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Understanding Performance Management, in Context When you hear the term “performance management,” what are a few thoughts that come to your mind? You may first think about the performance of an employee and then the performance appraisal process. If so, you are heading in the right direction. Many managers may be under the assumption that the performance appraisal process is the same as performance management; however, they are misinformed. The performance appraisal process allows a manager to assess an employee’s performance within a specified time frame. The performance appraisal is employee driven, whereas performance management focuses on methods used by an organization to remain competitive and aids the organization in developing strategic business goals. While the performance of an employee is one component of the operation, there are multiple layers required for an organization to reach the desired mission and goals. Sound performance management procedures require the employees, teams, and leaders to take a methodical approach in setting and achieving organizational goals. Within a performance management structure, you will find the following components to help employees attain career and organizational goals:

Employees and managers need to set objectives.

Managers must constantly assess the progress of employees.

Managers should offer constructive feedback and coaching.

Employees should be recognized and rewarded for achieving organizational goals.

When the HR professional implements a well-designed performance management system, it helps put the employees and managers on a positive track. The employees have clearly defined goals to achieve. The managers are able to set benchmarks to assess the results and reward employees according to their achievements. However, a performance system that is poorly designed can be considered unfair by employees, can spur on employee misconduct, and could possibly end in litigation.

The HR professional must ensure that the performance management system aligns with the strategic goals of the organization.

Components of a Well-Designed Performance Management System Performance management system is a tool a manager can use to assist the employees in meeting the organization’s operational and strategic goals. The manager can proactively use the performance reviews to manage employee performance, assess opportunities for areas of growth, and set new employee goals. Ultimately, a well-designed performance management system creates a win-win situation for all.

Review the tabs to know more about the components of a well-designed performance management system.

Oversee the employees’ performance.

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Example: Coaching, mentoring, and providing constructive and positive feedback on an area for improvement.

Constantly set new goals for the employees and assist them in achieving departmental and organizational goals. Example: Using SMART goals.

Ensure employees are knowledgeable of the job requirements by offering constant and continual training. Example: Offering on-the-job training or having the employees attend offsite training.

Reward and recognize employees when they receive exceptional reviews. Example: Giving nonmonetary and monetary rewards.

Evaluate, coach, and implement plans for corrective action and constantly communicate with the employees regarding their performance. Example: Combining the performance appraisal with SMART goals.

Additional Materials

From your course textbook, Performance Management, read the following chapter:

Performance Management and Reward Systems in Context

From the South University Online Library, read the following article:

Reward Management: Linking Employee Motivation and Organizational Performance 

South University

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Modeling Uncertainty in ML and NLP

Sunil Kumar Parisa

University of Cumberland’s

ITS 836 – Data Science and Big Data Analytics

Dr. Kelly Wibbenmeyer

26th July 2020

Abstract

Big data analytics is the capacity to deal with huge volumes of information with shifting arrangements and multifaceted nature from related in- formation, semi-organized information, weblogs, gadget information, and unstructured configurations. Capacity to get bits of knowledge about your items (brands), clients, and workers from online life information and connect with your exchange framework information. Big data analytics is mak- ing life is easier. Anything which you use on a daily basis might be a result of big data analytics. ML techniques are normally not computationally efficient or effective enough to handle big data features as well as vulnerability. NLP procedures can help with making new traceable interfaces and recoup detectability joins by finding semantic closeness among accessible printed ancient rarities.

