Let’s talk Data Culture: Power BI Lessons of Adoption #4

In most of the articles about the Power BI adoption, you may encounter the word “Data Culture.” Let’s dig into this concept.
Is the Data culture the same as Data literacy? 
No.
Of course, both relate and support each other.  Do we need Data culture for adoption? It is a chicken and egg situation, a robust data culture may help you with the adoption of the tool, but the tool can help you to build your data culture. And we can’t deny that Power BI may be the tool key to unlock this level of the game.

Data culture is the principle that requires all staff and decision-makers to focus on the information conveyed by the existing data, and make decisions and changes according to these results instead of leading the development of the company based on experience in the particular field.

EBM – Evidence-Based Medicine– is a tremendous example of it. Doctors are using all the available data and research–based on their quality– to take medical decision.

Are our companies already on this level of maturity?
Most of them aren’t- because of the data illiteracy but not only. The root issue may be in another place. Culture is the way we see ourselves. It is about perception. What is the perception and message about data in your company?
Does this word exist on his own in the company strategy and communication?
What is the IT department doing about it? Your IT department may not be aware of it. Because of this constatation:

Reading this hits me like a truck: I.T. provides tools instead of providing information/data. What is happening to the Information part of I.T.? 
It seems that even in the I.T. department, we may not have a robust data culture. Maybe the first people who need to change their minds are the people in the I.T. department? Even the BI teams themselves, although they are spending their days on data. I am part of those teams, and I am seeing them focused mostly on their ETL/ELT process, performance, modelization, reporting, and tooling. But they often miss the business point of view – because of that. They are not talking the same language. Even with the build of the semantic layer, often, it is like a deaf dialogue.

There is a change of paradigm that we MUST do.
And, another one is coming, being induced by the “Self-BI” concept and the whole CITIZEN development Story that I will develop later on this serie.

How can we start a Data Culture as it stands? -We need to check some symptoms in our companies to assess the situation.

Data fiefdoms existence ?	You have a CIO/Senior Technologist that restricts access to data and statistics with the team Your most important metrics are only contained in the computer of the data analyst Your organization consists of a bunch of disparate departments that rarely (if ever) communicate data insights You’re reluctant to train employees on slightly advanced operations related to data Metric-backed goals are siloed in departments without relation to the whole organization
Staff level of data usage	What is the staff structure as it relates to data reporting? Do staff members have the training they need to understand relevant data? Do staff members understand how to glean insights and actionable steps from data? Do staff members have excellent working relationships with data analysts?
Process stage	Are staff accessing and communicating data across teams well? Do staff act on data or regularly share learnings from experiments? Are goals set in a way that can be tracked through metrics? Does the organization use a Gather<Analyze<Insight method? How often do staff receive data feedback?
Product stage	Are tools in place to analyze large data sets (beyond Excel)? Are consistent naming and storage conventions in place across databases? Are dashboards and metrics updated as automatically as possible? Is data stored in a way that reporting can be done across the organization? Are semi-annual security audits and passwords changed?

Usually, we focused more on the product stage, although the two’s precedent stages may constitute up to 80% of the story. And again because as IT person we start from Technology.
This is also the reason why you need to go outside of your comfort zone and start to focus first on the Data story. If we have a look at the table below, we understand the massive effort we need to put in that. It can’t be achieved by the IT department alone. We are already overwhelmed by the third proposition that sounds like the holy grail of any Data team. But all of this can help you to understand how to position the Power BI adoption journey. To understand what are the challenge and the possible reason for difficulties and failure. It gives you also leverage on how to involve more deeply the company in a new era and ask for sponsorship. Sponsorship will be the subject of another post in the series.

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.