Is your company data-driven?
A data-driven company makes decisions based on data, not intuition. The 5 signs of analytics maturity and the most common gaps that prevent progress.
In an increasingly digital world, adopting the term data-driven has become today’s hot goal for all companies that want to remain relevant and demonstrate even greater value. And this is no coincidence…
The World Economic Forum report on the Fourth Industrial Revolution listed the key skills that employees of future companies need to have, proving that the (not at all distant) future of business is digital, and that the core capabilities for companies and professionals will be increasingly focused on data analysis and building more agile, assertive organizations.
With the covid-19 pandemic in 2020 and 2021, the world saw an acceleration of these goals, as an even greater number of companies went through very rapid digitalization, and innovation projects were taken off the shelf and implemented to seek a fast response to a chaotic scenario.
But what does data-driven actually mean? In this article, Erathos explains the 8 main characteristics of companies with this profile. Is yours one of them?
What is a data-driven company?
To start the conversation, the easiest way to describe this profile is by thinking about the basic concept of data-driven companies and organizations: they are those that place data analysis at the center and ahead of any business strategy and decision. Of course, on paper this can mean many things, but what does it look like in practice?
Being data-driven is much bigger than simply collecting data, installing the right software, and having the best plug-ins within your systems. It is a complex process that begins and ends with a strong cultural transformation, at a global level and not only within one department.
The Revolution starts from the inside out
An article published by Forbes Magazine in early 2022 revealed 12 reasons why digital transformations fail inside companies, and it is no surprise that almost all reasons translate to this: focusing so much on tools, numbers, and beautiful dashboards that creating an end-to-end data culture gets left aside. And in practice, that is what being data-driven means!
To find out whether this is already part of your company’s DNA, we’ve listed 8 characteristics of truly data-driven companies to help you understand where you should take action!
1) Data-driven companies understand that digital transformation is not a process that ends
A very common mistake is when an organization focuses on building a true data _case_ thinking only about infrastructure implementation: using and implementing the latest BI and artificial intelligence tools, building data warehouses, data lakes, or even implementing an efficient data lakehouse, and at the end of the process believing that “mission accomplished, now we are data-driven!”, when in fact these implementations are just one step in this process.
Being data-driven is a continuously built process, in every decision, and by every employee and manager. It is true that having the necessary tools is a very important step, but it does not end there!
2) They know their data maturity level
A company’s data maturity is an important metric for understanding how advanced its relationship is with the data it collects throughout its operations.
There are some frameworks used to measure each stage, but in short, they point to similar elements: the company’s strategic objective and vision for data use; not only the tools used for collection, storage, and analysis, but also who has access to data within the organization, and how these touchpoints happen; and finally, how this data guides action and decision-making.
Companies like Uber, Airbnb, and Ifood have such high data maturity that they can hardly be considered transportation, hospitality, and food companies, but rather technology companies, because they apply a scientific mindset that is accessible to everyone who interacts with their brands. In practice, this translates both for external customers and for their employees.
3) They have high Data Literacy
The concept of Data Literacy is used to define the ability to read, understand, produce, and communicate data efficiently. Unlike literacy as we know it for language, this term refers to a specific set of skills that need to be developed by everyone who works with data.
Companies with high Data Literacy—that is, a large share of their employees can communicate efficiently through critical data analysis—gain an edge in several important market criteria and tend to be extremely data-driven.
4) Their processes are automated
The use of artificial intelligence and automation in data collection and processing is extremely strategic, as it minimizes errors, improves the quality of collected information, and can show the results scenario in real time. This is important because it reduces response time for everyone involved in decision-making and in crisis and risk management.
5) Data is not treated as a hierarchical privilege
When it comes to data science, the companies that lead are those that invest in democratization.
Collecting data simply for the sake of collecting data is not an advantage—it is necessary to apply it to end-to-end business strategies; however, how can this be done with agility if from operations to senior leadership and even end users there are gaps regarding who can access, interpret, and analyze this information?
Within companies that are less democratic with their data, there is usually this bottleneck in information distribution, which remains concentrated in top management, with low cross-functionality. This is not positive because it delays decision-making.
