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Planning doesn’t have to be the enemy of agility

Planning has long been one of the cornerstones of management. At the beginning of the 20th century, Henri Fayol defined the work of managers as planning, organizing, directing, coordinating and controlling. The ability and willingness of managers to plan developed throughout the century. Management by Objectives (DPO) became the height of business craze in the late 1950s. The world seemed predictable. The future could be planned. So it seemed sensible for managers to identify their goals. They could then focus on managing to achieve those goals.

It was the capitalist equivalent of the five-year plans of the communist system. In fact, a management theorist from the 1960s suggested that the best-run organizations in the world were the Standard Oil Company of New Jersey, the Roman Catholic Church, and the Communist Party. The belief was that if the future was mapped out, it would happen.

Later, the MBO evolved into strategic planning. The companies created large corporate units dedicated to this. They were deliberately detached from the day-to-day reality of the company and emphasized formal procedures around numbers. Henry Mintzberg defined strategic planning as “a formalized system to codify, develop, and operationalize the strategies that companies already have.” The fundamental belief remained that the future could be largely predicted.

Now, strategic planning has fallen out of favor. In the face of incessant technological change, disruptive forces in one sector after another, global competition, etc., planning seems like a meaningless illusion.

And yet planning is clearly essential for any business of any size. Look at your own organization. Having a workplace equipped for it, and having you and your colleagues work on a specific project at a specific time and place, requires some planning. The reality is that you have to make plans about the use of a company’s resources all the time. Some are short-term, others extend into an imagined future.

Universally valuable but desperately old-fashioned, planning waits like an old maid in a Jane Austen novel for someone to recognize its worth.

But executives are wary of planning because they find it rigid, slow, and bureaucratic. Fayol’s legacy persists. A 2016 HBR Analytics survey of 385 executives revealed that most executives were frustrated with planning because they believed speed was important and that plans changed frequently anyway. Why go through a slow and painful planning exercise if you won’t even stick to the plan?

The frustrations with current planning practices intersect with another fundamental management trend: organizational agility. Reorganization around small, self-managed teams – enhanced by agility methods like Scrum and LeSS – is emerging as the route to the organizational agility needed to compete in the changing business reality. One of the key principles underpinning team-based agility is that teams autonomously decide their priorities and where to allocate their own resources.

The logic of long-term centralized strategic planning (done once a year at a fixed time) is the antithesis of an organization redesigned around teams defining their own priorities and assigning resources on a weekly basis.

But if planning and agility are necessary, organizations have to make them work. They have to create a Venn diagram with planning on one side, agility on the other, and a practical and workable sweet spot in the middle. Therefore, the search for a rethinking of strategic planning has never been more urgent and critical. Planning in the style of the 21st century must be reconceived as agile planning.

Agile planning has a number of characteristics:

  • Frameworks and tools capable of facing a future that will be different
  • The ability to cope with more frequent and dynamic changes
  • The need to invest quality time for a true strategic conversation rather than just a numbers game;
  • Availability of resources and funds in a flexible way for the opportunities that arise.

The intersection of planning with organizational agility generates two other overriding requirements:

A process capable of coordinating and aligning with agile teams

Agile organizations face the challenge of managing local squad autonomy (bottom-up input) consistent with a broader vision represented by tribe goals and interdependencies between tribes and communities.
strategic priorities of the organization (top-down vision). Controlling this tension requires new planning and coordination processes and routines.

Consider the Dutch financial services company ING Bank. It restructured its operations in the Netherlands by reorganizing 3,500 employees into agile squads. These are autonomous multidisciplinary teams (up to nine people per team) capable of defining their work and making business decisions quickly and flexibly. Squads are organized into a Tribe (of no more than 150 people), a set of squads working in related areas.

ING Bank revised its process and introduced routine meetings and formats to create alignment between and within tribes. Each tribe produces a QBR (Quarterly Business Review), a six-page document outlining priorities, objectives and key results at the tribe level. It is then discussed in a large alignment meeting (called the QBR Marketplace) attended by tribal leaders and other relevant leaders. This meeting addresses a fundamental question: when we add everything together, does this contribute to the strategic objectives of our company?
The alignment within a tribe occurs in what is called a Portfolio Market event: representatives of each of the squads that make up the tribe meet to agree on how the set objectives will be achieved and to address opportunities for synergies.

