While most businesses see the value of analytics, only 20% have fully realised its potential and used Analytics at scale. Being data-centric allows companies to have better and predictable business outcomes rather than insight or opinion-centric, which leads to slower progress.?
Companies are collecting so much data that some of it is falling through the cracks. That’s where a data transformation strategy fits in. It fills in the cracks by keeping track of all the data that’s being collected. It also explains why the data is being collected, what you’re doing with it, and who is in charge of it. Those four pieces will help organisations get the most out of the collected data.
Institutions are now engaged in transformation programmes aimed at reshaping their business models by leveraging data's enormous potential.?
A 2016 survey found several common challenges that hold institutions back:
- Lack of front-office controls, resulting in poor data input and limited validation.
- Inefficient data architecture.
- Lack of business support for the value of data transformation.
- Missing or soiled data
- Lack of transparency
The problem is, data strategies are complex. There are a lot of moving parts to them, so creating one can be difficult. That’s where the Five-Step Data Transformation framework comes into play. It helps in identifying what exactly needs to go into a data strategy.
Smart, Systematic Five-step process of Data Transformation
- Defining a clear strategy- setting up specific and quantifiable goals can make it easy for companies to get a hold of required data. A clear ambition gives way to a successful data transformation. As per a survey, most data value is achieved by improving compliance, lowering costs and higher revenues for example: a financial bank can save 30-40 per cent of its costs by reducing the response time taken for data requests by supervisors.
- Translate the defined strategy into use cases- business-value can be identified by creating use cases. These are critical to ensuring that everyone in the organisation is on board and dedicated to the transformation.
- Designing data architecture to support use cases- many organisations are redesigning and remodeling their data architecture to support the use-case roadmap and create an open data architecture that allows easy addition of components in future.
- Setting up a data governance to ensure data quality- poor quality of data from technology issues, human errors, and system issues can be reduced by setting up a robust data governance system. For example, a custom-designed search tool gives users access to key information about data elements used by the organisation.
- Adopting central governance- adopting use case approach by the entire organisation in its data transformation journey will lead to new capabilities and clear role definition.?
As creative ecosystems arise to break down barriers within and across industries, every organisition today competes in a world characterised by massive data sets, severe regulation, and frequent business changes.?
Organisation Is Key- once the framework is followed and data is put together, keeping the data organised plays a vital role.?
There’s nothing worse than collecting data and not knowing how to handle it!
Get in touch with us to make this transition as quick and easy as possible.