For example: if an executive is the only focal point for metric analysis, and metrics are reviewed only in weekly meetings—or even monthly, in some cases!—warning signs that could have been identified and resolved immediately end up going unnoticed.
6) Data from each department communicates with one another
Another important factor is breaking down Data Silos, which are sets of data isolated from the rest of the company, closed within a single department and not interacting with other areas. Intercommunication is important for making better data-driven decisions.
For example: if finance has access to real-time sales data or marketing metrics, response time to market changes decreases, enabling more targeted campaigns and motivating the team to create new strategies.
7) The company creates value through its data
As stated above, not having a strategy or goal for the use of collected data is one of the biggest mistakes made by companies that want to be data-driven. Using this information intelligently involves having a plan to direct data toward creating the best customer experience through personalized services and products, and by delivering exclusive service.
Some companies that do this very well are Nubank, Google, Ifood, Netflix, and Uber: through the data they have on their users, they deliver experiences and brand touchpoints that are adapted to each consumer profile’s reality. This personalization creates stronger loyalty and brings more value, reducing churn, minimizing the number of potential brand detractors, and also empowering users.
8) Data analysis is already part of the culture!
A truly data-driven company does not make decisions based on intuitive guesses, but generates insights based on real data. This is a factor rooted in culture: everything is done with a rational purpose to guide decisions, from purchasing tangible resources to employees’ journey with the company, marketing actions, or budgets for sales teams.
Contrary to popular belief, having this culture does not eliminate thinking or the creative process within organizations. On the contrary: it supports innovation by providing the tools needed to implement, test, and learn quickly from this process.
Conclusion
Companies that are truly data-driven have an extremely mature relationship with the information they produce. They have clear purposes, strategies focused on generating value through data, high data literacy, and efficient internal communication.
From the outside, it may seem like an intimidating process, but it is transformative for every part of the organization. For this reason, having a strategic partner supporting each stage of your data-driven journey reduces the time your business takes to reach data maturity and generate value.
Erathos was created with this purpose: to reduce the time-to-value of data initiatives, to co-create data-driven organizations. Discover our solutions and be part of the data-driven revolution!
In an increasingly digital world, adopting the term data-driven has become today’s hot goal for all companies that want to remain relevant and demonstrate even greater value. And this is no coincidence…
The World Economic Forum report on the Fourth Industrial Revolution listed the key skills that employees of future companies need to have, proving that the (not at all distant) future of business is digital, and that the core capabilities for companies and professionals will be increasingly focused on data analysis and building more agile, assertive organizations.
With the covid-19 pandemic in 2020 and 2021, the world saw an acceleration of these goals, as an even greater number of companies went through very rapid digitalization, and innovation projects were taken off the shelf and implemented to seek a fast response to a chaotic scenario.
But what does data-driven actually mean? In this article, Erathos explains the 8 main characteristics of companies with this profile. Is yours one of them?
What is a data-driven company?
To start the conversation, the easiest way to describe this profile is by thinking about the basic concept of data-driven companies and organizations: they are those that place data analysis at the center and ahead of any business strategy and decision. Of course, on paper this can mean many things, but what does it look like in practice?
Being data-driven is much bigger than simply collecting data, installing the right software, and having the best plug-ins within your systems. It is a complex process that begins and ends with a strong cultural transformation, at a global level and not only within one department.
The Revolution starts from the inside out
An article published by Forbes Magazine in early 2022 revealed 12 reasons why digital transformations fail inside companies, and it is no surprise that almost all reasons translate to this: focusing so much on tools, numbers, and beautiful dashboards that creating an end-to-end data culture gets left aside. And in practice, that is what being data-driven means!
To find out whether this is already part of your company’s DNA, we’ve listed 8 characteristics of truly data-driven companies to help you understand where you should take action!
1) Data-driven companies understand that digital transformation is not a process that ends
A very common mistake is when an organization focuses on building a true data _case_ thinking only about infrastructure implementation: using and implementing the latest BI and artificial intelligence tools, building data warehouses, data lakes, or even implementing an efficient data lakehouse, and at the end of the process believing that “mission accomplished, now we are data-driven!”, when in fact these implementations are just one step in this process.
