Harness the value of your data with data management

By Maëlys De Santis

Published: November 5, 2024

What is data management, and what can it do for your business? If you've ever searched for information without really knowing where to look, or whether the information you found was correct, then this article is for you. Can you estimate how much time is wasted each year searching for data?

Data management isn't just about managing IT data; it's a cross-functional project that concerns the whole company. By analyzing and exploiting the mass of data at their disposal (big data), data-driven companies create undeniable competitive value for their products or services.

28% of executives admit they still don't analyze their data (source: Decidio and YouGov, December 2018). After reading this article, you'll understand what's at stake in data management, and be convinced of the usefulness of training a data manager, implementing new processes and adopting new tools to ensure the quality of your data. Here's how it works.

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Data management: definition

What is data management?

Data management is a management discipline, but this time it concerns data.

The process involves collecting, verifying, storing, analyzing, protecting and processing data. The aim? To make it available to the company so that it can be put to the best possible use, as part of a Big Data strategy, for example.

Data is an inexhaustible source of information for businesses. How can they be managed intelligently and in compliance with current regulations?

The origins of data management

Initially, data management was born out of the need for best practices and tools to exploit the data generated by companies.

Which data are concerned?

Customer data,
marketing data
product data
HR data,
technical data, etc.

Examples of data include

user behavior on your site (browsing, time spent, average shopping basket, etc.),
users' personal data,
user interests (pages viewed, buttons clicked, etc.)

Data management and master data management (MDM)

Data management and master data management are closely related concepts, but they need to be differentiated.

💡Master data management (MDM) involves classifying and prioritizing data according to their degree of importance, to focus on the most qualitative data.

These methods make it possible to identify data from the quantity available to a company, validate its quality and ensure that it can be used without risk. This requires the creation of a data repository known as a " master file".

The diagram below summarizes the challenges of an MDM project:

The benefits of efficient data management

Improved productivity : no more time wasted searching for information. Reliable, qualitative data is available to teams who know where to find it and understand it easily.
Cost savings : no duplication of data, reduced storage and data processing costs, and time savings for data research and analysis.
Better ability to adapt to market expectations: if you delay making strategic decisions to adapt to the market, you'll quickly be overtaken by the competition. Having access to the right data at the right time will save you the trouble.
Improved risk management and reduced data loss: You gain control over your data by knowing where and how it is stored and exchanged, and are able to minimize the risks associated with data loss or even leakage, thanks to a data management plan.

The objectives of data management

Among the challenges of data management, the main one is undoubtedly to enhance the value of data as a company asset. Other objectives include

Ensuring data quality and reliability

The aim of data management is not simply to collect data for the sake of collecting it. You have to be able to do something with it: data management requires reliable, high-quality data that can be used.

It has to be said, however, that many companies' data management is not optimized. This lack of optimization can have several origins, but let's not forget that data management aims, among other things, to :

avoid data entry and processing errors,
avoid duplicates resulting from haphazard copying,
prevent data loss due to careless moving,
guarantee data traceability

➡️ What can be the consequences of poor-quality data?

imprecise or even erroneous reporting,
lack of visibility and anticipation,
distorted decisions,
high costs for finding, analyzing and storing quality data, etc.

✅ What are the solutions ?

Solution 1: define data quality criteria specific to your business. Every organization has its own issues, challenges and priorities: data quality varies from one company to another.

Solution 2: define rigorous processes to ensure that the entire company creates, disposes of, transmits and processes quality data that meets the needs of the company and its customers.

And consider the following tips in this article!

Consider the data life cycle

The data lifecycle consists of identifying where data is, to determine possible points of vulnerability.

Data can be vulnerable at any time:

collection
storage,
sharing,
analysis,
deletion.

Knowing these points of vulnerability enables you to put in place good practices and systems that guarantee the confidentiality and security of your data.

Integrating data

Why consolidate data in one place, in a single database ? This makes it possible to

make data easily accessible throughout the company,
facilitate data processing,
industrialize data flows.

A decision-making tool for management

Being able to analyze and exploit data is crucial to making the right strategic decisions, at the right time.

Lack of information increases the likelihood that decisions taken will not correspond to what is expected (by customers, users, company staff, etc.).

Having access to this data also enables us to anticipate needs, so we can make the right decisions with a head start.

Ensuring regulatory compliance

Data management means data collection, storage, processing and security.

