Big Data, Business Intelligence and Data Science… turning data into valuable information

Big Data, Business Intelligence and Data Science


It is clear that in a world where digital transformation is here to stay, every organization must have the ability to collect, select, organize, analyze and interpret a large volume of data. In this sense, the proper management and knowledge of data are key and strategic in any business sector. For this reason, more and more companies are deciding to invest in data analytics solutions based on the premise that information is power (and that data is the currency of the digital economy).

In the current context, there are a number of methodologies that seek in some way to facilitate data-driven decision making. Companies that take on the challenge of knowing and interpreting their data benefit in a variety of ways, either by saving costs, gaining speed, improving customer service, anticipating the competition, improving the operational management of the business, and identifying new business opportunities. In short, obtaining these benefits will depend on the right choice of the most suitable data analysis processes for the company. 

Concepts such as Big Data, Business Intelligence and Data Science have in common the intention of extracting value from information, although they do so in a different and complementary way. 

  • Big Data refers to the storage of large volumes of data and the procedures used to find repetitive patterns within this data. Big Data focuses on the capture and processing of data, working with a large amount of complex data (structured and unstructured) coming from various sources, such as sensors, smart devices, websites and social networks, among others. The amount and complexity of this data makes it difficult to analyze and manage if the appropriate tools are not used. 
  • Business Intelligence is responsible for data management, data organization and production of information from data. It is applied in organizations to fundamentally improve decision-making capabilities by performing data mining tasks, analyzing business information, and generating reports. Business intelligence is predominantly used for the analysis of stored historical data, impacting business performance, but without being able to predict future data. In this sense, it is oriented to the past, studying the historical evolution of the company to understand its development by finding analytical patterns. Business intelligence is the set of applications, methodologies and technologies capable of transforming data into valuable and structured information to be used for business purposes. It focuses specifically on internal company and industry data. Some examples of data analyzed through Business Intelligence are those related to marketing, customer service, sales, or data from the company’s human resources. It is precisely through Business Intelligence that the analysis of data obtained from Big Data can be carried out. 
  • Data Science could be considered as an evolution of Business Intelligence. Its objective is the generation of value from the collection, classification, visualization and corresponding interpretation of data. This more complex data analysis helps the company to generate new knowledge by discovering and answering new questions. To do so, it uses a range of techniques involving statistics, computer science, predictive analytics, and machine learning. In this way, Data Science makes it possible to analyze massive data sets, seeking solutions to problems that have not yet been thought of. The data being analyzed are both internal and external, for example, videos, emails and social media content. Data Science experts can predict potential trends by exploring seemingly unconnected data sources, finding better ways to analyze the information. 

This type of solutions based on data analysis to make better business decisions, are no longer seen as tools intended only for large companies, but are increasingly SMEs interested in these technologies and methodologies, working in an integrated way to get the most out of the growing volume of data.

At Macrotest we have the #DataLab division, seeking to help companies through comprehensive solutions for data management, data analysis and implementation of artificial intelligence for prediction and personalization of services.

We are at your disposal to solve all your data analysis needs!

It works on my notebook! Theory versus practice in Data Science

It works on my notebook! Theory versus practice in Data Science


When you start on the road to a
Data Driven company, you begin to understand how to use the tools that the current market offers such as artificial intelligence or Internet of Things. You hire data scientists who can solve your business problems without first asking yourself WHAT you need to do artificial intelligence and HOW you plan to do it.

The answer to the first question is simple, we need data. Now, the situation starts to get complicated when we try to answer our second question.

Everything seems perfect, but where do I have this data, what format does it take, is it easy to access, how often can I access it, is it complete, without errors, without null records, how long have I had this data, how long have I had this data? And assuming all this is solved, how easy is it to develop a Machine Learning model and implement it?

There are many questions, but there are also many answers depending on the problem to be solved.

When we talk about Data Science, we are not talking about a tool, skill or method, but more like a scientific approach that uses statistical theory, applied mathematics and computer tools to process large amounts of data. Data science is a detailed process that mainly involves preprocessing, analysis, visualization and prediction.

We all know that Data Science is a very powerful scientific approach, with all kinds of interesting applications. However, it is also well known that in Data Science there is a big gap between theory and practice: when it comes to theory, we know everything, but we don’t know how to apply it in real life.

