Machine Learning – Businesslab

Business Intelligence, Clients, Company, Data Governance, Data Marketing, Data Mining and Data Integration, Data Quality Management, Data Regulations, Data Warehouse, Machine Learning, Self-service Analytics, Technology

20 juin 20240

Getting started with Business Intelligence: practical tips

« Wisdom is about extracting gold from raw data; with sharp Business Intelligence, every piece of information becomes a nugget. »

This adage perfectly sums up the potential of BI, provided you follow a few practical tips. Existing information goldmines allow companies to turn them into nuggets of gold shaped in their own image.

Definition

Business Intelligence (BI) is a set of processes, technologies and tools used to collect, analyse, interpret and present data in order to provide actionable information to an organisation’s decision-makers and stakeholders. The main objective of BI is to help companies make strategic decisions based on reliable and relevant data.

BI is widely used in many areas of business, such as financial management, human resources management, marketing, sales, logistics and supply chain, among others. In short, Business Intelligence aims to transform data into actionable knowledge to improve an organisation’s overall performance.

Before looking at the practical tips, let’s look at the elements that define BI. To put BI into practice within your business, there are 5 main steps you need to follow to achieve relevant and effective BI.

Data collection	Data is collected from a variety of sources inside and outside the company, such as transactional databases, business applications, social media, customer surveys, etc.
Data cleansing and transformation	The data collected is cleaned, normalised and transformed into a format that is compatible for analysis. This often involves eliminating duplicates, correcting errors and standardising data formats.
Data analysis	Data is analysed using various techniques such as statistical analysis, data mining, predictive models and machine learning algorithms to identify trends, patterns and insights.
Data visualisation	The results of analysis are generally presented in the form of dashboards, reports, graphs and other interactive visualisations to facilitate understanding and decision-making.
Informations dissemination	The information obtained is shared with decision-makers and stakeholders throughout the organisation, enabling them to make informed decisions based on reliable data.

Practical tips

Now that we have a broad understanding of the definition of BI, it’s important to remember that getting started with Business Intelligence (BI) can be a challenge, but with a strategic approach and some practical advice, you can put in place an effective infrastructure for your business.

Here are some practical tips for getting started with relevant and effective Business Intelligence.

Clarify your objectives

Before you start implementing BI, clearly identify the business objectives you want to achieve. Whether you want to improve decision-making, optimise business processes or better understand your customers, clear objectives will help you focus your efforts.

Start with the basics

Don’t try to do everything at once. Start with pilot projects or specific initiatives to familiarise yourself with BI concepts and tools. This will also enable you to measure results quickly and adjust accordingly.

Identify your data sources

Identify your organisation’s internal and external data sources. This can include transactional databases, spreadsheets, CRM systems, online marketing tools, etc. Ensure that the data you collect is reliable, complete and relevant to your objectives.

Clean and prepare your data

Data quality is essential for effective BI. Put processes in place to clean, standardise and prepare your data before analysing it. This often involves eliminating duplicates, correcting errors and standardising data formats.

Choose the right tools

There are many BI solutions on the market, so look for those that best suit your needs. Considers factors such as ease of use, the ability to manage large sets of data, integration with your existing systems and cost.

Train your team

Make sure your team is formed to use BI tools and interpretation of data. BI is a powerful tool, but its effectiveness depends on the ability of your team to use it properly.

Communicate and collaborate

Involve stakeholders from the start of the BI implementation process. Their support and comments will be essential to ensure the long-term success of your initiative BI.

Start small and grow

Don’t try to implement all BI functionalities at once. Start with pilot projects or specific initiatives, and then gradually extend your use of BI according to the results obtained.

Involve stakeholders

Involve stakeholders right from the start of the BI implementation process. Their support and feedback will be essential in ensuring the long-term success of your BI initiative.

Measure and adjust

Track the performance of your BI and measure its impact on your business. Use this information to identify areas for improvement and make adjustments to your BI strategy over time.

By following these initial practical tips, you can get off to a good start with Business Intelligence and start leveraging your data to make informed decisions and drive business growth.

CONCLUSION

A Business Intelligence (BI) project is considered successful when it succeeds in adding value to the business by meeting its business objectives effectively and efficiently. Here are some key indicators that can define a successful BI project:

Alignment with business objectives: the BI project must be aligned with the company’s strategic objectives. It must contribute to improving decision-making, optimising business processes, increasing profitability or strengthening the company’s competitiveness.

