data management

All you need to know about data management

Data management challenges

Big Data is an expression you’ve heard before. The various information systems, business applications and other standard tools thanks to digitalization are full of information and data. Data is everywhere, all the time.

The challenge today is to know how to collect it, gather it, and organize it to get real benefits. Data must be used to make the right decision in order to improve efficiency and performance: this is data management. The data must be accurate, easily accessible and easily exploitable.

Finally, it must be RGPD compatible with increasingly strict regulations to preserve the security and confidentiality of each of us.

Data management must follow 4 key steps:

Data governance

This governance must give the strategy and the global policy of the company. In particular, it provides a framework for the collection and use of data. This is an essential step to guarantee data security, integrity and the company’s image.

Data integration

This step consists of deciding how to collect the data and where to place it so that it is accessible to the right person at the right time. Data integration must also include the quality of the data: how to ensure the reliability and accuracy of the data.

Data exploitation

What tools and methods are used to ensure that the data is exploitable and for what purpose? This step can include machine learning and AI to start an analytical phase of the data.

Data security

What processes and systems are in place to ensure the protection of the data against internal threats (destruction, corruption…) and external threats (theft, hackers etc.)?

Which tools for data management?

To carry out these steps, specific tools enable the collection and proper use of data:

Data storage tools

These solutions appeared as the volume of data to be stored became important and exponential. Most often in the cloud, these solutions allow to gather data in a secure and reliable place.

Data integration tools

Data integration tools (ETL - Extract, Transform, Load) are solutions that allow you to build bridges between the place where the data is stored and where it will be used.

Data transformation tools

These tools will help you customize the data according to your objectives and the use you will make of it. They will enable you to format the data to facilitate your exploitation. We are talking about Master Data Management (MDM) tools, Dataviz or analytics tools.

Data mining tools

These are analytical tools, based on often complex and sophisticated algorithms that allow you to categorize, analyze, find correlations, common points and even make predictions in order to make business decisions.

What are the benefits of data management?

Better coordination between the various departments

Secure the data and its use

Reduce the costs related to data collection and analysis

Align teams around a strategy based on facts

What are the uses of data management?

data management customer experience

Customer experience improvement

The most obvious is certainly marketing and customer relationship management. Data is essential for these professions in order to understand a market, user behavior, trends and purchasing behavior.

The interest of this use of data management is to be able to better transform these prospects into customers, or even ambassadors, by anticipating the needs and expectations of consumers and providing the best possible response.

Digital twins creation

Another use of data management can be the creation of “digital twins”. From the data, the company can create a digital twin and, thanks to AI, build simulation models to evaluate the impact of a given decision on an industrial or manufacturing device.

The interest of these digital twins is the time saving and the relevance of the decisions taken because they are based on a history of data and according to scalable assumptions.

data management digital twin
data management risk estimate

Risk estimate

Data also allows for better risk management. In the banking and insurance sectors, for example, the use of data is essential to evaluate the risks taken on investments and even on the granting of loans.

Data management is a lever to reduce risks and losses that could result from bad investments.

Process mining implementation

It enables, thanks to data, to put under real time control the business processes: customer experience, finance, supply chain to optimize its processes and automate the tasks with lower added value.

The benefits are both financial (time and performance savings) and qualitative (optimized customer promise, fluidity of paths, etc.)

data management process mining