New England will require large amounts of clean energy alongside some traditional power resources and robust transmission to maintain reliable supply of electricity while making the energy transition,

New England needs lots of clean energy, new power lines, grid operator's study says

Highlights  Reached constructive global settlement stipulation in 2024 GRC, including updates to the power cost recovery framework Third quarter results reflect challenging weather, power m. . .

Portland General Electric Announces Third Quarter 2023 Results

Pacific Gas and Electric Company (PG&E) reached an historic milestone this week, as it has begun the process to purchase California-produced renewable natural gas (RNG) for residential and small commercial customers.

Pacific Gas and Electric reaches milestone for renewable natural gas

carbon-free and carbon-neutral energy resources. For the first time in the company's history, it has begun the process to purchase California-produced renewable natural gas (RNG) for residential and small commercial customers. The company is also ...

PG&E Takes Historic Steps Toward Decarbonization as Company Announces New Production Pilot and Procurement of Renewable Natural Gas

General Electric (GE – Research Report), the Industrials sector company, was revisited by a Wall Street analyst today. Analyst Gautam

Hold Rating on General Electric: Evaluating Aviation, Renewables, and Supply Chain Factors

Federal grant will fund Anchorage-to-Kenai subsea cable and battery storage throughout Alaska Railbelt Energy Grid within eight years.

$206 Million Federal Grant Will Strengthen Alaska’s Electrical Grid Resilience

By Giulia Petroni TotalEnergies said that it won a 25-year contract to supply 1.4 gigawatts of renewable electricity to the state of New York through

TotalEnergies Joint Ventures Win Renewable-Energy Supply Contract in New York State

GE, one of the most storied U.S. companies, had announced in November 2021 it would break up into three companies focused on aviation, healthcare and energy ... rules to encourage more company listings and compete better with capital markets in New ...

GE's energy business to list on NYSE in 2024

General Electric raised its financial outlook and said it will shed its power business in the spring of 2024, the final step in a breakup of the industrial giant. The step will leave GE’s aerospace unit as a standalone business,

General Electric Raises Forecasts, Sets Power Spinoff for Spring 2024

Demonstrating its continued commitment to decarbonizing mobility, ANGI's HRS offers a complete, end-to-end solution for hydrogen refueling, enabling fleet operators and transit companies an effective means of transitioning to zero-emission vehicles.

Vontier's ANGI Energy Systems to Deliver Game-Changing Hydrogen Refueling Station for Zero-Emission Buses in California

General Electric

generalelectric

This is the second part of my series which focuses on Big Data Analytics, if this is your first time I recommend you read my first article which will introduce you to Big data analytics. here So what is a framework? A framework is a real or conceptual structure intended to serve as a support or guide for the building of something that expands the structure into something useful. For example, in computer systems, a framework is often a layered structure indicating what kind of programs can be built and how they would interrelate. Why do we need a framework for data analytics? In analytics, the framework allows you to move through data analysis in an organized way. It provides you with a process to follow as you scrutinize the data with your teams to identify and solve problems. data Imagine having a data-focused project with your team and start working on that project. If you’re not using a , there’s a good chance that different people will use different approaches to solve the same problem. Having different approaches will make it difficult to make a decision at different stages of your project and can be difficult to trace it back. framework The framework will allow you to focus on the business outcomes first and the actions and decisions that enable the outcomes. It helps you to focus attention on what generates value first before examining all the data that are available or data that are not available that needs to be procured. Types of Data Analytics As a or a data analyst, you might ask yourself “what analytic techniques can I use and what tools can help me to analyze my data”?. There are four types of data analytics, and the tools used to help build analysis: . data scientist Descriptive analytics, Diagnostic analytics, Predictive Analytics, and Prescriptive analytics The choice of an analytical approach based on what do you want to get or know from the data. This ranges from whether you want to identify a problem, propose a solution to solve problems, provide recommendations or actions that should be taken in the future. 1. Descriptive Analytics This helps you . It lets you look at what is happening today and what has happened in the past. This type of analytics typically provides summarized information to understand currently existing sales patterns or customer behavior, customer profitability, past competitor actions, etc. understand the current state of affairs in an organization Specific techniques might include simple box plots, histogram charts with means, minimums, and maximums. Plotting the in quartiles or deciles across a number of different variables. Or computing statistical measures like mean, mode, standard deviation, etc. data Descriptive analytics is very powerful for understanding the current state of affairs and for developing the hypothesis to anticipate where business problems and opportunities may lie. 2. Diagnostic Analytics This provides the . This type of analytics typically tries to go deeper into a specific reason or hypotheses based on descriptive analytics. reasons for what happened in the past While descriptive analytics cast a wide net to understand the breadth of the data, diagnostic analytics goes deep into the costs of issues. 3. Predictive Analytics Unlike descriptive or diagnostic analytics, predictive analytics is more forward-looking. Predictive analytics lets you . This type of analytics can help the client answer questions like, What are my competitors likely to do? What will the market look like? How will the future impact my product or service?. envision what could happen in the future what are my customers likely to do in the future? Predictive analytics typically predicts what could happen based on the evidence we have seen. (4.) Prescriptive Analytics This goes beyond providing recommendations to actually executing the actions, or making the decisions that are right for a particular situation. It does this by . looking at what happened in the past, the present state, and all the future possibilities Prescriptive analytics provides answers to the question, to achieve the desired outcomes? Often the intervention might be an optimal solution given the circumstances. Or the best possible action, given the uncertainty in the environment and the limited information available. what steps or interventions need to be taken (what is the solution) Prescriptive analytics is powerful in understanding the right actions needed today to address future possibilities and put an organization in the best possible position to take advantage of future conditions. So how can a company/organization apply this technique to solve their business problems? Let us take a look at the following case study. General Electric Company Case Study For Data Analytics Framework The following case study will help you understand how different organizations and companies can apply the framework to reach their decision and increase the number of customers and revenues. data analytics This is not going to be a complete one-to-one mapping but nevertheless to the extent that those connections exist it will help frame for you how the different levels of the analytics framework work and how the business practices and business strategy is changing as you move up. General Electric Company It is a large global company, it was founded more than a hundred years ago(1892) and it does many many things. makes light bulbs, health care stuff, energy stuff, windmills, gas turbines, jet engines, transportation equipment, railroad equipment, and so on. General Electric The use of data brought evaluation to General Electric company, about how data from industrial internet or internet obtain sources, sensors and so on are changing business, changing strategies, changing business practices, changing the management of talent in this environment and just completely revolutionizing business in some sense. Let’s look at the evolution of General Electric over the past several decades. 1. Transactional Model During the 1980s General Electric was selling different products to its customers such as light bulbs, jet engines, windmills, and other related products. Also, they separately sell parts and services this means they would sell you a certain product you would use until it needs repair either because of normal wear and tear or because it’s broken. And you would come back to GE and then GE would sell you parts and services to fix it. This was a transaction model during the time. There’s not much in the space of relationships and it’s one of the transactions of sales and services. 2. Contractual Model From the 1990s till about the 2010s, GE started moving to its more of a contractual model where they would guarantee the performance of their products. For example, they will sell you a windmill, but GE will guarantee that the windmill work for 90% of the time over the course of one year. And they give you a condition under which it would work and they will tell you what they would do if the uptime is less than 90% and so on. Now it’s no longer just a transactional relationship, where GE sells you the piece of equipment and then GE walks away. During this Model, GE has to engage with the client to work on operating, maintaining, doing preventative maintenance on the piece of equipment. To be able to guarantee this level of performance requires deeper engagement but also deeper analytics. 3. Expanded Customer Outcome Model Today, GE not only guarantees uptime but GE also helps its clients make the best use of equipment that’s has been sold to them. For example, GE now says " ". well okay, we sold this equipment, we have sensors and devices on this equipment to figure out or to compute their operating parameters to get their operational details. From that, we can do analytics to then actually tell you (client) how best to operate all of the equipment to maximize the value So for instance, if the client has bought a whole series of windmills, GE will help them optimize their setup. They may ask, " " And so on. When do you let the windmills run? How often is the wind too high that you just have to let the windmills spin freely? When do you schedule the maintenance across the different units so that overall the power output remains steady? Are you also maintaining and keeping up the lifetime of all of those windmills? So really partnering at an even deeper level with the client, to get the client to maximize their value. How Data Analytics Framework was applied in General Electric 1. Descriptive and Diagnostic Analysis The Transactional Model for GE was focusing on how much GE was selling, in sales of operational equipment, and in sales of parts and services. And what does GE need to do to drive up those sales? So in terms of analytics, GE needed to perform in order to increase its sales of equipment, parts, and services. descriptive and diagnostic analytics 2. Predictive Analytics During the level of the contractual model, GE would guarantee the performance of its equipment sold to its clients. Now they need to go into the level of predictive analytics. GE needs to predict when stuff will fail so that they can do preventive maintenance. They need to be able to predict how to operate this equipment so that it will actually stay up for 90% of the time. Finally, performance guarantees take GE to the level of predictive analytics. 3. Prescriptive Analytics. The last model for GE has expanded customer outcomes; this is at the level of prescriptive analytics. GE is telling the client(s) how they should use the equipment to maximize the value. While this provides much more value for the client, this is also much harder to do because GE needs to collect a lot more data and analyze it carefully. This has changed the business model because GE is actually selling not just the equipment, parts, and services, GE is selling the that means GE is going to be charging money for this partnership where GE can tell its clients how best to operate the equipment. So this is actually generating more value for both GE and for the clients. analysis Final Thoughts By using Big data and analytics to identify emerging trends, organizations will be able to create new service offerings for their consumers and generate new business. Using data and analytics isn’t limited to only high-tech industries. Whatever sector you operate in, be it finance, healthcare, education, Insurance, Transportation, Sports, Energy, Media, Manufacturing, retail, or just about anything else, big data and analytics can and will play a critical role. Therefore, organizations using big data solutions need to keep up with its evolving nature while those still reluctant to invest should rethink their organizational policies. This article was also published at https://becominghuman.ai/understand-data-analytics-framework-with-a-case-study-in-the-business-world-15bfb421028d