Being data-driven is a continuously built process, in every decision, and by every employee and manager. It is true that having the necessary tools is a very important step, but it does not end there!
2) They know their data maturity level
A company’s data maturity is an important metric for understanding how advanced its relationship is with the data it collects throughout its operations.
There are some frameworks used to measure each stage, but in short, they point to similar elements: the company’s strategic objective and vision for data use; not only the tools used for collection, storage, and analysis, but also who has access to data within the organization, and how these touchpoints happen; and finally, how this data guides action and decision-making.
Companies like Uber, Airbnb, and Ifood have such high data maturity that they can hardly be considered transportation, hospitality, and food companies, but rather technology companies, because they apply a scientific mindset that is accessible to everyone who interacts with their brands. In practice, this translates both for external customers and for their employees.
3) They have high Data Literacy
The concept of Data Literacy is used to define the ability to read, understand, produce, and communicate data efficiently. Unlike literacy as we know it for language, this term refers to a specific set of skills that need to be developed by everyone who works with data.
Companies with high Data Literacy—that is, a large share of their employees can communicate efficiently through critical data analysis—gain an edge in several important market criteria and tend to be extremely data-driven.
4) Their processes are automated
The use of artificial intelligence and automation in data collection and processing is extremely strategic, as it minimizes errors, improves the quality of collected information, and can show the results scenario in real time. This is important because it reduces response time for everyone involved in decision-making and in crisis and risk management.
5) Data is not treated as a hierarchical privilege
When it comes to data science, the companies that lead are those that invest in democratization.
Collecting data simply for the sake of collecting data is not an advantage—it is necessary to apply it to end-to-end business strategies; however, how can this be done with agility if from operations to senior leadership and even end users there are gaps regarding who can access, interpret, and analyze this information?
Within companies that are less democratic with their data, there is usually this bottleneck in information distribution, which remains concentrated in top management, with low cross-functionality. This is not positive because it delays decision-making.
For example: if an executive is the only focal point for metric analysis, and metrics are reviewed only in weekly meetings—or even monthly, in some cases!—warning signs that could have been identified and resolved immediately end up going unnoticed.
6) Data from each department communicates with one another
Another important factor is breaking down Data Silos, which are sets of data isolated from the rest of the company, closed within a single department and not interacting with other areas. Intercommunication is important for making better data-driven decisions.
For example: if finance has access to real-time sales data or marketing metrics, response time to market changes decreases, enabling more targeted campaigns and motivating the team to create new strategies.
7) The company creates value through its data
As stated above, not having a strategy or goal for the use of collected data is one of the biggest mistakes made by companies that want to be data-driven. Using this information intelligently involves having a plan to direct data toward creating the best customer experience through personalized services and products, and by delivering exclusive service.
Some companies that do this very well are Nubank, Google, Ifood, Netflix, and Uber: through the data they have on their users, they deliver experiences and brand touchpoints that are adapted to each consumer profile’s reality. This personalization creates stronger loyalty and brings more value, reducing churn, minimizing the number of potential brand detractors, and also empowering users.
8) Data analysis is already part of the culture!
A truly data-driven company does not make decisions based on intuitive guesses, but generates insights based on real data. This is a factor rooted in culture: everything is done with a rational purpose to guide decisions, from purchasing tangible resources to employees’ journey with the company, marketing actions, or budgets for sales teams.
Contrary to popular belief, having this culture does not eliminate thinking or the creative process within organizations. On the contrary: it supports innovation by providing the tools needed to implement, test, and learn quickly from this process.
Conclusion
Companies that are truly data-driven have an extremely mature relationship with the information they produce. They have clear purposes, strategies focused on generating value through data, high data literacy, and efficient internal communication.
From the outside, it may seem like an intimidating process, but it is transformative for every part of the organization. For this reason, having a strategic partner supporting each stage of your data-driven journey reduces the time your business takes to reach data maturity and generate value.
Erathos was created with this purpose: to reduce the time-to-value of data initiatives, to co-create data-driven organizations. Discover our solutions and be part of the data-driven revolution!