The use of Small Data allows companies to make a good interpretation of Big Data, enabling a more human-centric approach to data processing.
In this article, we discuss this new trend by introducing data consumption prediction and the evolution of data processing.
Data consumption
The amount of data consumed worldwide in 2022 was 947 ZB. And it is expected to reach 180 ZB in 2025 according to Statista data.
This increase in the use and consumption of data led companies to invest in new technologies. This is to manage and analyze this data in order to gain in-depth knowledge of their customers.
Thus, in 2021, investment in Big Data and data analytics solutions by companies increased by more than 10%. And it is expected that between 2021 and 2025 the annual growth rate will be 12.8%. This investment by companies foresees the business’s need to obtain qualitative information from the data collected.
Despite this, the human aspect of data processing often falls by the wayside in the face of companies’ imperative need to know their customers in depth to achieve business growth.
As a result, companies are beginning to change their metrics to better account for their customers. Thus, as many as 78% of corporate marketing departments have changed their metrics due to the pandemic.
In this sense, Mindquest analyzes how to move from ‘data centric’ to ‘human centric’ in data processing. And this transition involves the introduction of Small Data.
Small Data implementation
Small Data implementation has become critical for companies that want to make profitable use of Big Data. In fact, interpreting Small Data helps companies ensure a service or product that meets real customer needs.
It is no longer just a matter of collecting a large amount of data, but of deriving truly useful information from it.
Following are 3 ways to move from data-centric to human-centric in the data processing
1. Evolution towards Big Data Marketing
The application of data in marketing strategies is a common practice for marketing teams in all companies. Because of this, they have improved their digital strategies.
Thus, companies use Big Data for analytics, but despite its importance in the current context, its collection and analysis is increasingly complicated. This is due to the increased regulation and knowledge of users on the treatment of the same.
For all these reasons, companies must evolve in the treatment of their data and not only take them into account for the benefit of their business, but also to offer real and tangible benefits to their customers and users, taking them into account.
2. Small Data Implementation
Although companies have invested in Big Data in recent years, according to Gartner, by 2025 70 percent of companies will have shifted the focus of their data strategy from Big Data to Small and Wide Data.
“Small data is an approach that involves less data but still provides valuable insights. This approach includes some time-series analysis techniques or few-shot learning, synthetic data or self-supervised learning.” (Gartner, PR May 19, 2021)
The use of small data enables companies to interpret Big Data well, deepening their understanding of customers and their motivations for doing so. They do this by extracting useful information from each customer and opting for data quality rather than quantity.
Its use will be essential in the coming years as companies begin to base their business strategy on the customer. Consequently, they need to know the reasons that motivate their customers’ behavior in order to adapt to them.
The use of small data will enable companies to understand and draw conclusions from the large amount of data they already have on their customers.
3. Be aware of Wide Data
There are more and more data sources or points of contact between a company and its customers. So much so that marketers use data from an average of 15 sources
In this context, Wide Data is essential for companies. This is because it links together data from a wide range of sources to reach a meaningful analysis.
Thus, “Wide data allows analysis and synergy of a variety of small and large, unstructured and structured data sources. It applies X-analytics, where X is looking for links between data sources, as well as for a variety of data formats. These formats include tables, text, images, video, audio, voice, temperature, or even smells and vibrations:” (Gartner, PR May 19, 2021)
Its use allows them to understand customers’ use of each platform and gain a more comprehensive view of them. In this way, companies are able to adjust their strategies accordingly to better engage with their customers.
Conclusion
As important as data is to business strategy, it does not speak for itself.
The entire data analysis team needs to be able to draw conclusions from the data that truly impact the company’s relationship with customers.
In this sense, understanding the work behind data interpretation is essential for each company to enhance the value of its analytics team, which continue to play a determining role in the future of the company.
Also read our article about the differences between Business Intelligence and Big Data. Both work together on data, but they do not do it in the same way. Business Intelligence software helps companies make decisions based on data and metrics. But what does Big Data have to do with it?
