May 6, 2022
We are currently living in the best time in the history of humanity, at least in terms of the power of creativity and knowledge flow towards the concepts of Industry 4.0 and Web3.0 (what is Web 3.0?). Jobs, wages, hours, and benefits are far superior in the twenty-first century. Despite the pandemic, job projections show that everyone will have a different future with new trends.
One of the characteristics of this era is digitization, which applies to people and organizations alike. This trend is responsible for all the changes and innovations in our daily routines. Thanks to technology, our life (physically speaking) is much simpler. According to the Wall Street Journal, 50 percent of work activities done by humans can have a robotic replacement using the current technology.
However, this will not be a problem for job seekers for the next few years. The paragraphs below will talk about the four most important facts that will mark the future of jobs in this decade.
Remote Work and Hybrid Schedules
The pandemic made digitization a necessity for businesses, schools, and governments. The home office will be a common activity for the next few years, especially in digital marketing, health care, software development, and financial consulting. This trend also means hybrid schedules, which simultaneously save resources for companies and employees.
These schedules consist of working hours from home through digital devices such as computers or smartphones. Some companies plan to apply this modality one or two days per week, while others hire workers to cover 100 percent of their time remotely. At Aquarela Analytics for instance 100% of the total workforce operates remotely and only 40% lives nearby the headquarters.
Robotic Growth
Manufacturing and transportation companies will drastically change their production over the next ten years. The growth of robotic technology will be a historic victory in the factories of developed countries (and in the rest of the nations, eventually). You may see the negative side of this story, as thousands of manual jobs are lost, but this trend will also be favorable for many more people.
Companies will invest in personnel to maintain, manage, and repair these mechanical systems. In addition, patent companies will invest millions in hiring robotics engineers to develop these machines. On the other hand, we see the demand for other specialized occupations such as data science, cloud computing, and software design in an ever changing technological stack.
Training Centers Within Companies
Employers know the impact of technology and new trends in the job market. A trained team means better results, efficient production, and more profit for the company. The training centers are technical and cultural training strategies. For this reason, it is common to see companies encouraging their employees to attend a coding bootcamp to learn about advanced technology and current trends.
Competition is the main reason employers want updated staff. This is the rise of the self-taught era. If you want to know which job offer is right for you, evaluate the training programs, educational resources, and technical training they offer. Your level of preparation helps you become a more efficient professional prepared for any challenge.
AI for forecasts and dynamic pricing
The future may not be more predictable than it used to be, but the number of attempts to crack it will continue to grow as AI mines ever growing datasets to generate ever more complex combinations. This is at least a “predictable” prediction of the 4.0 industrialization process.
The ability to understand the new workflows of Industry 4.0 will have a high impact on the job market. Working to teach a computer has a significant impact on the ability to look at the data wearing a scientific glass to avoid any types of data bias.
Four Facts that will change the Job Market after industry 4.0 kicks in – Conclusion
In the paragraphs above, we saw the strong impact of technology on each of the trends for the job market in this century or at least this next decade. This field can be a great ally for you, so it is important to consider our own capabilities and interest in joining tech careers. Consider the facts, analyze the patterns of change in the labor market, and prepare for the challenges that lie ahead in the next ten years.
What is Aquarela Advanced Analytics?
Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.
Stay tuned following Aquarela’s Linkedin!
Author
Syed Ibrahim Imran, from Karachi, Pakistan, started working with Career Karma in July 2020. He is currently enrolled at Iqra University as a BBA student. He has contributed content to some of the best software and IT companies in Pakistan. He hopes to start his own digital marketing agency or real estate firm before he turns 30 years old.
Co-author
Founder – Commercial Director, Msc. Business Information Technology at University of Twente – The Netherlands. Lecturer in the area of Data Science, Data governance and business development for industry 4.0. Responsible for large projects in key industry players in Brazil in the areas of Energy, Telecom, Logistics and Food.
Jan 26, 2022
The food sector and food security are a global concern and Brazil is one of the main countries responsible for the world demand for food (Estadão). In this sense, what are the main challenges related to data management to optimize Brazil’s operational efficiency in the food/agribusiness sector, which today represents 21% of Brazil’s GDP?
This article addresses the issue with the bias of Aquarela’s experience in Advanced Analytics and Artificial Intelligence projects carried out in large operations in Brazil. The risk of a lack of information is as relevant as its excess and lack of analysis, which can impact the efficiency of the sector’s logistics chain as a whole.
Below, we have elaborated on some of these main risks.
Characterization of the food sector
The food sector is quite varied due to the large extension of the production chain, which ranges from agricultural inputs, industrialization, transport logistics to commercialization in consumer markets and finally the final consumer.
