Wie können Daten genutzt werden, um eine reaktionsfähigere Kultur zu schaffen?

Data means trust and action. It helps collectively, drive improvements and bond the group tighter to deliver goods. Data = lead to solve.

One mistake many organisations make is to just harvest all data that's available. Tracking 50 kpis at the same time is not going to give you any information. The key is to think first why and what you want to track before starting to track the right kpis. It's a time saver as well!

Data is more valuable when you use it guide where you take action to influence your culture. Make sure you show people what you've done as a result of analysing data, or explain why you haven't done anything.

I've found 'awareness' to be a key first step in creating a more responsive, change-enthusiastic team. Basically, everyone is aware of what’s going on. By effectively communicating what will be measured, why it is being measured, and the benefits it brings, you...can empower team members to actively engage and contribute to the transformation process. This creates a sense of purpose, aligns efforts, and instills a collective commitment to driving positive change throughout the org.

Data gives definitive shape, size, colors, distribution and trends about we generally talk in generic or vague terms. However, data will not automatically tell stories, we have to harness data to tell right stories or right way to understand it. Good stories bring emptions and emotions motivate people to commit for the cause. Story teller wins, whether it is an individual, department head, CEO or a president or a prime minister of a country. Data harnessed rightly helps to narrate better stories.

Data to me is like a guide, your "co-pilot". One should leverage data to get answers to your questions. Get data to work for you, help assist you in your work. But, the accountability is on the individual on how to use it. Data to me is the force which makes you more confident in your decision making.

When measuring culture, it's crucial to employ a recognized, quantifiable culture assessment tool that allows for actionable insights. Monitoring indicators like engagement survey scores, Pulse scores, and other relevant metrics is essential. Identifying the gap between the current culture and the desired culture is the initial step, and regular assessments are necessary to gauge the extent of progress achieved.

Organizations resemble living organisms, with informal networks playing a crucial role in their functioning and evolution. ONA provides valuable insights into these informal networks. SNA provides information on network density, connectors, and knowledge distributors. It is a powerful tool for understanding organizational dynamics and supporting change initiatives. SNA has been used to demonstrate networked flow and the importance of social networks for creative processes. This understanding is essential for building positive innovation networks, a cornerstone of future organizations (Riva, Gaggioli, & Milani, 2010).

Everyone mentions data quality but underestimates what it takes. In my experience, it is the first thing one learns by looking at data. The data quality probably reflects inconsistent processes and tooling and often exposes issues with user experience. Driving continuous improvement often starts with seeking to improve UX and standardise processes enough to make them measurable.

This is the place in your data pipeline where you need to utilize different tools like data cataloging, data lineage, data quality to ensure that data you are using is safe secure and trustworthy. Trust and security create different value for the data.

In organizations which don't have very strong data maturity, the terms 'data' and 'insights' are used frequently as synonyms. However, they mean very different things. Not all data will provide insights. Not all data has sufficient quality to be used for any decision-making. At the same time, I totally agree that insights are powerful and intend to drive action. This is why organizations strive to achieve strong data quality and diversify available data sources. For instance, blending behavioral data with ad-hoc research can allow companies to find answers to 'What?', 'How?' and 'Why?' questions, which eventually create some actionable business insights.

Data driven environments are super important but also difficult to create. This is often due to the mindset shift that is required and making decisions with assumptions. This requires a change management activity rather than assume it’s easy to transition.

The core enabler to a data-driven mindset is widely spread data literacy and analytics skills. The operating model can be a blocker to this. With overly centralised data services, such as under the Chief Data Officer, access to data becomes a bottleneck. Strong data governance will allow federation of data access to the whole organisation.

Data are useful only to the extent they are distilled into actionable insights. Otherwise, the data are merely in the "good to know" bucket. Think well on the modeling of your data in order to derive the most important insights for your organization.

Data is not useful without context. As such, we need to know what it relates to. If we don't know that then it's like that letter popped through the letter box which the unruly puppy got to first and tore to shreds: it's meaningless words scattered around on a medium which is no longer fit for the purpose of passing on a message. If lucky, you may still be able to determine what the pieces of that puzzle - which equate to the data - can somehow be pieced together to show who sent the message based on what is shown on the container it came in (that is, the envelope). Even then, some index might be required to allow the contact at the sender's end to determine what was sent and see if there is a reference to it for recovering the message

What works well is to have regular sessions where you deepdive into your objectives and see how the metrics are doing. Metrics moving? Great, go ahead. Metrics not moving? Maybe it's a good idea to redefine the associated actions. Having a regular, recurring moment helps to get data in everyone's mindset.

To use data to test assumptions, some additional cultural qualities are required. Psychological safety is the core. There has to be safety to test even the assumptions of leaders with positional power. Leaders need the humility to listen and to ask questions rather than asserting subjective viewpoints. Everyone needs curiosity and enough time and head-space to pursue that curiosity.

Data is the antidote against office politics and power struggles. Usually teams are torn between different demands of stakeholders. Using data, you can objectify the benefit of doing one thing or the other. This empowers the people because they can argument the foundation of their work without being overruled by management all the time.

Don't be afraid to experiment with the type of data you're using for reporting. You won't always get it right - constantly iterate and improve your data quality and the insight you can gain from it. It will also take time for others to wrap their head around the insights you're sharing; don't expect everyone just to be able to pick it up and start using it for decision-making. Providing training, coaching AND time for this to happen. Embedding an insight-driven culture doesn't happen overnight.

Data can create a more responsive culture by providing insights for decision-making and identifying areas for improvement. When analysing customer feedback, employee engagement, and performance metrics, leaders can make data-driven decisions and foster a culture of proactive problem-solving and continuous improvement. Sharing such data with employees promotes transparency and empowers them to contribute to decision-making. Leveraging data effectively enables organisations to adapt to changing needs and challenges, leading to improved performance and success. 🤩