Current State Analysis of Your Data – Part 3 – Data Culture
Read Part 3 of the State Analysis of Data, focusing on elements of Data Culture.
This article is the third in a series taking a deep dive on how to do a current state analysis on your data. This article focuses on data culture, what it is, why it is important, and what questions to ask to determine its current state. The first two articles focused on data quality and data freshness.
The questions are organized by stakeholder group to facilitate usability; hopefully you can use this as a template to start your Current State Analysis journey. A few definitions before we begin – note that these groups are not mutually exclusive:
People Who Input Data: These are people who collect and/or input data into the system. For example, sales people inputting their sales numbers, or survey creators.
People Who Manipulate and Analyze Data: These are people who organize the data and create analyses. This includes Data Engineers, Business Intelligence Professionals, and Data Analysts.
People Who Make Decisions Based on Data: These are the people who use the data to make decisions. This may be a sales manager deciding where to invest resources, a product manager understanding product use demographics, or an executive trying to cut costs.
What is Data Culture?
Data Culture refers to the way that people in an organization interact with data. Whether or not it has been intentionally curated, every organization has a Data Culture. It can be found in the way that people speak about data, if they are afraid of it, or how they include it in their decision making. A poor data culture can lead to confusing communication, inconsistent decision-making, and non-actionable insights, while a good one promotes robust, actionable, and data-driven insights.
Although good technologies and processes are important for responsible data use, it is equally important to understand how humans interact with data. Often, organizations will implement new data technologies and procedures, but will not think through how it affects the people who produce, use, and make decisions based on that data. Cultivating a strong Data Culture refers to the human side of data management – making sure that data is trusted and that everyone is using data responsibly.
Why is Data Culture Important?
Data Culture is important because even though data is often viewed as technical, it is ultimately consumed, used, and interpreted by people. If people are afraid of data or do not trust it, they will not use it to make decisions. Additionally, if they are not using the same vocabulary and metrics when discussing data, they will end up with confusion and miscommunication. Time will be wasted discussing the correctness of data rather than using it to make decisions.
Conversely, a strong data culture means that people will turn to data first when making decisions. There will be less time questioning the data and visualizations and more time spent interrogating the insights and decisions. There will be a strong foundation on which to base discussions because people trust and feel confident in the data.
Data Culture can often spiral, both for the better and worse. Left unchecked, different departments – and even individuals – can make different decisions with data, leading to inconsistencies. These consistencies lead to mistrust, and therefore discourage people from using data to make decisions. Each department becomes more entrenched in their data ways, which can lead to a downward spiral for data culture. Conversely, when people trust their data, they want to use it to make decisions. The more people discuss data in a positive light, the more data practices will converge across the organization, further engendering trust and encouraging data-driven decisions. Read more about Data Culture here.
Questions to Determine Current State of Data Culture
To Those Who Input Data
These questions are centered around understanding how connected the people who input data are to the data insights that are produced from their inputs. Oftentimes, the people who input data are not the same as those who consume it, and therefore it becomes a lower priority to the people who input the data. A strong data culture would try to close the feedback loop, showing those who input the data dashboards and metrics based on the data that they input. With more feedback and visibility, the potentially tedious task of data collection may gain more meaning.
Do you know how the data that you input is used? Do you know why it is important?
Who is the primary consumer of the data that you input?
Are you a consumer of any data or dashboards? How does that affect the way you do your role?
Is data a positive or negative influence in your role?
To Those Who Manipulate and Analyze Data
For the people who analyze the data every day, the Data Culture is most of their working culture. Not only do they reap the benefits of trustworthy and consistent data in their own work, but they also will be the ones to hear about any data issues from stakeholders across the organization. These are the people who have the strongest understanding about the Data Culture and have the most power to change it from the ground up.
These questions are meant to understand how much they trust data as users, but also how they think others use and perceive data across the organization. We are trying to understand if people are communicating about data in a positive way, but also if they are using consistent metrics so that everyone is speaking the same language.
Do you trust the data that you are using? Do you understand it?
Are people across the organization using the same vocabulary to refer to the same things?
If there are issues in the data, are they well communicated across the organization?
Are there discussions about data across the organization? Are they generally positive or negative?
How is your data organization currently structured? Do you have a democratized or centralized data team? (Read more about data organizations here)
To Those Who Make Decisions Based on Data
It is very easy for data decision makers to be detached from the source of their data. While it is not essential for them to understand the intricacies, it is good to know where their level of understanding is with the data. This helps to know if they are appropriately assessing the level of trust they should have in the data. Some decision makers may blindly trust the data because they have no reason to believe it is wrong, even though analysts have wide confidence intervals and issue many warnings with their analyses. Others may forgo data in their decision making altogether because they do not trust it, even if their analysts have a high level of trust. It is good for decision makers to have an ear to the ground for upstream data issues and have a high-level understanding of where the data comes from.
Do you understand where the data comes from (at a high level?)
Do you understand what goes into the metrics that are being used to make decisions and measure performance?
What factors would cause you to override a decision that is supported by data?
Data Culture is what happens when people interact with data. Data Cultures influence data outcomes just as much as Quality and Freshness, but is less discussed because it is not a technical issue. Without focus on Data Culture, you may have perfect data and no one to use it. Even worse, you could have people using it in inconsistent ways across the organization, causing confusion and slowing down processes. When doing a Current State Analysis on data, Data Culture is an integral piece of the puzzle.
This is the third in a series discussing the important considerations when assessing your Current State of Data. Follow along for the next article about Data Outcomes – how people use data to drive their organization’s goals and mission.