Use our template to create a compelling and comprehensive AI Expert job description to attract top talent.
Certainly, artificial intelligence (AI) is already part of our daily lives (personal assistants, smartphones with facial recognition, etc.) and it is becoming increasingly important in the business world, with numerous technological applications (chatbots, maintenance of installations, etc.). In this AI expert job description you will find everything you need to know about the AI expert job, required skills, training, education, and salary expectations.
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AI expert: the job
What is the role of the artificial intelligence expert?
The main task of the Artificial Intelligence (AI) Expert is to design computer programs capable of performing tasks similar to those performed by a human being. Both a researcher and a computer scientist, an AI expert can work in a wide range of fields.
The mission of the artificial intelligence expert is to solve complex problems. Research and analysis are therefore at the heart of the job. The AI expert must possess very advanced computer skills, as his or her expertise is constantly sought in the development of artificial intelligence projects. Since AI is still a relatively new field of expertise, the AI expert must constantly keep abreast of technological developments.
Understanding and analyzing problems
Since the role of the AI expert is to create software that mimics human reasoning, the expert must be able to analyze the human brain in relation to a problem and thus develop intuitive human-machine interfaces.
Developing and designing solutions
The AI expert can work on extremely diverse and varied projects. His or her day-to-day tasks are generally algorithm design, error checking, and programming.
Required skills of the AI expert
Strong technical skills
The job of Artificial Intelligence Expert requires advanced technical skills because AI-based applications cross many technologies (web crawling, data mining, data science, machine learning, deep learning, etc.).
Strength of proposal, and ability to listen
As far as soft skills are concerned, the AI Expert must show a strong spirit of initiative, interpersonal skills, and good listening skills. Since these qualities will enable him/her to carry out his/her projects successfully, communicate with all stakeholders, and call on external experts.
Ability to work in a team
To conclude, research in Artificial Intelligence is definitely not a solitary job. In fact, the AI Expert will have to work with various experts and will have to know how to federate these experts and listen to their advice to make progress in his research.
Expertise in Artificial Intelligence is very rare and therefore highly sought after. As a researcher and computer scientist, the AI Expert is highly qualified and can work in different fields of activity. Such as in ESN, in industrial companies, in research laboratories…
In this post, we discuss AI in the workplace with our Chief Digital Officer, Felix Lemaignent.
Salary
The average daily rate of an Artificial Intelligence Expert is between 700 and 1500€.
Training and education
To become an Artificial Intelligence Expert, you need to have a 5-year degree. You can enter this profession by having studied mathematics or computer science. However, you will need to continue your education and obtain a master’s degree or an engineering diploma. A specialized master’s degree or even a doctorate.
After years of research and a lot of work, an AI expert can easily move on to new projects and join innovative start-ups in R&D, large companies, or research centers.
Use our template to create a compelling and comprehensive Technical expert job description to attract top talent.
The technical expert is generally a specialist who not only assists and controls but also informs and advises. He or she can particularly be involved in a project as a whole or in part. Here is everything you need to know about the technical expert job, the skills, education and training, career and salary expectations.
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Technical expert: the job
Definition of the technical architecture
Always in accordance with the client’s strategy, the technical experts must analyze the company’s needs to implement the best possible solution to improve the business process. To do so, they will have to carry out internal audits to analyze the existing tools and possibly replace them with more efficient solutions.
They will then have to adapt the products in place to perfectly match the company’s structure. This phase will enable the solutions put in place to be tested to adapt them as much as possible to the company’s needs.
Training of the team
This phase is essential as it consists of training future users about the new product. The expert will assist in the deployment of the new products in order to provide maximum support to the new users.
Monitor and test the implemented tools
The technical expert must set up permanent tests to check the correct functioning of the elements that he/she has installed. In the event of a malfunction, it is important to be able to intervene quickly by analyzing and understanding the cause of the breakdown. As a problem can happen quickly, it is crucial to implement solutions to solve the problem.
Solving complex situations
The expert will have to explore all kinds of dysfunctions and propose a procedure to solve these complex problems. Of course, this procedure will have to be deployed in the company so that everyone can access it.
The technical expert must have an excellent command of the technical solutions that fall within his or her field of competence. But it is also valuable to have a more general knowledge of properly technical solutions, such as a good understanding of one’s client’s field of activity.
Knowledge of English
Good knowledge of English is also important for understanding software documentation. In fact, much software is translated into English. The technical expert can attend training sessions in English on certain tools.
