As organizations pursue their digital transformation journey, Cloud Computing has become an essential foundation for business performance. However, the unlimited flexibility of Cloud services is sometimes accompanied by rising costs, prompting companies to consider ways of controlling expenditure without degrading employee usage. To do so, they are implementing a Cloud financial management approach, also known as Cloud FinOps.

Does the term FinOps ring a bell? Derived from the contraction of Financial Operations, the term refers to a financial management methodology applied in Cloud Computing. The emergence of Cloud FinOps is linked to the need to control costs associated with the exponential growth in the use of Cloud services. This approach aims to reconcile the actions of financial, operational, and technical teams to optimize Cloud spending and guarantee optimal use of resources.

Cloud Finops focuses on cost transparency, identifying optimization opportunities, and empowering teams to take responsibility for their use of Cloud resources. By fostering collaboration between IT, finance, and business teams, Cloud Finops improves visibility, cost predictability, and operational efficiency, enabling companies to maximize the benefits of the Cloud while maintaining strict financial control.

How does Cloud Finops work?

Cloud Finops works through a combination of specific practices, processes, and architecture. In terms of architecture, cost monitoring tools, such as Cloud Financial Management platforms, are deployed to collect real-time data on resource usage. This information is then analyzed to identify opportunities for optimization.

In terms of processes, Cloud Finops encourages close collaboration between financial, operational, and technical teams, establishing regular review cycles to evaluate costs and adjust resource allocations. This iterative approach enables you to optimize spending on an ongoing basis, ensuring that your company makes efficient use of Cloud services while creating the conditions for total cost control.

What are Cloud FinOps best practices?

The practice of Cloud FinOps relies on a combination of methods, tools, processes, and vision. To take full advantage of your Cloud Finops approach, you’ll need to foster the emergence of a number of best practices.

Transparency & Synergy

The founding principles of Cloud FinOps are based on cross-functional collaboration. This involves the close involvement of financial, operational, and technical teams. This synergy enables a common understanding of business objectives and associated costs, promoting continuous optimization of Cloud resources.

Automation & Control

Automating processes is essential to ensure optimum cost management on a day-to-day basis. The use of automation solutions for automatic resource provisioning, instance scheduling, and all repetitive cloud management tasks, improves operational efficiency and avoids unnecessary waste.

Reporting & Analysis

To guarantee cost transparency, you need to be able to provide detailed, accessible reports on resource utilization. These reports enable teams to make informed decisions. This greater visibility encourages users to take responsibility and makes it easier to identify areas for improvement.

What are the main challenges for Cloud Finops?

To deliver its full potential, Cloud FinOps must overcome the complexity of Cloud pricing models. Indeed, the diversity of these models, which vary from one Cloud provider to another, makes it difficult to accurately forecast costs. As a result, expenditure can fluctuate according to demand, making budget planning more delicate.

Finally, compliance management, data security, and Cloud migration considerations are also complex aspects to integrate into an effective FinOps approach.

What does the future hold for Cloud Finops?

As companies move further along the road to cloudification, the future of Cloud FinOps looks brighter month after month. Tools and platforms specializing in the financial management of Cloud resources, offering advanced cost analysis, automation, and forecasting capabilities, are likely to continue to grow in line with Cloud adoption.

Closer integration and collaboration between financial, operational, and technical teams will enable companies to place greater emphasis on financial governance in the Cloud, integrating FinOps principles right from the start of their Cloud projects.

Edge Analytics enables data-driven companies to go straight to analyzing their data after it has been collected by IoT devices. It helps eliminate data processing bottlenecks.

Learn more about Edge Analytics, its benefits, and concrete use cases to better understand this new data trend.

Speed up data processing and analysis, and reduce the number of steps between collecting and using your data assets: That’s the promise of Edge Analytics. This method of data processing is all about proximity to the data source. It avoids all the steps involved in sending data to a data processing center.