As fundamental characteristics, the food sector is directly linked to factors that can have great variability and little control, such as:
- Climate (temperature, water volume, luminosity and others);
- Economic factors such as currency fluctuations;
- Infrastructure;
- Domestic/external market demand.
In addition to these factors, below we list some related to data management. We also show how they, if well organized, can help mitigate the effects of uncontrollable variables in the food supply chain.
The supply chain is quite large. This makes the data complex and difficult to interpret due to the different phases of each process, culture and region. In addition, it causes many important planning decisions to take place with very limited information and high risk. In other words, decisions are made without a vision of the complete scenario of the chain, largely following the manager’s intuition.
The lack of quality information is a big risk. If data is lacking today, imagine what the scenario was like 10 or 20 years ago.
In recent years, the industry and retail have shown great advances in their computerization processes with various traceability solutions. With the evolution of Industry 4.0 technologies (IOT and 5G) in the coming years, it is likely that the food market, from the agricultural and industrial sector to the commercial sector, will hold more complete information for decision making than what is currently available today.
02 – Data from multiple sources
If data is becoming more and more present with the development of informatization and communication, then the next problem is trying to analyze data from multiple and disconnected sources.
Different data is often stored on different systems, thus leading to incomplete or inaccurate analyses. Combining data manually to form datasets (what are datasets?) for analysis is quite heavy and time-consuming work and can limit insights into the reality of operations.
What is sought is the construction of Data Lakes adherent to the type of management to democratize access to data by market professionals, thus optimizing their activities with increasingly powerful analytics solutions. This not only frees up time spent accessing multiple sources, it also allows for cross-comparisons and ensures that the data is complete.
03 – Low quality data
Having incorrect data can be just as or more harmful than not having it. Nothing is more harmful to data analysis than inaccurate data, especially if the idea is to use data science and machine learning practices. Without a good input, the output will be unreliable.
One of the main causes of inaccurate data is manual errors made during data entry, especially when information is collected manually. Another problem is asymmetric data: when information from one system does not reflect changes made to another system and thus makes it out of date.
Analytics strategic planning projects seek to mitigate and/or eliminate these problems. This happens from systematic processes of data dictionarization, survey of processes, functions, and so on.
04 – Lack of data talents
Some organizations and companies, in general, are not able to achieve better levels of efficiency in operations, as they suffer from a lack of talent in the area of data analysis. In other words, even if the company has consistent technologies and data, the manpower to execute the analysis and action plans still counts a lot at the end of the day.
This challenge can be mitigated in three ways:
- Develop an analytical technology stack that is always up-to-date and adherent to the business and with up-to-date training materials.
- Add analytical skills to the hiring process. In addition, invest in the constant training of the team on new data technologies related to the technological stack of the operation.
- Use analytics outsourcing to accelerate the process. In this article, for example, we list the main aspects to be considered when choosing a good supplier.
05 – Customization of values and product characteristics in the food sector
Although, according to Embrapa, about 75% of the entire world food sector is based on just 12 types of plants and 5 types of animals, there are thousands of different products, marketed in multiple ways, prices and deadlines in the final consumer market.
Just as an example, in the area of animal protein, the process of marketing cattle meat requires investments, infrastructure, deadlines and processes that are quite different from what would be for the production of pork or even chicken.
Since the processes are different, the data generated by the production chain also becomes different, requiring customizations in information systems and databases. As a consequence, there are changes in models of:
The recommendation is to parameterize the systems based on the most common classifications in the market and focus on the most important products from a strategic point of view (contribution margin, volume or sales price).
5 real data challenges in the food sector – Final thoughts
In this article, we have collected some relevant points about the real challenges of data in the area of food, a sector in which Brazil stands out as one of the main global players.
It is a complex area with several risk factors and great opportunities for optimization with the increasingly intensive use of data. Previously, we wrote an article related to data strategies for energy trading and which in part has the same challenges related to decision making in the food sector.
We, at Aquarela Analytics, constantly work with these challenges of making the complex things simple and with good risk mitigation. So if you have any questions, get in touch with us!
What is Aquarela Advanced Analytics?
Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.
Stay tuned following Aquarela’s Linkedin!
Author
Founder – Commercial Director, Msc. Business Information Technology at University of Twente – The Netherlands. Lecturer in the area of Data Science, Data governance and business development for industry 4.0. Responsible for large projects in key industry players in Brazil in the areas of Energy, Telecom, Logistics and Food.
