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?
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)
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.
The second part of our interview with Microsoft cloud solution architect Wesley Backelant, in which he talked about his role, what the job entails and what’s ahead for the cloud industry.
A Microsoft insider, Wesley Backelant is a Belgian cloud solutions architect who works together with the company’s customers to engineer and deploy impactful data and advanced analytics projects. Among other things, Wesley is an expert on the various components of the Azure AI platform. He is also a frequent speaker at numerous community events and regularly shares Azure news and tips.
Interview with Wesley Backelant: What part of being a cloud architect at Microsoft do you enjoy the most?
There are a couple of aspects that make it very interesting to me, personally. One of them, and that’s probably one of the benefits of being at a company like Microsoft, is leading the space in terms of innovation and cloud. You are at the forefront of technology, and that’s what I like. I like new stuff. Every time I get to learn something new, I see it as an incredible challenge, and I really love doing these things and getting my hands on new stuff and new technologies. That’s what drives me from a technological standpoint.
But the other thing that I really like about being in the data and BI space is that it puts you in a position where you are not just talking about technology, you’re also talking about business and societal outcome. One of our customers, for example, is in the public transportation space, and the fun part there is that, when you do something that’s innovative with them, you know that it´s going to have an impact on people and that it’s going to improve the experience of a hundred thousand or millions of people. To me, that’s one of the more rewarding parts of my job. Seeing a project succeed is fun, but seeing it drive real impact is the real deal.
At the technical level, what do you love most doing as part of your job?
Making it all work together. Azure is a continuously evolving platform. New things pop up all the time. Figuring out where all those things fit together and how you can make things more efficient and cost-effective is one of the role’s key drivers and, honestly, quite fun. Matching what we deliver as a technology to something that can actually provide value to the customer by leveraging existing building blocks.
Also, coding is not so much part of my job officially, but it’s still something I love to do, because it’s quite tangible. You get to see the result pretty immediately, and that’s a lot of fun.
I would be obliged to say Azure
SQL Database is my favourite tool, as SQL is my old love, but that wouldn’t be
entirely fair, especially, if you look at what we’re doing today with open
source. I’d like to split my answer between cloud and open source.
Containers and Kubernetes are
clearly changing the way people are building software these days. I talk a lot
with start-ups and partners, and pretty much all of them are building stuff
based on containers and mostly Kubernetes as an orchestrator. It’s impressive
to see what the impact of these tools has been in the relatively little time
that they’ve been around for. We are also seeing them as a big foundation for our
own services.
Then there’s open source. For example, something people sometimes forgets is that, whenever you have a service that has to run in production, you need proper monitoring and alerting. It’s not the most fun part of building a solution, but it’s one of the most crucial steps when you want to do something seriously. We have Azure Monitor, which is a great tool that I strongly recommend, but at the same time, I’m a huge fan of open-source solutions like Prometheus and Grafana. Luckily, they have integrations with Azure Monitor, so that makes them even more interesting. What amazes me of the open-source world is the community, and the power of the community, how the community sets the direction in a lot of ways. Even the big players follow the direction set by the community. That’s quite nice to see.
As a data expert, what’s the biggest issue you are seeing with data these days?
I think we all know the challenges from a more technical point of view, so data quality, governance, etc, to me things that between quotes we can easily solve with technology. But one of the biggest challenges I see with customers is being more data-driven. It sounds like a marketing term, but it really is not.
I really believe most companies are aware there’s a lot of value in the data they have, in applying machine learning or even good reporting. But getting into thinking what’s the difference I can make with this data, and do I have the proper environment and organisation in place to really benefit from it? That’s where I still see a lot of companies struggle. If I were a c-level person, that’s probably the question I would ask myself – are we organised to benefit from all the data and what it can bring to our company? Becoming data-driven is more than just saying you are data-driven. It also means having a proper organisational structure in place together with technology and tools.
Interview with microsoft Wesley Backelant: What’s next for cloud?
One of the things is that I still
see too much of is that there’s still a strong focus on infrastructure. I still
see a lot of IT departments that can tell you what server or specific IP
address is hosting an application or service.
Things can change, the infrastructure can scale in or out. It shouldn’t matter to you. I really believe that infrastructure part is one of the things will see disappearing over time. One of the best things about cloud is agility, and when people start really to adopt the whole serverless idea, and also DevOps as a philosophy, that’s the point where we’ll really see the whole power of cloud.
Secondly, at Microsoft we strongly believe in the intelligent cloud and edge. Bringing some part of that computing power, or at least how it works closer to where the data is, is definitely also an area where there is a lot of innovation happening these days. The big challenge is, obviously, making it all work seamlessly together. We are definitely doing progress there. But I still think there’s going to be a lot of new developments in this area still. And, thirdly, there is the abstraction of where things are running. If you look at tools like Azure Arc, basically, Kubernetes is having a big impact on that whole story. Being able to host your solutions be it on the public cloud, your own cloud or multiple clouds and having it all seamlessly work together from an operational standpoint and management point of view – that’s also where cloud is going.