Keywords: Big data analytics, machine learning techniques, natural language processing

Addressing Uncertainty in ML and NLP

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Addressing Uncertainty in ML and NLP

When working with big data analytics, ML is usually used to develop prediction models as well as knowledge gathering to enhance information-dri- ven dynamic. There are several ML procedures recommended for big data assessment; these procedures comprise element learning, deep learning, move to learn, circulated learning, and dynamic learning (Hassani, 2018). Feature learning involves several methods that help a system automatical- ly find the designs needed to collect data and the classification of unprocessed data. ML algorithm's performance is mainly affected by the choice of information depiction. Deep learning algorithms are meant to break down as well as generate critical information from huge sums of information and also information collected from various sources; however, current deep learning models require a high computational cost. Distributed learning can moderate the adaptability application of traditional ML via completing computations on informational indexes adopted among a few workstations to scale up the learning process. Transfer learning involves the use of information gathered from one source to a new source; at the same time, enhancing data movement from one area by moving data from a related space. Dynamic learning involves calculations that use ver- satile information collection forms that subsequently change parameters to gather the most useful information as fast as it could reasonably be ex- pected to accelerate ML activities and avoid naming challenges (Lue, 2019). The vulnerability challenges of ML procedures can be basically ascribed to gathering information with low integrity i.e., dubious and inadequate information as well as information with low value irrelevant to the present issue. Among the ML techniques, active learning, deep learning, as well as fuzzy rationale hypothesis, are extremely recommended to assist in vul- nerability test that reduces the level of risks. Risks can have a big impact on ML so long as poor or uncertain training tests, indistinct classifica- tion limits, and harsh information on the objective information. At times, the information is presented without names, which can be a challenge.

Representing Uncertainty Resulting From Big Data Analytics

Marking big data physically can be a challenge in terms of cost and exhausting in terms of labor. At the same time, using unlabeled data is very difficult as classifying information with hazy rules muddled outcomes. Active learning has addressed this problem by determining a subset of the most significant event for marking. Deep learning is another learning technique that can deal with inadequacy and irregularity challenges in the classification methodology. NLP has a reputable set of techniques and tools which cover both written and spoken languages (Walker, 2015). NLP is also applicable in many areas such as machine translation, information gathering, speech recognition, optical character recognition, spell checking, and many others. Machine Learning (ML), on the other hand, is an approach that could be used in Natural Language Processing and many other fields such as data sciences, decision-making systems, and artificial intelligence. We can easily say that NLP is an interdisciplinary computing field, while ML is a set of strategies and tools to address as well as solve different challenges in a variety of computing fields, including NLP. However, we should not forget that these topics are so getting entangled and intertwined, which makes it difficult to establish a clear line between their defini- tions. Natural language processing provides clarification to the above-mentioned problems using the vocabulary selection method, understanding synonyms, antonyms, homonyms using wordnet, lexicon formation, relationship identification, and Name entity recognition Stanford parser. NLP is an aid to ML and also Deep futuristic learning. Moreover, NLP augmented to ML reduce the search space and make it a guided search.

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As a result, classier don't overfit while training and accuracy are improved. The addition of Semantics to NLP is a major thrust in today’s Learn- ing community.

Enhancing ML and NLP to Handle Big Data

NLP technique is integrated with ML, which helps gadgets to assess, decode, and even create content. NLP and big data handle huge amounts of content information and gradually get an incentive from such a dataset. Some common NLP practices comprise lexical procurement, word sense disambiguation i.e., determining which type of word is used in a sentence in an event a word has different implications and grammatical feature (POS) labeling i.e., hinder mining the capacity of the words through marking classes, for example, action word, thing, and so forth. Several NLP- based techniques have been used to conduct mining, including data gathering, theme demonstration, content outline, classification, grouping, ques- tion feedback, and supposition mining. For instance, financial and fraud detection may include finding proof of wrongdoing in huge datasets (Mora- bito, 2017). NLP technique especially named content extraction and data recovery can help oversee and scan through colossal measures of factual data, for example, criminal names and bank records, to support misrepresentation evaluation.