Nov 24, 2021
The benefits and positive impacts of the use of data and, above all, artificial intelligence are already a reality in the Brazilian Industry. These benefits are most evident in areas ranging from dynamic pricing in education, forecasting missed medical appointments, predicting equipment breakdowns, and even monitoring the auto parts replacement market. However, to achieve these benefits, organizations need to reach a level of analytical maturity that is adequate for every challenge they face.
In this article, we are going to discuss the concepts of AI and Analytics Strategic Planning and also look at which characteristics of the scenarios demand this type of project within the Digital Transformation journey of companies towards Industry 4.0.
What is AI and Analytics strategic planning?
The AI and Data Analytics Strategic Planning is a structuring project that combines a set of elaborate consultative activities (preferably by teams with an external view of the organization) for the survey of scenarios, mapping of analytical processes, elaboration of digital assets (systems, databases, and others) to assess the different levels of analytical maturity of teams, departments and the organization as a whole.
As a result, shared definitions of the vision, mission, values, policies, strategies, action plans, and good data governance practices are accomplished to leverage the organization’s analytical maturity level in the least possible time and cost.
Symptoms of low analytic maturity scenarios
Although there are many types of businesses, products, and services on the market, here we present emerging patterns that help to characterize the problem of companies analytical maturity and can generate interesting reflections:
- Is it currently possible to know which analytics initiatives (data analytics) have already taken place and are taking place? Who is responsible? And what were the results?
- In analytics initiatives, is it possible to know what data was used and even reproduce the same analysis?
- Does data analysis happen randomly, spontaneously, and isolated in departments?
- Is it possible to view all data assets or datasets available to generate analytics?
- Are there situations in which the same indicator appears with different values depending on the department in which the analysis is carried out?
- Are there defined analytic data dictionaries?
- What is the analytical technology stack?
- Are data analytics structuring projects being considered in strategic planning?
Other common problems
Organizational identity
Scenarios with low analytic maturity do not have data quality problems in isolation. There are usually systemic problems that involve the complexity of business processes, the level of training of teams, knowledge management processes, and finally, the choice of technologies for operating ERP, CRM, SCM and how these transactional systems are related.
Security Issues
Companies are living organisms that constantly evolve with people working in different areas. Thus, over time, control of the access levels of each employee is lost, causing unauthorized people to have access to sensitive information and also the opposite when people cannot access the data they need for their work.
Excessive use of spreadsheets and duplicates
Spreadsheets are one of the most useful and important management tools and for that reason, they are always helping in various processes. The big side effect of excessive use of spreadsheets is the maintenance of knowledge of each process. When there are two or more people and the volume of information and updates starts to grow, it becomes difficult to manage the knowledge that travels in blocks with spreadsheets. Additionally, many duplications occur and make it virtually impossible to securely consolidate data in large volumes.
What are the benefits of AI and Analytics strategic planning?
Data-driven management is expected to provide not just drawings and sketches of operations or market conditions, but a high-resolution photograph of present and future reality. Thus, it provides subsidies for corporate strategic planning in the short, medium, and long term with the following gains:
- Procedural and technological readiness for data lakes projects and Advanced Analytics and AI labs.
- Increased intensity of application of scientific techniques to businesses, such as comparative analysis, scenario simulations, identification of behavior patterns, demand forecasting, and others.
- Increased accuracy of information.
- Security of access to information at different levels.
- Acceleration of the onboarding processes (entry of new team members) who in turn learn more quickly the work scenario and also begin to communicate more efficiently.
- Greater data enrichment from increased interaction of teams from different sectors for analytical challenges.
- Increased visibility into analytics operations, Organization for localizability, accessibility, interoperability, and reuse of digital assets.
- Optimized plan of change for data-driven Corporate Governance.
- Incorporation of Analytical and AI mindset in different sectors.
- Homogenization of data policies and controls.
AI and Analytics strategic planning – Conclusions and recommendations
The preparation of strategic AI and Analytics planning is an important step to reach the level of data governance that allows the intensive use of analytics and artificial intelligence in operations since the high failure rate of analytical projects is linked to low quality of data, processes, and even the correct use of technologies (training).
Structuring projects, such as AI strategic planning and Analytics are, or at least should be, the first step in the journey of digital transformation of traditional companies. Therefore, we are convinced that in the future every successful company will have a clear and shared idea (vision, mission, and values) of what data means to them and their business model, in contrast to investments in data technology purely and simply because of the competition.
We believe that the focus on orchestrated (tidy and synchronized) data will be reflected in almost every area, for example: in the range of services, in revenue models, in key resources, processes, cost structures, in your corporate culture, in your focus on clients and networks, and in its corporate strategy.
