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Glossary

Data Architecture

A data architecture provides a framework for collecting, storing, sharing, processing, analyzing and reusing data, thus turning it into value.

As organizations generate and use more and more data, from a wider range of sources, they need to implement a structured information system to manage this data. The goal is to simplify data management, in terms of collection, storage, processing, analysis, sharing, etc.

This is what a data architecture aims to do. What is a data architecture? What are the benefits? And how do you implement one?

What is a data architecture?

A data architecture provides a framework for collecting, storing, sharing, processing, analyzing and reusing data. This can apply to any kind of data, including reference data, product catalogs, inventories, or supplier contracts. The goal is to deliver the right information to the right people at the right time.

With growing volumes of information and its increasing importance, an effective data architecture is vital for organizational success. Without a clear infrastructure, access to data remains in the hands of data specialists, rather than being easily available to the wider business. Having to use data specialists to provide business information is time-consuming and complex, and hinders decision-making as managers cannot access the information they need, when they need it. For companies, increasing the speed of data access is critical to becoming data-driven.

A data architecture therefore provides a framework for leveraging data and creating new uses for it that benefit the entire organization.

What are the benefits of a good data architecture?

In a world where data is becoming increasingly prevalent, implementing a data architecture is essential for:

  • Improving efficiency: organizations no longer need to call on a data expert when they want to access important information. This saves valuable time in the decision-making process.
  • Promoting innovation: with better data management, employees have all the tools they need to develop new services.
  • Encouraging communication: organizing data in a data architecture requires companies to create a universal language and terminology to describe data and what it means. This makes communication and information sharing easier.
  • Increasing involvement: as data becomes more accessible to all, employees will be more likely to make use of it in their daily working lives.
  • Promoting collaboration: since a data architecture aims to reflect the company’s vision, it allows all employees to buy-into overall objectives. This promotes collaboration on projects.
  • Deepening customer knowledge: since data is better structured, organizations can better understand their target audiences and deliver tailored products and services.

How do you implement a data architecture?

Implementing a data architecture benefits all organizations. However, to be successful you should follow these best practices:

1 – Appoint a data architect

This is the person who will be responsible for defining the roadmap. This includes everything from data collection to data analysis. In order to do this, the data architect will convert the needs of each department into data, and integrate this data into the system.

2 – Define a structure

To do this, we recommend asking the following questions:

  • Who generates different datasets?
  • Who can use them?
  • What type of data do you want to share?
  • Where is the data stored?
  • How is the data managed?

This work of standardizing the use of data within the organization must be done together with the data steward who is part of the organization’s governance strategy.

3 – Collaborate

An organization’s IT architecture must allow it to share data and make it accessible to everyone. The idea is to promote collaboration between decision-makers, the data architect and all employees. Data must be easily accessible, and not stuck in silos.

4 – Prioritize data quality

Defining the data architecture is part of the data governance policy. It is therefore essential to ensure the quality of the information collected, particularly through the involvement of a data steward.

5 – Adapt to the needs of the organization

The data architecture must rely on flexible technologies. This is crucial as tools and data evolve over time. It is therefore essential to base your data strategy on scalable technologies which are not rigid or closed.
There are different trends in data architecture structures. You must therefore choose an architecture that corresponds to your organization’s business goals, IT tools, and skills, for example.

6 – Keep the user in mind

The data architecture must be user-experience (UX)-oriented in order to allow all stakeholders to benefit from data. If access to data is too complex, employees without technical skills will not be able to use it.
It is therefore necessary to keep the architecture simple so everyone can understand and work with it. This user-centric approach should cover all areas, from data transfers to how data is displayed and shared.

7 – Automate

To allow access to continuously updated data, it is essential to automate data flows within the data architecture as much as possible. In this context, artificial intelligence and machine learning are essential. They make it possible to alert and adjust the structure when new needs or constraints emerge.

8 – Be flexible

Be willing to adapt according to needs, supply and demand. In periods of high activity, the data architecture must make it possible to scale to store as much information as possible. On the other hand, during lower usage periods you need to be able to reduce storage capacity. This flexibility is best achieved by storing data in the cloud.

9 – Secure the roadmap

Data, whether it is sensitive or not, must always be protected at all stages of data processing. Confidential and personally identifiable information must also be protected from unauthorized access by both external third parties and internal staff.

 

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