Impacts of Natural Language Programming in Big Data

Moreover, NLP and big data can be utilized to assess news stories and foresee rises and falls on the composite stock value file. The vulnerability affects NLP in big data in various ways. For instance, the catchphrase search is an exemplary methodology in data mining that is used to deal with a lot of factual information. Watchword search acknowledges as information a rundown of applicable words or expressions and searches the ideal arrangement of information for events of the significant words. The vulnerability can affect catchphrase search, as an archive that contains a watch- word isn't a confirmation of a report's pertinence. For instance, a catchphrase search, for the most part, coordinates accurate strings and overlooks words with a spelling error that may at present be important. Boolean administrators and fluffy pursuit innovations license more prominent flexibil- ity in that they can be utilized to scan for words like the ideal spelling. While big data using AI holds a ton of guarantee, a wide scope of challenges is presented when such methods are exposed to vulnerability. For example, every one of the attributes presents various sources of vulnerability, un- structured, inadequate, or noisy data. Moreover, the vulnerability can be installed in the whole assessment process. For instance, managing inade- quate and loose data is a basic test for most information mining and ML procedures (Hussain, 2016). Also, an ML algorithm may not get the ideal outcome if the preparation information is one-sided in any capacity. Scaling these worries up to the big data level will effectively exacerbate any er- rors or inadequacies of the whole investigation process. Accordingly, a moderating vulnerability in big data analytics must be at the cutting edge of any robotized procedure, as the vulnerability can have a significant influence on the exactness of its outcomes.

Conclusion

Data Analytics and Data Science can solve any and all business problems regardless of whether we have big data or regular data. However, the only difference with the big data analytics will be that we will be typically dealing with large and unstructured data on some sort of distributed computing such as Hadoop, AWS, etc. E-commerce issues with optimization of raw material stocks, rotation of goods, a decrease in warehouse space, and lo- gistics cost can be solved with the help of linear programming and the methods of big data analysis.

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Student paper 99% Student paper 97%

References

Hassani, M. (2018). Overview of efficient clustering methods for high-dimensional big data streams. Clustering Methods for Big Data Analytics, 25-42. https://doi.org/10.1007/978-3-319-97864-2_2

Hussain, A., & Roy, A. (2016). The emerging era of big data analytics. Big Data Analytics, 1(1). https://doi.org/10.1186/s41044-016-0004-2

Lue, R. (2019). Data science as a foundation for inclusive learning. 1.2. https://doi.org/10.1162/99608f92.c9267215

Morabito, V. (2015). Big data and analytics innovation practices. Big Data and Analytics, 157-176. https://doi.org/10.1007/978-3-319-10665- 6_8

Walker, R. (2015). Impact of analytics and big data on corporate culture and recruitment. From Big Data to Big Profits, 184-201. https://doi.org/10.1093/acprof:oso/9780199378326.003.0009

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University of Cumberland’s ITS 836 – Data Science and Big Data Analytics

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University of the Cumberland’s ITS- 836 Data Science & Big Data Analytics

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Big data analytics is the capacity to deal with huge volumes of informa- tion with shifting arrangements and multifaceted nature from related in- formation, semi-organized informa- tion, weblogs, gadget information, and unstructured configurations. Ca- pacity to get bits of knowledge about your items (brands), clients, and workers from online life information and connect with your exchange framework information. Big data an- alytics is making life is easier. Any- thing which you use on a daily basis might be a result of big data analytics.

Original source

Big data analytics is the capacity to deal with huge volumes to informa- tion with shifting arrangements and multifaceted nature from organized information, semi-organized infor- mation, weblogs, gadget information and unstructured configuration Ca- pacity to get bits of knowledge about your items (brands), clients and workers from online life information and connect with your exchange framework information Big data an- alytics is making life is easier Any- thing which you use on daily basis might be result of big data analytics

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Student paper 89%

Student paper 74%

Student paper 81%

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NLP procedures can help with mak- ing new traceable interfaces and re- coup detectability joins by finding se- mantic closeness among accessible printed ancient rarities.

Original source

Additionally, NLP procedures can as- sist with making new traceable inter- faces and recoup detectability joins by finding semantic closeness among available printed ancient rarities

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Big data analytics, machine learning techniques, natural language processing

Original source

Analytics techniques in data mining, deep learning and natural language processing

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Distributed learning can moderate the adaptability application of tradi- tional ML via completing computa- tions on informational indexes adopted among a few workstations to scale up the learning process.