How Edge Analytics works

Edge Analytics responds to a very different logic than traditional data analysis, with which data is generally transferred to a remote processing center, such as a server or cloud, and the analysis is performed. In the case of Edge Analytics, connected devices or sensors located at the edge of the network collect data in real-time from various sources such as industrial machines, vehicles, surveillance equipment, IoT sensors, etc.

The raw data collected is pre-processed locally – it is then filtered and prepared for immediate analysis. The purpose of this local pre-processing is to clean, sample, normalize and compress the data, in order to reduce the quantity of data to be transferred and guarantee its quality, prior to analysis. Once this preliminary phase has been completed, data analysis is also carried out on-site, at the edge of the network, using algorithms and models previously deployed on local devices or servers.

With Edge Analytics, you can fine-tune your data analysis strategy by transferring only essential data or exceptional events to a remote processing center. The objective? Reduce network bandwidth requirements and save storage resources!

What are the benefits of Edge Analytics?

If the proximity between the source of the data and the means of processing and analyzing it appears to be the main advantage of Edge Analytics, you’ll be able to reap five main benefits:

Benefit #1: Accelerate real-time decision-making

Less distance between the place where data is collected and the place where it is processed and analyzed means the prospect of time savings on two levels. As Edge Analytics processes data at the network edge, where the data is generated, this enables real-time analysis, eliminating the latency associated with sending data to a remote location. Another advantage of this real-time dimension is that it enables autonomous data analysis.

Benefit N°2: Reduce latency between data collection and analysis

Edge Analytics is a promise of real-time exploitation of your data assets because data processing is done locally. In the case of applications requiring rapid responses, such as the Internet of Things (IoT) or industrial control systems (production or predictive maintenance, for example), proximity data processing drastically reduces latency and optimizes processing times.

Benefit N°3: Limit network bandwidth requirements

Traditional data analysis almost always relies on the transfer of large quantities of data to a remote data processing center. The result: intensive use of network bandwidth. This is particularly true when your business generates large volumes of data at high speed. Edge Analytics has the advantage of reducing the amount of data that needs to be transferred, as part of the analysis is carried out locally. Only essential information or relevant analysis results are transmitted, reducing the load on the network.

Benefit #4: Optimize data security and confidentiality

As you know, not all data have the same level of criticality. Some sensitive data cannot be transferred outside the local network for security or confidentiality reasons. Edge Analytics enables this data to be processed locally, which can enhance security and confidentiality by avoiding transfers of sensitive data to external locations.

Benefit N°5: Embark on the road to scalability

Because Edge Analytics enables part of the data analysis to be carried out locally, it enables a significant reduction in network load. In so doing, Edge Analytics facilitates scalability by avoiding bandwidth bottlenecks and paves the way for the multiplication of IoT devices without the risk of network overload.

Data analysis can be distributed across several processing nodes, facilitating horizontal scalability. Adding new devices or servers at the edge of the network increases overall processing capacity and enables you to cope with growing demand without having to reconfigure the centralized processing architecture.

What are the main use cases for Edge Analytics?

While the Edge Analytics phenomenon is relatively recent, it’s already being used massively in many business sectors.

Manufacturing

Edge is already widely used in manufacturing and industrial automation. In particular, it helps to monitor production tools in real-time, in order to detect breakdowns, optimize production, plan maintenance, or even improve the overall efficiency of equipment and processes.

Healthcare

In the healthcare and telemedicine sector, Edge Analytics is used in connected medical devices to monitor patients’ vital signs, detect anomalies, and alert healthcare professionals in real-time.

Smart cities and mobility

Edge Analytics is also well suited to the urban mobility and smart cities sector. In the development of autonomous urban transport, for example, real-time analytics can detect obstacles, interpret the road environment, and make autonomous driving decisions.

Security & surveillance

The surveillance and security sector has also seized on Edge Analytics, enabling real-time analysis of video streams to detect movement or facial recognition.

With 60% of enterprise data now stored in the Cloud, and companies around the world turning to Cloud solutions to manage their data, many are finding that general-purpose Cloud platforms are not always able to meet the specific needs of their industry. They must then turn to an Industry Cloud Platform.

