Why Businesses Should Take a Cultured Approach to Data
Becoming data-driven is a cultural shift that has to be supported by technical progress, not the other way around.
Data brought a slowly moving revolution to the business world. While it has completely changed, and may still change, the way we approach processes, many companies still struggle with it. There's an implicit understanding that being data-driven is beneficial, yet no one can quite pinpoint how to become data-driven.
Most organizations build data analytics like they would create any new department. It's a somewhat isolated project that's intended to provide valuable insights to others. Unfortunately, such an approach can lead to many organizational errors that make data teams inefficient.
A common red flag in a business is if the data team produces little to no impact on daily operations. It may become visible due to an internal complaint or through day-to-day operation analysis. However, there are ways to avoid this issue.
Data silos and other problems
Businesses, in a poetic way, woke up one day to find themselves with plenty of data. Due to the accessibility of various tools that are now commonly used in business operations, tons of data is being collected almost passively. CRMs, sales automation tools, marketing trackers — all of these solutions bring forth a vast array of various data.
Unfortunately, all of these solutions progressed as independent software. Most departments get these tools as a way to enhance their work. While such an approach is understandable and even necessary, little thought has been given to integrating solutions together.
Additionally, many businesses run on legacy systems that have little room for integration. In the end, data silos (i.e., isolated environments where all information is stored) are created. These restrict the efficiency of data in more than one way.
There are many technical reasons to move towards data lakes or data warehouses. In simple terms, they're just much more efficient for any data analysis and business operations. Data silos, however, unintentionally cause procedural and cultural issues.
Silos make it seem natural that some or most data is the privilege of a small subset of people working in the organization. These usually include the creators of the data and possibly the data analysis department. As a result, little thought is given to how data could be transferred and shared.
Related: Key Challenges For Data Governance
Building a sharing culture
The primary goal of being data-driven is to enhance decision-making. Business decisions, however, permeate all levels of the organization. As such, data, regardless of how it's acquired, is the prerogative of everyone. Getting to the point where everyone understands these concepts and implements them daily is a tough issue. I think, partly, it's a common problem of becoming data-driven. It's not enough to start a data department. A culture around data has to be fostered first.
Building the necessary architecture and infrastructure is important as there would be no other way to become data-driven. Getting employees to use those tools, however, is the essential second step. Therefore, once all the technical requirements are in place, it's important to take an inventory of all decision-making going on in the company. All of these touchpoints can be enriched with data. Such a task may seem daunting at first, but it's not as complicated as it may seem.
Leading through example is the best approach. Higher level management can analyze key decision-making stages within a department and get data scientists and analysts closely involved for at least a short period of time. As the teams work together, the department in question will learn better ways to engage with data and realize the importance of sharing information. Additionally, data scientists will be able to promote important governance and data care ideals that will eventually become habits.
Data scientists, on the other hand, will feel and will be more involved with daily operations. It will give them a better understanding of how different departments handle their challenges and how they approach solutions. Further down the road, when a specific department requests data analysis, they will be able to better conceptualize the needs expressed.
Another common pitfall is the approach of doing data training (for non-data people) in one go or once a year. While data-driven people discover their passion for working with data, many others find it hard to get themselves involved. It doesn't help that analysis often involves mathematics — something not many people truly enjoy.
Therefore, yearly training is not nearly enough. It takes a lot of time to ease people into working with data. If they don't have the necessary background or innate curiosity about it, most of what was learned in training will quickly be forgotten. It's not that these yearly training sessions have no value. They impart critical information to people who want to start working with data. But analysis is a process that requires constant and continual efforts to maintain the skill levels required.
Not only should the work of data analysts and scientists be cross-departmental, but access to warehouses and lakes should be nearly unrestricted, at least in reading capacity. A single source of information with easy access provides the necessary proving grounds to hone analysis skills.
Finally, if a large data analysis project is overtaken, the results should be visible to everyone in the company. Any successful endeavor that involves data should be promoted, as it will generate higher morale for other departments.
Becoming data-driven is a cultural shift that has to be supported by technical progress. It, however, is often done the other way around. Technical solutions are easier to implement than something seemingly vague like data culture. Yet, those solutions are only as good as the people using them.
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