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Introduction to AppDynamics Tool

Web development, programming languages, Software testing & others

Explanation of AppDynamics Tool

The approach taken by these tools is not similar to other tools. They use analytics, and the details of every application are taken into consideration. This will not only help the AppDynamics but also the logs of the applications. For every transaction done in the application, the tool collects the details, whether it is basic or detailed, and this makes the AD tool more friendly to anyone who uses it. Because of any detail, be it basic or more detailed, can be seen in the reports or logs of this tool.

The AD tool can be used in production as well as development environments. In a production environment, all the transaction with its performance is noted down by the tool. This is mainly built for production environments as the tool itself follows an agile approach within itself. Normal performance is noted down, and alarms are given if any issues are found in the application. This gives the proper response time for the application in the tool.

A comparison is done for the response time and the user’s response time. This comparison helps to find out the tool’s performance with the self-learning curve. Troubleshooting can be done easily as the agent automatically collects all the details, even for normal behavior. This is an example of an analytics method, and this helps the tool to find out the alerts set for different problems. Hence the user can fix it very well as these are set before any major impact so as to ensure proper working of the application with the help of the tool.

We know that if the data is captured deeply, all the information related to the working of the application and its environments can be identified easily. But this data capturing is not easy as it requires more resources and more storage. AD tool has come as a rescue over here as it records the entire working of the application even if the app does not show any mode of failure. This makes the tool to use its analysis power to manage the performance of the application.

This recording helps to monitor the application and use it in testing and also in pre-production environments. Every request and every transaction are noted down or recorded by the AD tool, which helps to monitor the application’s performance. If the developer is left with any other work, they can instruct the tool to work by itself or stop when the transaction volume is more than the limit. Hence, the tool stops by itself if it feels the volume is increased or that enough information is collected. This alert helps the system to work well, and its performance is not halted due to overworking hours.

Agents are working along with the AD tool, and they monitor the performance of the application, support the infrastructure of the tool, and know the tool in and out. This makes the tool to work efficiently with human support. They know the entire application ecosystem and its environment, and the performance data logs are taken. This log data is sent to the controllers so that the application performance can be seen. This visual representation is done through a user interface in the system.

Accessibility issues will not happen in this tool as it is designed to work in any environment suitable for the application with all the access rights. Agents are working with the controllers, and real-time performance is seen. This helps boost the performance and orchestrate the bandwidth of the application and the tool being used.

The application in any environment will make many requests. The agents know these requests through the tool, and these requests are made to create a request map. This helps to manage and visualize the performance of the application along with the transaction history.

When a transaction is made, be it a business transaction or file transfer, the details, including a request made, response time, resolution time, and the files corresponding to the transaction, is taken care of. This helps to manage the requests in a log analytics manner with all the details in hand. Failure of the application will not be a worry, or can I say that the application will not fail if proper monitoring is done along with the AD tool monitoring?

Being part of Cisco, AppDynamics tools have incorporated machine learning and artificial intelligence to monitor and manage data. Several machine learning techniques such as anomaly detection, regression, and many others will be helpful to monitor and detect spams and unauthorized requests. This will reduce the work of agents who monitor the AD tool in the system. Also, machine learning helps to gather data and create logs by itself rather than creating reports in the system with external tools. Business performance metrics are identified and created with the help of machine learning. This helps to diagnose the problems in the application and to manage them perfectly. Autoscaling is another technique used in the AD tool with the help of Artificial Intelligence.

Conclusion – AppDynamics Tool Recommended Articles

This is a guide to AppDynamics Tool. Here we discuss the explanation of the AppDynamics Tool in detail for better understanding. You can also go through our other related articles to learn more –

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Managing Multiple Databases On A Single Server

Setting up a single database per instance or server makes database management a piece of cake. However it can very quickly drive up the cost of your database solutions as you now have to purchase a new server and new SQL Server license for each database you wish to host.

In order to combat this expensive setup, people will typically host multiple databases (and therefore multiple applications) on a single server or instance. While this will begin to ease the costs of hosting all these various databases, it increases the complexity of managing these systems as you now have multiple Service Level Agreements (SLAs) and maintenance windows to work with.

When you decide to host several databases on the same server the first thing to look at is whether these systems have complementary maintenance windows. If one system cannot be slow or offline in the middle of the night, and another cannot be slow or offline during the middle of the day, these may not be the best systems to share a server as you will have effectively no maintenance window to work with in the event that you need to patch the system or take the system offline for other reasons.

The next deciding factor you need to look at is the SLAs for the systems. Systems that require 99% uptime can reside together since you will likely build a much more robust environment for these (a clustered solution perhaps) than you would for systems that are not mission critical. This can save you some additional costs as you now have fewer high-end systems to purchase. The systems with the higher service level agreements also probably have similar maintenance windows, so these systems will probably be complementary to begin with.

Of course the most important thing to consider when combining databases onto a single SQL Server is if there are enough CPU and memory resources to handle the load that the clients will be placing on the database server. If a single server cannot provide the required CPU and memory resources then combining the databases onto that server would not be a good choice.

After you have gone through this entire decision process and have put the databases onto the same server, how do we keep the systems healthy and running at their peak? Like any other database solution you will still need to handle your backups, index defrags and rebuilds, and patching of both the operating system and the SQL Server.

The biggest trick to handling the maintenance on SQL Servers with several databases on them is timing. You need to make sure that your maintenance tasks can all be performed within the scheduled window of all your databases being hosted on the SQL Server. Running the maintenance tasks outside of the maintenance window for any of the databases will cause all the databases to run slower as disk and CPU resources are now being taken up with maintenance activities instead of handling normal database queries.