In this article, find out everything you need to know about Industry Cloud Platforms.

All industries have specific requirements for managing and securing their data. Industry Cloud Platforms are Cloud platforms designed to meet the specific requirements of a given industry or sector.

Unlike general-purpose Cloud platforms, such as Amazon Web Services (AWS) or Microsoft Azure, Industry Cloud Platforms offer features and services tailored to industries such as healthcare, finance, logistics, retail, energy, agriculture, and many others. The popularity of these platforms continues to grow.

According to Gartner, nearly 40% of companies have already considered adopting an Industry Cloud Platform. 15% of them are already engaged in a pilot project. Even better, about 15% more are considering deployment by 2026. As a result, Gartner predicts that by 2027, companies will use Industry Cloud Platforms to accelerate more than 50% of their critical business initiatives, up from less than 10% in 2021!

How does an Industry Cloud Platform work?

Industry Cloud Platforms provide robust and scalable cloud infrastructure, along with features and services that are ideally suited to the specific needs of companies in each industry. This can include features such as data analytics tools, supply chain management platforms, industry-specific security solutions, and custom business applications.

Industry Cloud Platforms can help companies improve operational efficiency, reduce costs and innovate faster by providing easy access to specialized cloud services for their industry. In addition, these platforms can help companies better manage risk and comply with industry-specific regulations.

What are the advantages and benefits of Industry Cloud Platforms?

Using a specialized Industry Cloud Platform for your sector provides you with data analysis tools and customized business applications. The primary benefit of being able to rely on tailored tools and services is that you gain operational efficiency and productivity.

But that’s not all; Industry Cloud Platforms help reduce the cost of purchasing, maintaining, and upgrading your IT infrastructure by taking an industry-specific approach. The “hyper-specialization” of these Cloud Platforms and the services they contain means that you only have the solutions you really need, and you don’t have to invest in expensive infrastructure that you rarely use to its full potential. This is a “best of need” rather than a “best of breed” perspective.

Moreover, since Industry Cloud Platforms are designed to be scalable and flexible, they will enable you to adapt quickly to the growth of your business. You can easily add or remove Cloud resources as needed to quickly adapt to market fluctuations.

Finally, the use of an Industry Cloud platform increases your capacity to innovate by giving you access to data analysis technologies, adapted to your activity.

Examples of Industry Cloud Platforms for different industries

There are many major players in the Industry Cloud Platforms market, each offering specific solutions and services for a particular industry or sector. Here are some examples of major players:

• Salesforce is one of the leading Industry Cloud Platform players in the sales and marketing industries, with its Salesforce Customer 360 platform.

• Microsoft offers a range of cloud solutions for different industries, such as Dynamics 365 for Finance and Operations for the finance and manufacturing sector, and Azure IoT Suite for the Internet of Things.

• IBM is positioning itself in this market segment with a dedicated cloud platform for several industries, including healthcare, financial services, and supply chain, with its Watson Health, IBM Cloud for Financial Services, and IBM Sterling Supply Chain Suite solutions.

• Amazon Web Services (AWS) offers a range of cloud services for different industries, including AWS Healthcare for healthcare and AWS Retail for retail. These offerings are distinct from Amazon Web Services’ general-purpose offerings.

• SAP has developed a cloud platform for several industries, including manufacturing, retail, financial services, and healthcare, with its SAP S/4HANA, SAP Commerce Cloud, and SAP Health solutions.

Unlock the value of your enterprise data by connecting your Industry Cloud Platform to Zeenea

Our integrated scanners and APIs automatically collect, consolidate, and connect metadata from your data ecosystem so your users can discover, trust, and understand their data.

For more information about our data discovery platform and its connectivity:

Cloud Security refers to the technologies, policies, controls, and services that protect data, applications, and infrastructure in the cloud from both internal and external threats.

Between essential protection and uncompromising lockdown, it is critical to find the best compromise between security practices and the flexibility that is essential for business productivity.

