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Data driven

In order to support our clients in the design and implementation of Data Driven strategies, our team is composed of profiles with Data Science skills. We help our clients in their Data Driven approaches, from data acquisition strategy with the identification and collection of data, to the valuation of this data or internal data via data mining, as well as the implementation of machine learning algorithms and data visualization.

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Data driven Data Science Agence Cabinet Experts Spécialistes Conseil ConsultingChallenges related to data driven approaches and data driven strategies

  • What is a data driven approach and what is data science?

Data Driven refers to an approach to managing a project or a company through data, that generates an environment in which decisions are supported by data analyses or machine learning algorithms, promoting rational and efficient management.

Data Science refers to all the algorithmic techniques associated with statistics that make it possible to select the right data, to analyze data, and even to create prediction algorithms (referred to as machine learning).

  • What are the challenges related to data driven approaches?

Companies are generating, retrieving and storing more and more data. Data Driven approaches allow the implementation of decision-making methods based on this data.

Implementing a Data Driven strategy covers issues in change management to implement this strategy, as well as in operational execution:

The first challenge is to identify the relevant data on which the company wants to base its decision-making, whether it is about a specific project or an entire part of its activity. For example, the implementation of a Data Driven marketing strategy requires data on potential clients, with fine granularity, at a consumer level for instance, while the optimization of inventory management requires logistics data.

What is the critical data to consider depending on the decisions to be made? How could this data serve my business objectives?
This may involve capitalizing on data already generated, to be recovered internally or externally. Identifying internal data can be an issue when the organization is large, which is why companies tend to structure this digital resource as much as possible to make it visible and accessible. Specific projects may need to be implemented to generate the strategic data that will allow Data Driven decision making. External data sources are varied and their use may be subject to certain restrictions (restricted access with file preparation, cost, etc.) related to their holder and to the regulations in force in different countries, in particular in the case of personal data, especially for health data.

Where can the relevant data be found or how can it be generated? What are the steps to follow to retrieve the data and to be authorized to process it?
Depending on the project and the use of the identified data, it is important to adapt the form of the data collected to the project for which it was collected. For example, a one-off marketing campaign analysis project does not involve any specific issue in the storage or accessibility of data; local storage is sufficient. In contrast, the creation of an inventory prediction tool must fit into the incoming data flow so that the tool can measure risk in real time. The integration of data for real-time decision-making, most often at the operational level as in Industry 4.0, is linked to the concept of Smart Data.

What are the processes to put in place to make the most of the data?
When data is collected, stored and valued, a major challenge is to analyze it in order to extract the most relevant information or to create machine learning algorithms that will enable prediction, automation or in-depth analysis. Another challenge is to create relevant analytical frameworks, so that the recommendations are all the more impactful and that the models used are the most suitable.

What lessons can we learn from data? What risks or business opportunities can the data possessed make it possible to anticipate?
Finally, the integration of raw data, analyzed or modeled to carry out a Data Driven strategy, must be supported by the implementation of internal work processes and an acculturation of the teams to ensure the sustainability of the approach and continue to give meaning to decision-making.

How can teams become familiar with a data-based strategy? What change management processes should be followed?

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How do we support you in your data driven projects or in the implementation of your data driven strategy?

For more than 25 years, Alcimed has supported its clients in their innovation and new business development projects. With the evolution of technologies and associated practices, data is now at the heart of business activities, and its intelligent use is becoming a necessity. In this context, the consideration of a Data Driven reflection is often essential regardless of the type of project we are working on with our clients.

Beyond placing data at the heart of our reflection and models in our projects, our team supports you in your specific Data Driven projects and in the implementation of your Data Driven strategy.

Depending on your project, your context and your challenges, we support you in particular in:

  • The identification of your strategic data:

Our experience in our clients’ businesses and our ability to lead work meetings enable us to identify critical data to meet our clients’ business challenges and strategic imperatives. If this critical data is available, it must be recovered. However, if it does not exist yet, we can create solutions with our clients to generate such data, to gather it, and to find what to do to achieve the necessary objectives.

  • The identification of data sources and the collection strategy:

We support our clients in identifying sources of internal data and in retrieving such data internally. We define how this data can be used to provide answers to their questions. We also help clients handle external data by sharing our knowledge of open data, of the various databases available on the market, of the procedures to follow in order to access both public databases and databases that we use.