This must be done in compliance with French and European legislation regulating the collection and use of data:

the General Data Protection Regulation (GDPR), in force since May 2018, breathes a wind of respectful use and traceability into the world of data ;
other regulations apply to a particular sector, such as the Bale and Solvency regulations, which frame the management of banking and insurance sector data respectively.

Data management vs data governance

Data governance complements data management in the sense that it brings together all the procedures required for effective management:

it operates on a company-wide scale;
it puts data management into practice and ensures its continuity;
it seeks to extract value from data.

How can data be combined to create value? How can data be used to support strategy? How can we take advantage of regulations governing data management?

Data governance seeks to answer these questions. It involves structuring the company, processes and tools to get the most out of your data.

Here are a few best practices to follow, according to Inventiv IT:

How to manage data efficiently within the company?

Ensuring data security

Data security is one of the keys to successful data management. So how do you go about it?

deploy and maintain an IT infrastructure that guarantees data security,
limit the number of entry points to tools and applications,
ensure that data exchanges, both internally and externally, do not render them vulnerable (leaks, losses, alterations), etc.

Define processes

Organization is the foundation of a healthy business that functions properly. This also applies to data management. Without organization, without clearly defined processes known to all, you can't have a successful data-driven company.

The more your company is structured around processes, the better it is able to manage quality data.

Without clearly defined processes, it's difficult to move from quantitative big data, a "mass of data", to actionable data from which to derive value for your business and your customers.

ℹ️ How do you go about it?

appoint one or more data management managers (quality manager, data manager, dedicated department, etc.),
adopt the appropriate tools,
define business rules,
materialize the processes and share them with all employees, so that they themselves adopt best practices.

Note: you will always have the luxury of relying on tools to automate tasks, save time or perform advanced calculations. But if you're not properly organized, all that added value isn't really there.

Manage access

Who has access to which data? Access management must be precisely defined in your processes. You need to be able to identify who has access to what data, who can store or archive it, modify or consult it, and also to restrict access.

This identification helps protect data from loss, alteration or theft.

ℹ️ This process is to be implemented as part of compliance with regulations, and in particular the RGPD.

Appoint a data manager

The data manager is the company's data management expert. This new profession has several main missions:

understand, synthesize and respond to data management needs ;
implement big data processes within the company;
define common tools to simplify data processing;
ensure that data is used appropriately, etc.

Specific training courses are available, including masters in data management.

Opt for the right data management tools

Data exchange platforms

If you're planning to extract data from one application to another (in order to add value, for example), you'll need a data exchange tool. Indeed, the use of this type of solution is often a prerequisite for data management.

There are many different types of tools available for this purpose. For example

ESBs (Enterprise Service Buses): they enable applications to communicate with each other,
ETLs (Extract Transform Load): synchronize information from different sources.

Which data exchange platform should I choose?

🛠️ Crosscut

Advantages of the solution :

data exchange from all sources (on premise, cloud, etc.),
fast, simple application flow creation,
technical and operational monitoring,
integration into existing work environments,
good value for money.

Data Management Platform (DMP)

A data management platform is a tool for storing and processing data. Data is integrated and consolidated into a single platform from a multitude of sources (CRM, website, emails, social networks, files, etc.).

The objective is simple: easily analyze data to provide users with high-quality, accurate, transparent and reliable data.

Which DMP software should you choose?

🛠️ Hadoop (Apache)

Advantages of this solution :

open source big data platform,
adaptable to your needs, through specific developments,
storage and processing of very large volumes of data,
high incident tolerance,
low cost.

🛠️ SAS Viya

Benefits of the solution :

collaborative for all professions (data manager, data scientist, developer, decision-maker),
ultra-fast data processing,
integrated artificial intelligence to improve data quality,
analysis models,
available in SaaS, on-premise or hybrid mode.

🛠️ Talend Data Services Platform

Solution benefits :

suitable for small, medium and large companies,
platform integrates with your applications (Oracle, Microsoft SQL Server, Salesforce, NetSuite, etc.),
data mapping,
data profiling,
graphical data visualization for enhanced analysis.

Business Intelligence (BI) solutions

A Business Intelligence tool, based on data analysis and automatic report generation, will enable you to easily obtain actionable information from a wide range of sources.

As a result, you'll be able to define your company's strategic direction more precisely, and instill a data-driven culture in all your teams thanks to the centralization and accessibility of data to the various business lines.

Which Business Intelligence solution should you choose?

🛠️ MyReport