Business applications, data storage, complete virtual machines… Almost everything can be governed by Cloud Computing. According to forecasts and observations made by IDC, it appears that spending on cloud infrastructure will increase by 22% in 2022 compared to 2021. This means that it will exceed the $90 billion mark by the end of the calendar year. A record number as this is the highest annual growth rate since 2018! But the more our businesses become cloud-based, the more the issue of Cloud Security becomes a top priority.

However, behind a concept as vast and complex as Cloud Security, it must be understood that it is based on a set of strategies, technical means, and control solutions that ensure both the protection of data stored, the availability and reliability of applications and infrastructure services essential to the operation of the cloud.

Protection against external and internal threats and vulnerabilities, resilience, and resistance to cybersecurity issues, Cloud Security is a concept intrinsically linked to Cloud Computing.

Private, public, hybrid… each cloud has its own security challenges

To fully understand Cloud Security, we must first differentiate the types of Cloud Computing. Public clouds are hosted by third-party cloud service providers.

When you use a public cloud, you get a turnkey cloud and have no latitude to configure and administer it, and the services are fully managed by the cloud provider.

If, on the other hand, you move to a private cloud service, you have a more secure and potentially more customizable space.

Finally, hybrid cloud services combine the scalability of public clouds with the greater resource control of private clouds while offering lower pricing than private clouds.

In all cases, whether you choose a public, private or hybrid cloud service, it is critical to ensure Cloud Security.

Why is Cloud Security important?

The more your company leverages the cloud, the more agile it becomes. Even better: the cloud enables small and medium-sized businesses to have the same tools and functionalities as very big companies. The downside is that the power at your disposal increases the cloud usage of your teams and, consequently, increases your exposure to threats that in the past only concerned larger companies.

That’s why Cloud Security is more important than ever. Unleashing usage and productivity through the cloud mechanically increases your dependency on the cloud.

Without the cloud, nothing is possible. Therefore, Cloud Security becomes a priority issue, especially when it comes to data protection. Preventing data leakage and theft is essential to maintaining your customers’ trust. Cloud Security, by guaranteeing data protection, makes it an element of trust between you and your customers.

The challenges of Cloud Security

The availability of the cloud is a major issue for companies. In this context, the first challenge of Cloud Security is to guarantee maximum availability, particularly by protecting infrastructures from Denial of Service (DDoS) attacks. Analysis of traffic on cloud servers, and detection of suspicious packets, are all practices that are essential to Cloud Security. The fight against data breaches and by extension, against data loss, are two other prominent challenges of Cloud Security.

Finally, the last key challenge of Cloud Security is user identity verification. As employees become increasingly mobile, they connect to cloud services from anywhere in the world, making identity verification more and more complex. This is why multi-factor authentication is highly recommended by cloud players.

Anticipation, visibility, transparency, reactivity, and proactivity are the levers to be used on a daily basis to guarantee Cloud Security.

The recent COVID-19 pandemic has brought about major changes in the work culture and the Cloud is becoming an essential part by offering employees access to the company’s data, wherever they are.  But why migrate? How to migrate? And for what benefits? Here is an overview.

Head in the clouds and feet on the ground, that’s the promise of the Cloud which, with the health crisis, has proven to be an essential tool for business continuity.

In a study conducted by Vanson Bourne at the end of 2020, it appears that more than 8 out of 10 business leaders (82%), accelerated their decision to migrate their critical data and business functions to the Cloud, after facing the COVID-19 crisis. 91% of survey participants say they have become more aware of the importance of data in the decision-making process since the crisis began. 

Cloud and data. A duo that is now inseparable from business performance.

A reality that is not limited to a specific market. The plebiscite for Cloud data migration is almost worldwide! The Vanson Bourne study highlights a shared awareness on an international scale, with edifying figures:

• United States (97%),
• Germany and Japan (93%),
• United Kingdom (92%).

Finally, 99% of Chinese executives are accelerating their plans to complete their migration to the Cloud. In this context, the question “why migrate to the Cloud” is unequivocally answered: if you don’t, your competitors will do it before you and will definitely beat you to it.