Original source

Distributed learning can be utilized to moderate the adaptability issue of customary ML via completing com- putations on informational indexes appropriated among a few worksta- tions to scale up the learning procedure

3

Student paper

Dynamic learning involves calcula- tions that use versatile information collection forms that subsequently change parameters to gather the most useful information as fast as it could reasonably be expected to ac- celerate ML activities and avoid nam- ing challenges (Lue, 2019). The vul- nerability challenges of ML proce- dures can be basically ascribed to gathering information with low in- tegrity i.e., dubious and inadequate information as well as information with low value irrelevant to the present issue.

Original source

Dynamic learning alludes to calcula- tions that utilize versatile informa- tion collection (i.e., forms that conse- quently alter parameters to gather the most helpful information as fast as could reasonably be expected) so as to quicken ML exercises and over- come naming issues (Dasgupta, 2018) The vulnerability difficulties of ML procedures can be basically as- cribed to gaining from information with low veracity (i.e., dubious and inadequate information) and infor- mation with little value (i.e., irrele- vant to the present issue)

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Student paper

Risks can have a big impact on ML so long as poor or uncertain training tests, indistinct classification limits, and harsh information on the objec- tive information. At times, the infor- mation is presented without names, which can be a challenge.

Original source

The vulnerability can affect ML as far as inadequate or uncertain training tests, indistinct classification limits, and harsh information on the objec- tive information At times, the infor- mation is spoken to without names, which can turn into a test

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springeropen 71%

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Student paper 96%4

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Representing Uncertainty Resulting From Big Data Analytics

Original source

Uncertainty in big data analytics

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Student paper

Active learning has addressed this problem by determining a subset of the most significant event for mark- ing. Deep learning is another learn- ing technique that can deal with in- adequacy and irregularity challenges in the classification methodology. NLP has a reputable set of tech- niques and tools which cover both written and spoken languages (Walk- er, 2015). NLP is also applicable in many areas such as machine transla- tion, information gathering, speech recognition, optical character recog- nition, spell checking, and many others.

Original source

Active learning has explained this is- sue by choosing a subset of the most significant occasions for mark- ing Profound learning is another learning strategy that can deal with inadequacy and irregularity issues in the classification methodology NLP has an established set of method- ologies, tools, and techniques that cover both written and spoken (not to mention signed) languages (Nas- raoui & N'Cir, 2018) Also, it has large application areas such as machine translation, information extraction, speech recognition, optical character recognition, spell checking, and such

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Student paper

Machine Learning (ML), on the other hand, is an approach that could be used in Natural Language Processing and many other fields such as data sciences, decision-making systems, and artificial intelligence. We can easily say that NLP is an in- terdisciplinary computing field, while ML is a set of strategies and tools to address as well as solve different challenges in a variety of computing fields, including NLP. However, we should not forget that these topics are so getting entangled and inter- twined, which makes it difficult to es- tablish a clear line between their de- finitions. Natural language process- ing provides clarification to the above-mentioned problems using the vocabulary selection method, understanding synonyms, antonyms, homonyms using wordnet, lexicon formation, relationship identifica- tion, and Name entity recognition Stanford parser.

Original source

Machine Learning (ML), on the other hand, is an approach that could be used in Natural Language Processing and many other fields such as data sciences, decision-making systems, and artificial intelligence We can per- haps say that NLP is an in- terdisciplinary field in computing, while ML is a set of approaches and tools to address and solve different problems in a variety of computing fields, including NLP However, we should not forget that these topics are so getting entangled and inter- twined, which makes it difficult to es- tablish a clear line between their de- finitions Natural language process- ing provides clarification to the above-mentioned problems using the vocabulary selection method, understanding synonyms, antonyms, homonyms using wordnet, lexicon formation, relationship identifica- tion, and Name entity recognition (Stanford parser)

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Student paper 100% Student paper 77%3

Student paper

NLP is an aid to ML and also Deep futuristic learning. Moreover, NLP augmented to ML reduce the search space and make it a guided search. As a result, classier don't overfit while training and accuracy are im- proved. The addition of Semantics to NLP is a major thrust in today’s Learning community.

