by Emma Lo Russo, CEO
What defines a truly data-led company? It starts with data, but continues with building people, accountability and capabilities, all of which are essential.
This was the topic I discussed at a recent event hosted by Digivizer in partnership with the Australian Computer Society (ACS), alongside Dan Locke of IMB Bank, Teena Wooldridge of LinkedIn, Satya Upadhyaya of Lution, and Theresa Eyssens of Accenture.
One message was clear, especially in the context of data. Data may start with the marketing department, but it has to extend across the entire organization.
Creating a culture of data-first, data-always
Support and adoption across an organization starts with the board or the leadership team. Organizations where data, and data-led teams, have a place at the table rely on data to make better decisions. When organizations know how well programs or initiatives work (or don’t work), any lingering tendency to back favorites is replaced by backing the best runners. And they stop those programs that are not performing, based on form and results.
For this to happen, the culture of the organization has to allow it. Openness to evidence has to be nurtured. If actions speak louder than words, data speaks louder than hunches. In short, “show me the data” becomes the catch cry.
This reliance on data is important because an improvement of 2% can be a very big gain in an enterprise. For smaller companies, improvements of 20-30% are possible. In both cases, using data to set targets and then track performance against them creates the accountability demanded by the organization.
In this context, targets are important because they are rarely definitive or absolute. Progress is always a work-in-progress. Having real-time data to hand, that can be acted on in as little as a few days, is the only way to be able to reach that target using finite resources in the most cost-effective manner. As Dan Locke put it, it’s about increasing the speed of decision making and performance tracking.
Changing the organization
In an economy that sees disruption from all sides, most companies are planning transformations. Some have them under way, others are desperately trying to work out how to react to new competition. The explosion of all things AI is just the latest example. Operational transformation leverages data, and data increasingly informs transformation strategies. This applies in the direction any such transformation might take, and in developing the strategy itself.
Strategy (and therefore data) must come ahead of technology. As Theresa Eyssens reminds us, “don’t just put technology in”. Without a strategy to define the purpose and direction of a transformation, technology-driven transformation will likely fall. This includes defining what success looks like, and the people capabilities required to support it, .
Reinvention through thought transformation is a continuous pursuit, with the customer at the very center of its success. Customer satisfaction and customer delight defines everything. Data is needed to measure the satisfaction. It’s also needed to understand the options, aspirations and needs customers have. Without data, companies essentially guess everything.
Ownership and trust
Who owns data is defined around trust. Organizations, and everyone within any organization that has inputs to data or relies on data to make better decisions, will need to trust that the data is accurate, meaningful, and timely. If that trust is ever broken, the data will likely become worthless. And this might will last for a considerable period of time, even if objectively it is materially accurate.
If an organization’s culture is to be accountable for its data and the decisions it takes based on data, there will be significant consequences through the organization. These will be around its ability to create better customer experiences, around compliance, being confident enough to react to new competitors, and to be agile when required.
Being data-led is therefore about understanding and interpreting and drawing insights, more than just “having access to data”.
Data-led means being skills-led
Satya Upadhyaya reminds us that martech currently has a mixed reception. The complexity of the typical organizational martech stack has made the stack unusable in many organizations. For too many, the skills required to operate martech solutions simply don’t exist inside the company. Martech should be no different to other company operations: skills need to match the needs of the company, as do the technology solutions. Skills gaps therefore need to be closed. Reinvention needs to be approached step by step. Organizations need to be recreated around these skills and the associated culture. Better still, a simpler, more accountable, more accessible martech stack with self-service support, that doesn’t need heavy training , is where the opportunity lies.
So, how might we define a data-led organization?
There were some key lessons from the event, summarized below:
- Data & digital transformation starts at the top and needs to be driven from the board table
- Data is intrinsic to the success of leadership – you no longer need to ‘be friends’ with the budget holder, you need hard data points to unlock what you need and for your team to make good decisions from
- Data is the strategy – not just the plan on a page
- Technology that supports the strategy can transform a business, as long as the skills and technology map to each other
Consider how your business ingests data and makes decisions around data. Then design where the data should go within the business, be clear about who makes the decisions about what the data should be.
Data-led organizations are those in which data works best because it’s available to all, with clear rules of engagement, clear lines of responsibility, a clear understanding of accountability, and a cultural backdrop that says, “we lead with data, and are led by data”.