Listening, curiosity, rigor
The expert must be able to listen to the customers to respond as much as possible to their needs. Then, curiosity is essential to constantly seek new technical developments. Moreover, rigor seems indispensable. Indeed, to remain competitive, they must thoroughly assess the company’s needs to respond to them as best they can.
Self-education and versatility
The technical expert is a constantly evolving profession. In fact, always on the lookout for new technologies, the technical expert is in constant training to remain competitive. Versatility is also predominant in this profession. The expert must solve technical problems and train future users of the tools in-house.
Be a good teacher
Finally, he or she must be a good teacher in order to train future users of the tools that he or she will have put in place within the company. This training is provided both internally and externally.
Internally, the technical expert is in contact with the technical consultants, the support manager, the designers, and developers, and the product managers. Externally, he/she works with service providers, the client, the IT department, and suppliers. They are also in constant contact with future users.
Careers and Salary
Before working as a technical expert, it is preferable to have at least three years of experience in professions such as design and development engineer or IT project manager.
It will be possible for the technical expert to progress to the professions of business engineer, project manager, or technical director.
The average daily rate will be between €550 and €700.
Training and education
It is possible to become a technical expert after obtaining a Bac+2 (DUT or IUT) but also with a Bac+5 (Engineering school, Master). It is then possible to train in a more specialized field. Finally, a doctorate may also lead to technical expert job.
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Certainly, to implement an efficient ERP is a crucial task; choosing a cloud-based solution can make the process faster. However, since an ERP covers the entire range of business functions, it is important to perform all the necessary steps carefully and methodically.
Switching to a new system, or activating it from scratch, requires a major preliminary analysis, a careful migration and testing process, scrupulous staff training, and fine-tuning of the system.
For this reason, we at Mindquest have summarized the various phases of this process in the following 6 steps.
Firstly, discovery and planning is perhaps the most important phase: if done methodically, it helps reduce time, cost, and risk. This phase consists of researching and selecting a system, establishing a project team, and defining detailed system requirements.
2. Design
Then, by analyzing the hardware and software infrastructure in place, new and more efficient workflows and other business processes can be designed to take advantage of the system.
In particular, if the choice falls to a cloud-based ERP, it is critical to check the quality, stability, and security of Internet access. Systematic analysis of information flows is critical here. Single systems may, in fact, perform less well than industry-specific products.
At this stage, it is also critical to define a team responsible for the process. Since the implementation is a complex task, dedicating resources makes it easier to interface with the support team.
3. Development
In addition of having performed the audit and mapped the information flows, assigned functions and responsibilities, and identified the most suitable solution, the actual implementation phase begins.
This step consists of the configuration of access and permissions. The ERP is used by several users and in different capacities. It is therefore important to set permissions and roles for access according to one’s user profile.
It also includes the preparation of the data and processes to be migrated. Preliminary analysis helps to resolve any format incompatibilities in time. Centralized data management eliminates redundancies and duplicates
4. Testing
During this phase, it is valuable to continuously test the functions of the system and refine the development to solve any emerging problems.
Fine-tuning, it is to say, testing the system to gradually verify the results of the migration process and adjust any discrepancies in use and access.
5. Deployment
Once this phase is also completed, we move on to the actual operational verification of the new ERP. This is accompanied by staff usability testing.
With the new ERP fully operational, it is possible to observe its actual operation. Preliminary analysis and partial testing are useful, but the go-live is the real litmus test.
6. Support
In the initial break-in period, the work of the support and service team is critical. This is also the reason for choosing not only the ERP, but also the company that provides it.
Maintaining the ERP implementation after deployment helps keep users satisfied and ensures that the company achieves the desired benefits.
Last but not leas, the project team may remain responsible for the ERP system during this phase, but will focus on listening to user feedback and adjusting the system accordingly.
Further development and configuration may be needed as new features are added to the system. It is also critical to train new staff on the system to implement an efficient ERP.
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IDC estimates that the business intelligence market will continue to grow at a rate of 8 percent through 2022. But despite the success of these types of business software solutions, most projects fail at some point in their implementation. What are the causes? How can they be avoided? To help you, we at Mindquest collected a list of 8 mistakes to avoid when it comes to Business Intelligence.