Business Intelligence (BI) analyst job description
Use our template to create a compelling and comprehensive Business Intelligence Analyst job description to attract top talent.
The job of Business Intelligence (BI) analyst is one of the most sought-after positions by IT employers. This professional plays a key role within an organization. He or she is responsible for collecting data, analyzing it and transforming it into decision-making tools.
When the BI analyst starts a new mission, his or her first task is to define the needs and constraints of the company’s various stakeholders (production team, users). They are also responsible for planning and estimating project costs.
Defining the data warehouse architecture
The Business Intelligence Analyst must then model the data warehouse and data marts dedicated to a particular function of the company. They must also define the data storage and structuring solutions, determine the data acquisition and extraction tools, and finally implement the best technical solutions to handle these large volumes of data.
Accompany the client in the implementation of the project
The BI Analyst then configures the analysis and reporting tools. He or she then restores the data and trains users through reports. Presents the data according to the user’s needs and trains the user to use the decision-making tools.
Required skills of the Business Intelligence Analyst
Dual technical and functional skills
The Business Intelligence expert must be familiar with database tools. These include Microsoft, SQL Server, Reporting Services, and Analysis Services. They must also master some BI tools, such as Business Object, Cognos, Hyperion and SAAS. They must also be familiar with database management systems (DMS).
Interpersonal skills
The Business Intelligence expert is in contact with different kinds of people, such as business specialists, company management, development teams, IT production, and others. They must therefore possess good interpersonal skills.
Synthesis and analysis skills
They must be able to synthesize to have an overall view of the results to be achieved. They must also be good analysts. Finally, they provide their stakeholders with elements that enable them to make choices based on the expected ROI (return on investment) and their urgencies.
Disclosure of technical subjects
The subjects on which the Business Intelligence Analyst work can sometimes be complicated. They must therefore be able to explain them in simple terms so that all their stakeholders can consider the technical issues of IT.
Context
Already widely used in large companies, Business Intelligence is becoming increasingly important in SMEs. Today everyone is aware of the importance of taking into account data related to Internet activities.
The BI Analyst is hierarchically linked to the director of studies, information systems, programs, IS professions, the project manager, or the head of a functional department in the company.
In large companies, his or her duties may vary depending on the hierarchical level.
Salary
The Business Intelligence Analyst may have previously worked in professions such as IT project manager, technical architect, or I.S. He or she can professionally progress towards training functions or towards management by becoming a project manager.
The average daily rate of a BI Analyst is between 500€ and 600€. It varies according to the size of the project, the level of responsibility, and the type of expertise.
Education and training
In conclusion, to become a BI Analyst, you need to have completed a five-year degree in the digital and IT sectors.
University courses such as a Master’s degree in project management, computer science, statistics, mathematics, and others or an engineering school in computer science, telecoms, or a generalist field can lead to this job.
Despite the success of business intelligence solutions, do you know the main reasons why most projects fail at some point in their implementation?
Use our template to create a compelling and comprehensive Data Center Manager job description to attract top talent.
The Data Center Manager job is to manage an infrastructure that houses a huge amount of data and applications for various customers who seek security and availability in this type of center.
Depending on the type of organization, the data center manager is assigned the function of technical and/or business manager of the data center. The Data Center Manager can either work for companies whose main activity is hosting or for a company that has its own data centers.
Technical and operational management of the data center
Firstly, the Data Center Manager is responsible for the administration of the data center. They ensure the correct technical administration of the servers. Therefore, it is important that the IT infrastructure that supports the data (disk space, network) is flawless.
The data center manager must therefore have solid technical skills in systems, networks, and programming languages. They ensure proper security levels are maintained to protect the structure and customer data.
Supervision and leadership
The Data Center Manager is also a supervisor. In fact they are responsible for developing schedules, anticipating hosting capacity extensions to avoid saturation or contention issues, monitoring service providers, and talking with vendors to match needs with existing technical solutions. Serves as the leader of the technical team and the team responsible for the operation of the data center in terms of staff management.