The main benefits of Cloud migration

Ensuring successful Cloud data migration is first and foremost a question of guaranteeing its availability in all circumstances. Once stated, this benefit leads to many others! If data is accessible everywhere and at all times, a company is able to meet the demand for mobility and flexibility expressed by employees. 

A requirement that was fulfilled during the successive confinements and that should continue as the return to normalcy seems finally possible. Fully operational employees at home, in the office or in the countryside, not only promise increased productivity but also a considerable improvement in the user experience. HR benefits are not the only consequences of Cloud migration. 

From a financial point of view, the Cloud opens the way to a better control of IT costs. By shifting data from a CAPEX dimension to an OPEX dimension, you can improve the TCO (Total Cost of Ownership) of your information system and your data assets. Better experience, budget control, the Cloud opens the way to optimized data availability. 

Indeed, when migrating to the Cloud, your partners make commitments in terms of maintenance or backups that guarantee maximum access to your data. You should therefore pay particular attention to these commitments, which are referred to as SLAs (Service Level Agreements). 

Finally, by migrating data to the cloud, you benefit from the expertise and technical resources of specialized partners who deploy resources that are far superior to those that you could have on your own.

How to successfully migrate to the Cloud

Data is, after human resources, the most valuable asset of a company.

This is one of the reasons why companies should migrate to the Cloud. But the operation must be carried out in the best conditions to limit the risk of data degradation, as well as the temporary unavailability that impacts your business.

To do this, preparation is essential and relies on one prerequisite: the project does not only concern IT teams, but the entire company. 

Support, reassurance, training: the triptych that is essential to any change management process must be applied. Then make sure you give yourself time. Avoid the Big Bang mode, which could irritate your teams and dampen their enthusiasm. Even if the Cloud migration of your data should go smoothly, put all the chances on your side by making backups of your data. 

Rely on redundancy to prepare for any eventuality, including (and especially!) the most unlikely. Once the deployment on the cloud is complete, ensure the quality of the experience for your employees. By conducting rigorous long-term project management, you can easily identify if you need to make adjustments to your initial choices. 

The scalability of the Cloud model is a strength that you should seize upon to constantly adapt your strategy!

2020 has marked a turning point in companies’ processes for digital transformation. The coronavirus and its procession of health measures, restrictions and precautions have strongly impacted human societies as well as the business world. Let’s take a look back at the data management challenges related to COVID19.

With the closure of physical points of contact, the widespread use of remote work, logistical tensions and uncertainties all around the world, throughout 2020, companies have had to reinvent codes, implement new methods and develop new strategies. The challenge? To maintain the link between employees and customers, who are kept at a distance by more or less strict measures.

According to a study by Gartner, 69% of companies believe that the health crisis has accelerated their digital business initiatives. For 60% of them, digital transformation is a perspective to improve their operational efficiency. Another study conducted by Pega Systems reveals that 56% of companies have increased their budgets related to their digital transformation. Sixty-nine percent consider that the health crisis is leading them to become more empathetic to their customers. At the heart of these challenges: data management.

Accelerating digital transformations… with data

According to estimates from a study carried out at the end of 2020, the COVID-19 crisis has accelerated enterprises’ digital strategy at an average of 6 years. In fact, 97% of business decision-makers believe that the pandemic has accelerated their digital transformation.

The same study reveals that in the face of the crisis, 95% of companies are looking for new ways to engage customers and 92% say that the transformation of digital communications is critical to meeting today’s business challenges.

Behind these findings, there is a compelling need: to make the most of data. Indeed, beyond identifying new levers to engage customers and keep in touch with your audiences, it’s first and foremost about understanding their needs. To show empathy, you need to know who you are talking to and which channel you can interact effectively with them.

Data management is the foundation of this whole process of accelerating your digital transformation!

Data allows you to define your marketing strategies as well as define the axes of your campaigns (which is essential to preserve the continuity of your business activity in times of crisis). It also conditions your priorities in terms of R&D. In fact, the customer knowledge provided by data management allows you to include innovation in a data-driven dimension.

