Opinions expressed by Entrepreneur the collaborators are his.
The data was a slow revolution in the business world. Although it has completely changed, and can still change, the way we approach processes, many companies are still struggling. There is an implicit understanding that being data-driven is beneficial, but no one can pinpoint how it can become data-based.
Most organizations create data analytics as if they were creating any new department. It is a somewhat isolated project that aims to provide valuable information to others. Unfortunately, this approach can lead to many organizational errors that make data teams inefficient.
A common red flag in a business is whether the data equipment has little or no impact on day-to-day operations. It can be visible due to an internal complaint or by analyzing the daily operation. However, there are ways to avoid this problem.
Related: The hidden advantages of data-driven sales teams
Data silos and other issues
The companies, in a poetic way, woke up one day and came across a lot of data. Due to the accessibility of various tools that are now commonly used in business operations, tons of data are being collected almost passively. CRM, sales automation tools, marketing trackers – all of these solutions offer a variety of data.
Unfortunately, all of these solutions progressed as standalone software. Most departments receive these tools as a way to improve their work. While this approach is understandable and even necessary, little thought has been given to integrating solutions together.
In addition, many companies operate with older systems that have little room for integration. In the end, data silos are created (that is, isolated environments where all the information is stored). These restrict the efficiency of the data in more ways than one.
There are many technical reasons to move towards date lakes or data warehouses. In simple terms, they are much more efficient for any data analysis and business operations. Data silos, however, inadvertently cause procedural and cultural issues.
Silos make it seem natural that some or most of the 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 analytics department. As a result, little thought is given to how data could be transferred and shared.
Related: Key challenges for data governance
Build a shared culture
The main goal of being data-driven is to improve decision-making. Business decisions, however, permeate all levels of the organization. As such, the data, no matter how it is acquired, is everyone’s prerogative. Getting to the point where everyone understands these concepts and implements them on a daily basis is a difficult topic. I think that’s partly a common problem with being data-driven. It is not enough to start a data department. A culture around data must first be fostered.
Building the necessary architecture and infrastructure is important, as there would be no other way to become data-driven. However, getting employees to use these tools is the second essential step. Therefore, once all the technical requirements are met, it is important to make an inventory of all the decisions that are made in the company. All these points of contact can be enriched with data. This task may seem daunting at first, but it is not as complicated as it may seem.
Taking the lead is the best approach. Senior management can analyze key milestones in decision-making 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 relate to the data and realize the importance of sharing information. In addition, data scientists will be able to promote important data governance and care ideals that will eventually become habits.
Data scientists, on the other hand, will feel and be more involved with day-to-day operations. It will give them a better understanding of how different departments manage their challenges and how they approach solutions. Later, when a specific department requests data analysis, it will be able to better conceptualize the expressed needs.
Related: All companies can work more efficiently with better data
Another common drawback is the approach to doing data training (for people who don’t have data) once or once a year. While data-driven people discover their passion for working with data, many others find it difficult to get involved. It doesn’t help that analysis often involves math, which a lot of people don’t really enjoy.
Therefore, annual training is not enough. It takes a long time to make it easier for people to work with data. If they do not have the necessary background or innate curiosity about it, most of what has been learned in the training will be quickly forgotten. It’s not that these annual workouts have no value. They provide critical information to people who want to start working with data. But analysis is a process that requires constant and continuous effort to maintain the required skill levels.
Not only should the work of data analysts and scientists be interdepartmental, but access to warehouses and lakes should be almost unrestricted, at least in readability. A single, easily accessible source of information provides the testing ground needed to hone your analytical skills.
Finally, if a large data analysis project is passed, the results should be visible to everyone in the company. Any successful effort involving data needs to be promoted, as it will generate higher morale for other departments.
Becoming data-driven is a cultural shift that must be supported by technical progress. However, it is often done the other way around. Technical solutions are easier to implement than something seemingly vague like data culture. However, these solutions are only as good as the people who use them.