Understand Data Analytics Framework Using An Example From General Electric Company

In the early 2000s, the concept of evidence-based medicine emerged. Dr. David Sackett defined it as using current best evidence in patient care decisions, and it has gained traction. Despite abundant research, only about 15% of medical decisions are based on evidence, with many relying on outdated knowledge, traditions, experience, and vendor information. Similarly, managers often need to pay more attention to reliable evidence, leading to less effective decision-making. An evidence-based approach in management could provide a competitive edge, though it is challenging due to the variability among organizations and the weaker evidence base. Adopting better logic and evidence in management practices can enhance organizational performance. What is wisdom? When doctors or managers make decisions based on something other than the best evidence, it is often due to reliance on personal experience over research. Practitioners may find firsthand information more convincing than journal data despite acknowledging the limited sample size of their observations. Managers similarly rely on past practices without considering differences between organizations. Specialists often default to familiar treatments, and marketing can skew the information practitioners receive, promoting products with exaggerated benefits. Ideology also affects decisions. For example, stock options are defended as fostering an ownership culture, though evidence shows no consistent performance benefits. The first-mover-advantage myth persists despite mixed evidence, and cultural beliefs further reinforce this notion. Uncritical emulation, or casual benchmarking, is another issue. While benchmarking can be helpful, copying top performers without understanding the underlying logic can be ineffective. Failed attempts by dozens of ride-sharing apps across the globe to imitate Uber’s success are notable examples. Six common substitutes for evidence: outdated knowledge
personal experience
specialist skills
marketing hype
ideology
casual benchmarking Illustrate why evidence-based decision-making is rare. Relying on these substitutes is not optimal for evaluating practices. Evidence-based management can improve outcomes, as demonstrated by companies successfully implementing it. Is Forced Ranking good? At Oxford’s Centre for Evidence-Based Medicine, decision-making starts with framing an answerable question, like whether to adopt forced employee ranking. Popularised by General Electric, this system ranks employees on a bell curve, rewarding the top 20%, developing the middle 70%, and dismissing the bottom 10%. Many companies consider this system due to GE’s success and its promotion in business literature. However, evidence suggests that forced ranking may not be beneficial. Evidence-based management would question if their company is similar enough to GE to expect similar outcomes. They would scrutinize the supporting data, noting flaws such as incorrect causality assumptions in studies like The War for Talent. Negative evidence includes findings that performance improves with team continuity and that wide pay disparities harm productivity and morale. Studies show that forced ranking can decrease collaboration and trust, and pay dispersion correlates with poorer performance, even in sports like baseball. Additionally, research in the auto industry indicates that systems emphasizing teamwork and minimizing status differences outperform those focusing solely on individual talent. This evidence suggests that adopting forced ranking may yield different results than the desired ones. How to reinforce your company with evidence-based managers Implementing evidence-based management is challenging but essential. Managers should prioritize training, technologies, and practices that support informed decision-making. Encouraging a culture of inquiry, where evidence of efficacy is demanded, logic is scrutinized, and experimentation is rewarded, fosters better decisions. Cultivating an "attitude of wisdom" ensures continuous learning and applying the best available knowledge. Here are some tips to create such an environment within a company. Demand evidence Kent Thiry, CEO of DaVita, emphasizes evidence-based management to turn around the struggling company. Thiry and his team implemented systems to track performance, ensuring data-driven decisions. At DaVita Academy, Thiry supports claims of industry-leading treatment quality with specific, quantitative comparisons. Reports start with patient care effectiveness data; monthly reports include a DaVita Quality Index. Missing but important metrics are noted as "not available" to motivate data collection. This focus on evidence contributed to a 50% decrease in voluntary turnover, superior patient care, and strong financial results. Emphasizing evidence-based decision-making in an environment that requires complete transparency is crucial. Scrutinise Logic More than merely requesting research to support proposals are required for accurate, evidence-based management. Managers must examine gaps in logic, exposition, and inference, especially since most management studies are non-experimental. Understanding research limitations and thinking critically are essential. Senior executives should unpack assumptions behind policies or practices, fostering a culture of critical evaluation. They should explore whether assumptions make sense, asking if they align with organizational realities. Be wary of consultant claims; for example, Bain & Company claims that clients outperform the market but lack proof that their advice is the cause. Adopt the Attitude of Wisdom True wisdom in evidence-based management comes from recognizing how much remains unknown. Effective managers act on the best available knowledge while continually questioning their assumptions. This balance of humility and decisiveness can be achieved by supporting ongoing professional education and encouraging inquiry and observation even without rigorous evidence. For example, executives at a struggling computer company discovered product issues firsthand by visiting stores and gathering qualitative data. Such practices demonstrate that real-world observations are crucial for informed decision-making, fostering a culture that values evidence over guesswork and belief. What will be the impact? Like evidence-based medicine, evidence-based management relies on sound data for decision-making. Initial studies show improved medical outcomes, but business settings need broader evidence. The theoretical argument remains strong: decisions grounded in solid evidence foster better outcomes. Practical studies, like those showing stand-up meetings saving time without sacrificing decision quality, exemplify its benefits. However, adopting this approach may challenge traditional power dynamics, as data-driven decisions level hierarchies. Leaders must choose between preserving their status and improving organizational performance. Embracing evidence-based management transforms managerial thinking, leveraging logic and facts for effective leadership and better organizational results. Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments! In the early 2000s, the concept of evidence-based medicine emerged. Dr. David Sackett defined it as using current best evidence in patient care decisions, and it has gained traction. Despite abundant research, only about 15% of medical decisions are based on evidence, with many relying on outdated knowledge, traditions, experience, and vendor information. Similarly, managers often need to pay more attention to reliable evidence, leading to less effective decision-making. An evidence-based approach in management could provide a competitive edge, though it is challenging due to the variability among organizations and the weaker evidence base. Adopting better logic and evidence in management practices can enhance organizational performance. What is wisdom? When doctors or managers make decisions based on something other than the best evidence, it is often due to reliance on personal experience over research. Practitioners may find firsthand information more convincing than journal data despite acknowledging the limited sample size of their observations. Managers similarly rely on past practices without considering differences between organizations. Specialists often default to familiar treatments, and marketing can skew the information practitioners receive, promoting products with exaggerated benefits. Ideology also affects decisions. For example, stock options are defended as fostering an ownership culture, though evidence shows no consistent performance benefits. The first-mover-advantage myth persists despite mixed evidence, and cultural beliefs further reinforce this notion. Uncritical emulation, or casual benchmarking, is another issue. While benchmarking can be helpful, copying top performers without understanding the underlying logic can be ineffective. Failed attempts by dozens of ride-sharing apps across the globe to imitate Uber’s success are notable examples. Six common substitutes for evidence: outdated knowledge personal experience specialist skills marketing hype ideology casual benchmarking outdated knowledge personal experience specialist skills marketing hype ideology casual benchmarking Illustrate why evidence-based decision-making is rare. Relying on these substitutes is not optimal for evaluating practices. Evidence-based management can improve outcomes, as demonstrated by companies successfully implementing it. Is Forced Ranking good? At Oxford’s Centre for Evidence-Based Medicine, decision-making starts with framing an answerable question, like whether to adopt forced employee ranking. Popularised by General Electric, this system ranks employees on a bell curve, rewarding the top 20%, developing the middle 70%, and dismissing the bottom 10%. Many companies consider this system due to GE’s success and its promotion in business literature. However, evidence suggests that forced ranking may not be beneficial. Evidence-based management would question if their company is similar enough to GE to expect similar outcomes. They would scrutinize the supporting data, noting flaws such as incorrect causality assumptions in studies like The War for Talent. Negative evidence includes findings that performance improves with team continuity and that wide pay disparities harm productivity and morale. Studies show that forced ranking can decrease collaboration and trust, and pay dispersion correlates with poorer performance, even in sports like baseball. Additionally, research in the auto industry indicates that systems emphasizing teamwork and minimizing status differences outperform those focusing solely on individual talent. This evidence suggests that adopting forced ranking may yield different results than the desired ones. How to reinforce your company with evidence-based managers Implementing evidence-based management is challenging but essential. Managers should prioritize training, technologies, and practices that support informed decision-making. Encouraging a culture of inquiry, where evidence of efficacy is demanded, logic is scrutinized, and experimentation is rewarded, fosters better decisions. Cultivating an "attitude of wisdom" ensures continuous learning and applying the best available knowledge. Here are some tips to create such an environment within a company. Demand evidence Kent Thiry, CEO of DaVita, emphasizes evidence-based management to turn around the struggling company. Thiry and his team implemented systems to track performance, ensuring data-driven decisions. At DaVita Academy, Thiry supports claims of industry-leading treatment quality with specific, quantitative comparisons. Reports start with patient care effectiveness data; monthly reports include a DaVita Quality Index. Missing but important metrics are noted as "not available" to motivate data collection. This focus on evidence contributed to a 50% decrease in voluntary turnover, superior patient care, and strong financial results. Emphasizing evidence-based decision-making in an environment that requires complete transparency is crucial. Scrutinise Logic More than merely requesting research to support proposals are required for accurate, evidence-based management. Managers must examine gaps in logic, exposition, and inference, especially since most management studies are non-experimental. Understanding research limitations and thinking critically are essential. Senior executives should unpack assumptions behind policies or practices, fostering a culture of critical evaluation. They should explore whether assumptions make sense, asking if they align with organizational realities. Be wary of consultant claims; for example, Bain & Company claims that clients outperform the market but lack proof that their advice is the cause. Adopt the Attitude of Wisdom True wisdom in evidence-based management comes from recognizing how much remains unknown. Effective managers act on the best available knowledge while continually questioning their assumptions. This balance of humility and decisiveness can be achieved by supporting ongoing professional education and encouraging inquiry and observation even without rigorous evidence. For example, executives at a struggling computer company discovered product issues firsthand by visiting stores and gathering qualitative data. Such practices demonstrate that real-world observations are crucial for informed decision-making, fostering a culture that values evidence over guesswork and belief. What will be the impact? Like evidence-based medicine, evidence-based management relies on sound data for decision-making. Initial studies show improved medical outcomes, but business settings need broader evidence. The theoretical argument remains strong: decisions grounded in solid evidence foster better outcomes. Practical studies, like those showing stand-up meetings saving time without sacrificing decision quality, exemplify its benefits. However, adopting this approach may challenge traditional power dynamics, as data-driven decisions level hierarchies. Leaders must choose between preserving their status and improving organizational performance. Embracing evidence-based management transforms managerial thinking, leveraging logic and facts for effective leadership and better organizational results. Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments! Did you have a chance to work in an evidence-based and managed team? Share your experience in the comments!

Evidence-Based Management in Business: Will it make a difference?

