Data democratization: a winning strategy for businesses
From "reserved for a small group of specialists" to "accessible by every department in the company", data is undergoing a transformation within organizations; jumping on the data democratization bandwagon can quickly turn into a competitive advantage. Find out why in this article, and how your company can benefit.
What is the democratization of data?
The " democratization of data" could be defined as turning data into a strategic asset, and making it accessible to everyone in the company.
This transformation requires the popularization of data, so that it can be interpreted by everyone, to speed up decision-making.
Historically, data has been viewed through a technical prism, and not mastered by those who need it.
The transformation of uses brought about by this democratization must be part of a long-term approach. We need to raise awareness among company managers: they themselves must be convinced that making data accessible to everyone in the organization is an essential process in the development of society.
What's at stake for companies?
Giving priority to the influence of data in the ecosystem enables all company departments to measure and factualize their activity, to better understand it and, finally, to better predict it. In this way, the use of data will bring added value to all processes:
- Optimization of internal operations (inventory management, predictive maintenance, supply chain improvement)
- Continuous improvement of the company's products and services
- Strategic decision-making
- Improving the customer experience
Conversely, misinterpreting data can lead to the wrong decision.
The challenges of understanding and reading data are therefore colossal, and will also require our teams to develop their skills in order to achieve greater autonomy.
In addition, governance must be put in place to protect the company's assets.
Today, in concrete terms, the democratization of data is far from being a widespread reality. Indeed, the vast majority of organizations provide only partial access to data. Some departments or employees believe that they retain a form of power by not sharing their data, and in fact prevent it from being used to its full potential, thus holding back the generation of large-scale value that data could bring.
Making data part of everyone's daily life is therefore essential, and goes hand in hand with a plan to acculturate employees to the benefits of data and its sharing, which should enable the company to develop a genuine data culture.
Putting information in the hands of the right people confers a competitive advantage that no company can ignore. By making data accessible to all, companies can :
- Optimize their processes
- Innovate faster
- Personalize their services to better meet customer needs.
It also encourages employee autonomy and spreads a culture of innovation among them. In this way, democratizing data enables companies to be more agile, adapting more easily and more quickly to market changes.
The principles and best practices of data democratization
Developing a Data strategy
The company needs to have a precise and real vision of its "information assets". In particular, it needs to assess its robustness and reliability, in order to determine the extent to which its employees can rely on it.
Appointing a Chief Data Officer
Within companies, the Chief Data Officer, the conductor of this governance, has emerged and has now become a pillar of organizations' data strategies, and consequently a key player in the democratization of data.
Its role is to define a data strategy and implement the associated roadmap. Its purpose is to manage and exploit data as a lever of economic growth for the company, and to act as a link between the business divisions, the IT department and senior management.
Get a clear picture of employees' Data maturity
Understanding the flow and use of data varies from one department to another. Certain departments, such as IT, finance and marketing, often have long-standing expertise in handling and analyzing data. This is not the case for all departments. It's also worth noting that, within a mature department, there can be significant disparities among employees.
The maturity levels can be hierarchized as follows:
- Beginner: has no data culture and uses no data analysis or visualization software
- Intermediate: familiar with corporate data, its challenges, players and tools, and may be required to use data consumption software.
- Advanced: masters data analysis methods and often carries out analyses or studies
- Expert: a data specialist who masters all existing methods and anticipates their evolution.
It is important for the company to establish a diagnosis of its needs and the desired level of autonomy of its employees, and HR will play a key role in training, coaching and recruitment to align the entire organization to the same level of maturity.
Defining data governance
Governance involves defining the organization and processes around data, adapting them to the maturity of the company and its employees.
To achieve this, the CDO needs to surround himself with a data office (central or otherwise) to implement the actions that will enable data to respond to business challenges. He or she must also draw on the support of all the company's departments to link data to its business value, establish best practices in analytics, and prioritize initiatives.