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Business Intelligence: decision-making technology
The purpose of Business Intelligence (BI) solutions is to provide information that facilitates decision-making with real-time data. Therefore, in an ever-changing environment, BI software is increasingly indispensable.
Moreover, the union between BI and Data Science is expanding the horizon of possibilities of Business Intelligence to limits that were unimaginable just a few years ago.
But in order for your company to benefit from all this business decision-making technology, it is necessary to carry out a good implementation.
The following are the 8 most common mistakes to avoid when it comes to Business Intelligence.
Business Intelligence mistakes that companies often make are often the same. Therefore, let’s take a look at the “manual of bad practices” in Business Intelligence implementation.
Firstly, avoiding a BI software implementation problem means anticipating it, which is why it is necessary to know in advance.
1. Not defining the objectives of the software properly in the planning phase
To start, it is a big mistake to think that just by setting up a BI solution it will work by itself, as if by magic. Business Intelligence is just a tool, and it will work as long as it is handled with skill.
For it to work, the objectives to be achieved with the implementation need to be set from the outset. These must also be aligned with the business objectives. This is the only way to get a return on the investment in Business Intelligence.
2. Give all the power over the BI tool to the IT department
Related to the previous point, for the software to be
aligned with business objectives, the implementation must transcend the IT
department.
In other words, the more business-oriented managers and executives must actively participate in defining the objectives that the BI must meet.
3. Choosing a Business Intelligence technology that does not meet the requirements of the business
There is a multitude of software vendors with different technical and functional solutions on the market, and then there are customized solutions. Whatever your company may select, the software must be tailored to your business needs.
Be suspicious of one-size-fits-all solutions. Since the best business intelligence technology will depend, in most cases, on the size of your company, the sector in which you operate, the type of activity, etc.
4. Not doing a good job of integration
For the BI solution to deliver the desired results, integration with the company’s databases is crucial.
Companies that still rely on Excel for everything have a problem in this regard, and need a complete overhaul of their systems. BI that is well integrated with data from ERP, CRM, etc., is crucial.
5. Neglecting data quality
One of the consequences of not doing a good job of
integrating with the company’s databases is poor data. But there are other
reasons why data may be of poor quality, irrelevant or incomplete.
There must be controls in place to avoid loading erroneous data into Business Intelligence, ETL (Extract, Transform, Loud) processes, etc.
6. Prioritize the front-end and leave the back-end in the background
Although the purpose of a BI tool should be to present dashboards, reports, and charts visually that facilitate the analysis of information (front-end), the configuration of internal processes (back-end), which are responsible for processing all the information that is then to be displayed, should not be overlooked.
Giving equal importance to the back-end and front-end is crucial for choosing the right technology when implementing or developing a Business Intelligence solution.
7. Not sufficiently protecting your BI data
Certainly, developing a solution with self-service options that democratize data and extend it to more internal users is often beneficial to a company.
Mobility also enables more practical use of technology,
allowing, for example, access to reports from a smartphone or other device from
anywhere.
But all this can also pose a serious security problem when an employee views information to which he or she should not have access or an employee loses his or her smartphone, opening the company’s doors to any stranger. Effective controls need to be put in place to ensure legal compliance and company security.
8. Forgetting the end user
Last but not least, training the employees and professional profiles that must handle the Business Intelligence solution is fundamental if we want them to use it.
Low adoption is one of the main reasons why the
implementation of BI in the company can fail.
A good training program is very useful, but it is not enough. The employee must understand why it makes sense for the company to use Business Intelligence, and why it is important for them to use it.
To conclude, Business Intelligence is the ability to visualize data in an easily interpretable way with powerful top-down navigation that makes it easy to get to the source of the detected problem.
If we associate it directly with information technology, we can say that BI is the set of applications, technologies, and methodologies that can collect and transform data into valuable and structured information that can be used and analyzed directly.
For this reason, it is important to know the most common mistakes to avoid in Business Intelligence, to convert information into valuable data for decision-making.
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difference between big data and business intelligence
Although they are two closely related concepts, Business Intelligence and Big Data are not the same thing. They are not even interchangeable. In this article, we compare Business Intelligence and Big Data to see their main differences.
Differences between Business Intelligence and Big Data
What can we expect from a solution like Business Intelligence (BI) and Big Data? BI or Business Intelligence software helps companies make decisions based on data and metrics. But what does Big Data have to do with it?