  • Data analysis:

Our data scientists and consultants have the analytical and technological tools for analyzing our clients’ data, enabling the analysis of qualitative data and quantitative data.

  • The implementation of machine learning algorithms:

Some missions for our clients require the implementation of prediction algorithms or in-depth analysis. Our data scientists develop machine learning algorithms coded specifically for each project, adapted and adjusted to the challenges of our clients.

  • Ensuring your teams are familiar with data subjects:

We participate in developing the internal culture of our clients around the use of data through training, discussion, organization of seminars or workshops, as well as the co-construction of internal methods and processes with our clients’ teams.

The types of projects we carry out for our clients in this field are:

  • Regulatory framework analysis 
  • Test & Learn approaches 
  • Strategic foresight 
  • Commercial strategy 
  • Valorization 
  • Business models 
  • New services 
  • New offers 
  • Open innovation 
  • Workshop 
  • Strategic audit 
  • Strategic positioning 
  • Innovation process 
  • Innovation strategy 
  • Collaborative projects 
  • Search for partners 
  • Roadmap 
  • Opportunity evaluation 
  • Patient pathway 
  • Market access 

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EXAMPLES OF RECENT PROJECTS CARRIED OUT FOR OUR CLIENTS IN THE FIELD OF DATA DRIVEN APPROACHES

One of our clients, a pharmaceutical industry leader, wanted to rethink the promotional model of a drug in its portfolio (which physicians to target, how often, through which channels) using a Data Driven approach. The objective of our project was to find, through a quantitative analysis, the promotional model that would enable the best ROI, by optimizing the targeting of physicians and the promotional mix, all based on a mix of sales data, budget data, data on the promotion carried out and also targeting data from external sources. As our quantitative analyses were limited (little data, sometimes poorly informed), we supplemented them with a qualitative analysis to find the ideal model enabling the best return on investment for our client.
We supported one of our clients, a leader in the healthcare sector, who wanted to explore the opportunity to diversify its activities through the integration and use of digital solutions for the generation and collection of real-life data (Real-World Evidence, RWE). For this project, our teams evaluated the different data capture technologies available on the market, their characteristics, their advantages and their limits, as well as the existing approaches for their use in France in RWE. Following our analysis, we defined 4 approaches enabling our client to integrate and to set up these new selected digital data services and established an operational action plan to carry out pilot projects. In the end, a pilot was successful and our client was able to launch a new differentiating offer.
We developed a global customer engagement indicator for one of our clients. The objective of this indicator was to use all available customer data, particularly in terms of responses to communications, to manage activities: to understand what actions triggered customer engagement to make the best future decisions. Our methodology consisted of two parts. The first part was to create a common definition of what “customer engagement” was for our client and to define the data available for the creation of this indicator. This resulted in an external investigation (bibliographic research and interviews with key players) as well as an internal investigation via exchanges with the various stakeholders in the company. The second part consisted in retrieving this data to bring out, in real-time, the engagement indicator at different levels of granularity for our client.
One of our clients, an industrial player, wanted to set up a European project based on the collection of sensitive data in external databases. After having carried out a first pilot which consisted in collecting data and setting up a machine learning algorithm in a European country, our client wanted to work its way through the regulatory context of the European Union and of several other European countries. The objective of our project was to better understand how to implement the collection of sensitive data and its processing in these other countries in Europe. We therefore focused on the GDPR and national laws to provide a global vision of the regulatory environment and the different steps necessary to set up the project that our client wanted to implement.

Founded in 1993, Alcimed is an innovation and new business consulting firm, specializing in innovation driven sectors: life sciences (healthcare, biotech, agrifood), energy, environment, mobility, chemicals, materials, cosmetics, aeronautics, space and defence.

Our purpose? Helping both private and public decision-makers explore and develop their uncharted territories: new technologies, new offers, new geographies, possible futures, and new ways to innovate.

Located across eight offices around the world (France, Europe, Singapore and the United States), our team is made up of 220 highly-qualified, multicultural and passionate explorers, with a blended science/technology and business culture.

Our dream? To build a team of 1,000 explorers, to design tomorrow's world hand in hand with our clients.

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