How do you navigate conflicting data interpretations among different teams in your company?

A lot of confusion arises when people are using the same words but mean different things. Before diving into the data, it’s crucial to align on what key terms and metrics actually mean. Take the time to document these definitions and share them widely, so everyone has a common reference point.

This is a classic and extremely difficult issue for any legacy large business undergoing digitization phase of digital transformation. We find extreme creativity for KPIs in different parts of the organization. Everybody will vouch for their own definition and a monthly review meeting can turn very funny sometimes (mildly put !!!). Unique truth, the holy grail of digital transformation cannot be achieved without coherent definition of data throughout the organization. I cannot make it any clearer to my Clients to avoid spending enormous amounts on digital transformation journey before addressing data. Mapping the organizational data is the starting point and having a consistent definition of each data point is the key to data quality.

Data doesn’t exist in isolation. It’s important to provide context so that everyone understands where the data comes from, how it was collected, and what it’s intended to represent. Sharing this backstory helps people see the data from the same perspective and reduces the likelihood of misinterpretation.

If your company has established data standards or best practices, now is the time to lean on them. Ensure that all teams are following consistent data formats, definitions, and reporting structures. This uniformity helps to prevent discrepancies in how data is understood and interpreted. If such standards don’t exist, it might be worth the effort to develop them—it will save time and reduce confusion in the long run.

If different teams in your company interpret data differently, encouraging dialog is key to resolving differences ... Encourage open communication: Create a platform where all teams can openly discuss their interpretations. This will ensure that every perspective is heard and considered, reducing misunderstandings. Establish a common language: Standardize terminology and data definitions across all teams. This helps to ensure that everyone can agree on the meaning of the data and minimizes the risk of conflicting interpretations. Joint analysis: Conduct joint analysis sessions where teams examine the data together. This collaborative approach promotes shared understanding and builds consensus.

Technology can be a powerful ally in managing data interpretations. Consider implementing data governance tools that provide a single source of truth for the data your teams are using. These tools can help automate data validation, flag inconsistencies, and ensure that everyone is working with the most accurate and up-to-date information. They can also facilitate collaboration by making it easier to share insights and annotations directly within the data platform.

After decisions are made based on data interpretations, it’s important to monitor the outcomes. Set up mechanisms to track the results of these decisions and compare them to the expected outcomes. If the results differ from what was anticipated, dig into the data and the interpretations that led to the decision. This process not only helps identify where things went wrong but also provides valuable insights for future decision-making.

Data is dynamic, and interpretations can change as new information becomes available. Encourage teams to remain flexible and open to revisiting their assumptions and conclusions. Create a culture where it’s okay to pivot or adjust strategies based on updated data or new insights. This adaptability is key to staying responsive in a fast-paced environment.