Nowadays, there are many companies that use data as a resource. They rely on it to support strategic decisions that help to grow and improve the business. In this aspect, both Big Data and Business Intelligence work together on that data. However, they do not do it in the same way, since we can find differences between them.
But before we look at the differences between Business Intelligence and Big Data, we should give a small definition of both. This will give us enough context to explain their differences.
Big Data it is a set of technologies and tools that allow us to manage and process large amounts of data at high speed and in real-time, whether they are structured, semi-structured, or unstructured.
The data come from various sources (smart devices, sensors, social networks, websites, etc.). However, it is not so much the quantity as the quality of the data that matters. In other words, they are usable to generate relevant ideas and make good strategic decisions.
Therefore, when we talk about Big Data, we are referring to a large volume of complex data that is difficult to manage and analyze without the right tools.
BI is the combination of software applications, infrastructure, and practices that make it possible to access and analyze the information collected by companies. Info they then use to improve decision-making processes.
It is through BI and its tools that we can carry out quality analysis of the data obtained from Big Data.
With Business Intelligence tools, companies can make decisions based on data already processed and treated to convert them into information.
Analyzing the information
The analysis of all this information makes it possible to obtain new data and exploit previously collected information. These processes are useful to:
Generate new information from the analysis of existing data, e.g. for demand forecasting or people classification methods.
Identify alarms or exceptional situations to review/study in order to take appropriate action.
The value obtained by these companies translates into:
Cost savings
Speed
New products and service
The anticipation of the competitors
Better operational management, etc.
Now that we know what they are, we can see the main differences between Business Intelligence and Big Data.
Main differences between Business Intelligence and Big Data
Comparing these concepts is like talking about two worlds. They are still under exploration, but which constitute the closest reality for companies. Both allow extracting the value of information in totally different but complementary ways.
BI is a set of business management techniques that enable companies to make decisions based on data; Big Data, on the other hand, are the tools that can obtain, store and process data.
In other words:
Business Intelligence provides access to data sets that are already organized and stored so that the user can easily navigate them,
Big Data focuses on massive processes for a large volume of data, with very different organization and origins, in order to obtain new information.
This means that with Business Intelligence, companies can carry out analyses and draw conclusions, produce reports, graphs, maps, tables, etc. with 100 per cent detailed information. With Big Data, the opposite is true.
In short, we can summarize the main divergence between the two as ‘innovation and discovery vs. questions and answers‘. Some of the processes that business intelligence uses to deepen data are: the use of software, the feeding of knowledge systems, the transformation of data into actionable intelligence, etc.
Another difference concerns the type of data with which both methodologies work.
In Big Data all types of data, structured or unstructured, are collected.
Business Intelligence, on the other hand, only works with structured data, which has previously been stored in a database hosted on a server (also called a data warehouse), which allows it to work with offline data.
Business Intelligence and Big Data do not store data in the same way
Speaking of data storage; we have already pointed out that Business Intelligence stores data in a database hosted on a server and this must be done prior to processing and analysis.
Big Data, in order to work at the speeds it does, must use several servers to store the large volumes of data, i.e., it must use distributed file systems in nodes to store the information, such as Hadoop. These systems, which are much more flexible (they allow data to be stored without labelling) and more secure, since if one of the nodes fails, the information will be replicated on other nodes.
Data are not processed in the same way
Data processing is not carried out in the same way either.
As we have already mentioned in the previous point, Big Data uses a system of files distributed in nodes, which allows parallel processing of data, thus optimizing the speed at which data is handled. It does this by executing several instructions at the same time, comparing the results obtained, grouping and analyzing them before presenting the final solution to users.
In Business Intelligence, queries must be made to the database to obtain the solutions sought.
They also differ in how they perform data analysis
If Big Data can store and process structured and
unstructured data, it also has the tools to be able to analyse and visualise
large amounts of data, regardless of its type and origin. This is particularly
useful for companies, since the vast majority of data currently collected comes
from various sources on the Internet and only around 20% is structured.
In addition, Big Data has the ability to work with data from the past as well as in real time, which makes it possible to make more accurate predictions.
Business Intelligence, since it can only work with previously stored, processed, classified and converted data, always works with data from the past.
Main differences between Big Data and Business Intelligence
The professional profile is not the same either
Finally, Big Data and Business Intelligence also differ in
the type of professional profile dedicated to each speciality.
On the one hand, the professional profiles for Big Data usually include mathematicians, computer engineers or statisticians. In addition, they belong to the technology department and report to the CTO (Chief Technology Officer).
