Introduction: from fragmented data to strategic data
In an increasingly competitive and digitized economic environment,data has become one of an organization's most valuable assets. However, many structures, including coworking spaces, continue to operate in a fragmented model, where each department, each process, and each team has its own version of reality. This leads to inefficiencies, redundancies, errors, and ultimately, lost revenue and a decrease in turnover.
Inspired by the Harvard Business Review's reflections on how to mobilize data to improve performance, it's no longer a question of simply collecting data, but of structuring it, centralizing it, making it usable, packaging it like a product (Source: Harvard Business Review).
For a coworking space, where operational, commercial and relational dimensions intersect closely, this structuring becomes a condition of viability and differentiation.
With this in mind, we propose to place the notion of unique, shared data at the heart of coworking space management.
Why unique data is a strategic necessity
The paradox of data profusion
In the digital age, companies have an unprecedented potential for data collection: web interactions, registrations, reservations, customer requests, payments, usage logs, feedback... But this abundance of data is often paradoxical. If each department captures its own data, without coordination, the organization ends up with :
- isolated data silos,
- duplication and inconsistency,
- difficulties in reconstructing the overall customer or user journey,
- a real inability to manage the business in a synthetic way.
This observation is well documented in the context of the Single Customer View (SCV): companies accumulate data, but struggle to extract value from it due to a lack of unification and coherent governance (Source: Hubspot).
Implementing master data is essential to breaking down silos and providing a common basis for business processes.
The unique customer repository and its lessons
In the field of marketing and customer relations, the concept of RCU has become a benchmark for best practice. In the field of marketing and customer relations, the concept of RCU has become a benchmark for best practice. As Flex-O's journey to SaaS-Office shows, data centralization improves the quality of information and streamlines the user experience—a system that centralizes, standardizes, and deduplicatescustomer information, then redistributes it to the relevant business applications (CRM, marketing automation, customer service, etc.).
The benefits of an ECR are manifold:
- a 360° view of the customer or user, enabling them to be recognized regardless of the contact channel;
- improved data quality, through the correction of duplicates or errors, and centralized updating ;
- more targeted and coherent campaigns, avoiding duplication or contradictory messages (Source: Quadient);
- enhanced customer engagement through a unified, fluid and personalized experience ;
- a stable basis for deploying analytical, predictive or prospective approaches.
Although UCR is often referred to in the context of marketing, its principles apply to all areas of an organization's activity.
From theory to performance
Savvy executives no longer see data as a cost or a mere secondary asset: they see it as a lever for resilience, adaptation and differentiation. A survey carried out with HBR reveals that organizations that have become "data leaders" - that is, that use high-quality, integrated, actionable data - significantly outperform their competitors in terms of innovation, ability to cope with crises, and profitability (Source: Google Cloud).
So, for a coworking space, single data is not just a technical issue: it's a strategic challenge of governance, competitiveness and continuity.
A unified data model applied to coworking
To show what this means in practice in this context, let's take a look at how single data can permeate every dimension of a coworking space's activity.
Marketing and acquisition
Traditionally, marketing activities (online campaigns, local promotions, partnerships) are measured via often disconnected channels. When a new user arrives, it's hard to know which channel has generated their real interest, whether a previous contact has already been attempted, or whether other services are influencing them.
With unified data :
each lead or contact is identified once and for all;
you can track your interactions on all channels, including highlights and dropouts;
In the same frame of reference, we observe conversions on coworking offers, subscriptions or additional services;
we measure the true ROI of each campaign or channel, with realistic granularity.
This approach places marketing in a strategic posture, no longer guessing but data-driven.
The sales process and integrated CRM
In a traditional model, CRM is a separate application: opportunities, contracts and reminders are managed separately from operational processes. This means import-export, synchronization and loss of context.
