We all hear about future trends on data-driven marketing that focus on helping marketers and brands activate machine learning, artificial intelligence, predictive modelling.
Ultimately one may think mature organizations living and breathing data analytics DNA are ships steered by a captain with a north star, strong winds behind it and a nimble and focused crew. In reality the journey to data-driven maturity is like navigating the rough seas: a combination of experimental finger exercises and investments welcoming a performance-oriented mindset.
The good news is that data-driven organizations aren’t born that way, they’re made step-by-step
That’s why it’s critical to explore the process of building a data-driven organization as an industry. Asking how brands can evolve with strong data analytics foundations, and thinking ahead about whether there is a way to accelerate this evolution.
The IAB SEA+India Analytics Committee had a lively discussion reverse-engineering the factors that allow an organization to get started, go through the data-driven journey and get ahead of these trends. Imagine you had a blank sheet of paper. What best mindset would you adapt from the masters to accelerate the adoption of data-driven maturity?
Here are three things we’ve seen those with data analytics DNA get right.
1. They ask how to get tools, technology, and people right in parallel towards building a data-driven marketing organization
An analytics-mature organization balances the formula that connects technology and tools with its people. Instead of asking which comes first: tools and technology, or people (which is the perennial debate), we recommend considering these two aspects as interconnected and interdependent materials for data analytics DNA:
- Secure tools and technology: whether it’s a platform, a data-aggregation and normalization approach, or a discipline, ensuring granularity of organizational data that is of the highest fidelity at a user level for clean, usable data sets
- A shared data analytics services team that is adequately staffed to provide horizontal support and unify cross-functional data silos to democratize access among those who will use it to make decisions
2. They ask what the steps to an analytics-first approach are, and figure out where their organization is
An analytics-mature organization is self-reflective in their growth mindset. Instead of waiting for change to come from outside influence, asking the question: is my organization in the nascent, emerging or mature phase of data analytics DNA?
They reflect on the impact of their use of data across these dimensions – whether they manifest in more data-driven decision-making in day-to-day, or if there is structural friction that should be removed in current workflows, to fully socializing data-driven approaches in organizations. Some of the questions they would ask are:
- Housekeeping: What are the basics that must be in place? People, tech, the right business-aligned questions, awareness of data-driven approaches, etc.
- Frequency: How frequently is data vs. gut/opinion used in your organization? Daily, weekly, monthly, etc.
- Volume: Which functions or disciplines use data in your organization? Finance, marketing, sales, etc.
- Value: What tangible business impact have you derived from data, and how has it changed the way you run the business?
- Velocity: What speed is the organization about to implement changes, and accelerate the maturity of the organization’s use of data?
3. They ask how to get culture, capabilities, and commitment right to mutually reinforce one another, towards ultimately scaling the organization to a data-mature state
An analytics-mature organization is defined by leadership that ensures that the 3 C’s of culture, capabilities, and commitment complement one another.
These organizations understand that the shift is a cultural one that depends on top-down organizational will to create the right environment to bring a data analytics advocate’s vision to action.
- Culture: What are the ways that data-driven decision-making is rewarded, incentivised, or mandated? Are we a performance-driven organization – if so, what does this look like?
- Capabilities: Is there an experimental mindset towards optimization against core KPIs? Are new technologies like automation and self-learning systems being incorporated?
- Commitment: How persuasive is the leadership in bringing vision to action? Do they prioritize resources to achieve this?
In summary, the single most important decision that brands can prioritize today to get their fundamentals right on their journey to data maturity, is deciding to invest in the journey. All it takes is a single data-driven step by the leadership, such as a directive to bring a data-driven proof point with you when taking a decision to management.