The ING Bank example shows how the planning process is still necessary and essential for an agile company, albeit in a different way, with different processes, mechanisms and routines.
As more and more companies transform into agile organizations, agile planning is likely to become the new normal to replace the traditional central planning approach.

A process that uses both unlimited hard data and human judgment

Traditionally, planners have been obsessed with collecting hard data about their industry, their markets, and their competitors. Soft data – networks of contacts, conversations with customers, suppliers and employees, use of intuition and the vine – have been practically ignored.
Starting in the 1960s, planning was built around analysis. Now, thanks to Big Data, the ability to generate data is practically unlimited. This does not necessarily allow us to create better plans for the future.
Soft data is also vital. “Although hard data can inform the intellect, it is to a large extent the soft data that generates wisdom. They can be difficult to ‘analyze’, but they are indispensable for synthesis, the key to developing strategy,” says Henry Mintzberg .

Businesses need to imagine the possibilities first, and choose the one with the most compelling argument second. To decide which is the most convincing argument, they must take into account all the data that can be obtained. But, in addition, they must use qualitative judgment.

In an agile organization, teams use design thinking and other exploratory techniques (in addition to data) to make quick decisions and change course on a weekly basis. Decision-making is carried out by a team of people, thus compensating for the possible biases of a single person who makes a decision based on their individual judgment. To some extent, an agile team-based organization allows the ability to leverage qualitative data and judgment – combined today with infinite hard data – to make better decisions.

Relying solely on hard data has undoubtedly killed many would-be large companies. Take Nespresso, the pioneer of coffee pods developed by Nestlé. Nespresso took off when it stopped heading to offices and started marketing to homes. There was little data on how households would respond to the concept, and available information suggested a consumer perceived value of just 25 Swiss cents, compared to a company-required threshold of 40 cents. The Nespresso team had to skillfully interpret the data to present a better case to top management. Because he firmly believed in the idea, he forced the company to take a higher risk than usual. If Nestlé had been guided solely by quantitative market research, the concept would never have taken off.

The traditional approach to planning needs to be revised to better serve the purposes of the agile business of the 21st century. Agile planning is the future of planning. This new approach will require two fundamental elements. First, replace the traditional obsession with hard data and the numbers game with a more balanced coexistence of hard and soft data in which judgment also plays an important role. Second, the introduction of new mechanisms and routines that ensure alignment between the hundreds of self-organized local autonomous teams and the general objectives and directions of the company.

Why do more employees need data knowledge?

By Maria Korolov
August 04, 2020

Encouraging employees to learn data can benefit any business. Read on for some of the benefits and resources that can be leveraged in building those skills.

Research shows that data-driven organizations are more successful, but employees often lack the skills to handle data.

According to a 2020 survey by Sapio Research, 80% of decision makers considered that opening access to data has a positive impact on their organizations and 74% said that employees have access to data what do you need. But 53% of those surveyed reported employee reluctance to use the data.

Meanwhile, research has consistently shown that data-driven businesses are more successful. A 2019 survey by McKinsey & Company, a management consulting firm, found that companies in which employees consistently use data in decision-making are 1.5 times more likely to report growth in revenue. income of more than 10% in the last three years.

The difference comes down to data literacy.

“It’s crucial in today’s world where data is ubiquitous,” said Shreeni Srinivasan, director of business analytics and application delivery at Sungard Availability Services. “Data literacy can empower employees to make fact-based analytical decisions that are more grounded in reality than those made by instinct or intuition.”

According to the same McKinsey & Company survey, the proportion of executives in high-performing organizations who understand data concepts is 44% higher, the proportion of managers who understand data is 39% higher, and the number of employees in first line that comprise the data is 12% higher than that of other surveyed companies.

However, there are significant obstacles to understanding the data.

