4.03. From Evaluation to Management: Making Data Speak Differently

The cultural sector has experienced rapid digitization, much like the rest of the economy, which has led to a massive and continuous production of data about their activities. Numerous techniques and tools have gradually emerged to handle this intense data production and extract valuable insights. Here, we focus on two of them: Data Visualization, which involves visually translating data to better grasp its meaning, and Business Intelligence. When used properly, these techniques offer valuable support for decision-making and management of cultural institutions, as well as an effective way to communicate about activities and report internally or to governing bodies.

So how can we make data "speak" effectively through Data Visualization or Business Intelligence without falling into the pitfalls of "over-monitoring"?

It is important to remember that data, in itself, holds no intrinsic value. Its relevance and potential impact depend entirely on how it is used and interpreted (see related data sheet). Conversely, misinterpretation or a careless approach to data processing can lead to counterproductive decisions. This is why maintaining high data quality, ensuring regular cleaning and updating, is essential. This is a prerequisite for any data exploitation project, whether carried out using spreadsheets or more advanced tools discussed in this sheet.  

Data visualization - also known as data visualisation or Dataviz - consists of encoding and converting numerical data into a visual format to make it more understandable and accessible. This transforms raw data into a "refined, interpreted" and visual form (according to Datactivist, a cooperative listed in the resources of this page). Its origins date back to the 18th century with pioneers like Scottish engineer and economist William Playfair (1759-1823), who created the first bar and line charts (see the resource "Data By Design" for more information). The discipline spread further with the digital revolution of the 20th century.

The rise of computing and the explosion of produced data have created a demand for data processing and mediation tools. Indeed, this method, increasingly used by journalists, offers genuine educational and mediating value regarding the subject it documents. Within organizations, it also fosters shared understanding among employees with different roles, sensitivities, and technical skills. In the cultural sector, it is now common to find a data visualization section integrated into ticketing or CRM software.

Proper use of Dataviz requires respecting several principles. First, it demands a solid understanding of the raw data as well as a clear angle or message that the produced image must convey. It also requires choosing the appropriate level of representation (the right granularity) and the most relevant type of graph, since the same information can be perfectly clear or completely confusing depending on the chosen representation. For guidance, see Nazirah Jetha's advice on the Tableau platform blog, applicable to any tool. Finally, it is essential to ensure the legibility of the visualization (presenting enough data to maintain statistical fidelity without sacrificing clarity) and to find the right balance of aesthetics to enhance rather than detract from the message.  

Many tools are available that allow for creating simple or sophisticated data visualizations. As noted above, the key is to choose the tool based on the needs and skills of the teams so that it provides real added value and eases the workload of the teams.

Business Intelligence, or BI, encompasses the same functionalities as decision support systems. Like Data Visualization, its goal is to transform raw data into actionable information, but BI goes beyond simple visualization: it integrates a data analysis process to enable activity management and guide strategic decisions. BI applications collect, cross-reference, analyze, and format data to produce dashboards. In a cultural context, a major benefit of such a tool is to facilitate dialogue between departments and leadership and enhance decision-making through broader, finer, and more objective data-driven management.

Data management breaks down into key steps aimed at maximizing the value and usefulness of data:

1. BI tools collect data from various sources such as ticketing systems, websites, social networks, spreadsheets, or other data sources (accounting, cultural activity reports, sponsorship, etc.).
2. Once collected, data undergo cleaning and organization to ensure reliability and relevance.
3. The data is then analyzed to identify significant trends and develop informed forecasts.
4. Finally, results are shared and disseminated as reports or accessible dashboards, facilitating understanding and use by different stakeholders.

In a cultural institution, BI tools can help manage the organization's or department's activities, identify priority actions, evaluate the medium-term impact of initiatives targeting specific audience categories (origin, age, etc.), compare activity across periods in real time, and analyze the return on investment for advertising campaigns.

There are two main categories of BI solutions today. The first focuses mainly on data visualization and requires integration with data storage and ETL (extract, transform, load) software. The second category includes full BI functionalities. Two leading market tools often highlighted are:

• Tableau: appreciated for its intuitive interface, it allows creation of interactive visualizations and connection to multiple data sources. It offers great flexibility and an active user community.
• Power BI (Microsoft): seamlessly integrates with the Microsoft ecosystem (Excel, SharePoint). Ideal for small organizations, it provides customizable visualizations and powerful analysis capabilities.

Other notable solutions include Qlik Sense, SAP BI4, Oracle BI, Sisense, IBM Cognos Analytics, Domo, and MicroStrategy. In the cultural sector, the Pim's software also offers dashboard functions related to BI.  

Before deploying these types of tools, it is important to conduct an internal reflection to optimize data processing, improve data quality, and implement better management. Indeed, properly structuring data and carefully selecting management tools allows for quickly identifying relevant indicators and easing the workload of teams. Once deployed, the tools presented in this document are expected to play a critical role in managing the organization and evaluating its activities. That is why we stress the necessity of carefully designing and sizing these tools.

Preliminary assessment of needs is a critical step, as it must be aligned with the team's capacity to use the tools. The tool should be sized coherently with the team's skills and appetite for these matters. Complexity has no intrinsic virtue; a simplified tool that meets needs will be more valuable than a sophisticated tool ill-suited to reality. Moreover, this ideation phase often leads to multiplying wishes and monitoring indicators, resulting in an accumulation of dashboards with little real use that burden teams instead of helping them. It is therefore at this stage that priorities must be asserted, superfluous monitoring discarded, and focus placed on indicators and information that provide real added value to the organization and its management.

Such projects must involve all concerned stakeholders. They should precisely describe what they expect from Business Intelligence or Data Visualization, the information to be included in reporting, the periodic analyses to be carried out, or the visuals to be produced. Furthermore, despite their relative accessibility, Data Visualization and BI tools require prior training for proper handling. Skimping on this stage risks project failure due to lack of skills.

To succeed in these crucial steps, we suggest referring to the guides "How to Define Your Strategic Objectives and Goals?" and "How to Successfully Express Needs and Deploy a Project?"  

• [EN] The beauty of Data Visualization (external link), TED Talk, David Mc Candless, november 2012
  
• [FR] #DataGueule (external link), Websérie produit par France TV
  
• [EN] Data By Design (external link), MIT Press Klein et All, 2024
• [EN] A brief history of Data Visualization (external link), Jeffrey Michael Heer, Associate Professor of Computer Science & Engineering at the University of Washington, 2009
• [FR] Culture générale des données et de la visualisation de données (external link), Cours de Sylvain Lapoix, Samuel Goëta, Antoine Blanchard, Datactivist, juillet 2022
  
• [EN] How to... create infographics with impact (external link), Nifty Fox, 2021