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Beyond Data – Data management

Beyond Data

Data is valuable. Data which has been analyzed, understood and used strategically even more so. Our data management expertise allows us to maximize the value you can extract from the data in your systems, making it available to your business, thanks to our command of Big Data, Machine Learning and Data Analytics.

Value your data !

Data management: key to your success.

Data centric, data driven, data cleansing, data lineage, data and business analytics, artificial intelli-gence, machine learning… There’s no lack of subjects around data! Finding a way to give data real business value is not easy. Our experts are here to help. Data is the new wealth. Our experts can support you to clean and structure the data present in your information systems in order to exploit it for your business lines.

Big Data

Big data and a data-driven approach make for better decision making. Our solutions allow you to manage increasing volumes of data, the regulatory constraints that force you to keep track of data for several years, and allow you to take advantage of all your data in order to remain competitive.

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Machine Learning

Companies accumulate client, technical, commercial, marketing and other data. Data can bring wealth. How can machine learning identify, prepare and exploit data? How can we explore data and draw value from it?

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Our Partners

Invivoo develops its partner network to offer its customers efficient and flexible solutions. Each partnership shows our common desire to support you through your digital transformation.

Partnerworks Consultant Program

Analysing data. Empowering the future.

Data Wrangler

Réconciliation de données

Are you passionate ? So are we !

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Our latest publications on Data Management

Why should you use a consultancy for Data Management?

What is data management?

Data management is a subject that groups a set of tools and standards to take advantage of digital data. It covers data collection, storage, processing, cleansing, analysis and visualisation.
Data management expertise is built upon data governance, a set of policies and procedures that ensure data quality and security. Data management also relies on centralized data storage in a data lake to avoid data siloing, duplication and inconsistencies.
The final stage of a data management expertise is the transformation of raw data into valuable information. This information can enable companies to become data-driven.

What is a data driven company?

A data driven company exploits its data to improve its products, performance and strategy. Thanks to Big Data and to the exponential increase of the amount of data generated, companies can obtain a huge quantity of information, but it still needs to be leveraged. First, we have to identify and collect the data relevant to our business needs. Which data is re-quired to meet those needs or what will be needed in the coming years? Collecting a large amount of data is going to be expensive, so it’s important to make sure that every piece of data will be use-ful. Poor quality data cannot be used to make good decisions. Data cleansing obtains reliable data. Quality depends on the absence of duplicates and a single source of truth. If different departments of the company do not have the same data, the risks of making poor decisions becomes significant. Data loses its value over time, so it is necessary to ensure that the useful data for the business lines are readily available. With the aid of visualisation and analytics tools, as well as their own business intelligence, partners can better understand, manage and exploit data. Its only at this point that data becomes valuable and aids better business decision making. And where does machine learning fit in? Machine learning enriches data by automatically creating additional indicators. It also improves data quality at the data cleansing stage. Finally, machine learning offers the possibility of automating certain decisions based solely on the data.

Public cloud, private cloud, hybrid cloud, on premise: what are we talking about here?

In the 21st century, data hosting infrastructure can take many forms. Historically, companies had their own infrastructure but lately, more and more companies are switching to a private or public cloud and sometimes they choose both. This would be referred to as a hybrid cloud. Local private cloud (on premise): The company has its own internal infrastructure in one or multiple data centres. It owns its servers and handles them itself. This solution requires a significant initial investment and a dedicated team for server management. It offers a high level of security if the cloud is well handled. External private cloud: The company hires a provider to host its servers. However, these servers are not shared with other companies. This solution has the advantage of a high level of security, without having to deal with the infrastructure management. Public cloud: AWS, Azure, GCP… The company hires a third-party that owns data centres. The serv-ers are not dedicated to a certain company. The advantage of this solution is the possibility of quickly increasing or decreasing the amount of available servers. Hybrid cloud: The company uses both a public cloud and a private cloud. The management of this kind of solution generates an extra cost, but allows a choice about the nature of data and where it will be processed. Sensitive data will remain inside the company servers. Occasionally processing can be done on external servers, but data will be returned as soon as the processing is completed.