Effective use of data: a successful BI project makes effective use of data to provide usable information. This means collecting, cleansing, analysing and presenting data in the right way to meet business needs.

User adoption: end-users must adopt BI tools and use them on a regular basis to make decisions. A successful BI project is one that meets users’ needs and is easy to use and understand.

Improved performance: a successful BI project translates into improved business performance. This can take the form of increased sales, reduced costs, improved productivity or any other performance measure relevant to the business.

Positive return on investment (ROI): a successful BI project generates a positive return on investment for the business. This means that the benefits gained from using BI outweigh the costs of implementing and maintaining the project.

Scalability and flexibility: a successful BI project is capable of adapting to the changing needs of the business and evolving with it. It must be flexible enough to support new needs, new types of data or new usage scenarios.

Management support and commitment: a successful BI project benefits from the support and commitment of the company’s management. Management must recognise the value of BI and provide the necessary resources to support the project throughout its lifecycle.

In summary, a successful BI project is one that contributes to achieving the company’s business objectives by effectively using data to make informed decisions. It is characterised by its alignment with business objectives, its adoption by users, its positive impact on business performance and its positive return on investment.

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Artificial Intelligence, Business Intelligence, Company, Data Governance, Data Marketing, Data Mining and Data Integration, Data Quality Management, Machine Learning, Self-service Analytics, Technology

10 juin 20240

Informed decision-making: fast and effective

« Promptness in decision-making is the pillar of success, but data insight is the foundation »

This adage perfectly sums up the subject of effective and rapid decision-making, which in the majority of businesses is based on data.

In today’s business world, data has become the fuel that drives strategic decision-making. From planning day-to-day operations to developing long-term strategies, businesses are now leveraging data to guide their choices and improve their overall effectiveness.

Here’s how data-driven decisions can radically transform your business. Whether you’re a leader in your sector or expanding into a new market, you’ll inevitably have to make strategic decisions that will affect your business.

Knowing that the wrong decision can have serious consequences for your project, and even for your company, it’s essential to have the right processes, decision-making tools and, above all, data.

Accuracy and relevance

Data-driven decisions are based on tangible, factual information, eliminating guesswork and hunches that are often prone to error. By using accurate, up-to-date data, businesses can make more informed and relevant decisions, reducing the risk of costly errors.

Identifying trends

By analysing large data sets, businesses can identify significant trends and recurring patterns. This enables them to anticipate market changes, identify new opportunities and stay ahead of the competition.

Personalising customer experiences

Customer behaviour data enables businesses to create personalised, tailored experiences. By understanding individual customer needs and preferences, businesses can offer better-tailored products and services, boosting customer loyalty and satisfaction.

Using technology to accelerate & optimise the process

Operational data enables companies to optimise their internal processes. By identifying inefficiencies and bottlenecks, companies can make precise adjustments to improve productivity, reduce costs and increase overall operational efficiency.

Data processing technologies such as artificial intelligence (AI), machine learning and predictive analytics can accelerate the decision-making process by automating repetitive tasks and providing actionable insights in real time. Advanced algorithms can detect subtle patterns in data, helping decision-makers to make better and faster decisions.

Data-driven decisions: the key to agility & agile decision-making

With real-time access to data, businesses can make decisions faster and more agilely. Using real-time dashboards and analysis, decision-makers have the information they need to react quickly to market changes and new opportunities.

Informed decision-making depends on access to accurate, up-to-date data. Companies that invest in data collection, analysis and visualisation systems are better equipped to make rapid, informed decisions. By exploiting available data, they can quickly assess market trends, understand customer needs and identify opportunities for growth.

Speed without compromising quality

While speed is essential in a competitive business environment, this does not mean sacrificing the quality of decisions. Data provides an objective framework on which to base choices, reducing the risk of costly errors associated with impulsive or ill-informed decision-making. By combining speed and accuracy, businesses can make effective decisions while maintaining a high level of quality and relevance.

The importance of a data culture

Beyond tools and technologies, informed decision-making depends on an organisational culture that values data and fosters collaboration. Companies that foster a data culture are better equipped to collect, analyse and effectively use information to make decisions. By encouraging transparency, communication and collaboration, these companies can fully exploit the potential of data to drive innovation and growth.