Original source

NLP is an aid to ML and also Deep futuristic learning Moreover, NLP augmented to ML reduce the search space and make it a guided search As a result, classier don't overfit while training and accuracy are im- proved The addition of Semantics to NLP is a major thrust in today's Learning community

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Student paper

NLP technique is integrated with ML, which helps gadgets to assess, de- code, and even create content. NLP and big data handle huge amounts of content information and gradually get an incentive from such a dataset. Some common NLP practices com- prise lexical procurement, word sense disambiguation i.e., determin- ing which type of word is used in a sentence in an event a word has dif- ferent implications and grammatical feature (POS) labeling i.e., hinder mining the capacity of the words through marking classes, for exam- ple, action word, thing, and so forth. Several NLP-based techniques have been used to conduct mining, includ- ing data gathering, theme demon- stration, content outline, classifica- tion, grouping, question feedback, and supposition mining.

Original source

NLP is a strategy integrated into ML that empowers gadgets to assess, decipher, and even create content NLP and big data handle large mea- sures of content information and can get an incentive from such a dataset progressively Some stan- dard NLP practices include lexical procurement, word sense disam- biguation (i.e., figuring out which feeling of the word is utilized in a sentence when a name has different implications), and grammatical fea- ture (POS) labeling (i.e., hinder min- ing the capacity of the words through marking classes, for exam- ple, action word, thing, and so forth) A few NLP-based methods have been used to content mining, includ- ing data extraction, theme demon- strating, content outline, classifica- tion, grouping, and question feed- back, as well as supposition mining

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For instance, financial and fraud de- tection may include finding proof of wrongdoing in huge datasets (Mora- bito, 2017). NLP technique especially named content extraction and data recovery can help oversee and scan through colossal measures of factual data, for example, criminal names and bank records, to support mis- representation evaluation.

Original source

For instance, financial and extortion examinations may include finding proof of wrongdoing in massive datasets (Morabito, 2017) NLP strategies (uniquely named sub- stance extraction and data recovery) can help oversee and filter through colossal measures of literary data, for example, criminal names and bank records, to support misrepre- sentation evaluation

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Impacts of Natural Language Pro- gramming in Big Data

Original source

Natural language processing and big data

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Moreover, NLP and big data can be utilized to assess news stories and foresee rises and falls on the com- posite stock value file. The vulnera- bility affects NLP in big data in vari- ous ways. For instance, the catch- phrase search is an exemplary methodology in data mining that is used to deal with a lot of factual in- formation. Watchword search ac- knowledges as information a run- down of applicable words or expres- sions and searches the ideal arrangement of information for events of the significant words.

Original source

Moreover, NLP and big data can be utilized to investigate news stories, and foresee rises and falls on the composite stock value file Vulnera- bility influences NLP in vast informa- tion in various ways For instance, a catchphrase search is an exemplary methodology in content mining that is used to deal with a lot of literary knowledge Watchword search ac- knowledges as information a run- down of applicable words or expres- sions and searches the ideal arrangement of data for events of the significant words

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Student paper 97% Student paper 87%

Student paper 97%

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The vulnerability can affect catch- phrase search, as an archive that contains a watchword isn't a confir- mation of a report's pertinence. For instance, a catchphrase search, for the most part, coordinates accurate strings and overlooks words with a spelling error that may at present be important. Boolean administrators and fluffy pursuit innovations license more prominent flexibility in that they can be utilized to scan for words like the ideal spelling. While big data using AI holds a ton of guar- antee, a wide scope of challenges is presented when such methods are exposed to vulnerability.