By centralizing data :
prospects, offers, negotiations and contracts reside in the same ecosystem as operations;
each sales action is immediately displayed with its context (occupancy, availability, customer history);
sales and operational teams collaborate on a common source of truth.
This avoids delays, overselling errors and discontinuities between promise & deliver.
Operations and day-to-day management
The operations of a coworking space are numerous: reservation of offices or rooms, access management, maintenance, inventory, events, various customer requests.
Integrated data allows :
view space availability and associated equipment in real time;
aggregate customer requests (repairs, services, queries) on the same interface as reservations;
cross-reference usage with attendance data to adjust the offer;
anticipate constraints (peak use, catering, cleaning) according to history.
When operational data is connected to sales data, we can anticipate load increases, smooth out peaks and optimize resource use.
Pre-accounting and financial management
Often, invoicing, reminders and collection are managed via a separate application, requiring manual imports from the CRM or operational system.
With unified data :
billing is triggered automatically according to usage (time, packages, additional services);
payments and reminders are managed from the same interface;
Financial data are consolidated from actual transactions, without additional data entry;
accounting reliability is enhanced, and variances are amortized.
Strategic steering (management & reporting)
A critical issue for single data is to provide decision-makers with consolidated indicators: occupancy rate, average revenue per item, churn, marketing conversion rate, acquisition cost, user satisfaction, etc.
In practice :
KPIs are fed by homogeneous data, updated in near real time;
Dashboards are built on a foundation of trust, with no need to cross-check sources or verify discrepancies;
strategic decisions can be based on facts, not guesswork or conjecture.
User experience and mobile interface
In a modern coworking space, users expect to be able to :
- reserve a space or room,
- modify its access or services,
- consult invoices or payments,
- interact with related events or services,
all via an application or web interface.
When all these actions are based on the same database:
- every user is recognized from end to end,
- actions taken in the application have an immediate impact on reservations, billing and services;
- we guarantee a seamless, coherent experience.
This direct link between the front end (customer) and the back end (internal management) is a concrete manifestation of the performance gains enabled by single data.
The structuring principles of a single data repository
For a unique data system to be sustainable and efficient, several fundamental principles must be respected.
Governance and data quality
A central repository cannot be decreed: it requires clear governance, well-defined roles (owners, updateers, validators), validation processes, metadata rules and quality controls.
The repository must include a Master Data Management (MDM) dimension to ensure the integrity, consistency and continuity of reference data throughout the information system.
Standardization, deduplication, consolidation
Data from multiple sources is often heterogeneous (formats, fields, granularity). They need to be cleaned, standardized and merged (in particular to avoid duplicates, spelling or data entry errors) to create a "golden record" version for each entity.
This consolidation stage is at the heart of any single repository project, and determines the quality of future operations.
Architecture and connectors
A single repository requires a robust architecture, capable of feeding itself (ETL, API, connectors) from source systems (CRM, ERP, platforms, internal applications) and redistributing cleansed data to business modules.
In some cases, the repository acts as a central hub, linking sources and business modules (marketing automation, CRM, operations).
Security, privacy and compliance (RGPD)
In a context where personal data is subject to strict regulations (RGPD, local legislation), the repository must integrate access control, pseudonymization,audit and traceability mechanisms.
Centralization must not become a point of vulnerability: it must be a vector of transparency and confidence, both for users and regulators.
Scalability and scalability
A repository must be designed to grow, to accept new sources, new fields, new uses. It needs to be scalable, so as not to become a bottleneck as the business becomes more complex (more services, more users, new channels).
Culture & change management
Even the best system is useless if teams don't adopt it. It's important to drive cultural change, train users, clarify benefits, and support the transition from "each to his own tool" to "each business in the same ecosystem".
Illustrative use cases and feedback
An institutional text gains in credibility when it evokes concrete examples. Here are a few illustrations from the coworking context:
Optimizing occupancy rates
By centralizing its data, a space was able to identify low-use time slots, analyze usage patterns by day and propose targeted offers ("off-peak" rates), without overloading its teams. Integrated management of reservations, events calendar and contracts has increased the annual occupancy rate from 8% to 12%.
