Data analytics is gaining skyrocketing popularity in businesses and yet, it is hard to become a data-driven organisation.
67% of U.S. senior managers are not comfortable accessing or using data from their business tools.
77% of U.S. firms say business adoption is a challenge and 95% of it appears to be cultural and organisational issues.
After investing a huge sum to generate business value from data, companies are unable to make the cultural shift. Despite having all the tools available, companies are still not entirely data-driven. The culture is missing. What do we do?
Four steps to transforming a company from intuition-driven to data-driven
1. Inspire the Change
Just like every cultural change starts at the top, so does the data-driven culture. An organisations’ top management should demonstrate decision making by relying solely on the data evidence and not on educated gut instinct.
2. Make Data Accessible & Trustworthy
The more accessible the data is, the more potential there is for people to spot insights from it. Define key stakeholders who will manage data and ensure timely availability of reports.
Effective data analysis may need analytics experts to help in laying out the data infrastructure.
3. Bring Teams together and Standardize KPIs
In organisations with multiple teams operating independently, it becomes essential to have data experts work cross-functionaly. Most data projects fail because data is present in silos with different teams.
Have a central data repository with all the teams aligned on the key metrics and definitions. When it comes to data, the entire organisation should speak the same language. The way the growth team defines acquisition should not vary from how the finance team defines it.
4. Make it a part of the Process
The resistance to change is not always because of unwillingness. Often times, It is because operating in default-mode is easier and quicker. The challenge here is to change the default settings.
This can only be done by making data an integral part of the company’s day to day operations. For every new proposal that comes in, data-based evidence should be made an essential requirement. For every new change that ships out, data-based success measurement should happen.
To sum it all up,
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