Original source

The vulnerability can affect catch- phrase search, as an archive that contains a watchword isn't a confir- mation of a report's pertinence For instance, a catchphrase search, for the most part, coordinates accurate strings and overlooks words with a spelling error that may at present be important Boolean administrators and fluffy pursuit innovations license more prominent flexibility in that they can be utilized to scan for words like the ideal spelling While big data using AI holds a ton of guar- antee, a broad scope of difficulties is presented when such methods are exposed to vulnerability

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Student paper

For example, every one of the attrib- utes presents various sources of vul- nerability, unstructured, inadequate, or noisy data. Moreover, the vulner- ability can be installed in the whole assessment process. For instance, managing inadequate and loose data is a basic test for most informa- tion mining and ML procedures (Hussain, 2016). Also, an ML algo- rithm may not get the ideal outcome if the preparation information is one-sided in any capacity.

Original source

For example, every one of the V at- tributes present various sources of weakness, for example, unstruc- tured, inadequate, or noisy data Moreover, the vulnerability can be installed in the whole assessment process For instance, managing in- sufficient and lose data is a basic test for most information mining and ML procedures (Ghosh & Liv- ingston, 2019) Also, an ML algorithm may not get the ideal outcome if the preparation information is one-sided in any capacity

3

Student paper

Scaling these worries up to the big data level will effectively exacerbate any errors or inadequacies of the whole investigation process. Accord- ingly, a moderating vulnerability in big data analytics must be at the cut- ting edge of any robotized proce- dure, as the vulnerability can have a significant influence on the exact- ness of its outcomes.

Original source

Scaling these worries up to the high data level will effectively exacerbate any errors or inadequacies of the whole investigation process Accord- ingly, a moderating vulnerability in big data analytics must be at the cut- ting edge of any robotized proce- dure, as vulnerability can have a sig- nificant influence on the exactness of its outcomes

7/26/2020 Originality Report

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Student paper 81%

Student paper 100%

Student paper 96%

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Clustering Methods for Big Data An- alytics, 25-42.

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Clustering methods for big data analytics

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Hussain, A., & Roy, A.

Original source

Hussain, A., & Roy, A

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The emerging era of big data analyt- ics. Big Data Analytics, 1(1). https://doi.org/10.1186/s41044-016- 0004-2

Original source

The emerging era of Big Data Analyt- ics Big Data Analytics, 1(1) doi:10.1186/s41044-016-0004-2

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Student paper

Big data and analytics innovation practices. Big Data and Analytics, 157-176.

Original source

Big Data and Analytics Innovation Practices Big Data and Analytics, 157-176

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Student paper

https://doi.org/10.1007/978-3-319- 10665-6_8

Original source

doi:10.1007/978-3-319-10665-6_8

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Impact of analytics and big data on corporate culture and recruitment. From Big Data to Big Profits, 184- 201. https://doi.org/10.1093/acprof:oso/9 780199378326.003.0009

Original source

Impact of Analytics and Big Data on Corporate Culture and Recruitment From Big Data to Big Profits, 184-201 doi:10.1093/acprof:oso/9780199378 326.003.0009

South University

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Understanding the Performance Management Process You are a new HR Manager for a company with 250 employees. During your third week of employment, you have discovered that none of the employees have job descriptions. Some employees have worked for the company over five years. Employees have been classified as exempt when, in fact, they are nonexempt employees according to the Fair Labor Standards Act (FLSA). In addition, the company has never established a performance management process. Employee morale is low, employees are not performing at an optimal level, and employee reviews have not been conducted for two years. What are the necessary steps to take in order to get the organization on track?