Conclusion

By adopting a data-driven approach, businesses can transform the way they make decisions, moving from an approach based on intuition to one based on tangible, verifiable data. As a result, they can improve operational efficiency, drive growth and maintain competitiveness in the ever-changing marketplace. Ultimately, businesses that fully embrace data-driven decision-making are better positioned to thrive in the modern economy.

Informed, data-driven decision-making offers an undeniable competitive advantage in the modern business environment. By combining speed and efficiency with the accuracy of data, businesses can adapt quickly to market changes, seize opportunities and maintain their position as leaders in their sector. By investing in advanced data processing technologies and fostering a data-driven culture within the organisation, businesses can successfully navigate an ever-changing world and thrive in the face of uncertainty.

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Business Intelligence, Company, Data Governance, Data Marketing, Data Mining and Data Integration, Data Quality Management, Data Regulations, Data Warehouse, Machine Learning, Technology

23 mai 20240

Basic SQL : what is it?

For a very long time, SQL was reserved for knowledgeable and technical people in the IT department, and not just any company entity or department could do it. It used to be the exclusive preserve of the company’s IT department. Now, with the spread of « IT », many departments are able to access their company’s data using SQL to query their databases, including marketing, accounting, management control, human resources and many others!

Are you a company specialising in e-commerce, healthcare, retail or simply an SME / SMI? Do you have a set of data stored in a database?

It’s essential to know the basics of structured query language (SQL) so that you can quickly get answers to your queries.

DEFINITION

SQL, or Structured Query Language, is a programming language specially designed for managing and manipulating relational databases.

It provides a standardised interface enabling users to communicate with databases and carry out operations such as inserting, updating, deleting and retrieving data efficiently.

THE BASICS OF SQL

Remember that SQL is nothing more than a way of reading the contents of a relational database to retrieve the information a user needs to meet a requirement.

DATA STRUCTURING

SQL is based on the relational model, which organises data in the form of tables. Each table is made up of columns (fields) representing specific attributes, and rows containing the records.

Table structure :

In the world of SQL, table structure is crucial. Each table is defined by columns, where each column represents a particular attribute of the data you are storing. For example, an « employees » table might have columns such as « surname« , « first name« , « age« , etc. These tables are linked by keys. These tables are linked by keys, which can be unique identifiers for each record, facilitating relationships between different tables.

The main operations (or commands / basic SQL queries)

SELECT : Used to extract data from one or more tables. The SELECT clause is used to specify the columns to be retrieved, the filter conditions and the sort order. This clause is one of the most fundamental in SQL. The WHERE clause, often used with SELECT, is used to filter the results according to specific conditions. For example, you might want to retrieve only those employees whose age is greater than 30, or as in the example below, only those employees in the sales department.

SELECT last name, first name FROM employees WHERE department = ‘Sales’;

INSERT: Used to add new rows to a table

INSERT INTO customers (last name, first name, email) VALUES (‘Doe’, ‘John’, ‘john.doe@email.com’);

UPDATE: Used to add new rows to a table

UPDATE products SET price = price * 1.1 WHERE category = ‘Electronics’;

DELETE: Used to delete rows from a table under certain conditions

DELETE FROM orders WHERE order_date < ‘2023-01-01‘;

Filtering and sorting

To filter the results, SQL uses the WHERE clause, which allows you to specify conditions for selecting the data. In addition, the ORDER BY clause is used to sort the results according to one or more columns.

Filtering and sorting are essential operations in the SQL language, making it possible to retrieve specific data and organise it in a meaningful way. Let’s explore these concepts with some practical examples

Filtering with the WHERE clause

The WHERE clause is used to filter the results of a query by specifying conditions. This allows you to select only the data that meets these criteria.

–Select employees with a salary greater than 50000

SELECT last name, first name, salary

FROM employees

WHERE salary > 50000;

In this example, only employees with a salary greater than 50000 will be included in the results.

Filtering with the ORDER BY clause

The ORDER BY clause is used to sort the results of a query according to one or more columns. You can specify the sort order (ascending or descending)

–Select customers and sort alphabetically by name

SELECT last name, first name, email

FROM customers

ORDER BY name ASC;

In this example, the results will be sorted in ascending alphabetical order by customer name.

Filtering and sorting can also be combined, i.e. combining the WHERE clause and the ORDER BY clause to filter the results at the same time

–Select products in the ‘Electronics’ category and sort by descending price

SELECT product_name, price

FROM products

WHERE category = ‘Electronics

ORDER BY price DESC;

There are other ways of filtering and sorting with operators, but this becomes SQL that is no longer basic, but for a more experienced audience.