SQL has a long and proven history. It has survived the fuss around NoSQL. And even if not perfect, it has been demonstrated to be the best available language for data. This is no surprise! The story began in the 1960s with the development of databases—an era marked by the introduction of the Integrated Data Store (IDS) at General Electric. However, it was Edgar Codd's relational model that revolutionized data handling. His model, which turned data into a series of tables (or, more strictly, relations), has influenced database systems ever since. This era also saw the birth of SQL (Structured Query Language), which became the standard language for interacting with relational databases, including MariaDB and others. The utility of relational database systems So, why do we need all this database stuff? Let's imagine you're building an app, maybe a simple to-do list to keep track of your daily tasks. Initially, you might think, "Why not just save each task directly to a file?" After all, my programming language has constructs and libraries to save and read data from disk. Also, implementing this seems straightforward: create a task, write it to a file, delete a task, and remove it from the file. These are good points; however, as your app gains traction, users start to aggregate, and suddenly, you have thousands of users trying to add, delete, and modify tasks simultaneously. At this point, the simplicity of files becomes fragile. Imagine one user is updating a task at the exact moment another tries to delete it. Or maybe two users are editing the same task at the same time. With a simple file system, you're likely to end up with corrupted or lost data because there's no inherent mechanism to handle such conflicts. Databases handle these situations gracefully through the . Essentially, a set of principles ensures that even if your app crashes midway through an update, the data remains consistent, and no half-completed tasks are left hanging. Back to the to-do app example, imagine trying to move your task "Buy groceries" from to which also requires changing the property, but your app crashes right in the middle. With a relational database, it's all or nothing—either the task is marked complete and the property reflects the new time value, or it's like you never tried to update it in the first place, avoiding those incorrect half-states. ACID properties pending completed last_updated last_updated Now, let's consider data relationships. In your app, tasks might belong to different categories or users. In a file system, maintaining these relationships is cumbersome. You might end up with a separate file for each category or user, but then how do you quickly find all tasks across categories or ensure two users don't end up with the same task ID? Databases have the ability to manage complex relationships, making it easy to query all tasks for a specific user or category or even more complex queries like "show me the number of completed tasks for user U grouped by category C during the last month." Security is another biggie. In a file system, if someone gains access to your files, they have your data. Databases offer robust security features, like access controls and encryption, safeguarding your data from unauthorized eyes. And then there's the issue of growth. Your simple to-do app might evolve into a complex enterprise project management tool over time. With a file system, every change can feel like renovating a building with people still inside. Databases are built to be flexible and scalable, meaning they're designed to grow with your needs, whether you're adding new features or handling more users. In the end, choosing a database over a simple file system is about preparing for success while standing on solid ground. It's about ensuring that as your app grows, your data remains secure, consistent, and manageable and your users happy. After all, no one likes losing their to-do list to a random crash or waiting forever for their tasks to load because the system is bogged down handling conflicts and searches! A bit of history It was Edgar Codd who proposed the relational model for databases, and since he was a mathematician, he formalized the concepts by creating what is called relational algebra and relational calculus. All this was theoretical until IBM and others started to implement the concepts in academic and research projects. They also wanted to come up with a standard language for querying data in relational databases. At first, they invented QUEL (Querying Using the English Language) at the University of California, Berkeley. At IBM, researchers wanted to come up with their own language and started a project that I perceive more as a game between colleagues called SQUARE (Specifying Queries Using a Relational Environment). This led to a query language that had a scientific-like notation with subindexes and super-indexes, which was hard to type on computer keyboards. To solve this, they redefined the language to only use standard characters and, in an ingenious and probably friendly mockery way, called it SEQUEL. This name, however, was a trademark in the UK, which prevented them from using it. They removed the vowels in SEQUEL, and boom! SQL was born. By 1986, SQL would become an ISO and ANSI standard. As a curious historical remark, although their inventors had to rename SEQUEL to SQL, they continued to call it "sequel." Even today, many software developers and IT professionals continue to pronounce it "sequel." The name Structured Query Language (SQL) would appear later. The utility of SQL SQL is a declarative language, meaning that you specify what you want to get and not how to get it. The database is in charge of doing whatever needs to be done to get the data requested. SQL isolates database complexity. A database is a complex piece of software with tons of algorithms implemented in it. These algorithms deal with different ways to get data stored in disk or memory. Different algorithms are more efficient in different circumstances, which include different queries and different datasets. For example, in MariaDB, a component called the is in charge of deciding what algorithms to use given a SQL query and stats gathered on the actual data. The query optimizer analyzes the SQL query, the data structures, the database schema, and the statistical distribution of the data. It then decides whether to use an index, which joining algorithm is the best, and how to sequence the operations. This process involves a remarkable amount of complexity and mathematical precision, all of which the database abstractly manages for you. As a developer, you only need to worry about constructing the query to get the data you need and let the database figure out whether to use an index or not (with some datasets, not using an index could be faster), B-trees, hash tables, and even whether to add the data to an in-memory cache, as well as many other things. query optimizer SQL also allows you to handle writes, that is, creating and updating data. It also allows you to define the schema of the database, or in short and over-simplifying, the tables and their column structure. In fact, there's much more that SQL allows you to do, and its functionality can be divided into four categories: Creating and manipulating the schema. Data definition language (DDL): Inserting, updating, and deleting data from the database. Data manipulation language (DML): Retrieving data from the database. Data query language (DQL): Dealing with rights and permissions over the database and its objects. Data control language (DCL): In my more than 15 years of experience in the industry, I have rarely seen the previous categories used in a work environment, with the exception of DDL, which refers to activities related to handling database schema updates. These categories are useful mostly in academic circles or in teams implementing relational database management software. However, it's good to know that these terms exist and are used by others as they help in discussions around database technology. With this in mind, let me briefly touch on one of such discussions. Some would say that developers have to deal only with DML and DQL, while DDL and DCL are DBAs' concerns. In practice, this division is not so easy to make. Developers need to understand how database objects (like tables and columns) are created and how access to these objects is managed. However, it is true that developers spend most of their time writing SQL statements to modify and query data. You'll see that this book focuses on DML and DQL while explaining other categories as needed. On the other hand, DBA's are experts on everything database—from infrastructure and general database management to SQL query optimization and migration, a DBA is always a valuable brain to have in your team. Conclusion So, in conclusion, databases solve real problems that application developers face, thanks to their ability to ensure data integrity through ACID properties, manage complex relationships, and provide robust security features. I only scratched the surface here, but this should be enough to give novice IT practitioners a quick refresh on the importance of relational databases and SQL. Also published . here

