Exploit the value of your data with data management

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 is not just about managing IT data; it's a cross-functional project that concerns the whole company. By analysing 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 managers admit that they still don't analyse their data (source: Decidio and YouGov, December 2018). After reading this article, you will have understood the challenges of data management, and will be convinced of the usefulness of training a data manager, setting up new processes and adopting new tools to ensure the quality of your data. Here are some explanations.

See all software Data Analytics

Data management: definition

What is data management?

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

The process involves collecting, checking, storing, analysing, protecting and processing data. The aim? To make it available to the business 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 practice and tools for exploiting the data generated by businesses.

What data is involved?

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

Examples of data :

• user behaviour on your site (browsing, time spent, average 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 similar concepts, but they need to be differentiated.

💡Master data management (MDM) involves classifying and prioritising data according to its degree of importance, in order to focus on the most qualitative data.

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

The diagram below summarises the challenges of an MDM project:

The benefits of effective data management

• Greater productivity : you no longer waste time looking 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 data storage and processing costs, and time savings when searching for and analysing data.
  
• Better ability to adapt to market expectations: if you are slow to take strategic decisions to adapt to the market, you will quickly be overtaken by the competition. Having access to the right data at the right time will help you avoid this problem.
  
• Improved risk management and reduced data loss: You can take control of your data by knowing where and how it is stored and exchanged, and are able to minimise 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 make the most of data as a company asset. Other objectives include

Ensuring data quality and reliability

The aim of data management is not just to collect data for the simple pleasure of accumulating 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 that the data management of a good number of companies can be optimised. This lack of optimisation can have several causes, but let's not forget that one of the aims of data management is to :

• avoid data entry or processing errors
• avoid duplicates resulting from careless copying,
• lose data because it has been moved without due care,
• guarantee data traceability

➡️ What are the possible consequences of poor quality data?

• inaccurate or even erroneous reporting,
• a lack of visibility and anticipation,
• distorted decisions,
• high costs for finding, analysing and storing quality data, etc.

✅ What are the solutions ?

Solution 1: define the data quality criteria specific to your business. Every organisation 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, accesses, transmits and processes quality data that meets the needs of the company and its customers.

And take on board the following advice in this article!

Consider the data lifecycle

The data lifecycle consists of identifying where the data is, in order to determine any 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,
• make it easier to process
• industrialise data flows.

A decision-making tool for managers

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

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

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

Ensuring regulatory compliance

Data management means collecting, storing, processing and securing data.

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

• the General Data Protection Regulation (GDPR), which has been 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 govern 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 good management:

• it is applied throughout the company ;
• It puts data management into practice and guarantees its long-term viability;
• it seeks to extract value from the data.

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

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

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

How can data be managed effectively 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 make them vulnerable (leaks, losses, alterations), etc.

Defining processes

Organisation is the foundation of a healthy business that functions properly. This also applies to data management. Without organisation, without clearly defined processes that everyone is familiar with, you cannot have a successful data-driven business.

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

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

ℹ️ In practical terms, how do you go about it?

• Appoint one or more people to be responsible for data management (quality manager, data manager, dedicated department, etc.),
• Adopt the appropriate tools,
• define business rules,
• formalise the processes and share them with all employees so that they themselves adopt best practice.

Note: you will always have the luxury of relying on tools to automate tasks, save time or make advanced calculations. But if you're not organised properly, 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 it or consult it, and also to restrict access.

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

ℹ️ This process must be put in place to comply with regulations, particularly the RGPD.

Appoint a data manager

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

• understanding, synthesising and responding to data management needs ;
• implement big data processes within the company;
• defining common tools to simplify data processing;
• ensuring that the data is used appropriately, etc.

Specific training courses exist, including masters in data management.

Opting for the right data management tools

Data exchange platforms

If you are planning to extract data from one application to another (in order to add value, for example), you will need a data exchange tool. Using this type of solution is often a prerequisite for data management.

Note that there are different types of tool for this purpose. For example

• ESB (Enterprise Service Bus): these enable applications to communicate with each other,
• ETLs (Extract Transform Load): they synchronise information from different sources.

Which data exchange platform should you choose?

🛠️ Crosscut

Advantages of the solution :

• Data exchange from all sources (on premise, cloud, etc.),
• fast, simple application flow creation experience,
• technical and operational monitoring,
• integration with the existing work environment,
• good value for money.

The 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 aim is simple: to analyse the data easily and 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,
• a tool that can be adapted to your needs, by means of specific developments,
• Storage and processing of very large volumes of data,
• high incident tolerance,
• low cost.

🛠️ SAS Viya

Advantages 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

Benefits of the solution :

• Suitable for SMEs and large accounts,
• platform that integrates with your applications (Oracle, Microsoft SQL Server, Salesforce, NetSuite, etc.),
• data mapping,
• data profiling
• graphical visualisation of data 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 usable information from a wide range of sources.

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

Which Business Intelligence solution should you choose?

🛠️ MyReport

Advantages of the solution :

• Broad functional coverage (creation, management, distribution, publication and alerting of reports, data visualisation, ETL, etc.),
• integration for all business departments (General Management, Finance, Sales, HR, Marketing, Logistics, etc.),
• easy to use, with no technical skills required,
• automated reporting and dashboards,
• adapted to the needs of SMEs and in the familiar Excel environment for greater simplicity.

See all software Data Analytics

Take control of your data

Data is the new gold for businesses. But they often have no idea just how rich they are. The data is there, it exists, and millions are created every minute. All you have to do is exploit it! If you don't, your competitors surely will.

Are you ready to create value from your data?

Updated article, originally published in October 2019.