Last but not least, it is worth pointing out that, for a successful structuring to happen, a long-term holistic approach must be taken. This means investments in optimized technology, people, and processes to enable continued business growth.
How Aquarela has been acting
Developing new technologies and new data-driven business models in a vision that the amount and availability of more data will continue to grow, taking the business to new heights of optimization.
What we do specifically for companies:
- We analyze data-generating enterprise ecosystems.
- We determine analytic maturity and derive action fields for data-driven organizations and services.
- We develop and evaluate data-based services.
- We identify and estimate the data’s potential for future business models.
- We design science-based digital transformation processes and guide their organizational integration.
For more information – Click here.
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What is Aquarela Advanced Analytics?
Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.
Stay tuned following Aquarela’s Linkedin!
Authors
Founder – Commercial Director, Msc. Business Information Technology at University of Twente – The Netherlands. Lecturer in the area of Data Science, Data governance and business development for industry 4.0. Responsible for large projects in key industry players in Brazil in the areas of Energy, Telecom, Logistics and Food.
Business and Project Manager at Aquarela Analytics. Postgraduate in Project Management (MBA), Bachelor in Business Administration, specializing in Systems, Business Process Modeling (BPM) and Project Management.
Master in Production Engineering with a degree in Transport Engineering and Logistics. During his master’s degree, he deepened in the areas of macrologistics and regional economics, and developed research in the areas of reverse logistics, relocation of production chains, logistics outsourcing and operational research.
Oct 13, 2021
Do you know the Aftermarket market segment of the auto parts industry? This term refers to the automotive aftermarket segment, which supports the continuity of operations of approximately 42.6 million vehicles (motorcycles, cars, trucks and even agricultural machinery) in Brazil. The turnover of this industrial segment ranges between 75 and 85 billion reais per year (data by Issuu).
The automotive aftermarket process involves a large amount of data on a large number of parts (SKUs) produced and sold over decades. This usually makes it difficult to identify market share and new business opportunities. But, how to overcome this challenge?
Market share case on the auto replacement aftermarket
To answer this question, we prepared a material presenting our success story in the automotive sector in the Aftermarket segment, in which we show how advanced analytics and artificial intelligence strategies can result in great benefits to the commercial operation.
The case study addresses our client’s business problem, immersed in several challenges, such as the difficulty in understanding the sales behavior of some part groups; the journey, marked by the development of a system capable of presenting the evolution of the organization’s market share; and the results generated for our client.
Our goal is to assist marketing managers, commercial managers and administrators who work in large-scale operations.
Automotive Aftermarket – Conclusion
Identifying market share and new business opportunities in the auto parts sector is a challenge, but that can be overcome through tools like AI and Advanced Analytics.
However, its implementation process is complex, demanding artificial intelligence as well as qualified, market-recognized data analytics providers.
Also read – How to choose the best AI and Data Analytics provider?
Do you have any doubts about our success case in the automotive aftermarket? So leave your comment.
What is Aquarela Advanced Analytics?
Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.
Stay tuned following Aquarela’s Linkedin!
Authors
Founder – Commercial Director, Msc. Business Information Technology at University of Twente – The Netherlands. Lecturer in the area of Data Science, Data governance and business development for industry 4.0. Responsible for large projects in key industry players in Brazil in the areas of Energy, Telecom, Logistics and Food.
Head of Marketing at Aquarela Analytics. Advertising and specialist in Strategic Marketing.
Mar 13, 2021
Choosing a artificial intelligence provider for analytics projects, dynamic pricing, demand forecasting is, without a doubt, a process that should be on the table of every manager in the industry. Therefore, in case you are considering to speed up the process, an exit and the hiring of companies specialized in the subject.
A successful implementation of analytics is, to a large extent, a result of a well-balanced partnership between the internal teams and the teams a analytics service provider, so this is an important decision. Herein, we will cover some of key concerns.
Assessing the AI provider based on competencies and scale
First, you must evaluate your options based on the skills of the analytics provider. Below we bring some for criteria:
- Consistent working method in line with your organization’s needs and size.
- Individual skills of team members and way of working.
- Experience within your industry, as opposed to the standard market offerings.
- Experience in the segment of your business.
- Commercial maturity of solutions such as the analytics platform.
- Market reference and ability to scale teams.
- Ability to integrate external data to generate insights you can’t have internally.
Whether developing an internal analytics team or hiring externally, the fact is that you will probably spend a lot of money and time with your analytics and artificial intelligence provider(partner), so it is important that they bring the right skills to your department’s business or process.
Consider all the options in the analytics offering.