Why Databases and SQL Matter: The Pillars of Modern App Development

INTERNAL COMBUSTION ENGINES

We’ve witnessed business visionaries like Steve Jobs, whose groundbreaking presentations captivated audiences worldwide, read countless articles and books on the subject, and yet wonder how such people drive progress. And most importantly, how can we follow in their footsteps? The crux of the matter lies in our approach. The business focuses on selling, which is quite logical, of course. But at every stage of product development, our minds are consumed by sales. Everyone, from engineers to marketers, is immersed in formulas, analytics, and advertising strategies. These aspects are essential and valuable, yes. However, more than these processes are needed to become proper technology and marketing revolutionaries. To surpass mediocrity, one must transcend the singular focus on sales and embrace ideas and meaning above profit. On that unforgettable day when Steve Jobs unveiled a personal computer from a simple bag, more powerful and productive computers already existed. Future advancements were in the works as well. Yet, people weren’t dreaming about RAM capacities or processor speeds. Instead, they yearned for personal computers that mirrored the sleekness, convenience, and aesthetics of everyday household appliances like televisions and video recorders. Twenty-three years later, when Steve Jobs took the stage, unveiling the first iPhone, there were already “smart” mobile devices offering superior communication and longer battery life. However, people truly craved a handheld computer — a device that would fulfil their dreams. Apple’s secret sauce was to bring people’s dreams to life, capture their imagination, and deliver what was envisioned. But to accomplish this, you must be a dreamer yourself. In a serendipitous twist, Jobs once referenced a refrigerator while contemplating the ideas behind the Macintosh. It’s hard to fathom now, but refrigerators and computers share a similar origin story. Once, refrigerators were massive industrial structures found in warehouses or aboard ships. A century ago, even the most innovative and modernist architects didn’t consider refrigerators when designing houses and apartments. Instead, they incorporated ventilated storage rooms or cold cellars, as preserving food was a constant challenge, particularly during summer. Only a fortunate few could afford to install a home cooling unit, which required a separate room for the engine and compressor. It was a complex and cumbersome affair. The dream of a portable refrigerator that could fit into a small space, no larger than a closet, seemed unattainable. However, in 1927, General Electric introduced the Monitor-Top home refrigerator — the first mass-market model that turned this dream into reality. Despite its hefty price tag of $525 (equivalent to $7,500 to $8,000 today) and the looming Great Depression, the Monitor-Top sold over a million units, profoundly impacting the concept of comfort, the food industry, and architectural design. Steve Jobs had a knack for finding relatable examples; one of his favorites was the refrigerator. Like how you effortlessly store and retrieve food without worrying about spoilage, Jobs believed computers should be equally intuitive and hassle-free. The focus should be on using them rather than understanding them. In 1968, Alan Kay introduced the concept of Dynabook, a device encompassing the features of today’s personal computers, laptops, tablets, and smartphones. Kay’s visionary sketch contained elements that would later become a reality in these devices. However, at the time, such advancements only existed in science fiction. Even Alan and other experts understood that they were dreaming, realizing that widespread use of such technology lay in the distant future. The arrival of widespread computerization depended on dreamers like Jobs and Wozniak, who dared to push boundaries and make dreams a reality. Let’s take a moment to acknowledge that Apple wasn’t the sole master of turning dreams into reality. Other pioneers dared to transform ideas that seemed straight from science fiction into tangible products. A massive wave of excitement surrounded the release of the Motorola DynaTAC 8000X cell phone in 1984. How many of those eager customers were avid fans of the intrepid Captain Kirk and his crew’s adventures aboard the Enterprise? In those very adventures, the heroes wielded wireless handheld devices for communication, pushing the boundaries of imagination. Surprisingly, the need to create and bring a cell phone to the mass market wasn’t even apparent to futurists then. In Stanley Kubrick’s visionary film “2001: A Space Odyssey,” Dr. Heywood Floyd utilized a specialized cabin apparatus to make wireless calls. Motorola took a risk, charging a hefty $3,995 for the novelty (equivalent to $10,000 to $11,000 today). Still, they bet on the dream and emerged victorious, solidifying Martin Cooper as the “father of the cell phone.” This principle extends beyond the realm of information technology. Consider Lee Iaccoca and his invention of the Mustang. Did you know that the initial base model of the Mustang wasn’t a bona fide sports coupe? Iaccoca tapped into the aspirations of millions of Americans (and beyond) who yearned for sports cars but couldn’t afford the high price tags. By crafting a car based on a family sedan that exuded the essence of speed even when motionless, Iaccoca captivated the hearts of dreamers everywhere. Heinrich Nordhoff had a similar vision when he transformed the Volkswagen Beetle into an international automotive phenomenon. His dream was to create a car that transcended social status and could be purchased without concern for neighbors’ or colleagues’ judgments. Dreams have guided the paths of illustrious figures like Thomas Edison, the Wright brothers, Henry Ford, Guglielmo Marconi, and the Lumiere brothers, propelling them towards remarkable commercial success. By now, you’ve likely deduced the conclusion we’re driving at: to become a truly successful techno guru poised to etch your name in the annals of civilization and reap substantial rewards, you must dare to dream. Embrace ideas that others envision, even if they appear crazy and fantastical. Instead of merely “solving the user’s problem” or “selling a product,” you must embrace the art of giving dreams away — for a modest fee.

Take the Dream Out of the Bag and Hide it in Your Pocket

Analysts and statisticians during World War 2 often analyzed the aircrafts that returned from war. While some experts preferred to reinforce the places that had been shot, the experts who prevailed in the debate preferred to fortify specific non-damaged locations. The evidence showed that particular regions missing the bullet holes were the areas that, when shot, caused planes to go down. The conclusion was that the bullet holes of the planes that returned proved that certain areas could be hit and still allow the jet to return. This is known as , referring to the tendency to examine survivors rather than those who do not survive. The same principle can be applied to information security. Instead of applying security controls where we wish them to be or are most afraid, what’s more important is to take note of the real risks that confront an organization and optimize accordingly. Survival Bias What is Insider Threat? According to , 82% of breaches involve “the human element.” This isn’t an insider threat alone, but the main point is that humans cause most breaches. So, security should focus on the people problem. Verizon’s 2022 DBIR One of these human factors is insider threat. But what is that? , an insider is “any person who has or had authorized access to or knowledge of an organization’s resources…” According to CISA As mentioned , “For many enterprises, the biggest risks to their data and intellectual property come from trusted insiders such as employees, partners, and contractors.“ here Insider threat, aka insider Risk, is presented by anyone with approved access to a company’s assets. These threats and risks are, therefore, always present. Is there any way around this risk? An old proverb says, ‘where there are no oxen, the stable is clean.” If you don’t want any insider threats, don’t have any people involved. Given this, there’s always an insider threat. Types of Insider Threats While there are many ways to present the categories and subcategories, I’ll narrow it down to two main groups of : insider threats ●       Malicious (e.g., Privilege Abuse) ●       Error (e.g., deletion of important files) Motivations such as financial gain or vengeance are factors, but we won’t focus on that here. We all make mistakes. For example, the risk of insider error in Healthcare is 2.5 times greater than maliciousness. Whatever the error, actions such as misclicks, an email inadvertently sent to the wrong person, or even a VM accidentally deleted, and in whatever the industry, one of the primary protections against error and maliciousness is a solid backup and restore process. While error is statistically more common in some industries, an organization’s primary activity is performing its own risk analysis (you’re not just a statistic, right?). Whether through error or malicious actions, results such as deleted files, sending private emails to the wrong people, and botched software deployments can make for bad times for a company and must be included in the risk analysis. What an insider threat looks like in real life In 2021, two GE (General Electric) employees were to federal prison for stealing trade secrets from GE from 2008 to 2019. They used the stolen data to create their own competing company. The data was leaked primarily through uploads to private repositories and personal email addresses. fined and sentenced In 2022, 1.5 million files were exposed via an . These were the PII of airport workers, including photo IDs, and this is an example of a security misconfiguration, which is #5 on the issues. unsecured S3 bucket OWASP Top Ten Cost of Insider Threat Because of all the factors involved, e.g., amount and type of data breached, legal and regulatory fines, public recompense, lost revenue, and reputation - it’s not possible to give one average to rule them all. But according to ,” the combined average of some leading insider threat activities – phishing, malicious, accidental data loss, and cloud misconfiguration – totals over 17 million USD. IBM’s “Cost of a Data Breach Full Report 2022 What can be done to mitigate the risk? Gene Spafford, renowned professor, and computer security expert, famously quipped: “The only truly secure system is one that is powered off, cast in a block of concrete, and sealed in a lead-lined room with armed guards - and even then, I have my doubts.” The best way to secure a building is to lock it up, so no one gets in. The only way to make a boat go faster is to get it out of the friction of the water. The hypothetical list goes on and on, but we know it can’t be done those ways. Appropriate action that balances protection and productivity is to be taken by those who have calculated the risk. When calculating risk, the usual formula is . If you want something more complex, you could also use Risk = threat x vulnerability x information value – but we’ll keep it simpler for now. risk = threat x vulnerability Security Controls Here are a few ideas for implementing the proper administrative, technical, and physical safeguards to reduce insider threats. Attitude The attitude must avoid treating employees like criminals while protecting the business from destructive actions. This includes . This also called left of Bang, is a military term that means mitigating and hopefully stopping risks and threats by thinking ahead and planning, preparing, & training accordingly. It could be considered Shift-Left in SDLC and Incident Response policies and procedures. thinking Left of Boom Leadership , "Leaders must fully buy into the importance of cybersecurity; only then will they make the business decisions necessary to protect the organization." Leadership – which goes beyond the title to include influence – needs to understand and take action on the need for proper cybersecurity initiatives and implementations. Business leadership provides much-needed direction and funding. SecurityIntelligence notes Technical Controls Even though we’re talking about people, solutions must include technology to keep up with the pace 24/7/365. The chosen technologies must, at minimum, provide analytics that are data-aware and intelligent, with better options, including capabilities such as behavior detection and real-time actions. Orientation toward results Insider threat is a reality, but so are ways to prevent and mitigate it. What’s your next step in protecting your organization? Anything is progress.