User support and assistance
As a support officer, the Data Center Manager is responsible for communicating with customers whose computing resources are hosted on data center servers. Also manages all documentation related to hosting and operations (technical manuals, user resource site…). And ensures continuous dialogue and regularly interacts with users to understand their requests.
Required skills of the Data Center Manager
General Knowledge
The Data Center Manage has a solid understanding of hardware and software. They must also be comfortable with the various hardware elements that compose the entire system: network, fiber, firewall, etc. In addition, this professional is familiar with machine architectures, systems with various multiprocessors, and networks (TCP/IP). Finally, they must be proficient with scripting languages such as Bash/Python/Perl.
Interpersonal skills
Being a Data Center manager requires maintaining a dialogue with vendors, service providers, and users to best meet the expectations of each of them and those of the data center. Therefore, to be able to manage teams, it is crucial to be good at dealing with people.
Availability and responsiveness
These are the watchwords of a good Data Center Manager. He or she must be able to respond quickly to technical and service problems, which can arise at any time. Being as organized as possible is therefore necessary to prioritize your tasks and be as responsive as possible.
Context
The data center manager usually begins a career as a technician or engineer. After being distinguished for the ability to manage a team, it is possible to advance to the position of Data Center Manager.
Salary
The position of Data Center Manager varies depending on many factors such as the size of the data center, the size of the customer base and the size of the team. It usually requires two years of experience in technical support to access this position.
The average salary is between 450e and 600e. After a few years, he/she may be offered a responsibility in a larger structure. The career of a Data Center Manager can also be oriented towards the pre-sales of hosting services.
Education and Training
In conclusion, for this position, it is necessary to have at least an operations technician or Bac+2 engineer degree. With lower-level experience, you can qualify for the Data Center Manager position.
A Data Scientist is an expert in Data Science, whose job is to extract knowledge from data in order to be able to answer the questions he or she is asked.
Use our template to create a compelling and comprehensive Data Scientist job description to attract top talent.
A Data Scientist is an expert in Data Science, whose job is to extract knowledge from data in order to be able to answer the questions he or she is asked.
In this article you will find all you need to know about the job of Data Scientist, the skilled required, education and training, and the salary expectations.
For the past few years, the job of data scientist has become increasingly relevant due to the growing popularity of Big Data. It is one of the most promising career paths in the IT sector.
Nature of the work
The Data Scientist’s job is based on 4 main missions:
Identify the needs and problems that the company entrusts to him/her (several possible areas: marketing, HR, customer loyalty, etc.)
Define a statistical model that will enable him/her to respond
Build the appropriate tools to collect the data
Collect and organize the data to exploit the results. The data can come from various sources.
Required skills of the Data Scientist
Ability to analyse and synthesise
The Data Scientist must be able to anticipate information needs and constantly seek new sources of information.
Technical skills
The mastery of certain technical skills is essential for the Data Scientist. Indeed, they must master NoSQL databases (MongoDB, Hadoop), R programming language, C programming with the Python language…They must also have a solid foundation in statistics as well as notions of machine learning, which can be a real asset.
Curiosity and open-mindedness
To work in this profession, you must also be able to detect the most interesting data. In addition, a passion for information processing and Big Data issues is obviously a plus.
Context
Two engineers from Facebook first used this term in 2008. Harvard Business Review voted Data Scientist as the “sexiest job of the 21st century”. As a result, in large companies, the job is divided into several sub-categories: the data miner (collects data), the data analyst (administers and creates databases), and the data scientist (interprets the data).
Thus, data scientists can be found in different fields, such as the commercial sector or security.
Salary
Depending on the company, Data Scientists work in several areas such as marketing, information systems or the finance department.
Their salary varies between 500 and 800 euros.
Data Scientist: Training and Education
In conclusion, to embark on a career as a data scientist, you need a minimum of 5 years of higher education, with a master’s degree in statistical analysis or computer programming. Many also have a doctorate (bac +8)
Use our template to create a compelling and comprehensive Data Protection Officier job description to attract top talent.