By understanding these filtering and sorting concepts, you will be able to extract specific data from your SQL databases in a targeted and organised way.

Joins

Joins are essential for combining data from several tables.

Common types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN and FULL JOIN, each offering specific methods for associating rows between different tables.

Example of a simple join:

SELECT customer.name, orders.date

FROM customers

INNER JOIN orders ON customers.customer_id = orders.customer_id;

Types of joins :

INNER JOIN: Returns the rows when the join condition is true in both tables.

LEFT JOIN (or LEFT OUTER JOIN): Returns all the rows in the left-hand table and the corresponding rows in the right-hand table.

RIGHT JOIN (or RIGHT OUTER JOIN): The opposite of LEFT JOIN.

FULL JOIN (or FULL OUTER JOIN): Returns all rows when the join condition is true in one of the two tables.

Constraints for data integrity and Indexes to optimise performance

Constraints play a crucial role in guaranteeing data integrity. Primary keys ensure that each record in a table is unique, while foreign keys establish links between different tables. Uniqueness constraints ensure that no duplicate values are allowed in a specified column.

Indexes are data structures that improve query performance by speeding up data searches. Creating an index on a column makes searching easier, but it is essential to use them wisely, as they can also increase the size of the database.

Conclusion

SQL is a powerful and universal tool for working with relational databases. Understanding its fundamentals enables developers and data analysts to interact effectively with database management systems, making it easier to manipulate and retrieve crucial information. Whether for simple tasks or more complex operations, SQL remains an essential part of data management.

It offers a range of tools for interacting with relational databases in a powerful and flexible way. By understanding these basic concepts, you’ll be better equipped to effectively manipulate data, create custom reports and answer complex questions from large datasets. Whether you’re a developer, data analyst or database administrator, mastering SQL is an invaluable asset in the world of data management.

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Business Intelligence, Company, Data Governance, Data Marketing, Data Mining and Data Integration, Data Quality Management, Data Regulations, Data Warehouse, Machine Learning, Self-service Analytics, Technology

06 mars 20240

Data Warehouses vs Data Lakes: a comparative dive into the Tech World

In the ever-evolving world of technology, two terms have been making waves: Data Warehouses and Data Lakes. Both are powerful tools for data storage and analysis, but they serve different purposes and have unique strengths and weaknesses. Let’s dive into the world of data and explore these two tech giants.

Data Warehouses have been around for a while, providing a structured and organized way to store data. They are like a well-organized library, where each book (data) has its place. Recent advancements have made them even more efficient. The convergence of data lakes and data warehouses, for instance, has led to a more unified approach to data storage and analysis. This means less data movement and more efficiency – a win-win!

Moreover, the integration of machine learning models and AI capabilities has automated data analysis, providing more advanced insights. Imagine having a personal librarian who not only knows where every book is but can also predict what book you’ll need next!

However, every rose has its thorns. Data warehouses can be complex and costly to set up and maintain. They may also struggle with unstructured data or real-time data processing. But they shine when there is a need for structured, historical data for reporting and analysis, or when data from different sources needs to be integrated and consistent.

On the other hand, Data Lakes are like a vast ocean of raw, unstructured data. They are flexible and scalable, thanks to the development of the Data Mesh. This allows for a more distributed approach to data storage and analysis. Plus, the increasing use of machine learning and AI can automate data analysis, providing more advanced insights.

However, without proper management, data lakes can become « data swamps », with data becoming disorganized and difficult to find and use. Data ingestion and integration can also be time-consuming and complex. But they are the go-to choice when there is a need for storing large volumes of raw, unstructured data, or when real-time or near-real-time data processing is required.

In depth

DATA WAREHOUSES

Advancements

1. Convergence of data lakes and data warehouses: This allows for a more unified approach to data storage and analysis, reducing the need for data movement and increasing efficiency.

2. Easier streaming of real-time data: This allows for more timely insights and decision-making.

3. Integration of machine learning models and AI capabilities: This can automate data analysis and provide more advanced insights.