For this reason it is important to prioritize when working with data. This list may change depending on the company, but most of them agree on many of these points.

Step 1 – Define the business problem

This first step is fundamental, and requires much more of the human factor for the understanding of the problem to be solved, the agreement of criteria for the definition of the objectives, scope and timeframe, than of the system itself that will be used as a means to reach them.

Surely the data scientist has many ways to solve a problem, but the one who must set the course of the solution must be the one who knows the business. Interaction and teamwork are essential.

Step 2 – Data acquisition

This step is perhaps where we find the biggest difference between theory and practice. In theory, when we want to make a machine learning model, all we need to do is download a dataset from sites such as Kaggle or Github and we will have clear, neat and well described information. In practice, sometimes the sources can be:

  1. Very varied: which would take a previous work of ETL’s, modeling, etc.
  2. Poorly described or without description: Without having a clear description of what variable we are working with we do not know what we have and if it can help us to solve our problem.
  3. With erroneous data / null records: As it is often said in Data Science, Garbage in / Garbage out.
  4. Unknown: In a sectorized company where the data are within the area that worked with them, the opportunity to combine them or use them for other business purposes may be lost.
  5. With restricted access: Depending on the data security standards within the company, accessing data often becomes a titanic task and involves a bureaucratic process that is difficult to measure over time.

These and many problems with data sources can be solved with proper data governance and fundamentally a very well communicated organization.

Step 3 – Data preparation

This step involves data cleansing and data transformation, data cleansing is the most time consuming as it involves handling many complex scenarios such as inconsistent data types, misspelled attributes, missing values and duplicates. Then, in data transformation, we have to modify the data based on the defined mapping rules.

Step 4 – Exploratory Data Analysis

With the help of Exploratory Data Analysis we define and refine the selection of variables to be used for the development of our model. It is important to always keep in mind the solution we want to target.

Step 5 – Data modeling

The main activity of a data science project is known as data modeling. In this step, we repeatedly apply machine learning techniques of type strength such as KNN, decision trees, Naive Bayes, etc. to the data so that we can identify the model that best fits the business requirement. We train the model on the training dataset and test it to select the best performing model.

Step 6 – Visualization and communication

This point is perhaps the most relevant of all because we can have the best data extraction and transformation process, the best trained Machine Learning model, but if we do not know how to visualize it, explain it, communicate it and give value to the business, all the previous work will not matter much. It is essential to reinforce soft skills at this point to know how to reach stakeholders.

Step 7 – Implementation and maintenance

And finally, in this step, the data scientist implements and maintains the model, tests the selected model in a pre-production environment before implementing it in the production environment, which is the best practice. After implementing it, we have to get real-time analytics and monitor and maintain the performance of the project.

As you will see, there is a huge difference between what we study (Theory) that practically starts and ends in a local Notebook versus what is needed to carry out the whole process in real life (Practice). It is for this reason that it is often overwhelming and sometimes frustrating to try to work with data and generate results.

That is why Macrotest #DataLab helps you all the way with our end-to-end solution so that you have a complete understanding of the tools, methodologies and processes.

What does agile cell work consist of?

Working with agile cells

 

Nowadays, the development of projects through the organization in agile cells is widely spread. As its name suggests, this form of work organization seeks to resemble the behavior of a living cell, in constant change and dynamism. This model is in a way in opposition to the traditional model based on hierarchies, which no longer works successfully in a highly changing environment and with increasingly demanding end customers.

Following this living cell model, in an agile work cell each team member retains his or her autonomy but is in constant interrelation with the other members. In this sense, while each member has a very clear role, this role is complemented by the roles and tasks of the others.

This type of work cells are generally composed of a small number of interdisciplinary professionals who share certain characteristics. Among them, proactivity and creativity stand out, considering that these cells work in a self-managed manner and are free to find solutions in the process of responding to a specific need. It is also key to build good interpersonal relationships, seeking to make the work work work as a whole. Likewise, collaborative work is the essence of this type of cells, where each member contributes to the achievement of the objectives with a focus on the needs of the client, who is always in contact with the project. 