The Data Protection Officer job, better known as DPO, is to protect personal data and information. The DPO’s main task is to ensure that the use of data collected by the company complies with the regulatory framework.
As of 25 May 2018, following the implementation of the Data Protection Regulation (GDPR), many companies and administrations whose activities give rise to some form of personal data management, as well as public bodies, are obliged to appoint a DPO.
But, what are the main tasks of a Data Protection Officer?
Ensuring compliance with personal data legislation
Any company that collects or uses personal data must comply with the law. Whether that data is used internally (for recruitment purposes, for example); or externally for commercial purposes (for an email campaign, for example).
Finding alternative uses for data that comply with the law
In order for the company to be able to maintain its activities, while respecting the law concerning the collection and processing of personal data, it is important that the DPO be able to propose alternatives and adapted structures.
Ensuring compliance with the law within the company
In order to inform the various entities of the company of the challenges represented by the data protection law and the importance of compliance with it, the Data Protection Officer must be able to raise awareness and train the internal teams on this subject.
Required skills of the Data Protection Officer
Computer literacy and legal knowledge
The DPO must be familiar with the regulations on the protection of personal information, the laws on ICT, and the various methods and techniques for protecting this data. They must also have knowledge of cyber security.
Versatility
The DPO must be versatile in dealing with different subjects, whether legal or IT-related. Furthermore, his/her job is cross-functional within a company as he/she has to work with various and varied entities such as the marketing, sales, and HR departments, etc., as well as externally with the company’s partners and suppliers.
Context
As mentioned before, for more and more companies, it is now becoming necessary to have a DPO. On the one hand, since the law of 25 May 2018, in companies and administrations where the activity gives rise to any kind of management of personal data, as well as public bodies, it is mandatory to have a DPO. On the other hand, data has become a crucial element in companies. To ensure its security and legal use, companies are also increasingly calling for a DPO profile.
Salary
The DPO’s salary varies between 600-800€.
Data Protection Officer: Training and Education
To become a DPO there are training courses from BAC+3 to BAC+5. They can be undertaken in engineering schools or in university courses. As the profession is relatively new, training courses are not available everywhere, but here are some examples:
Paris II Panthéon Assas University Diploma in Data Protection Officer;
Master of Management in Data Protection from ISEP;
IESIA Master in Information and System Security;
DPO/CIL diploma from the University of Franche-Comté.
Use our template to create a compelling and comprehensive Big Data Engineer job description to attract top talent.
The Big Data engineer job consists of developing, maintaining, testing, and evaluating Big Data solutions. They create large-scale data processing systems. They are also experts in data warehousing solutions and database technologies.
Big Data, or the processing and analysis of massive data, has become a real phenomenon in our hyper-connected societies, where the volume of information exchanged is increasing exponentially. This has led to the emergence of a new high-tech profession: the Big Data engineer.
What kinds of assignments does a Big Data Engineer perform?
Adding value to a company’s data
To do this, the Big Data Engineer needs to analyze hundreds of millions of data using highly specialized software and classify the information collected according to the company’s needs and requirements.
Designing and implementing appropriate architecture and solutions
The Big Data Engineer also designs solutions for processing large volumes of data pipelines, which must be sufficiently secure and readable.
Implementing algorithms and technical tests, and monitoring the results
This professional must also test his/her designs and monitor the results. They must optimize the processing and revise the codes if necessary. Moreover, they must constantly update themselves on the technologies and languages is use.
Required skills of the Big Data Engineer
Excellent technical skills
The Big Data Engineer must have a good command of the technologies used by the company, and of digital data systems. He/she also needs to be proficient in technical English and advanced mathematics. In addition, development skills such as Java or Python are greatly appreciated.
Development Infrastructure skills
This professional must be familiar with frameworks such as Hadoop, Hive, Spark, Storm or Pig. He/she must also know how to use MongoDB or Cassandra tools.
Communication skills
These skills are invaluable for reporting. He/she must also be able to work in a team and often be flexible.