According to Gartner, 50% of organizations lack sufficient knowledge

on data to achieve business value, and 35% of CIOs said

that the lack of knowledge about data is one of the main obstacles to the

effective use of data and analysis, right behind cultural challenges and lack of

resources and financing.

What is data literacy?

Data literacy is the ability to write and understand data in a way similar to how we view literacy with reading. This may include understanding where the data comes from, communicating the information derived from the data to others, and knowing where to use different analytical tools and methods.

“When companies have more data-savvy employees, they understand that data is no longer just the domain of the data team,” said Andrew Stewardson, data manager for Farm Credit Services of America, a provider of credit to farm operators. and ranches based in Omaha, Neb. “Having a higher level of knowledge of the data means that we can better serve our customers.”

Stewardson’s organization took an unusual approach to data literacy training by creating an internal person, Walt, to answer data-related questions from employees.

“The key to fostering the data literacy training was getting Walt to connect with various people within the organization,” said Michael Meyer, data engineer for Farm Credit Services of America. “We also created a blog where users could ask questions about everything related to data.”

That took the pressure off data teams to drive change, he added.

“Simply putting data in the hands of individuals in an organization does not automatically increase knowledge of the data and build an organization on it,” Stewardson said.

In fact, launching data projects without paying attention to data literacy can be a costly mistake. ”

For example, Penny Wand, chief technology officer for West Monroe Partners, a Chicago-based technology and management consulting firm, was working on a project for a manufacturing company to deploy pricing strategy analysis.

“People just rejected it,” he said. “They didn’t understand the results.”

The project was a failure, and the company not only wasted the time and money it spent creating the analysis, it also lost millions of dollars in missed opportunities, Wand said.

“It cost them money because they couldn’t optimize their pricing strategy,” she said.

“They lost money by not being able to put into practice what they learned from the data.”
Many people have been out of school for a long time, and their math and data analysis skills are not on a level playing field, Wand said. And this not only hurts basic analysis projects.
“Forget AI, without a certain level of knowledge of the data, it’s not going to get there”,
said.

Who is responsible for data literacy education?
Unfortunately, there aren’t currently many best practices and guidelines to follow when it comes to teaching employees in data literacy, Wand said.
“We are in the infancy of formal data literacy programs,” he said.
One approach is to make education specific to the role the employee has in the organization, he said. That is what Coursera is doing with its Academy of Data Science. Another approach is to get education to people as they need it, as they are using their data tools, rather than a formal training program.
Then there is the challenge of measuring success.
Many companies are tackling the challenge of data literacy by moving data scientists out of data science departments and into business units where they work closely with end users, said Bryan Coker, senior data and analytics consultant at AIM Consulting Group, a Seattle-based management consultancy.
“I think that’s a trend almost everywhere,” he said.
Another approach is to provide specific, hands-on workshops for employees, focusing on the specific analytical tools used in the business and the specific business challenges around data that the business and its employees face.
These workshops can be led by tool vendors or independent consultants, said Justin Richie, director of information science for Nerdery, a technology consultancy.
“I firmly believe that people learn best by doing,” Richie said. “So having the ability to get your hands on a keyboard or laptop and do something is about creating that contextual awareness of doing it yourself. It’s better than sitting in a college-sized auditorium and listening to someone speak for eight hours.”
Or, these days, the equivalent of the Zoom, he added.

How can companies promote literacy of data?
Not everyone wants to run off to learn math.
“That would be a good day at work for me, just taking math classes,” said Jeff Herman, a data science instructor at the Flatiron School in New York.
Other employees may need some support.

In Herman’s previous job at a railroad company, data scientists led training sessions for other employees on data and how to use it to do their jobs better.
“We were talking about basic statistics,” he said. “We were talking about different databases:
Here’s a locomotive database, here’s where the trains go, here’s the finances, and here’s how to access the data. ”
Companies looking to do something similar should look for communication skills when hiring data scientists.
“People who can communicate with non-technical stakeholders and are comfortable conducting the training,” Herman said.