In short, it is about generating an environment of innovation and total openness, pursuing the ultimate goal that the whole is more than the sum of its parts. This way of working seeks to speed up software development times, recognizing the importance of human resources even above technologies.

When is it recommended to outsource by hiring an agile development team?

Agile cells are very useful when knowledge and experience are needed. There are different ways of working according to each project, and the cell can be managed by the client itself, by the company that provides the technology professionals or even jointly. Agile cells are also very useful when looking for an efficient end-to-end development, since they seek to minimize deviations from the initial estimate. 

Outsourcing by hiring agile cells of IT professionals, allows the client to focus on the core of their business, reducing the technical barriers of new projects, investing only in what is needed according to the different types of profiles required. 

In Macrotest we have work cells available for your projects, integrated by multidisciplinary and modular specialized teams that are integrated into the structure of your organizations to develop the technological solutions you require. If you want to know more about our agile cells service, please contact us and we will gladly provide you with all the necessary information. At your disposal you will find all our experience, highly qualified resources, the highest quality standards and our framework that has been evolving for years. All this expertise is at the service of our clients, seeking to optimize their business processes and ensure quality and efficiency in each of the projects entrusted to us.

In the next article we will tell you about a particular type of agile cell, which are the mentored cells. We look forward to sharing this next note with you!

 

What is cloud computing?

Cloud computing

Cloud computing is a technology that allows remote access to software, file storage and data processing using the Internet, which is an excellent alternative to running on a personal computer or local server.

Beyond its definition, what is really important is that cloud computing has radically transformed business models and business strategies, making it possible to accelerate innovation processes and reduce costs, not only for large companies but also for small and medium-sized enterprises.

Cloud computing provides companies with greater flexibility in relation to their data, being able to access it from anywhere and at any time, which is ideal not only for companies with headquarters in different locations around the world, but also to facilitate collaborative work between individuals in the same location.

In short, cloud computing is an intelligent way to take advantage of the benefits of the hyperconnection provided by the Internet, helping to eliminate hardware maintenance costs and software upgrades, and allowing the integration of all company information in a single place.

And what does it bring to companies?

The main advantage is that it makes it possible to have all the company’s information updated in real time. In this sense, the cloud allows control of the data, being able to choose which users have access to what type of data and with what level of permissions. Another great advantage is the virtually unlimited storage capacity, which frees up the computer and improves its performance.

It also helps to generate a much more fluid communication between departments, avoiding errors, duplications, inefficiencies and wasted time. Especially if employees work remotely or in different departments, cloud computing favors collaborative work, allowing documents and information to be shared in real time. Another of its great advantages is especially valued in unforeseen situations of data loss, since data recovery is one of the key aspects. Through cloud computing, data will always be available, even if we delete it by mistake. In addition, it is a multiplatform technology, which means that it can be accessed from a laptop, tablet, smartphone or computer just by having access to the Internet.

In short, cloud computing provides businesses of any size, the ability to access a set of applications designed to manage all information related to different business processes remotely and integrated.

In Macrotest we have experience in the cloud, mainly Azure and Aws, with all our implementations we accompany the progressive implementation of this type of technological strategies, to improve and add value in the different business processes. Contact us!

Our 2021 in Macrotest

Our 2021

2021 has been an extremely active year for Macrotest… do you want to know why?

In these lines we tell you about some of the activities we have been involved in!

First of all, we have continued to develop products together with other partner companies, such as Dimensiona. As a result of these developments we have launched different applications, from incident tracking for companies and public administrations, to wine tastings, auctions, easycommerce for e-commerce, Dex and C-Control for the implementation of remote monitoring centers.

For the management of these new projects we have expanded our team and added great professionals, with commercial profile, business development and institutional communication. Thanks to these new additions our team continues to grow and add value day by day, adding different perspectives to a multidisciplinary team that always thinks about our clients and the development of new opportunities.

We have also been present in a large number of events related to R&D and technology, with the active participation of our R&D&I director. At the Inter-American Development Bank (IDB) and Talentia Summit, he presented a webinar on Tools to develop an R&D project, while in his role as mentor he participated in Prendete, a contest of innovative business initiatives that seek to enhance the development and strengthening of the skills and capabilities of entrepreneurs, in addition to facilitating access to new networks and tools to increase their competitiveness.