The objective: to design and develop products and services corresponding to the expectations and needs of your target audience. A study by Solocal, published at the end of 2020, highlighted that for 81% of companies seeking to accelerate their digital transformation, their objective is to solicit and respond to customer feedback. 

Certain sectors at the forefront of digital transformation

Integrating real-time data into business strategies, analyzing customer journey, deploying predictive analytics solutions to accelerate commitments or detecting weak signals in order to anticipate them… The scope of data management is widening every day.

During the COVID-19 crisis, some business sectors had to completely reinvent themselves and, thanks to their data assets, managed to find levers to maintain business continuity.

For example, a recent study by the consulting firm QuantMetry, published last October, showed that 68% of companies have maintained or even increased their data-related budgets in 2020. During the health crisis, Uber for Business noted an explosion in demand for meal delivery. With the careful use of their data assets, the company managed, in just a few months, to design a new offer for marketing departments. The concept? To offer meal delivery vouchers not only to employees working from home, but also to corporate customers as a loyalty lever.

The offer, with a B2B2C strategy, is also positioned as an alternative to all those moments of sociability linked to professional events. These vouchers are also first-rate marketing tools because many indicators are related to them (type of use, type of meals ordered, geographical area…). These are all useful KPIs for Uber for Business, but also for the companies that use them to refine their customer retention strategies… or talent retention strategies.

If you are in the middle of digitally transforming your business, check out our tips on how to succeed your transformation!

Today, data impacts all sectors; all companies are confronted with data management challenges, in one way or another. In spite of these observations, most of them are still struggling to really transform their enterprises into data-driven organizations. One of the reasons why they do not succeed is that they are often faced with complex and time-consuming information systems, which represent a real obstacle for their digital transformation. 

Indeed, few organizations are able to truly find their enterprise data, especially if their data ecosystem includes different formats, sizes, and varieties of data. Moreover, it is difficult to interpret them, or even to know if they are of quality when they are poorly, or simply not, documented. 

In this article, we share some best practices so that companies can find the keys to start their digital transformation through data discovery.

Rethink your corporate IS

For many years, data and data management challenges were reserved for “Tech Giants”, such as GAFAM for example (Google, Apple, Facebook, Amazon, Microsoft). Being actors of the web and working mainly with digital resources, their IS and infrastructures were already thought and developed around data. It was therefore more difficult for other market players to implement new models and strategies: they were at a disadvantage!

These other organizations were confronted with technologies built on an accumulation over time. It is obvious that it is more complex to undertake digital transformation in this case.

A striking example can be found in the banking sector. While banks have been able to standardize data due to a succession of international directives, they have had to deal with new digital banks, offering much more agile and efficient services. In order to remain competitive, they realized that it is necessary to change their strategic model to stay in the digital race.

Another example can be found in the automotive sector. Buyer behaviors tend to gradually change over time, and these changes have not come unnoticed. The increased demand for “eco-friendly” travel through the use of bicycles, access to VTC services, or shared mobility with the increase in carpooling. These facts mainly show that the mobility market is changing. Demand is no longer based on the acquisition of new goods but rather on the mobility services made available to them.  Thus, in order to remain competitive and meet the expectations of their users’ new behaviors, automotive market players must diversify. 

Getting the right data solutions

In order to meet the needs of data users, it is essential to equip your data teams with the right solutions. This means answering the question: which cross-functional technologies across all the company’s silos need to be deployed in order to have an agile configuration? To do this, IT resources need to be organized to cover three main stages:

1. data discovery,
2. the preparation and transformation of these data,
3. the consumption of these by the different departments of the company.

For more information on data discovery, here are detailed articles:

• What is data discovery?
• Download our White Paper: Data Discovery through the eyes of Tech Giants

These three steps are essential to start its transformation to a data-driven enterprise.