“Data literacy is not only a benefit for the company, but also for you. It will open more doors for you, it will make you more marketable. Jeff Herman Information Science Instructor, Flatiron School ”.

Flatiron School also has a free information science prep course in addition to its regular information science curriculum, he said. Khan Academy also offers many free courses that cover everything from basic statistics to data analysis.
But it’s not just about making training available, Herman said.
“Companies need to talk about what the benefit is for the employee,” he said. “It is not only a benefit for the company, but also for you. It will open more doors for you, it will make you more marketable.”
Plus, data literacy starts at the top, with the executive team.
“They have to be comfortable with the idea of ​​making decisions based on the data,” Herman said.

Benefits of improving data knowledge
When the only people looking at the data are data scientists, important insights can be lost. For example, at the railroad company where Herman used to work, a key indicator was the downtime of the locomotive.
“We thought it was a waste of fuel,” Herman said.
But when other people in the company outside of the data analytics teams started using Power BI, they were able to see the same data from a different perspective.
“The people closest to the locomotive operations knew there were specific reasons why it sometimes had to idle,” he said. “They were able to make a dashboard for when a locomotive was idling, when it shouldn’t be, and focus on where we could really save fuel.”
Not surprisingly, in a recent Forrester Research survey, 90% of respondents and analytical decision makers saw increasing the use of data in business decision making as a priority.
But with only 48% of decisions based on quantitative information and analysis, there is plenty of room for improvement.

Should Robots Conduct Job Interviews?

November 2020. The I.A. is playing a new role in recruiting. By RACHEL WITHERS.

“Congratulations, you have been selected for an interview for the position of professional mini-player at the Open Mind Corporation,” announces a robotic voice over a blank screen. “My name is Alex. I will guide you in the interview. The whole process will not take more than 10 minutes. Let’s hear your voice. … Smile at the camera. … Welcome to the interview.”

This is the beginning of “Interview with Alex”, a dystopian interactive online experience that takes viewers to a “job interview” conducted by an AI hiring manager, one that measures the tone to score users on a ” Mental State Index “. Carrie Sijia Wang, the multimedia artist behind the project, writes that her work is intended to “critique the present by speculating about the future.” But you’re not that far off what your next job interview might be, if you’re applying for a high volume of low-skilled (or even some high-skilled) jobs. A growing number of real-life recruiters are turning to A.I.-led job interviews, using programs that interview and screen candidates before a human recruiter even sets eyes on them.

“Chances are, if you apply for the type of job that attracts a lot of applicants, you will be interviewed by an A.I. eventually”.

Alex won’t interview you, but it could be Hubert, or Ella, or Tengai, or Phai, or just automated words on a screen. Most bots aren’t running the decision-making process from start to finish (although they sometimes do – see Ryan Fan’s OneZero article “I got a job at an Amazon warehouse without speaking to a single human”) . Instead, recruiters generally use artificial intelligence at the “top of the funnel” to rank or rank candidates before they reach a still human stage. Like humans, these bot recruiters have their own unique interview styles. Some are only looking for logistical information, such as availability and ongoing interest, while others may be looking to assess drive, initiative, teamwork skills, adaptability, or even your tendency to jump from one job to another. Some will ask everyone the same set of questions in the same order, while others will tailor their questions to you, verifying that you can actually do the things that you say you can.

For many positions, each applicant receives an automatic link for the interview. Applicants are free to enter the “interview” on their own time, and there will often be practice questions that they can try before tackling the official questions. Some are text-based, while others require applicants to be videotaped. Questions, asked by a bot or a prerecorded message (or, in one case, by a disembodied head), are usually fairly common probes: Tell us about your previous experience, why are you interested in this company? But there is no human listening. Responses are recorded and analyzed by A.I., marking the candidate’s suitability on certain traits, before human recruiters use this analysis to decide who to invite for another interview or hire. By the time the answers are reviewed by a human (if at all), I.A. has already passed judgment.

Chances are, if you are applying for the type of job that attracts many applicants, you will eventually be interviewed by one. So often is the Fast Company running an article telling readers “4 Things You Must Do to Prepare for an A.I.-Powered Job Interview,” while LinkedIn publishes a free A.I. Interview Practice Tool. on video. And as much as the companies behind them tell me otherwise, you may not like it.