4. Faster identification and resolution of data issues: This improves data quality and reliability.

Setbacks

1. Data warehouses can be complex and costly to set up and maintain.

2. They may not be suitable for unstructured data or real-time data processing.

Best scenarios for implementation

1. When there is a need for structured, historical data for reporting and analysis.

2. When data from different sources needs to be integrated and consistent.

DATA LAKES

Advancements

1. Development of the Data Mesh: This allows for a more distributed approach to data storage and analysis, increasing scalability and flexibility.

2. Increasing use of machine learning and AI: This can automate data analysis and provide more advanced insights.

3. Tools promoting a structured dev-test-release approach to data engineering: This can improve data quality and reliability.

Setbacks

1. Data lakes can become « data swamps » if not properly managed, with data becoming disorganized and difficult to find and use.

2. Data ingestion and integration can be time-consuming and complex.

Best scenarios for implementation

1. When there is a need for storing large volumes of raw, unstructured data.

2. When real-time or near-real-time data processing is required.

In conclusion, both data warehouses and data lakes have their own advantages and setbacks. The choice between them depends on the specific needs and circumstances of the organization. It’s like choosing between a library and an ocean – both have their charm, but the choice depends on what you’re looking for. So, whether you’re a tech enthusiast or a business leader, understanding these two tools can help you make informed decisions in the tech world. After all, in the world of data, knowledge is power!

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Artificial Intelligence, Business Intelligence, Change and Project Management, Data Governance, Data Marketing, Data Mining and Data Integration, Machine Learning, Self-service Analytics, Technology

29 février 20240

Mastering your Data: the essence and impact of the data catalogue

In today’s hyper-connected world, where data is seen as the new gold, knowing how to manage and exploit it is essential for businesses wishing to make informed decisions and remain competitive. The concept of the data catalogue is emerging as a key response to this challenge, offering a compass in the vast and often tumultuous ocean of data.

This article aims to shed light on the challenges and benefits of data catalogues, modern libraries where metadata is not just stored, but made comprehensible and accessible. Through the automation of metadata documentation and the implementation of collaborative data governance, data catalogues are transforming the way organisations access, understand and use their valuable information.

By facilitating the discovery and sharing of trusted data, they enable organisations to navigate confidently towards a truly data-driven strategy.

But also...

A data catalogue is a centralised tool designed to effectively manage data within an organisation. According to Gartner, it maintains an inventory of active data by facilitating its discovery, description and organisation.

The basic analogy would be to say that it is a directory, where readers find the information they need about books and where they are: title, author, summary, edition and the opinions of other readers.

The aim of a data catalogue is to make data governance collaborative, by improving accessibility, accuracy and relevance of data for the business. It supports data confidentiality and regulatory compliance through intelligent data lineage tracing and compliance monitoring.

Here are 5 reasons for your data teams to use a data catalogue:

Data analysts / Business analysts

They use the data catalogue to find and understand the data they need for their analyses. This enables them to access relevant data quickly, understand its context and guarantee its quality and reliability for reporting and analysis.

Data Scientists

The data catalogue is essential for locating the datasets they need for their machine learning and artificial intelligence models. It also makes it easier to understand the metadata (where the data comes from and the transformations it has undergone), which is vital for data pre-processing.

Data Stewards

They are responsible for data quality, availability and governance. They use the data catalogue to document metadata, manage data standards, and monitor compliance and the use of data within the organisation.

Compliance and security managers

The data catalogue helps them to ensure that data is managed and used in accordance with current regulations, such as the GDPR for the protection of personal data. They can use it to track access to sensitive data and audit data use.

Data architects and engineers

These technicians use the data catalogue to design and maintain the data infrastructure. It provides them with an overview of the data available, its structure and its interrelationships, making it easier to optimise the data architecture and integrate new data sources.

It’s important to note that business users are not left out of this tool either. Although they are not technical users, they benefit from the data catalogue to access the information and insights they need to make decisions. The directory enables them to find relevant data easily, without the need for in-depth technical knowledge.

Key points

A data catalogue is used to:

Improve data discovery and access

Strengthen data governance

Improve data quality and reliability

Facilitate collaboration between teams

Optimise the use of data resources

With Data Catalogues, just as we now do with our own revolutionary DUKE solution, navigate the complex data landscape today, with the luxury of effectively accessing, managing and exploiting data to support informed decision-making and business innovation.

Let your Data teams shine today and dive straight into the heart of our DUKE project.