On the one hand, they help implement appropriate security measures to prevent the loss of sensitive data and avoid devastating financial and reputational consequences for the company. On the other hand, it enables data teams to drill down into the data to identify the specific elements that reveal the answers and find ways to show the answers. 

All this with a data catalog

At Zeenea, we define a data catalog as being:

“A detailed inventory of all data assets in an organization and their metadata, designed to help data professionals quickly find the most appropriate data for any analytical business purpose.”

With the help of a data catalog, both data teams and managers will be able to start their digital transformation based on their company’s data. 

Choosing Zeenea is choosing:

• An overview of all of an enterprise’s data assets through our connectors,

• A Google-esque search engine that enables employees to intuitively search for a dataset, business term, or even a field from just a single keyword. Narrow your search with various personalized filters (reliability score, popularity, type of document, etc.).

• A collaborative application that allows enterprises to become acculturated to data thanks to collective knowledge, discussions, feeds, etc,

• Machine learning technology that notifies you and gives suggestions as to your catalogued data’s documentation,

• A dedicated user experience that allows data leaders to empower their data explorers to become autonomous in their data journeys.

Learn more about our data catalog 

Start Accelerating your data initiatives now

If you would like more information, a free and personalized demo, or if you just want to say hello, do not hesitate to contact us and our sales team will answer you as soon as we receive your request 🙂.

The benefits of implementing a Hybrid Cloud architecture

Air France’s initial questions regarding the move to the Cloud were :

• Air France KLM aims to standardize its calculation and storage services as much as possible.
• Not all data is eligible to leave Air France’s premises due to regulations or sensitive data.
• All the tools already used in UDA platforms are available both on-premise and in the public cloud.

Éric says that a hybrid Cloud architecture would allow the firm to have more flexibility to meet today’s challenges:

“Putting our UDA on the Public Cloud would give greater flexibility to the business and more options in terms of data deployment.”

According to Air France, here is the checklist of best practices before migrating to a Hybrid Cloud:

• check if the data has a good reason to be migrated to the Public Cloud
• check the level of sensitivity of the data (according to internal data management policies)
• verify compliance with the UDA implementation guidelines
• verify data stream designs
• configure the right network connection
• for each implementation tool, choose the right level of service management
• for each component, evaluate the locking level and exit conditions
• monitor and forecast possible costs
• Adopt a security model that allows Hybrid Cloud security to be as transparent as possible.
• Extend data governance in the Cloud

Where is Air France today? 

It’s clear that the COVID-19 crisis has completely changed the aviation sector. Every day, Air France has to take the time to understand new passenger behavior and adapt flight schedules in real time, in line with the travel restrictions put in place by various governments. By the end of summer 2020, Air France will have served nearly 170 destinations, or 85% of their regular network. 

Air France’s data architecture has therefore been a key catalyst for the recovery of their airlines:

“a huge thanks to our business users (data scientists) who every day try to optimize services in real time so that they can understand how passengers are behaving in the midst of a health crisis. Even if we are working on artificial intelligence, the human factor is still an essential resource in the success of a data strategy”. 

Contemporary business initiatives such as digital transformation, are facing an explosion of data volume and diversity. In this context, organizations are looking for more flexibility and agility in their data management.

This is where Cloud strategies come in…

Data management definition

Before we begin, let’s define what data management is. Data Management, as described by TechTarget is “the process of ingesting, storing, organizing and maintaining the data created and collected by an organization”. Data management is a crucial part of an enterprise’s business and IT strategy, and provides analytical help that drives overall decision-making by executives.

As mentioned above, data is seen as a corporate asset that can be used to make better and faster decisions, improve marketing campaigns, increase overall revenue and profits, and above all: innovate. As a result, organizations are seeing cloud technologies as a way to improve their data initiatives.

Cloud strategies are the new black in data management disciplines

It is an undeniable fact that Cloud service providers are becoming the new default platform for database management. This phenomenon provides data management teams with great advantages:

• Cost-effective deployment: Greater flexibility and a more rapid configuration.
• Consumption-based spending: Pay what you use and do not over provision
• Easy maintenance: better control over the associated costs and investments 

By knowing this, there is no doubt that data leaders perceive cloud as a less expensive technology, driving this choice even more.