Read More

What is a Data Warehouse and how do you know when you need to implement it? by PowerData Newsroom

In the Data 4.0 era where information is increasingly essential for business decision-making, modernized, agile and intelligent data management is essential. Along these lines, understanding what a data warehouse is and the different options that the company has when storing data implies having a clear data strategy and accepting that it is a key element to ensure normality and the smooth running of business operations.

Before entering the world of data warehouses, it is necessary to understand what a data warehouse is and why it is important for companies today.

A data warehouse is a unified repository for all the data collected by the various systems of a company that can be hosted in a data center or in the cloud. This data storage architecture enables business executives to organize, understand, and use their data to make strategic decisions.

You may be interested in continuing reading The future of the Data Warehouse is in the cloud

The future of the Data Warehouse is in the cloud

Data repository, efficient operations

The centralization of information combines historical records with current data and, in this way, the reporting function is enriched. Any report is made from data from different sources (marketing, sales, production or finance, for example) and, in addition, the business gains visibility, increasing its chances of discovering trends and developing agile and precise answers. Having a data warehouse reduces the time it takes to find and analyze important data, making operations more efficient.

¿How do you know when the business is ready?

The complexity of the operations is one of the determining factors when considering the construction of a warehouse of this type. When the volume of data to process and analyze is almost overwhelming, it is necessary to have a good information management plan, it cannot be improvised.

Minimizing risk depends on decisions like this. And it is that, traditional methods such as spreadsheets, are designed to work with a fixed amount of data that, if exceeded, begins to generate problems of agility, reliability or completeness.

It is much easier to control data quality in a centralized data warehouse than in multiple independent repositories. The appearance of duplication is one of the clearest evidence of this type of data quality issue. But it is also that not putting the necessary means to promote interdepartmental collaboration has its consequences. The lack of a data warehouse, coupled with reliance on spreadsheets, makes data governance difficult.

Imagine a situation that can be very familiar in many organizations:

  • Spreadsheets used by almost every department in the company.
  • Diverse business owners.
  • Need to work based on data, of all kinds, historical and current.
  • Generation of manual reports.
  • Volume of information growing progressively.

In this scenario, where is the efficiency? Who controls the information? How can the time invested in each process be reduced? A centralized data warehouse does it.

Keep reading Cloud storage a green option?

 

Benefits of implementing a data warehouse

The results of implementing a data warehouse do not take long to be seen, promoting benefits such as the following:

  • Reporting agility: optimizing the time required for reporting is one of the first signs of working with a data warehouse. It is no longer necessary to go to different sources to check if the data is updated, or to manually keep it updated. There is no longer any lost or isolated information. Everyone knows that all the data, in the best quality conditions, are in the central warehouse.

  • Reduction of waiting times:eliminating inefficient processes and even tensions between departments. Sometimes users lack time to be able to deal with sharing certain information and, other times, the problem is that they do not even know where to find the data that solves the query they must manage. Implementing a data warehouse can help centralize data and make quality information available to all members of the organization more efficiently.
  • Single version of the truth:how many times have discrepancies appeared between reports from different departments, and even between data and reports. What is the valid option? Which one can you trust? It takes a long time to resolve these types of conflicts, which, if undetected, lead to serious errors. However, by implementing a data warehouse, duplicate records are eliminated, errors and inconsistencies disappear, and the information used as the basis for reporting is accurate, complete and up-to-date. In short, data warehouses allow companies greater efficiency, reliability, accessibility and speed when it comes to storing and accessing information for making business decisions.

Source: blog.powerdata.es

The true promise of synthetic data

Researchers at MIT launch the Synthetic Data Vault, a set of open source tools aimed at expanding access to data without compromising privacy.

Every year, the world generates more data than the previous year. In 2020 alone, an estimated 59 zettabytes of data will be “created, captured, copied and consumed,” according to the International Data Corporation, enough to fill about a trillion 64-gigabyte hard drives.