The aim of governance is to solve the equation: sharing, securing, protecting and managing risks.
Conversely, digital technology has also made it possible to obtain more frequent data on customers, such as web pages consulted or orders placed, enabling a better understanding of the customer, a more personalized experience with the brand, and thus an improved brand image.
Making data accessible
There are different ways of making data accessible, depending on its use: communication, consultation, analysis, modification. Each form of data consumption can change the way it is accessed. The tools available on the market today simplify sharing by storing information in a centralized location, and enable this multiplicity of uses.
It is also important that access to data is adapted to the maturity of employees' understanding of the data, so as to avoid drowning a neophyte in an overly complex form of data. In this respect, Datavisualization and storytelling aim to simplify the interpretation of data, even for the most novice of audiences.
Self-service data access encourages the daily use of data.
Sharing a data dictionary
Data is a form of language, and as such needs a dictionary to remove any ambiguity in communication. It is essential for all employees working with the same information to share the same definitions and vocabulary, so that they can understand each other during exchanges. The multiplication of players brought about by democratization makes this need even more urgent.
Ensuring data quality
Data quality is of paramount importance, as the democratization of data can only be initiated on the basis of reliable data. As access to data expands throughout the organization, it becomes even more crucial to ensure that information is reliable, to avoid incorrect interpretations. What's more, quality data strengthens employees' confidence in systems and processes, and encourages them to use them more proactively and regularly.
The "Data as a product" approach
Companies must treat their data with the same level of importance as any other corporate product. To do so, it must use the same techniques and methodologies, and in particular rely on multi-disciplinary teams to build up its data assets.
What conditions must be met and what obstacles must be avoided?
Democratizing data is a cross-company process. As such, it requires a strong commitment from management, in line with its strategy. It must also take account of the realities on the ground.
It's an ongoing process made up of stages defined in collaboration with everyone involved, and adapted to operational issues and problems.
One of the challenges is to get employees to adopt this new way of doing business, and to understand the value of sharing data.
The biggest obstacle is resistance to change: democratizing data means moving from a highly centralized and restricted situation, i.e., data reserved for a small number of people within the company, to data distributed everywhere, or even self-service data.
On the one hand, data experts need to understand that they have a vested interest in seeing data manipulation, enhancement and exploitation extended to everyone. On the other hand, everyone needs support to familiarize them with data, this new decision-making tool, so that its use becomes as natural as possible.
To guarantee the success of this democratization, it is also essential to put in place the appropriate data governance for the company. In particular, the Chief Data Officer will have to acculturate the teams to these issues, and then define the notion of dataownership.
This new organization must not only de-silo data, but also guarantee its quality and proper use. The CDO will have to define access, the framework for use and control the use made of the data.
Use by a wider audience can introduce a risk to the security of the company's Data asset. As a result, all employees need to be made aware of the issues and risks of data security, as well as the regulations in place depending on the geographical area (CSRD or GDPR in Europe, PIPL in China, CCPA in the USA...), or depending on the business sector.
In particular, the key principles of consent, human rights, transparency and accountability must be widely understood.
In target, each user is responsible for his use of the data and to maximize this use, he must know :
- Where data is stored
- Where data comes from
- If the data is reliable
What tools will help democratize data?
We now know that data is an essential asset for companies, and that making it more accessible to the business is a significant competitive advantage.
But the question is: are there gas pedals to democratize data?
The answer lies in the tools used, although this is not a miracle recipe. If the tools implemented in companies are to contribute to the dissemination of data within the business, they must be adapted to the maturity of employees and regularly enriched by the many technological innovations still to come in this field.
What different types of tools are needed?
Tools for upgrading skills
The successful democratization of data also includes an HR component, with employee skills development and training, after identifying their level of maturity and needs.
It forms part of the employee training plan, and is regularly updated in line with developments in the sector and changing needs.