Data leaders will embrace the cloud as an integral part of their IT landscape in the coming years and months. However, we strongly believe that the rate at which organizations migrate  to the cloud will differ by organization size. Small or midsize organizations will migrate quicker , while larger organizations will take months, even years to migrate. 

Thus, the Cloud is going to become a default option for all data management technologies.  Many strategies appear including various deployment types or approaches. We have identified 3 main strategies:

• Hybrid Cloud: Made up of two or more separate Cloud infrastructures that may be private or public and that remain single entities
• Multicloud: Use more than one cloud service provider infrastructure as well as on-premises solutions.
• Intercloud: Where data is integrated or exchanged between cloud service providers as part of a logical application deployment.

The Cloud is also seen as an opportunity for data analytics leaders

The increased adoption of cloud strategy deployments regarding data management  has important implications for data and analytics strategies. As data is  moving to the cloud, the data and analytics applications they use must follow.

Indeed, the emphasis on the speed of value delivery has made cloud technologies the first choice for new data management solution development for vendors, and deployment for enterprises. Thus, enterprises and data leaders are choosing next-gen data management solutions! They will migrate their assets by selecting applications that connect to future cloud strategies and preparing their teams & budgets for the upcoming challenges they will overcome..

Those data leaders who use analytics, business intelligence (BI) and data science solutions are seeing Cloud solutions as greater opportunities to:

• Use a cloud sandbox environment for trial purposes in terms of onboarding, usages, connectivity and create a prototyping analytics environment before actually buying the solution.
• Facilitate application access wherever you are and improve collaboration between peers.
• Access to new emerging capabilities over time with ease, with continuous delivery approaches.
• Support heavy lifting with the cloud’s elasticity and scalability along the analytics process.

A data catalog, the new essential solution for cloud data management strategies

Data and analytics leaders will inevitably engage in more than one cloud where data management, governance and integration become more complex than ever before. Thus, data leaders must equip their organization to new metadata management solutions to assist in finding and inventorying data distributed across a hybrid and multi cloud ecosystem.  Failure to do so will result in a proliferation of data silos, leading to derailed data management, analytics and data science projects.

Data management teams will have to choose among the wide-range of data catalog in the market the most relevant one.

We like to define a data catalog as a way to create and maintain an inventory of data assets through the discovery, description and organization of distributed datasets.
If you are working on the data catalog project, you will find: 

• On the one hand by fairly old players, initially positioned on the Data Governance market.
  These players provide on premises solutions with rich but complex offers, which are expensive, difficult and time-consuming to deploy and maintain, and are designed for cross-functional governance teams. Their value proposition is focused on control, risk management and compliance.

• on the other hand by suppliers of data infrastructures (Amazon, Google, Microsoft, Cloudera, etc.) or data processing solutions (Tableau, Talend, Qlik, etc.), for which metadata management is an essential block to complete their offer. They offer much more pragmatic (and less costly) solutions, but are often highly technical and limited to their ecosystem.

 We consider those alternatives as not sufficient enough. Here are  some essential guidelines to find your future data catalog. It must:

– Be a cloud data catalog enabling competitive pricing and rapid ROI for your organization.
– Have universal connectivity, adapting to all systems and all data strategies (edge, cloud, multi-cloud, cross-cloud, hybrid).
– Have very advanced automation for the collection and enrichment of data assets as well as their attributes and links (augmented catalog). The automatic feeding mechanisms, as well as the suggestion and correction algorithms reduce the overall cost of the catalog and guarantees the quality of the information it contains
– Be strongly focused on user experience, especially for business users, to improve solution adoption.

To conclude, data management capabilities are becoming more and more cloud-first and in some cases cloud-only.

Data leaders who want to drive innovation in analytics will need to leverage cloud technologies from data assets. They will have to go from ingestion to transformation without forgetting to invest in an efficient data catalog in order to find their way in an ever more complex data world.