The data analyst is the leading expert for all business database operations. They assemble and process data in order to evaluate business activity and make appropriate recommendations. Their work enables them to ‘make the data speak’ by interpreting it.
On the other hand, professional profiles for Business Intelligence come from fields such as business administration, economists or marketing, although they may also include engineers or technicians.
For example, QlikView is a Business Intelligence platform facilitating self-service data interpretation. Thus, the QlikView solution enables big data analysis to be transformed into actionable insights. As a consequence, the role of the QlikView developer is to prepare prior data processing to adapt the tool to the business needs and the activities of the company.
They are usually found in the company’s management department and report either to the CSO (Chief Strategy Officer) if there is one, or directly to the CEO.
Businesses are undergoing the shift towards digital transformation. More and more businesses are seeing the need to invest in data analytics solutions for one simple reason: information is power.
Through them, we can have full control of data and increase business insight, as well as:
Save time and costs.
Improve the service offered to customers.
Facilitate the consultation of data.
New business opportunities.
Obtain verified, transparent and reliable results.
Having exposed these advantages, we can affirm that having a BI platform is essential to achieve business success.
Although each company is a different world, all of them can find competitive advantages in BI. Solutions focused on business intelligence are no longer seen as a simple tool focused only on large companies, so more and more SMEs are becoming interested in its technology.
What you shouldn’t do in Business Intelligence
Here are the things you should avoid at all costs when it comes to BI:
Choosing a technology that does not meet your business requirements, needs, or problems.
Poorly defined software objectives in the planning phase.
Forgetting the role of the end user.
Lack of integration and protection of company data.
Leaving the back-end in the background and giving top priority to the front-end. They must be in balance.
So, Big Data vs Business Intelligence, which wins?
The truth is that neither, because it is not a competition between the two methodologies. But rather they must work together to get the most out of data collection and analysis.
Thus, the Business Intelligence team will work together with the Big Data team. They need to establish the datatocollect and then go on and analyze it. For its part, the Big Data team will look for patterns in the data to communicate them to the BI team.
The next Business Intelligence challenge: real-time analysis
If BI wants to remain relevant and not be displaced over time by Big Data tools, it must take the next step and be able to have its own tools for real-time data analysis.
In other words, BI will also need to carry out analysis on unstructured data and achieve a system in which it is possible to detect and respond to situations that occur in the market in a quick and agile manner.
This does not mean that Business Intelligence will stop working together with Big Data. This is because the process of collecting and storing massive data will continue to fall to the latter. But it does mean that the former will have tools that allow it to analyze these data in real time. This without having to process, treat and store them in a database as it has been doing until now.
You’ve probably heard of the role of project manager. And now you’re wondering what these professional roles consist of and what their tasks and career paths are. Well, in this article we will tell you all about the Project Manager roles, their competencies, skills, and salary expectations.
What is Project Management?
Firstly, let’s define what it is. Project Management is the process of planning, developing, and delegating responsibilities for an organization’s intended objectives of a particular project according to agreed criteria.
What is a Project Manager?
Then, based on the definitions found on the Internet, the Project Manager is the person responsible for planning and safeguarding the successful execution of the steps to carry out a project. In other words, it is the profile that coordinates the work of the team to meet the objectives.
Large companies have always invested in a similar role to take responsibility for the management and supervisory tasks. But, why are the roles and skills of the Project Manager more important today than ever before? What has changed so that all companies have decided to incorporate one in their ranks?
The answer is simple. The digital business context in which we find ourselves is very changeable and the strategy varies according to external demands. This is why today, more than ever, companies need Project Managers, who will keep the focus on objectives and take into account the external factors that the team is facing.
According to the data, 71% of Project Managers who have shared their salary say that it has increased in the last year. The demand for this profile continues to grow steadily, mainly in countries such as Mexico, Colombia, Peru, and Spain.
Of course, the salary depends to a large extent on the project management experience that each person brings. Among the most in-demand profiles, the average salary of a Project Manager with 2 to 5 years’ experience is between $30,000 and $40,000 gross per year, depending on the geographical area and the size of the company.
Skills of a Project Manager
The role of project manager, unlike other profiles, does not require purely technical knowledge. Rather, it requires a specific set of skills that are acquired while working in the profession. For example, the ability to plan, organize, coordinate and control work.
