‘A data culture is not just about deploying technology alone, it’s about changing culture so that every organization, every team and every individual is empowered to do great things because of the data at their fingertips.’
Satya Nadella, CEO at Microsoft
When data flows through the analytics value chain there are several touchpoints – some human, other technological – shaped by the prevailing culture that you might have in your organization. It is that culture that will influence who has access, what can be shared, and what data investments are made into people and tools. If you are interested in cultivating the right data culture in your organization, then you need to be aware of the critical ingredients to achieve success.
Open, trusting culture
All data-driven organizations provide broad access to data. This is not limited to only providing access to data to staff outside the core analytics organization but also sharing data among business units, teams, and key individuals.
To achieve that, you must make sure that there is a clear signal from your organization that the data is not ‘owned’ by any individual team. In fact, data ought to belong to the business as a whole. This will allow you to maximize the potential of the data by bringing it together to provide a more holistic and richer context.
Such an objective is not easy and having the right data leadership plays a crucial role. From the outset, such leadership will need to evangelize about benefits of sharing within the organization by ensuring:
- The right incentives are in place to eliminate the silos to share all data.
- The right mechanisms are in place to make sure that data sharing follows compliance and governance practices and does not present any potential risk.
Data-driven organizations are much more open and transparent when data is democratized and accessible by individuals in your organization. But to arrive at that ideal stage, a huge element of trust is required to ensure that data will not be abused, leaked to competitors, or used to fuel political battles.
There are different and successful strategies used by organisations across the globe to achieve the creation of this open and trusting culture. The most common are:
- Promotion of data sharing practices.
- Increase availability of training in data analytics.
- Communication of the benefits of data-driven decision making.
If more employees have access to the data that they need, the necessary skills to analyse and interpret it, within an environment of trust, then more decision-making can be democratized. Obviously, many decisions are still going to ‘bubble’ up to higher levels of management. However, those operational decisions that can be easily tackled at the margins will be tackled by empowered data-skilled employees.
Data-driven organisations require strong, top-down data leadership. They need leadership that inspires, promotes a data-driven culture, and actively drives and supports all aspects of the analytics value chain, from data collection through to data-driven decision making and institutional learning.
Data leaders have several constituencies:
- Support the analytics organization per se by providing the data, tools, and training that they need. They define the organizational structure, modifying it as appropriate whilst the company evolves, and should also provide a clear career path and incentives for the analysts to be happy, productive, and deliver great work.
- They need to get buy-in from the rest of organisation, especially the business. They really need to believe that a data-driven approach is a right way to go. The leader needs to show results, even if they are only small wins at first. With this, he/she will have a better chance of fostering a data-sharing culture, a culture in which the business units are fully sold and part of that organizational-wide effort.
Broad data literacy
Clearly, analysts need training in experimental design, critical thinking, data presentation, use of business intelligence tools, statistics… But you also need managers and other decision-makers to be data literate too. Why?
- They need to understand the value that data will bring to the organization.
- They are the ones signing off on disruption to their teams’ workflow and suffer reduced productivity as analysts take classes, train, and learn new tools. Taking the hit during the transition, so they must buy into the longer-term gains.
- Managers make the ultimate strategic and tactical decisions based on the analyses. So, they must understand the nuances of the analysis, have confidence in it, and be prepared to defend it.
This requires that there should be some responsibility from your management team to learn some of those basic metrics, terminology, tools …
Data-driven companies are ahead of their more gut-driven pears in offering employee training and implementation support as part of data initiatives. Every organization can start somewhere, there are plenty of resources to help, to develop their skills, and get comfortable with data, tools, and analysis.
Goals first culture
A focused organization has a clear direction, a commonly held vision of where the business is heading. The role of leadership is to gather people around that vision, align them, and get them working toward a common goal.
In a data-driven organization, the goal becomes more transparent with clearly defined Key Performance Indicators (KPIs), its associated definitions, clear targets, and a clear current state. Together, these factors constitute what is often called ‘a scorecard,’ and it should be broadly accessible to each team member, so everyone understands how their work contributes to reaching the overall goal of your organization.
When running a data project, it is always possible to put a post-hoc spin on the data, to pick out something that goes in the right direction, which shows some sort of positive ROI. It is for this interest that reason, in the interests of objectivity, that a data-driven organization should develop a goals-first culture. Define the metrics in advance of the go-live date. By doing this you will make a clear case of what is important to the organization and reduce the ability to game the system or cherry-pick data points to make a particular outcome win.
Iterative, Learning Culture
The lack of accountability is a key issue with decision-makers. Someone should be keeping score, not only to make decision-makers accountable but for the organization, in general, to learn and grow.
A way to solve this is to implement a generic feedback loop. You design and run an experiment, instrument it, measure results, analyse the data, interpret the results, learn, hypothesize, and build a new experiment.
In a data-driven organization, where everyone is watching the numbers, hypotheses can come from anywhere, and a large proportion of the staff is actively using data, there is a broad engagement and investment. When you have a clear set of goals and people are focused on the top-levels KPI, they will truly care when an experiment fails or a program soar. They will want to understand why and dig deeper and do even better.
Inquisitive, Questioning Culture
The experimentation mindset changes the conversation from opinions to hypotheses and thus that can be tested objectively. This means that a broader set of stakeholders are more likely to come up with a broader set of ideas.
In addition to giving everyone a voice, this should encourage an inquisitive culture. Creating an atmosphere of healthy debate where one can ask for additional information, challenge assumptions and discuss recommendations or additional tests.
Are you missing any of these ingredients to foster the right data culture in your organization? Would you like to improve the culture of your organization to become more data-driven? If you want to know more about data culture and building successful data-driven organizations reach out to us. Our trusted advisors can help you move forward in your data journey. Get in touch and schedule a free consultation with one of our experts on [email protected].