We have seen many organizations limit their options to Capgemini, EY, Deloitte, Accenture and other major consultancies or simply developing internal analytics teams. Although:
But there are many other good options on the market, including the Brazilian ones which are worth paying attention to the their rapid growth. Mainly within the main technological centers of the country, such as: in Florianópolis or Campinas.
Adjust expectations and avoid analytical frustrations
We have seen, on several occasions, the frustrated creation of fully internal analytics teams, be they for configuring data-lakes, data governance, machine learning or systems integration.
The scenario for the adoption of AI is similar, at least per hour, to the time when companies developed their own internal ERPs in data processing departments. Today of the 4000 largest technology accounts in Brazil, only 4.2% maintain the development of internal ERP, of which the predominant are banks and governments, which makes total sense from the point of view of strategy and core business.
We investigated these cases a little more and noticed that there are at least four factors behind the results:
- Non-data-driven culture and vertical segmentation prevent the necessary flow (speed and quantity) of ideas and data that make analytics valuable.
- Projects waterfall management style performed in the same manner as if the teams where creating a physical artifacts or ERP systems, this style is not suitable for analytics.
- Difficulty in hiring professionals with knowledge of analytics in the company’s business area together with the lack of on-boarding programs suited to the challenges.
- Technical and unforeseen challenges happen very often, so it is necessary to have resilient professionals used to these cognitive capoeira (as we call here). Real life datasets are never ready and are as calibrated as those of the examples of machine learning of the passengers of the titanic dataset. They usually have outliers (What are outliers?), They are tied to complex business processes and full of rules as in the example of the dynamic pricing of London subway tickets (Article in Portuguese).
While there is no single answer to how to deploy robust analytics and governance and artificial intelligence processes, remember that you are responsible for the relationship with these teams, and for the relationship between the production and analytics systems.
Understand the strengths of analytics provider, but also recognize their weaknesses
It is difficult to find resources with depth and functional and technical qualities in the market, especially if the profile of your business is industrial, involving knowledge of rare processes, for instance, the physical chemical process for creating brake pads or other specific materials.
But, like any organization, these analytics provider can also have weaknesses, such as:
- Lack of international readiness in the implementation of analytics (methodology, platform), to ensure that you have a solution implemented fast.
- Lack of migration strategy, data mapping and ontologies
- No guarantee of transfer of knowledge and documentation.
- Lack of practical experience in the industry.
- Difficulty absorbing the client’s business context
Therefore, knowing the provider’s methods and processes well is essential.
The pillars of a good Analytics and AI project are the Methodology and its Technological Stack (What is a technological stack?). Therefore, seek to understand about the background of the new provider, ask about their experiences with other customers of similar size to yours.
Also, try to understand how this provider solved complex challenges in other businesses, even if these are not directly linked to your challenge.
Data Ethics
Ethics in the treatment of data is a must have, therefore we cannot fail to highlight this topic of compliance. It is not just now that data is becoming the center of management’s attention, however new laws are being created as example of GDPR in Europe and LGPD in Brazil.
Be aware to see how your data will be treated, transferred and saved by the provider, and if his/her name is cleared on google searches of even public organizations.
Good providers are those who, in addition to knowing the technology well, have guidelines for dealing with the information of your business, such as:
- It has very clear and defined security processes
- Use end-to-end encryption
- Track your software updates
- Respect NDAs (Non-disclosure Agreements) – NDAs should not be simply standard when it comes to data.
- All communication channels are aligned and segmented by security levels.
- They are well regarded by the data analysis community.
Conclusions and recommendations
Choosing your Analytics provider is one of the biggest decisions you will make for your organization’s digital transformation.
Regardless of which provider you choose for your company, it is important that you assemble an external analytics consulting team that makes sense for your organization, that has a technological successful and proven business track that supports your industry’s demand.
What is Aquarela Advanced Analytics?
Aquarela Analytics is Brazilian pioneering company and reference in the application of Artificial Intelligence in industry and large companies. With the Vortx platform and DCIM methodology, it serves important global customers such as Embraer (aerospace & defence), Scania and Randon Group (automotive), Solar Br Coca-Cola (beverages), Hospital das Clínicas (healthcare), NTS-Brasil (oil & gas), Votorantim Energia (energy), among others.
Stay tuned following Aquarela’s Linkedin!
Author
Founder – Commercial Director, Msc. Business Information Technology at University of Twente – The Netherlands. Lecturer in the area of Data Science, Data governance and business development for industry 4.0. Responsible for large projects in key industry players in Brazil in the areas of Energy, Telecom, Logistics and Food.