Insider Threats: Unmasking the Foe Within Your Organization

The automotive market stands on the verge of a major transformation with the introduction of electric vehicles, addressing the concerns of global greenhouse gas emissions and the use of exhaustible power resources. The accelerated demand for these vehicles comes from the shift in three key areas: consumer behavior, regulations, and integration of AI technology in the process flow. As per , “a total of 10.5 million new BEVs (Battery Electric Vehicles) and PHEVs (Plug-in Electric Vehicles) were delivered during 2022, an increase of +55 % compared to 2021.” EV-Volumes.com “It is clear now that electric vehicles are the future, and that the future is coming faster than many anticipated. California and other states have already declared that no new gas vehicles will be sold after 2035,” said Dave Lewis, CEO of . MoveEV China despite sporadic Covid outbreaks and lockdowns, emerged at the top of the leaderboard with an increment of +82 percent year-over-year EV sales by the end of 2022. The Chinese government's subsidies for electric vehicles, the expansion of the mini-electric vehicle industry, and the emergence of more relatively affordable brands are a few factors contributing to the sector's explosive growth in China. Following closely up with China, Europe made up about 11% of the total revenue generated. Stringent climate laws and regulations drive the European transportation industry, its program defines how laws make “reaching the EU’s climate goal of reducing EU emissions by at least 55% by 2030 a legal obligation.” “Fit For 55” However, the EV market in Europe is in the clutches of an impending energy crisis in the wake of Russia’s invasion of Ukraine. The rising cost of electricity is impacting the affordability of driving electric vehicles, to the point where they may become more expensive to run than their gas-powered counterparts. This surge in electricity prices could potentially hinder the continent's shift towards electric transportation. “When we looked at this(transition to EVs) before the energy crisis, we were looking at a tipping point of around 2023 to 2024. But if you assume you have a tariff going forward of $0.55, the tipping point then moves to 2026,” , a partner at Ernst & Young. said Maria Bengtsson Several countries are framing their policies around the threat of climate change and exhausting renewable sources pool. The Biden administration is pushing America to decarbonize by a 50 percent electric vehicle target by 2030. introducing "Today, nearly all automakers have put zero-emissions vehicles at the heart of their long-term strategies – and, perhaps more importantly, their medium-term investment plans," according to . The report emphasized how EVs are no longer a budding industry but have gained enough traction to be mainstream in markets like China, Germany, France, and the UK. S&P Global Mobility The adoption of EVs is majorly driven by advancements in technology, the integration of AI into automobiles has led to the enhancement of features that have made driving effortless and safe. As per , “ the global Automotive Artificial Intelligence (AI) Market is predicted to thrive at a CAGR of 29.2% and valued at over USD 34.30 billion over the forecast year 2022-2030”. Research and Markets It further noted that the growth in the automotive AI market is a direct reflection of increasing demands “for sophisticated vehicles and the shift of the automotive industry towards electricity.” Challenges like limited driving range, long charging times, and integration with smart grids are being addressed by the introduction of AI Technology. “ The increased sensitization of an electric vehicle leads to more data from the vehicle being available. This data can be used by manufacturers, service representatives, and drivers,” said Cliff Rice from . Rockwell Automation He further explained that manufacturers can use this data to improve driver assistance algorithms, understand driving patterns for maintenance needs, and provide advanced diagnostics and software updates to enhance vehicle performance and increase their lifespan. Predictive analytics is playing a significant role in advancing battery technology. Using predictive maintenance, these technologies are improving the efficiency and reliability of batteries. This is  important as it ensures that batteries operate at optimal levels, delivering high performance and minimizing the risk of breakdowns. “Batteries are highly complex components – chemically, electrically, and in terms of software,” , Data Scientist at Volvo. “But data analytics can help us to understand them better: how they are used, how they are affected by the driver, how they are impacted by other components in the vehicle.” said Rikard Vinge Apart from user experience, engineers are using AI to improve the designs and working efficiency of components. “By using AI in our generative thermal design approach, we are able to create and simulate thousands of iterations of a design in a matter of days, fully autonomously. To fully grasp the scale and speed at play here, in the time it takes a thermal engineer to create one design, we’ve already created 1 000,” said Diabatix CEO, Lieven Vervecken. Several companies are collaborating with big tech names to provide their users with the best of both technologies. Ford has with Google to make use of its cloud services and expertise in artificial intelligence and machine learning. Tesla AI for updating the software over the cloud providing their users with remote services. Volkswagen and Microsoft have also to unleash the power of Azure IoT and PowerBI by offering telematics and productivity solutions. collaborated leverages joined hands The major OEMs(Original Equipment Manufacturers) are convinced that the transition to the e-mobility industry is now inevitable. BYD Auto's sales of BEVs and PHEVs tripled by concentrating its production and sales efforts on electric vehicles, making it the leading manufacturer of PHEVs and BEVs in 2022. Tesla dominates global BEV sales with a 17% market share, while the Volkswagen Group saw only a 10% increase in EV sales in Europe with declining PHEV sales. Established carmakers are reinventing their business models in the hope of adapting to a new world where electricity replaces traditional gasoline and diesel.  Automakers are buying up they can get as factories undergo renovations to build electric vehicles. Some businesses are being forced to find partners due to the high expense of creating electric vehicles, while others are becoming acquisition candidates. every battery The prominent players in the industry are revving up their traditional internal combustion engines to become the frontrunner in eliminating them altogether. Leading names like Tesla, Volkswagen, Toyota, General Motors, and Nissan are introducing new models in the market. The transition to the EV industry is bringing about a lot of opportunities for innovation in the field of battery technology, braking systems, steering systems, and the arrangement of propulsions.  In 2020, more than patent applications were made, with an average grant rate of 50%, according to global patent trends in the Electric Vehicle (EV) sector. 18,000 While the patent filing trends suggest the pace of technology, it also gives a fair picture of the new changes occurring in the industry. As per a published by European Patent Office(EPO) in 2020, patent filings for batteries and other electricity storage technologies saw an annual increment of 14 % worldwide, 88 percent of which offered innovation in battery technology. report Electric vehicles raise the highest demand for Lithium supply for the manufacturing of batteries. If today’s demand is forecasted to 2050, the US market alone would require a three-fold mining of what is currently produced globally, but does it have to be this way? A found that “Lithium demand can be reduced by up to 92 percent in 2050 in comparison to the most lithium-intensive scenarios. This can be done by employing three key policy interventions: decreasing car dependency, right-sizing EV batteries, and creating a robust recycling system.” recent report The existing charging infrastructure for these batteries is far from capable of meeting the growing demand. Although huge investments are underway, skepticism looms owing to cost, choice, and soaring in the areas where battery packs have previously failed to deliver considerable driving range and took long charging times. The fastest vehicle and charger- the Lucid Air can reportedly give of range in 20 minutes, is typical for average EVs. “range anxiety,” about 300 miles 100-200 miles per charging hour To alleviate such concerns, there has been a swift effort to establish fast charging networks throughout the country, with utilities looking to increase electricity sales. More than have joined forces to establish the National Electric Highway Coalition, which aims to develop a nationwide fast-charging network along major highways in the US. The number of fast chargers has already grown significantly, with over available today,  and there are even more 60 utilities 6,500 commitments underway. General Motors Co. and Pilot Co. to partner up and build a network of charging stations across the U.S., set to boost about 20% of the number of fast chargers available to American drivers. plan As the world transitions from a fleet of gasoline-powered vehicles to electric vehicles, it is estimated that millions of people will rely on the electric grid in various ways. This means that the power generation capacity will have to increase to accommodate the growing demand for electricity without causing strain on the grid. , US, there will be a 38 % increase in electricity consumption by 2050, mainly due to the high involvement of electric vehicles. According to the Department of Energy Research conducted at the University of Texas, Austin carried out state-by-state assessment scenarios where every personal car, truck, and SUV is converted to a plug-in electric model. The found that the state energy consumption jumped to 17 % in Wyoming and 55 % in Maine. \ study The ranged from 20-30 percent in most of the states. Some states showed an available capacity to take upon the excess loads whereas, for others, ideal case assumptions for charging times were made to channel the power. The capacity of grids for EV charging depends greatly on the time of the day they are plugged in. The chance of grids overloading falls drastically if charged at when consumption is lesser. consumption off-peak hours With all the speculations and possibilities, the imagined “zero emissions” world doesn’t seem to acknowledge what happens to fossil fuel-driven vehicles running on the roads for now. One idea circulating is the remodeling of traditional engine-driven cars to electric vehicles. At a recent industry event, Toyota's CEO, Akio Toyoda, signaled that the business would enter the conversion market by displaying two vintage Corollas that had been converted to electric and hydrogen power. “The reality is that we cannot achieve zero carbon emissions in 2050 simply by switching all new car sales to EVs,” said CEO Toyoda at the “It is important to provide options for beloved cars that are already owned.” press conference. While some experts support the idea of remodeling, others believe that electrifying cars is unlikely to be a viable option for individuals with limited financial resources. “ If you’re thinking of how do I retrofit an ICE car to be an EV, I don’t think that’ll happen. People will simply buy EVs as replacement cars as usual, but they may straddle the two technologies by having a plug-in hybrid car first,” said Trevor Curwin from . Sheeva.ai The rapid expansion of the EV industry, despite the challenges posed by external factors and limitations, provides a strong indication that electric vehicles are here to stay and will play a significant role in shaping the future of transportation.