The first part of the interview with Microsoft cloud solution architect Wesley Backelant to discuss his career trajectory and share some tips on how you can gain new skills and become a self-made IT pro like him.
A Microsoft insider, Wesley Backelant is a Belgian cloud solutions architect who works together with the company’s customers to engineer and deploy impactful data and advanced analytics projects. Among other things, Wesley is an expert on the various components of the Azure AI platform. He is also a frequent speaker at numerous community events and regularly shares Azure news and tips.
So, Wesley, tell us a bit about your professional
trajectory. How did you get started in your career?
I’m actually what they call a self-made man. I started studying ICT or IT, but I had been doing an internship at a company during high school, and that’s the company I got started in. They were looking for someone to help them and support them with software development and, because I worked there as an intern, they asked me to go work with them in my first year of university. They offered to teach me. There were some very senior people working there, who knew everything about development and so on. So, I left my studies and joined the company.
I decided to do
that the first year because, to be honest, the education system in Belgium at
the moment was not that strong at this level, so I was kind of frustrated. I
finished high school and I thought, okay, now I am going to study IT stuff, I
am going to learn how to program, and when I got there, there was a lot of
stuff that I had to do that was completely unrelated to IT. There was
accounting, economics, different languages. It was all super interesting, of
course, but I was there to learn how to develop, and even when we did that, it
was only old school stuff that we learnt, COBOL, and so on.
When I got that opportunity to get hands-on, I said, okay, let’s do this. It was a leap of faith too, but it turned out to be okay! A small company, so I was the IT department together with a few colleagues. We did a bit of everything.
Interview with Wesley Backelant from microsoft: What sort of career path eventually took you to become a cloud architect at Microsoft?
Well, sometime after that, I started getting interested in the database side of things, more than on development. I needed bigger challenges, so I got into outsourcing to have more variation, customers and technologies to get in touch with.
I then had the opportunity to become a database architect at a large enterprise in Belgium, and I took it because I knew they were hosting one of the most transaction-intensive SQL environments in Belgium at that moment. I thought it would be a great learning opportunity, which kind of proved to be true, because it put me on the next level of data, focusing on database design, performance tuning, and so on. They were also looking into some business intelligence solutions and, given my passion for data, I had been doing some reporting services work. We implemented together with a colleague the first reporting solution there, and that’s how I got into contact with some people at Microsoft. I figured that, with that experience, it was the right time to apply for a position at Microsoft, as they were looking for a pre-sales for their data and BI.
Back then I was not really a BI specialist, I was more focused on the database engine, but I figured, hey, new stuff to learn, fantastic, let’s take that leap. It was the same year I first saw the demo for what back then was called Red Dog, which is now known as Azure, at an internal event. Looking back, it was hard to imagine. It was the early, early days of Azure, and, seeing that demo, it was still hard to grasp what cloud would mean nowadays.
It was fun being there. Being at the birth of Azure, so to speak, put me in a nice position. I actually grew up and worked together with Azure at Microsoft, meaning that I was blessed to be able to go through this whole cycle of innovation and new services coming up. You know, looking back at something like SQL Server 6.5 and what keeps me awake at night today, it’s really amazing. We are so far from the traditional idea of the database now.
What educational resources do you turn to as someone who
is constantly learning new things?
Well, at Microsoft there’s a huge focus on learning these days. It’s one of the key priorities to make sure everyone is up-to-speed with technology. Especially with our roles as cloud architects, we need to be ahead of the curve in many ways. But it’s a broader concept, of course. Everyone at Microsoft needs to at least have some part of technical knowledge, and there’s a strong push for that. Most of that is coming from Microsoft learn these days, which is also open for external people, so I really would encourage people to look at it, as there is great content on the platform.
Additionally, I
like to look at blogs, from the official Microsoft blogs to the heroes in the
industry. For me that’s actually the queue, when I see something on a blog
post, I say, okay, let’s see how can I learn this, how can I get my hands dirty
with this.
Is that what you tend to favour, a more practical
approach to education?
What I’ve noticed works best for me — I have these “production-like” pet projects. It could be something you do at home, but where you try to have a scenario in mind. Playing with technology is one thing, but you only start to learn when you have requirements, I think.
At first, I had zero knowledge of Linux, for example. I saw it was going to become important. Since I had a server at home running Windows, I decided to make it a Linux server – that was before cloud, to be clear – so I said, okay, let’s use Linux, let’s start building RAID arrays, save some pictures on a central server, etc. I broke the server several times. Luckily, I had backups, thanks to experience. I think I had to re-install it like 3 times because I did something wrong with permissions and partitions in Linux, but that’s learning.
The point is that you need something that has real requirements so that you challenge yourself to try new things. And for me that’s what works best, getting your hands dirty with technology. It, of course, starts by reading and learning the basics. But then you need to start pushing yourself.
