover new opportunities.

Organisations are Struggling to Scale Data Analytics

There is a lot of work that goes into setting up an enterprise data analytics capability, and there is no ‘one size fits all’ solution for enterprises to adopt.

Customers often have to purchase and integrate multiple data analytics products from multiple vendors, which means that large teams are needed to build, integrate and support them all.

A typical enterprise data analytics architecture is broken up into three layers, each of which require their own large dedicated teams as illustrated below:

data analytics architecture

Without investment in automation and data democratisation, the rate at which you can execute on data analytics use cases — and realise the business value — is directly proportionate to the number of data engineers, data scientists, and data analysts you hire.

This scalability issue dramatically increases costs and is a major problem with big data analytics plans.

This is a complex problem and organisations typically do not have the time or resources to solve this on their own.

However, at Cangler we believe we have found the keys to solving many of the biggest problems with big data analytics today: automation and data democratization.