Electric Vehicles: Driving Towards a Sustainable Future With Artificial Intelligence

বৈদ্যুতিক যানবাহন: কৃত্রিম বুদ্ধিমত্তা সহ একটি টেকসই ভবিষ্যতের দিকে ড্রাইভিং

Elektrofahrzeuge: Mit künstlicher Intelligenz in eine nachhaltige Zukunft

Vehículos eléctricos: conduciendo hacia un futuro sostenible con inteligencia artificial

Véhicules électriques : vers un avenir durable grâce à l'intelligence artificielle

इलेक्ट्रिक वाहन: आर्टिफिशियल इंटेलिजेंस के साथ एक सतत भविष्य की ओर ड्राइविंग

電気自動車: 人工知能による持続可能な未来への推進

전기 자동차: 인공 지능을 통해 지속 가능한 미래를 향해 나아가다

Veículos Elétricos: Rumo a um Futuro Sustentável com Inteligência Artificial

Elektrikli Araçlar: Yapay Zekayla Sürdürülebilir Bir Geleceğe Doğru Yol Almak

Xe điện: Hướng tới một tương lai bền vững với trí tuệ nhân tạo

电动汽车：借助人工智能迈向可持续发展的未来

is something every investor wants to be a part of; however, it is difficult to predict what it will look like. This is why investors should learn from the past to see how trends have shaped the business industry today. The future of business This does not mean that the future would be a replay of the past. But investors can learn a great deal from foregoing patterns and make more strategic decisions for the future. For example, what happens when markets plunge, and how should you react in such situations? You might find yourself in a bind. That's because there are many behavioral patterns that become unhealthy during crisis times. As a result: buying on expectations rather than reality, selling on short-term emotions rather than long-term investments, delay in your withdrawals, resistance to change, and deterioration of values are all very dangerous. In such conditions, we experience unfounded market optimism that lacks a real foundation of fundamental valuation but instead rests on psychological factors, blind optimism, and tunnel vision. Globalization and the Future of Investment According to consensus in the 21st century, with their passive long-term strategy, buy-and-hold investors, in the long run, tend to outperform active investors who focus on market timing. However, show that this is not necessarily the case. For one, that consensus is derived from biased research, focused on the United State’s stock market only. insights from Global Financial Data Apparently, since the 1950s, the United States has experienced a sustained momentum of economic growth due to political instability, despite the occasional setbacks. However, that has not been the case for most other countries that have continued to be plagued with a tense political and economic atmosphere. In such situations, investing passively for the long term (buy and hold) is sure to deplete the value of one’s financial assets (stock returns, dividends, bonds, and all), especially with rising inflation. Nevertheless, the 21st century is an era of globalization, and you cannot afford to be satisfied with a limited worldview. Taking a global approach to building your portfolio protects you against unnecessary volatility and protects your asset classes in the long run. Growth vs Value Investing Even amidst the current stability of developed markets, , “It would be foolhardy to suggest that passive investing will continue unimpeded on its current trajectory.” as Edward Morris claims If you want to protect your assets and seize opportunities during major crises, you need to consider the consequences. Like historians, you often work with insufficient or bad data.  You have to extrapolate.  Remember, trend is not fate. According to , between 2000 and 2013, the risk-adjusted returns for value stocks were higher than that of growth stocks at various levels of capitalization. And while the former was more volatile, growth stocks performed better at shorter terms. research by John Dowdee As investors know, there are so many factors that determine how high or low the returns would be. If anything, the history of investing has consistently shown us that markets are too volatile for investors to hold on to changing rules of thumb. Investing is a strategic activity and you must always clarify your purposes, objectives, and expectations before taking the plunge. Crisis, Business Resilience, and Investment Decisions Periods of crisis strengthen an economy’s resilience and provide opportunities for innovation and surging business formation. In fact, some of the world’s biggest innovations today emerged out of the ashes of a preceding catastrophe. For instance, successful companies such as WhatsApp, Uber, Venmo, Slack, Square, etc. are innovations out of the 2008 global recession. Other in history include Netflix (1997), Microsoft (1975), General Electric (1876), and so on. companies that were started during various economic catastrophes Therefore, it is true that economic downturns provide significant opportunities to thrive; hence, these high-risk-high-reward situations can be transformative for investors. Of course, this does not mean that every investment during a crisis pays off eventually. There has to be an enabling environment for sustainable growth if economic activities must flourish afterward. In addition, a company’s strategies before, during, and after a crisis are very telling of how spectacular its recovery would be. The realize this and they have been investing even more heavily as the pandemic reached its high. FAMGA giants Volatility and Uncertainty So, how exactly do you manage market uncertainty? Volatility in a portfolio may prevent successful investment outcomes. As a matter of fact, the more volatility in your investment portfolio you have, the more difficult it is to achieve positive results. But by reducing your expected range of outcomes, you lower your anxiety and encourage more constructive and successful investment behavior. is to reduce your exposure to stock market risk by getting absorbed into a smaller set of assets. By keeping your financial footprint small, it becomes easier to manage your capital effectively over time. You can also focus on assets that matter more for your overall portfolio performance. The goal of diversification When markets are volatile, diversification can reduce destabilizing effects and increase returns. Sustaining long-term success requires disciplined and continuous effort. For this reason, investors must learn to identify their level of risk tolerance which enables them to direct their time, energy, and capital into a profitable activity. Conclusion When times are hard and there are no guarantees, having the right instincts is critical. Just as people make decisions in life by weighing up the pros and cons of various options, investors should think about their options through the lens of opportunity costs. It is worrying - and somewhat disconcerting - to think about the near-term prospects for the global economy. But history shows that these problems tend to get smoothed out, even if we find ourselves in a situation where things look suddenly unworkable. , “trend is not destiny”. Yes, we are in the midst of a technological revolution, but the truth is that human history has always been a journey — a journey toward progress and prosperity, always driven by those willing to explore new frontiers and discover what lies ahead. This is what history has taught us. As a Financial Times author puts it