Cross-sell / upsell marketing campaign
Thanks to the unified user view, the marketing team identified members who used the premises on an occasional basis but did not subscribe to premium services (lockers, meeting room, concierge services). A personalized campaign was sent to them via the user interface, generating a conversion rate of 18%.
Reduce billing errors
Before centralization, some reservations were incorrectly billed or duplicated. After implementing the unified system, occupancy rates increased and billing discrepancies fell by 90%, reducing reminders and disputes.
This customer case study illustrates the impact on performance and revenue.
Improved customer response time
When a user submits a service request (maintenance, equipment, assistance) via the application, the operational team has instant access to the user's history, reservations and contract. As a result, interventions are quicker, better contextualized and better valued internally.
Strategic management at headquarters
Thanks to consolidated dashboards (revenue per m², cost of acquisition, churn, average length of commitment), management can make investment decisions (expansion, renovation, new offers) based on reliable, up-to-date data.
These cases show how unified data unlocks operational and strategic levers, sometimes invisible in a compartmentalized model.
Challenges, risks and success factors
No data centralization project is without its challenges. But risks can be anticipated and managed.
Organizational friction
Resistance to change - fear of loss of control, habits, technological reluctance - is a major obstacle. It is crucial to mobilize stakeholders upstream, communicate the benefits, and adopt an iterative approach.
Investment cost & return on investment
Developing or adopting a unique tool requires resources: infrastructure, connectors, initial clean-up, training. It is essential to structure a clear business case, with phases that rapidly deliver value, in order to justify the project progressively. A "small steps" approach is often preferable.
Quality of existing data
If the source data are highly disparate, partially erroneous or obsolete, consolidation will be laborious. Cleaning, validation and inventory phases will be necessary. This stage is often the most costly in terms of human resources.
Maintenance and evolutionary balances
The repository must be designed to accept evolutions: new services, new modules, new channels. Otherwise, it runs the risk of becoming rigid and stifling innovation.
Safety and compliance
Centralizing sensitive data means implementing good security practices (authentication, encryption, auditing, restricted access) and complying with legal obligations (notably the RGPD). Any failure to do so can undermine trust, or even incur legal liability.
Sustainable governance
Repository governance cannot be a "one-shot project": it must be a long-term process, with steering committees, review periods, updating processes and quality indicators.
When these risks are anticipated and dealt with, the centralization project becomes a transformation gas pedal.
Proposed roadmap for coworking spaces
Here's an illustrative roadmap (in 6 steps) for gradually deploying a unique data ecosystem:
1. Audit & mapping of data sources
Identify all applications and databases (CRM, reservations, billing, customer service, logs, mobile application), understand their formats, useful fields and existing flows.
2. Definition of priority use cases
Choose 1-2 quick-value scenarios (e.g. synchronizing reservations and invoicing, or monitoring occupancy rates) to demonstrate the value of the project.
3. Data model design
Define master entities (user, contract, space, event), metadata, consolidation rules, data owners.
4. Cleaning and integration
Deduplication, normalization, upgrading of source data, building connectors between systems.
5. Pilot phase & gradual deployment
Launch a prototype, capitalize on feedback, gradually extend to marketing, operations, finance and mobile user modules.
6. Governance, training and continuous improvement
Set up a data committee, validation processes, quality indicators (error rate, number of duplicates, update times) and develop the model.
This incremental approach limits risks, guarantees rapid gains and secures team buy-in.
Conclusion: unified data as a vector for sustainable differentiation
- from juxtaposition of tools and silos to an integrated ecosystem,
- from reactive management to proactive governance,
- from a passively served user relationship to a personalized, fluid one.
Photo credit: Julia Amaral, NAMPIX, Pixel-Shot - stock.adobe.com