The skill enhancement focuses on 3 angles:
- On industry-specific data issues and risks
- On data analysis tools
- On the data itself
The format should be as varied as for any skills enhancement: webinars, teasers, face-to-face or remote training, coaching, e-learning or even GPS applications.
Data access tools
Different ranges of data access tools exist, depending on the maturity of employees:
- Office tools Excel or Google sheet: these let you enter and organize data, perform basic calculations, and create graphs and reports to visualize data.
- BI tools tools: they collect, organize and present data in a structured way via interactive dashboards (data visualization charts), prefabricated reports, dynamic graphs and advanced statistical analyses.
- Data visualization and exploration & analytics tools Data visualization tools such as Tableau, Power BI and Qlik Sense enable users to understand trends and relationships between different variables. Users can create interactive graphs, aggregate, segment, filter and sort data. With these tools, you can find hidden information in the data, detect anomalies and make evidence-based decisions.
- Data preparation tools Data preparation tools such as Alteryx or Dataiku can be used to enrich existing data, transforming it to create new information.
In recent years, numerous innovations have improved the range of tools available to users, and enhanced them with functionalities for an enhanced experience: AI, integration into collaborative tools, etc.
Governance tools
Understanding the data organization
This is a communication tool for the organization and its processes (flowchart integrating the players and their roles).
Knowing the context of the data
An essential tool for accelerating the democratization of data is the data dictionary. It enables definitions, exceptions and rules for using data to be shared with everyone. In its simplest form, the dictionary is a simple dashboard for communicating definitions integrated into access tools, or even a shared spreadsheet.
The Data Catalog is a more advanced form of data definition. A veritable metadata repository, it provides the context of a database and the information needed to interpret it. It's an intelligent, practical inventory of all the company's data.
What are the basic functions of a Data Catalog?
- Metadata dictionary
- Categorizing data with labels
- Search engine
- Authorization and access control management
In all cases, the data dictionary must start with the business definition, so that it can be understood by everyone. A complex data catalog limited to a technical vision will not be able to play its role as a catalyst for data consumption. Only the documentation of the business concept will bear fruit.
Knowing the level of data quality
Trust in data is the prerequisite for its consumption. To achieve this, data quality must be measured and communicated to users. This can be done by means of a quality barometer, for a 360° view of a business process, or by integrating the status of quality via visual indicators directly into management tools, enabling data to be validated as it is consumed.
This quality measurement is a recurring process, and must be integrated into every new data project. It enables the design team to set up monitoring tools and identify any quality issues as early as possible in the day-to-day use of the data.
Lastly, we need to involve users in incident reporting, by simplifying incident reporting and integrating it into management tools.
Understand how data is used
Knowing how to use data means looking at how best to use all the data available to a company, and how to get the most out of it.
We need to know how the data is being used for a number of reasons:
- Verify the legitimate use of information from a regulatory point of view
- Identify opportunities to automate certain processes
- Managing data obsolescence
This knowledge involves the use of consumption monitoring tools integrated with analytical tools, and must also be based on regular exchanges between the data office or IT department and business departments, in order to identify manual actions that could be automated.
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Democratization is under way, but what's next?
The democratization of data is an endurance race interspersed with targeted sprints to achieve rapid results.
Technologies and data, like their form and complexity, are constantly evolving.
By way of example, the breakthrough of AI hasbroadened the means of consumption without, however, eliminating the need to question the use made of data.
The volumes to be processed are exponential, and even if technologies have greatly increased processing capacity, it remains important tolink processing to a specific use.
Data democratization is a continuous, iterative process. Each step must be evaluated to measure the ROI of actions taken against initial objectives, not forgetting qualitative feedback from data consumers. To this end, surveys, interviews, focus groups or analysis tools can be used to assess the impact and value of data democratization initiatives. These elements will help to better define the content of the next steps andadjust the strategy accordingly.
At Micropole, we're convinced that democratizing data within companies boosts their performance tenfold. All departments benefit from a more precise, fact-based understanding of their business, which, among other things, shortens decision-making cycles.
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