Context
As Big Data is a rapidly expanding sector, companies are increasingly looking for this type of profile. Among them, are all types of structures: startups or large groups in the finance, telecommunications, marketing sectors, etc.
Generally, this professional is integrated into the R&D department, the Data Science division, or within a dedicated Big Data department.
Salary
The average daily rate for a Big Data Engineer is between 500 and 800€.
Big Data Engineer: Training and Education
In conclusion, to become a Big Data Engineer, it is necessary to have a Bac +5 in Computer Engineering School with a Master’s degree in Big Data. It is also possible to qualify for this profession after a Doctorate (Bac +8) with a specialization in statistics.
After a few years of experience, the Big Data engineer can progress to the position of IT Director.
Database Administrator Job descriptionin this job description his missions, his skills, his training or his salary.
Use our template to create a compelling and comprehensive Database Administrator job description to attract top talent.
The job of the database administrator is to design, manage and administer database management systems and to ensure the consistency, quality, security, and ongoing accessibility of information.
Data Administrator: the job
The following are the steps a database administrator takes to perform his or her job.
After taking into account the client’s specific requirements, particularly concerning the size of the database, the database administrator sets up standards and good practices for the development teams.
In collaboration with the various project stakeholders, he/she defines the database implementation choices. Following this, the administrator defines the database parameters, the security rules, models, and designs the tables and keys.
Administration and maintenance
Once the database has been set up, the administrator must implement the data on the technical support. In terms of administration, this means guaranteeing the availability and quality of the data, administering access authorizations, and dealing with security issues. On the other hand, in terms of maintenance, this means ensuring that the data is updated, backed up, and upgraded. It is also the Database Administrator’s responsibility to guarantee the recovery of data and the restoration of conditions following an incident, as well as the correction of any bugs.
Technological monitoring and control of the database
The role of this professional is also to monitor the evolution of database versions and to carry out tests and validation of their management. He/she will also have to anticipate technical developments with a daily technology watch.
Required skills of the Database Administrator
Technical skills
The Database Administrator is familiar with the main software (Oracle, MySQL, SyBase, SQL Server, etc.), the SQL query language, and security issues. Knowledge of Shell scripts under UNIX, Windows or MVS as well as knowledge of technical English is also essential.
Understanding the environment
For this professional, an understanding of the environment, its development, and its operation is essential. Good knowledge of the activities and of the client enables him/her to anticipate the latter’s needs and also to intervene more effectively when necessary.
Reactive and methodical
Methodical and synthetic are the keywords of the database administrator. As with all freelancers, they are also expected to be open-minded and adaptable.
Context
Since the administrator evolves on different supports: mobile databases, shared databases or datawarehouses, the functions of network architect and database administrator are often confused.
The administrator is a real link between the project managers and engineers and the users of the database in order to better define the needs of each person and the company. The system administrator is required to work on call. Indeed, the systems operate 24 hours a day and many operations require action outside office hours.
As far as the hierarchical reporting line for the freelance database administrator is concerned, it is most often the mission director or technical director.
This Database Administrator position requires previous experience but will also allow you to progress. For example:
Database Architect Expert consultant in database optimization Storage manager or infrastructure manager
Head of a DBA team Chief data officer
Database administrator: Training and education
To conclude, the Database Administrator has a profile with high technical added value. In other words, they may have a background in development with a specialization in databases or a generalist background in systems and networks.
Level bac + 3 License pro specializing in database administration or distributed systems…
Bac + 5 level Master’s degree in databases and distributed applications, decisional computing… Engineering degree with a specialization in database engineering or operation…
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.
Need advice on how to start or develop your freelance consulting business in tech or IT? Need to start a new permanent or freelance assignment? Join Mindquest and get support from our team of experts.
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.
Keeping a constant watch
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…
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.
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.
Need advice on how to start or develop your freelance consulting business in tech or IT? Need to start a new permanent or freelance assignment? Join Mindquestand get support from our team of experts.
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.
Would you like to find out more about our recruitment service for IT consultants? Post your requirements now, or find out more about our job offers directly on our Mindquestplatform!
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.