But just because data is proliferating doesn’t mean everyone can use it. Companies and institutions, legitimately concerned about the privacy of their users, often restrict access to data sets – sometimes within their own computers. And now that the Covid-19 pandemic has closed laboratories and offices, preventing people from visiting centralized data warehouses, sharing information securely is even more difficult.

Without access to data, it is difficult to create tools that really work. Introduce synthetic data: Artificial information developers and engineers can use it as a substitute for real data.

Synthetic data is a bit like diet soda. To be effective, they have to look like “real” ones in certain ways. Diet soda should look, taste, and bubble like regular soda. Similarly, a synthetic data set must have the same mathematical and statistical properties as the real-world data set it represents. “It looks alike, and has a similar format,” says Kalyan Veeramachaneni, principal investigator at the Data Laboratory for AI (DAI) and principal investigator scientist at the MIT Decision and Information Systems Laboratory. If run through a model, or used to build or test an application, it performs as real-world data would.

But – just as diet soda must have fewer calories than the regular variety – a synthetic data set must also differ from a real one in crucial ways. If it is based on an actual dataset, for example, it should not contain or even imply any information from that dataset.

Threading this needle is difficult. After years of work, Veeramachaneni and his collaborators recently unveiled a set of open source data generation tools – a one-stop shop where users can get as much data as they need for their projects, in formats from tables to time series. They call it the Synthetic Data Vault.

Maximize access while maintaining privacy
Veeramachaneni and his team first attempted to create synthetic data in 2013. They had been tasked with analyzing a large amount of information from the edX online learning program, and they wanted to bring in some MIT students to help. The data was sensitive, and couldn’t be shared with these new hires, so the team decided to create artificial data that students could work with instead, thinking that “once they wrote the processing software, we could use it in the real data, “says Veeramachaneni.

This is a common scenario. Imagine that you are a software developer hired by a hospital. You have been asked to build a dashboard that allows patients to access their test results, prescriptions, and other health information. But you are not allowed to see any actual patient data, because it is private.

Most developers in this situation will make “a very simplistic version” of the data they need, and they will do the best they can, says Carles Sala, a researcher at the AID lab. But when the board kicks in, there’s a good chance that “everything will fall off,” he says, “because there are some borderline cases that they weren’t taking into account.”

High-quality synthetic data, as complex as what it is intended to replace, would help solve this problem. Companies and institutions could share them freely, allowing teams to work more collaboratively and efficiently. Developers could even carry it on their laptops, knowing they weren’t putting any sensitive information at risk.

Refine the formula – and handle the constraints
In 2013, the Veeramachaneni team took two weeks to create a data pool that they could use for that edX project. The timeline “seemed really reasonable,” says Veeramachaneni. “But we completely failed.” They soon realized that if they built a series of synthetic data generators, they could make the process faster for everyone else.

In 2016, the team completed an algorithm that accurately captures correlations between different fields in a real data set – think about a patient’s age, blood pressure, and heart rate – and creates a synthetic data set that preserves those relationships, without any identifying information. When data scientists were asked to solve problems using this synthetic data, their solutions were as effective as those made with real data 70 percent of the time. The team presented this research at the IEEE International Conference on Data Science and Advanced Analytics in 2016.

For the next step, the team delved into the machine learning toolbox. In 2019, PhD student Lei Xu presented her new algorithm, CTGAN, at the 33rd Conference on Neural Information Processing Systems in Vancouver. CTGAN (for “Conditional Tabular Generative Adversary Networks”) uses GANs to build and refine tables of synthetic data. GANs are pairs of neural networks that “play with each other,” says Xu. The first network, called the generator, creates something – in this case, a row of synthetic data – and the second, called the discriminator, tries to tell whether it is real or not.

“Eventually, the generator can generate perfect [data], and the discriminator can’t tell the difference,” Xu says. GANs are most often used in artificial imaging, but they also work well for synthetic data: CTGAN outperformed classic synthetic data creation techniques in 85 percent of the cases tested in Xu’s study.