First, you must have a clear understanding of the organization’s mission and strategic goals and then create a list of jobs. Without a list of jobs, it will be virtually impossible to begin the process. There are multiple steps in understanding the performance management process:

1. Know the mission and strategic goals of the company.

2. Conduct a job analysis.

3. Write job descriptions.

4. Implement a well-designed performance management process.

5. Determine when performance appraisals will be conducted and how coaching feedback will be given.

6. Define the type of employee behavior required to attain the desired outcome.

7. Establish a set of new goals to attain and set benchmarks.

The scenario suggested above actually happened. After three weeks of employment in her first role as an HR Manager, this manager found the organization to be in total disarray. Prior to her onboarding, an employee had filed a grievance since he was classified as exempt when he was actually a nonexempt employee. Employees were disgruntled because they had not received a raise in over two years. The company did not have a clear idea of the mission or strategic goals. When the performance management process was clearly defined and linked to the strategic goals of the organization, employee morale lifted.

Benefits of the FLSA Employers are required to follow the guidelines established by the FLSA to ensure employees are properly compensated for the work performed.

Review the tabs to study the benefits of the FLSA through the given case studies.

Case Study 1

Case Study 2

South University

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The manager of your fast-food restaurant has advised all employees to clock out after their eight-hour shift; however, the manager insists that the employees cannot leave until the restaurant is cleaned and ready for the morning crew. The manager has been trying to remain within the monthly budget. A disgruntled employee filed a claim since she has not received overtime pay. The franchise is now facing a class action lawsuit.

Locate a court case to understand the repercussions for an employer failing to adhere to the FLSA.

What are some other options the manager could have used to remain within the monthly budget?

What are five critical areas that you have discovered from reading the court case you selected?

How can you apply this knowledge to your current or future organization?

Additional Materials

From your course textbook, Performance Management, read the following chapter:

Performance Management Process

From the South University Online Library, read the following article:

10 Performance Management Process Gaps 

South University

file:///C|/Users/CWATKIM/Desktop/Organizational%20Performance%20and%20Strategic%20Planning.html[7/15/2020 6:50:26 PM]

Organizational Performance and Strategic Planning Consider for a moment that you decide to go skiing. There is signage directing you on the safe slopes and advising you of the slopes that are not safe. Throughout the week, you followed the signs and adhered to the designated path for all skiers. You are now very confident with your ability to ski on the black run. The next day, you seek more of a challenge and take the path that is off-limits. As you are heading back to the lodge, a whiteout occurs. When it clears, you are totally disoriented and cannot find your way back to the lodge.

Lack of strategic planning is like a whiteout and can leave an entire organization disoriented. How can the HR professional prepare the employees, managers, and leaders to understand and utilize performance management and strategic planning on a daily basis? The HR professional must ensure that everyone understands the concept of strategic planning, which is to determine the organization’s future goals, recognize obstacles, develop a plan to ensure the organization has clear visibility to move forward, and maintain competitive advantage. The performance management system must depend on the strategic planning process that has been executed if it is to be of any use to the organization. “The behaviors, results, and developmental plans of all employees must be aligned with the vision, mission, goals, and strategies of the organization and unit” (Aguinis, 2013, p. 81). The HR professional must be sure that each manager and leader realize that a well-designed performance management system is the main component for the successful implementation of an organization’s strategic plan. All employees, managers, and leaders must adhere to the policies that have been established.

Reasons Why Strategic Plans Fail Strategic planning is a process that involves describing the organization’s destination, assessing barriers that stand in the way of that destination, and selecting approaches for moving forward. The main goal of strategic planning is to allocate resources in a way that provides organizations with a competitive advantage. There are several reasons strategic plans fail.

(Aguinis, 2013, p. 60)

Review the tabs to study the reasons behind the fail of strategic plans through examples.

Additional Materials

From your course textbook, Performance Management, read the following chapter:

Performance Management and Strategic Planning

From the South University Online Library, read the following article:

South University

file:///C|/Users/CWATKIM/Desktop/Organizational%20Performance%20and%20Strategic%20Planning.html[7/15/2020 6:50:26 PM]

The Missing Link to Strategic Planning: Meeting Methodology Lines Up Performance Management with Execution 