Business Intelligence, Data Governance, Data Marketing, Data Mining and Data Integration, Data Quality Management, Machine Learning

06 juillet 20210

RETAIL : 4 règles pour devenir Data Driven // S3E4

Face à des freins culturels et organisationnels, il est difficile de déployer la culture de la donnée dans les entreprises du retail. Diffuser la culture de la donnée en magasin veut dire donner le pouvoir aux employés de mieux vendre. La question principale est donc de dépasser les obstacles, et d’accompagner le changement.

Voici les 4 règles clés à suivre durant votre transformation :

1. Soyez soutenu(s) par votre hiérarchie

Mettre la culture de la donnée au cœur de l’organisation est une prérogative du haut management. Il faut emmener l’ensemble de vos collaborateurs dans la transformation. Il y a parfois des freins culturels, les personnes non issues de l’ère numérique, conservent des réflexes. Du jour au lendemain, elles sont invitées à repenser leurs habitudes. Il est donc nécessaire d’adopter une conduite de changement.

2. La communication, c'est la clé

Tout lancement d’un nouveau projet implique forcément des changements de processus et des changements organisationnels. Pour réussir, il vous faut communiquer pendant toute la durée du projet.

Pour créer une culture de la donnée (dite « Data Driven culture ») vous devez penser votre projet pour que les données puissent être communiquées à des non-spécialistes. Gartner précise qu’une des caractéristiques fondamentales d’une culture de la donnée est la mise à disposition de la donnée de manière simple et claire à toutes les personnes en entreprise. Par exemple, utilisez une solution logicielle de tableau de bord « retail » ou de visualisation de données pour restituer de manière claire vos données. Et par conséquent, prendre des décisions éclairées !

Vous pouvez même raconter des histoires avec vos données en leur donnant du contexte grâce aux solutions de « data storytelling » comme dans Tableau Story.

Vous pouvez rendre vos tableaux de bord simples personnalisables. Par exemple, chaque point de vente devrait être en mesure de s’approprier et d’analyser ses données « retail ». Il appréciera de pouvoir changer l’angle de vue en fonction de ses besoins. Passer d’une vision par produit, à une vision par client (B2B), ou d’une vue « directeur de magasin » à une vue « team leader », ou d’une vue produit à une vision par zone géographique, etc. La personnalisation de l’angle de vue est fondamentale pour que la donnée soit vulgarisée et comprise par l’ensemble du personnel en magasin. D’autre part, vu le nombre d’informations auxquelles il est exposé, il est important de rester simple pour une communication efficace.

Simplicité, efficacité ; n’est-ce pas ?

3. Focus : les motivations personnelles de vos collaborateurs pour améliorer le taux d'adoption des outils

Vous devez intéresser le personnel de vos magasins par les données qui sont à sa disposition. Vos collaborateurs doivent voir des solutions à leurs problématiques métiers dans le projet ; c’est une étape essentielle pour un projet data réussi. Par exemple, la rémunération variable du personnel est souvent en fonction des résultats des ventes du magasin. Lui donner des solutions concrètes pour mieux vendre est donc dans son intérêt.

Fournir des tableaux de bord retail personnalisés et simples, est un enjeu de votre projet. Imaginez un mini site internet fournissant au directeur du magasin le tutoriel sur la nouvelle disposition des articles en magasin, l’emploi du temps de la semaine, les performances de vente par produit…Une mini-plateforme personnalisée lui fournissant des informations pour lui et son équipe : le rêve !

Si vous souhaitez la réussite de votre organisation (on n’en doute pas une seule seconde !), vous devez penser « adoption par les collaborateurs » de votre projet.

4. Enfin : rendre toutes ces données actionnables et pertinentes !

Le défaut de nombreux projets data est qu’ils naissent sans être pensés pour des cas d’usage métier précis. La donnée est privilégiée au détriment de l’apport métier. Nous pensons que c’est une vision purement technique de voir les choses ! Avoir les données à disposition n’est pas le but du projet data. La finalité est de pouvoir fournir des informations actionnables à des professionnels et répondre à leurs problématiques.

La Data permet de réhabiliter l’efficacité des stratégies marketing en offrant aux retailers l’approche « ROIste » qu’ils réclament. Le Data Storytelling permet, lui, de légitimer et valoriser les choix en systèmes d’information qui récupèrent cette Data, en la racontant aux magasins. Ces derniers peuvent désormais prendre les meilleures décisions.