What Future Business Investors can Learn from Past Trends

Reverse mentoring occurs when young people become mentors of executives to guide them on various topics of strategic and cultural relevance. Why Reverse Mentoring? When we are thinking about mentoring and the relationship between mentor and mentee, we always think about experienced professionals guiding younger professionals who are starting their careers, with tips and contributions that they can teach after years of hard work and a lot of experience. We must remember that mentoring is not a new topic. To tell you the truth, the concept of mentoring comes from the Mythology. To be more specific, when Odysseus went to the front of the battle in the Trojan War, he handed over the care of his family to a slave named Mentor, who worked as a master and coach of his son Telemachus. The role of Mentor was not only to protect Telemachus, but to guide and contribute to better develop and prepare him so that he could face the responsibilities that he would have to assume later in the life. And the word “mentor” started to be used to designate a mentor, counselor, friend, tutor, teacher and the mentoring role have not changed drastically since that time. The whole idea of mentors not only remained, but also started to be applied by organizations. However, in the reverse mentoring, the opposite happens: younger employees offer a new fresh perspective on technology, social media, innovation, new work models and even help executives and organizations to adjust their “voice” and positioning, as many traditional companies still end up being very formal when talking to customers and this is not the best way when dealing with people so connected to the digital world. Where Did This Idea Of “Reversing The Roles” Come From? This is not a complete new thing in the corporate word. The credits for this model are from Jack Welch, who implemented, in the 90’s, a reverse mentoring program at General Electric. The practice consisted of young employees training leaders of the “older” generations to use new tools that needed new type of knowledge in technology, not only technical, but also behavioral. The orientation for the company’s executives (which included Jack Welch), was to find a “reverse mentor” to help them connect with the new digital world emerging with the internet and network technologies. Today, this type of program goes far beyond teaching and learning how to use the various technologies. A survey from Deloitte showed that just a half of Millennials said that the culture of their current employers encourages employees to implement new ways of working, while only 23% believe that their leaders prioritize the development of innovative products and services. But, What Is Reverse Mentoring? Reverse mentoring could be a solution to keep the organizational environment dynamic, in the sense of keeping its managers up to date, once managers learn that younger employees can provide different insights, perspectives and knowledge in a variety of areas. It inverts the perspective of conventional mentoring. New generations have grown up with technology around them and a have greater understanding of diversity. They have accumulated extensive experiences in the digital world and lived in a connected life with the internet, so they are willing to share these cultural insights. As a result, executives are beginning to realize that knowledge share is not a one-way street. The goal is to use the knowledge that resides within an organization to take advantage of it. A well-implemented reverse mentoring program can provide numerous benefits to the organization and potentially can provide the organization with a dynamic training solution for everyone. In an article with enormous repercussions in the Harvard Business Review, social psychologist and professor of leadership and organizational behavior, Jennifer Jordan, with Michael Sorell, said that many companies struggle to attract and retain Next-Gen talent. Some of these companies are experimenting the results of reverse mentoring programs to solve this problem. These programs can increase retention, help senior executives to become more sophisticated about social media, drive cultural change and promote diversity. In the same article, we saw the case of Mark Tibergien, CEO of Advisor Solutions, who, reflecting on the future of Pershing (a financial solutions company), realized that the company had a problem: the younger generations were not interested in working in financial services. In addition, the Millennials who joined the company were leaving the organization much more quickly than their older peers. Like Pershing, many companies struggle to retain talent from the digital generation and also to stay relevant to younger consumers. In response to these challenges, leadership teams from large companies around the world are implementing reverse mentoring programs, because it can connect younger employees with members of the executive team to guide them on various topics of strategic and cultural relevance. How Easy Is It To Implement A Reverse Mentoring Program? Senior leaders being mentored must be committed to participating in the sessions and prioritizing the mentoring relationship. Reverse mentoring relationships can be especially challenging, since the mentee and the mentor are assuming unknown roles. Another major challenge is that a program of this magnitude must be implemented consciously, in order to ensure that no one feels that they are not doing their job properly. Also, senior employees who have been with the organization for a long time can feel affronted, which is a real risk if it is not handled well or if the younger mentor does not handle the situation properly, receiving training to deal with these moments. There are still some executives who challenge themselves and do not agree with the idea of being mentored by a junior employee, but most influential leaders see this as an opportunity to exchange culture perspectives, where junior and experienced employees share their knowledge, increasing understanding of both groups and improving communication and collaboration in general for the workplace. For those who grew up in a world without technology, it can be a huge challenge not only to keep up to date on the latest social platforms, collaboration tools and technological trends, but to find out how to make them applicable to their business. But the current generation has grown up with social networks and various technologies. Its members are always connected. So, reverse mentoring take advantage of this mentality and drives it towards the growth of the enterprise, building a highly engaged and enriching employee base. This type of mentoring can be the key to new opportunities and for industries to push their limits with creativity and collaboration.

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