Statistical similarity is crucial. But depending on what they represent, the data sets also come with their own context and vital limitations, which must be preserved in the synthetic data. AID lab researcher Sala gives the example of a hotel ledger: a guest always leaves after checking in. Dates in a synthetic hotel reservation dataset should also follow this rule: “They have to be in the correct order,” he says.

Large data sets can contain a number of different relationships like this, each strictly defined. “Models cannot learn constraints, because constraints are very context-dependent,” says Veeramachaneni. So the team recently finished an interface that allows people to tell a synthetic data generator where those limits are. “The data is generated within those limitations,” says Veeramachaneni.

That accurate data could help businesses and organizations in many different industries. One example is banking, where increased digitization, coupled with new data privacy regulations, has “sparked growing interest in ways to generate synthetic data,” says Wim Blommaert, team leader for financial services at the ING. Current solutions, such as data masking, often destroy valuable information that banks could use to make decisions, he said. A tool like SDV has the potential to sidestep the sensitive aspects of data while preserving these important limitations and relationships.

One vault to rule them all
The Synthetic Data Vault combines everything the group has built so far into “a whole ecosystem,” says Veeramachaneni. The idea is that stakeholders – from students to professional software developers – can come to the vault and get what they need, be it a large table, a small amount of time series data, or a mix of many different types of data.

The vault is open source and expandable. “There are a lot of different areas where we find that synthetic data can also be used,” says Sala. For example, if a particular group is underrepresented in a sample data set, synthetic data can be used to fill in those gaps – a sensible endeavor that requires a lot of finesse. Or companies may also want to use the synthetic data to plan for scenarios they have not yet experienced, such as a large increase in user traffic.

As use cases keep popping up, more tools will be developed and added to the vault, Veeramachaneni says. It may occupy the team for another seven years at least, but they are ready: “We are only touching the tip of the iceberg.”

Robotic interviews, machine learning and the future of the recruitment of the workforce.

This would affect all aspects of HR functions like how HR professionals embark and hire people, and the way they train them.

Artificial intelligence (AI) is changing every aspect of our lives and that too at a rapid pace. This includes our professional lives as well. Experts hope that in the coming days, AI will become a more important part of our careers as all companies are making progress in adopting such technology. They are using more machines that use AI technology that would affect our daily professional activities. Very soon, we would see machine learning and deep learning in HR as well. It would affect all aspects of HR (human resources) such as the way HR professionals embark and hire people, and the way they train them.

Impact on onboarding and recruiting

Companies are also using machine learning and deep learning in HR to help provide on-the-job training to employees. Just because you’ve gotten a job and settled into it doesn’t mean you know everything. You need to get job-related training so you can keep improving. This is where experts expect AI to play a major role in the years to come. It will also help a generation of professionals in an organization transfer their skills to their successors. This will ensure that no company ever suffers from a skill shortage. Increase in the workforce Robotics in human resources will play an important role in improving the people who work in organizations in which the management applies that technology. One of the main reasons people are so afraid of using AI in an organization is that they feel that it would replace them and that they would do everything they can do now. This will consequently lead to job losses. However, in today’s scenario, AI is all about augmenting that workforce. This means that it would help you do your job more efficiently. Contrary to popular opinion, she would not replace you.

Surveillance of the workplace

Companies can also use machine learning and deep learning in HR to improve their workforce surveillance work. This is uncomfortable for many employees as they feel that such technology would invade the privacy of their workplace. Recently, Gartner conducted a survey that found that more than half of companies with an annual turnover of more than $ 750 million use digital tools to obtain data on their employees’ activities and monitor their overall performance. As part of this, they analyze their emails to find out how engaged and happy they are with their work.

The use of workplace robots

Aside from robotics in HR, companies today also use physical robots that can move by themselves. This is especially true for warehousing and manufacturing companies. Experts hope that this will soon become a common feature in many other workplaces as well. Mobility companies are creating delivery robots that can move around the workplace and deliver items directly to your desk. Tech companies are also developing security robots. Experts believe they would become commonplace because they can ensure the security of commercial properties against intruders. Companies are also developing software to help you park your cars in your office.