La Data est votre nouvelle monnaie. Mieux que de l’échanger, il faut la faire fructifier et la rendre exploitable. La question n’est plus « Pourquoi ?», mais « Quand ?». Faites-nous confiance, nous nous occupons du « Comment ?».

Nous espérons que cette mini-série spéciale « Data & Retail » vous a plu ! Nous vous encourageons à lire les articles précédents si ce n’est pas déjà fait…

Article 1 : https://www.datanalysis.re/businesslab/blog/business-intelligence/retail-meilleures-data-meilleurs-resultats-s3e1/

Article 2 : https://www.datanalysis.re/businesslab/blog/business-intelligence/retail-maitriser-vos-donnees-metiers-s3e2/

Artcicle 3 : https://www.datanalysis.re/businesslab/blog/business-intelligence/retail-data-science-insights-s3e3/

Nous vous préparons la rentrée avec d’autres mini-séries à venir! Des thématiques que vous souhaiteriez voir abordées par ici ? Ecrivez-nous !

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Business Intelligence, Data Governance, Data Marketing, Data Mining and Data Integration, Data Quality Management, Machine Learning

30 juin 20210

RETAIL: Data Science & Insights // S3E3

La Data Science est la science des données. C’est un ensemble de techniques et de méthodes qui permettent à une organisation d’analyser ses données brutes pour en extraire des informations précieuses permettant de répondre à des besoins spécifiques ou de résoudre des problèmes analytiques complexes.

La Data Science permet de découvrir des informations pertinentes au sein des ensembles de données

En plongeant dans ces informations à un niveau fin, l’utilisateur peut découvrir et comprendre des tendances et des comportements complexes. Il s’agit de faire remonter à la surface des informations pouvant aider les entreprises à prendre des décisions plus intelligentes.

Cette « fouille de données » peut se faire grâce à l’apprentissage automatique (Machine Learning). Ce dernier fait référence au développement, à l’analyse et à l’implémentation de méthodes et algorithmes qui permettent à une machine (au sens large) d’évoluer grâce à un processus d’apprentissage, et ainsi de remplir des tâches qu’il est difficile ou impossible de remplir par des moyens algorithmiques plus classiques.

La Data Science permet de créer un Data Product

Un data product est un outil qui repose sur des données et les traite pour générer des résultats à l’aide d’un algorithme. L’exemple classique d’un data product est un moteur de recommandation.

Moteur de recommandation

Il a été rapporté que plus de 35% de toutes les ventes d’Amazon sont générées par leur moteur de recommandation. Le principe est assez basique : en se basant sur l’historique des achats d’un utilisateur, les articles qu’il a déjà dans son panier, les articles qu’il a notés ou aimés dans le passé et ce que les autres clients ont vu ou acheté récemment, des recommandations sur d’autres produits sont automatiquement générées.

Optimiser votre gestion de stock

Un autre exemple de cas d’usage de la data science est l’optimisation de l’inventaire, les cycles de vie des produits qui s’accélèrent de plus en plus et les opérations qui deviennent de plus en plus complexes obligent les détaillants à utiliser la Data Science pour comprendre les chaînes d’approvisionnement et proposer une distribution optimale des produits.

Optimiser ses stocks est une opération qui touche de nombreux aspects de la chaîne d’approvisionnement et nécessite souvent une coordination étroite entre les fabricants et les distributeurs. Les détaillants cherchent de plus en plus à améliorer la disponibilité des produits tout en augmentant la rentabilité des magasins afin d’acquérir un avantage concurrentiel et de générer de meilleures performances commerciales.

Ceci est possible grâce à des algorithmes d’expédition qui déterminent quels sont les produits à stocker en prenant en compte des données externes telles que les conditions macroéconomiques, les données climatiques et les données sociales. Serveurs, machines d’usine, appareils appartenant au client et infrastructures de réseau énergétique sont tous des exemples de sources de données précieuses.

Ces utilisations innovantes de la Data Science améliorent réellement l’expérience client et ont le potentiel de dynamiser les ventes des détaillants. Les avantages sont multiples : une meilleure gestion des risques, une amélioration des performances et la possibilité de découvrir des informations qui auraient pu être cachées.

La plupart des détaillants utilisent déjà des solutions liées à la Data Science pour augmenter la fidélisation de la clientèle, renforcer la perception de leur marque et améliorer les scores des promoteurs.

Et vous, quand est-ce que vous ouvrez votre précieux sésame ?

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