Sometimes working with big data resembles dining at an “all-you-can-eat” restaurant: you get too much food and are busy swallowing it all without thinking of its quality or your health. You should never forget, though, that data is a double-edged sword and requires cautious handling. If gives businesses new power to organize, operate, predict and create value, but it entails numerous risks, including data-security questions, privacy concerns, and uncertainty about ethical boundaries, to name just a few.
To create a data-driven culture is becoming critical in times of global connectivity and data-driven organizations.
To create a data-driven culture is becoming critical in times of global connectivity and data-driven organizations.
“Data culture” is a relatively new concept which is becoming pivotal nowadays, when organizations develop more progressive digital business strategies and apply meaning to big data. It refers to a workplace environment that employs a consistent approach to decision-making through emphatic and empirical data proof. In other words, it implies that decisions are made based on data evidence, not on gut instinct.
Key Concepts of Data Culture
1. Data culture is a decision-making tool
Data analysis is not invented for the love of science; its fundamental objective is collecting, processing and deploying data to make better decisions with a focus on the outcomes. The insights, ideas, and innovation generated by the team shall be used to improve the existing capability, product and service in a digital feedback loop.
2. Data democratization ensures people’s acceptance
To create a competitive advantage, the demand for data should go bottom-up from the grass roots to excite people. Open access to data helps people to believe in it and to come up with solutions that don’t require an expert data scientist. When people trust data, they begin to change their behavior.
3. Risk management is another side of data culture
Risk management is one of the most important components of data culture that introduces analytics into key processes in a responsible manner. Data-driven solutions are beginning to help companies understand what’s happening inside the box. However, there always exists is the risk of getting analytics wrong and businesses should be aware of this and have alerts in place.
4. People are important
Even in data-driven organization, careful human resources management remains in focus. It is the responsibility of the CEO and the Board to raise the data culture, and they are the people on the front lines who should take up the call and lead the change. Building a data-driven culture requires people who can bridge data science and on-the-ground-operation. On the other hand, it is necessary to balance hiring new talents in data science and relying on the existing employees. Businesses should take a sharper look at the skills that data management require and put the right people at the right places.
Translating the Concepts into Actions
Based on the above mentioned concepts, we can speak about recommendations to build such a culture within a company and achieve effective data management:
1. Map data use and identify the gaps
To trust data, employees need to see the full picture. Data maps can give them evidence on how data is used and how their own data usage fits into the enterprise scheme. Mapping data can also show where gaps exist and how to fill them with alternative data that exists elsewhere. It can also reveal quality issues and help compensate them by letting people know about shortcomings and the respective data source.
2. Search for alternative uses of data
The hallmark of the data culture is understanding that data is flexible and multifunctional. Organizations need to educate their employees on how the data they use affects other parts of the organization. Instead of forgetting about data once it is used for a certain task, organizations can look for new applications of it by encouraging employees to identify other teams that may benefit from the same data.
3. Ensure data transparency
The only way to cultivate trust in data is to ensure its accuracy, security and reliability of its provenance. Organizations need to ensure that all data is accurate, timely and accessible for all who are entitled to use it. Besides, data requires openness, even if it is protected and kept private for regulatory reasons. Businesses can promote trust in data by tracking its quality and lineage, as well as by providing multiple use cases — including negative examples in which a data set should not be used.
4. Promote communication and feedback
A successful data culture implies a thorough understanding of how the teams and departments function and collaborate as well as where there is friction and contradiction. Organizations need to create an environment in which everyone can share information without being perceived as negative and provide data evidence based on facts, not emotions. Besides, open discussions of strategies and innovation goals provide employees with a clear view of data’s role in the company’s overall mission and reinforces their connection to the larger organization.
Building the right team
With the focus on people and understanding of the key principles of the data culture, it is possible to start building a data-driven team by promoting the following things:
1. Encouragement to use data — the team need to clearly see the value of using data clear and have no barriers to access it;
2. Encouragement to experiment — organization management need assist the employees to bring data into their everyday decision making freely and from internal and external sources;
3. Education in the use of data — the team need to receive training on how to use the tools at hand to access data, to make it informative, and to interpret results.
4. Fostering critical thinking — the organization needs to create an environment that would promote questioning biases, distrusting intuition, and displaying a healthy degree of skepticism but would celebrate critical thinking, curiosity, and the deeper desire to question things.
Data can be both a problem and a solution. When an organization’s data philosophy is detached from its business strategy, big data analytics is likely to fail. At the same time, when you find the right people to come to terms with data, it becomes a source of energy and momentum. Transitioning to a data culture may constitute a challenge that requires dramatic changes in the organization, but in the long run, everyone would turn to it. After all, technology is exciting and it does add value to the business!