1. Leadership
In the digital era, the role of the Project Manager must revolve around leading and driving the digital transformation within the company. A good leader must know how to transmit these values to their team. Also, they need to transmit their motivation to work and achieve the objectives.
Being a leader does not mean that you do not need to work in a team. On the contrary, you need to interact with many hierarchical levels within the company, and it is important to know how to do it with each one of them to clearly determine the objectives and guidelines to be followed.
3. Organization
To a large extent, the Project Manager’s job is to organize, organize and organize. The organization of the processes and actors involved in the project depends on him/her, as well as a good organization of the deadlines and times of the actions.
4. Communication
If there is one thing a Project Manager is expected to be, it is a great communicator. One of their tasks is to create good communication channels and ensure that all the agents involved in the project know and are clear about their role in the planning.
5. Conflict management skills
In addition, interpersonal relationships in project management are a fact of life, and it is inevitable that conflicts may arise at some stage. That is why it is important that the Project Manager knows how to manage this type of problem and act as a mediator to solve them and create a good working environment.
A good manager must also be aware of his or her limitations and assume that it is impossible to cover everything. Therefore, it is important that he/she knows when to delegate and trust his/her subordinates, respecting their functions and motivating them in their work.
7. Detail-oriented and attentive
The quality of the service provided is one of the main objectives to be met by the Project Manager. To this end, he/she must be demanding and attentive to detail, assessing at all times that the standards of excellence are met and being able to identify what goes wrong to make the right decision at all times.
8. Knowledge of the market
Undoubtedly, if the project has a market outlet, it is the Project Manager’s job to know the trends and the competition that may arise to adjust the focus of the objectives towards success.
The position of e-CRM (electronic customer relationship management) project manager lies at the intersection of the IT, marketing, and sales functions. This role occupies an important place in the field of web-based customer relations, as it coordinates the implementation of digital campaigns across all of a company’s digital platforms.
In addition to setting the main objectives within a project, unforeseen events can also arise. It is crucial that the Project Manager knows how to guide the team with quick and precise decision-making, and establishing deadlines.
10. Knowledge in evaluation and metrics
Having knowledge of analysis and metrics is essential for the Project Manager since one of its functions is to evaluate the efficiency, progress, performance, productivity, and quality of a project or product. In this sense, metrics help to know the status of the ongoing project in terms of time, costs, and profitability.
The budget indicates how funds will be spent during the lifetime of a project. The Project Manager needs to define in terms of cost all efforts invested in each task.
What you need to study to become a Project Manager
Professionals with specific and multidisciplinary training with a digital base who manage value, time reduction, agility, and reliability of objectives are becoming increasingly important in companies. Although there is no specific career with which to learn to cover Project Manager roles, you do need to have skills in management, business, and techniques such as design thinking, problem-solving, and Agile & Scrum.
Design Thinking
Design Thinking is a discipline that is based on the sensitivity and methods of designers to match the needs of people with what is technologically feasible.
Brands are constantly looking for digital experts that combine emotion and innovation. It is within this context that they seek the help of a Creative Technologist.
The Creative Technologist plays an important role in the digitization of an agency or brand. His or her main mission is to help position brands through technology and innovation. It is a hybrid job that combines expertise in technology, marketing, and design.
Problem-solving is the skill that determines why a problem arises and how to solve it. It starts with identifying issues, devising solutions, implementing these solutions, and evaluating their effectiveness.
Agile and Scrum
Agile and Scrum is the process of regularly applying a set of best practices to work collaboratively as a team to achieve the best possible outcome for a project. Moreover, Agile and Scrum are some techniques used to achieve objectives.
The Agile transformation can be a very difficult project for a company. Many reasons can lead to failure: management not open to agility, change of direction and goals… The Agile Coach is a change agent for companies on the road to agility. He helps companies transform over the long term.
On the other hand, the origin of the term Scrum comes from rugby. The job of the Scrum Master is analogous to that of the scrum-half. The Scrum Master has the responsibility to push others in the right direction. Promote team unity, and communicate with the outside world. The Scrum Master, therefore, acts as a guide. He or she helps and facilitates the work of the team, with a view to improvement and adaptation.
Jira is a multi-functional platform developed by Atlassian that facilitates the management of development and Agile projects. It is a tracking software enabling teams to define activities, identify blockages and share information. This tool is specifically designed to meet the needs of teams working in Scrum or Kanban.
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