Other networking and matchmaking events with potential clients and partners from different parts of the world have been Digital Enterprise Show, Global Innovation Summit, Women at the Forefront of the Economy, Digital America, TDD4Future, Smart City Expo World Congress, to mention just a few. This participation has allowed us to arrange a large number of meetings that have provided us with valuable contacts and opportunities to work together in the future.

In addition, we have published a variety of articles for our blog on current issues related to technology, such as digital transformation in sectors such as health or agriculture, notes on work methodologies such as scrum and project management, others on business such as e-business and R&D partners, and technologies such as cloud computing, blockchain, and artificial intelligence. The development of these notes has sought to inform about the latest technological trends and their benefits for our current and potential clients.

Other events in which we have participated during this 2021 are those linked to financing programs for business projects of Research and Technological Development and joint actions with other countries. Among them can be mentioned “CooperAR-EU 2021”, “Horizon Europe: Opportunities of the new European Union Framework Program”, “Marie Skłodowska-Curie Actions Staff Exchange”, “Argentina – Spain Bilateral Call”, “Industry 4.0”, “International Business Technology Collaboration Programs”. All these events have allowed us to participate in an active innovation ecosystem not only in Spain and Europe but also in relation to other continents.

We thank all our team, our customers and partners, for being part of Macrotest in some way and for growing together during this year. We hope that next year will find us with many new projects to work together!

Digital transformation… also in the healthcare sector

Transformación digital del sector salud

 

Digital transformation is a recurring topic today, where all sectors and social areas are impacted to a greater or lesser extent by this transformative wave, even without the actors themselves being aware of the depth of the changes it generates. In this sense, it is important to understand the real dimension of the concept of digital transformation, which implies a continuous and complex process affecting the very essence of what we are and what we do.  

The healthcare sector is also experiencing this digitalization phenomenon with changes that aim to improve the quality and efficiency of medical care by obtaining information and its appropriate use in order to optimize decision-making at all levels of the healthcare system. A clear example is the digital acceleration that the healthcare field has experienced due to the coronavirus pandemic. Thus, digitalization in the healthcare sector is not only about helping patients with their illnesses or doctors with their tasks, but also about speeding up workflows, providing more security for medical staff and hospitals, and offering more transparency to patients. Thus, creating a connected healthcare ecosystem, where interactions are smarter, faster and more accurate between people, devices, data, analytics and applications that are transforming the way healthcare is delivered.

What are the main advantages of digital transformation in the healthcare sector?

  • It optimizes the work of medical professionals by offering more accurate results and diagnoses that contribute to providing more appropriate comprehensive treatments.
  • It speeds up times by reducing the number of medical appointments, allowing check-ups and consultations to be made from home.
  • Enables the creation of a unified medical record that is continuously updated across devices 
  • Performs remote patient monitoring and virtual training to reduce costs and complications of chronic illnesses 
  • Allows the exchange of information in real time between doctor-patient, making a continuous follow-up from prevention to diagnosis and treatment, significantly reducing medical errors. 

What tools and technologies are needed?

The digital transformation in the healthcare sector is being driven mainly by the Internet of Things (IoT), which makes it possible to connect sensors, devices, software and intelligent networks via the Internet. In this sense, wearable clinical devices have become a trend in the sector in recent years and are being used especially to observe high-risk patients.

Macrotest’s mission is to contribute to the healthcare sector by providing products and solutions that allow us to continue improving patient care and the quality of medical services. In this sense, we have developed Rehabictus, a support platform that makes it easier for stroke patients to carry out their rehabilitation from home. Rehabictus is a technological development composed of an APP, a web platform and electronic devices based on artificial intelligence that monitor the movement of patients. The mobile application receives data from the device, which has functionalities such as exercises with personalized levels and visualization of achievements, and from the web platform, the medical professional can access the patient’s data, establish the frequency of exercise, note observations, verify the graphs of their evolution taking into account different parameters, details of exercises and achievements. 

In this way, with this development, Macrotest seeks to provide an accurate follow-up of patients’ rehabilitation and ensure greater success in their recovery. Contact us for more information on how to digitize your healthcare organization.