For example let’s say that each week you check your index fragmentation and it shows 70% fragmentation. So you run an index rebuild to clean up the indexes.

However what happens if you check the index fragmentation the next day? It will probably be about 8-10% fragmentation. So if you run the defrag job daily instead of running the reindex job weekly there will be much less work to do each day and the job will complete much faster, possibly within the daily maintenance window.

And even if you can’t take the system offline during that window, as a defrag operation is an online operation the system will continue to function just with a slower response rate than normal while the defrag operation is running.

Backups are another key issue to address when dealing with multiple databases on a single server.

Each database may have its own backup requirements. Backing up databases is probably the most brutal task that can be performed on a live SQL Server. Not because of the amount of CPU power or RAM it takes (which is typically very low unless you are doing compression on the database while it is being backed up) but because of the massive amount of disk resources which are required to backup up a large database.

When performing a full backup the entire database must be read from the disk. If you have a very busy disk system, the backup could bring performance to its knees. The best solution for this is proper timing. You may also need to look to a third party tool that allows for database backup compression while the backup is running. As this will increase the CPU load on the SQL Server, it will usually greatly decrease the time it takes to complete the backup as much less data needs to be written to the backup device.

These are only a couple of the techniques which can be used to help maintain the database server while running several databases on the single system. Hopefully you will find them useful while working on your database consolidation projects.

Nokia 6.1 Plus Performance Test: Great Performance All Around

The Nokia 6.1 Plus has been launched starting at Rs. 15,999 and unlike the Chinese variant (Nokia X6), this one comes only in the 4GB/64GB variant, sporting a Qualcomm Snapdragon 636 octa-core processor. We’ve already covered the battery test of this new smartphone from HMD Global, and if you’re wondering what the performance of the phone is like, we’ve done that for you too.

Nokia 6.1 Plus Specifications

Before we dive into the tests we performed on the Nokia 6.1 Plus and the way it handled different tasks, let’s get the specifications out of the way.

Display5.8-inch FullHD+ 2280×1080 pixels

ProcessorSnapdragon 636

GPUAdreno 509



Primary Camera16MP f/2.0 + 5MP f/2.4

Secondary Camera16MP f/2.0

Battery3,060 mAh

OSAndroid 8.1 Oreo

SensorsAmbient light sensor, Proximity sensor, Accelerometer (G-sensor), E-compass, Gyroscope, Fingerprint Sensor (on the back)

ConnectivityWiFi 802.11 a/b/g/n/ac; Bluetooth 5.0; GPS/AGPS+GLONASS

Now that we’ve gotten those out of the way, let’s jump into how the phone actually performs in various benchmarks and real world performance tests.


We ran the usual benchmarks on the Nokia 6.1 Plus to get an overview of what we can expect from the phone before we put it through its paces in our real world tests. We also benchmarked the Redmi Note 5 Pro since it’s in a similar price range so as to get a comparative idea of where the two phones stand.



We also benchmarked both the Redmi Note 5 Pro and the Nokia 6.1 Plus on AnTuTu and here the Nokia 6.1 Plus beats the Redmi Note 5 Pro, though not by a huge margin. While the Redmi Note 5 Pro scored 112,569 on AnTuTu, the Nokia 6.1 Plus got a slightly higher score of 115,398.

Real World Performance

While the benchmark scores of the Nokia 6.1 Plus are basically indicative of the fact that we can expect Redmi Note 5 Pro like performance from the phone. To test out the real world performance I put the phone through some gaming tests, and I used it as my daily driver for almost a week.

Gaming Tests

Starting off with Asphalt 9, the newest game from Gameloft has some incredible graphics and races that are more intense than ever. In terms of perceived performance, I think the phone went through the game like a charm and I didn’t notice any stutters or lags while racing through People’s Square or in Cairo. It was all pretty awesome and I loved it. In terms of frame rate, Asphalt 9 ran at a median frame rate of 30FPS, and had 89% stability. True, that could’ve been higher, but it’s still a decent score to get.

Moving on to PUBG Mobile, the game auto-set the graphics to Low, which I expected because of the Snapdragon 636 and 4GB of RAM that the phone has. Surprisingly though, PUBG wouldn’t even let me turn up the graphics settings. Anyway, in Low settings, the game performed smoothly (as it should) and I faced no issues with getting a Chicken Dinner. It was definitely a fun experience even though I’m used to playing on high settings with my OnePlus 5. In terms of frame rates, the phone got a median frame rate of 26FPS which is okayish, though nothing to be overly proud of, and had 87% stability in its FPS values which is definitely good for a game like PUBG Mobile.

I also played Marvel Strike Force on the Nokia 6.1 Plus and as expected, the game ran perfectly fine. I personally don’t enjoy this game all that much, but it’s definitely a fun game. In terms of frame rates, the phone churned out an easy 55FPS median frame rate with a stability of 77%.

Everyday Usage Tests

In terms of everyday usage, the Nokia 6.1 Plus won’t disappoint you. Thanks to stock Android running on Snapdragon 636 and with 4GB of RAM, the phone doesn’t lag even when I’m using multiple apps and constantly switching between them. I also found that app launches are generally fast on the Nokia 6.1 Plus, although sometimes apps like Instagram would take a millisecond longer to launch which can get infuriating, but I won’t blame the Nokia 6.1 Plus for that; I’ve observed Instagram behaving like this on my OnePlus 5 as well.

The phone has standard Android animations though, and those can sometimes make things feel slower than they actually are. I’d recommend you to turn down the ‘Transition animation scale’ inside Developer Settings to 0.5x to get a snappier feel without feeling like the phone is absolutely not animating anything at all.

Nokia 6.1 Plus Performance Test: A Great Performer for Its Price

So basically, the Nokia 6.1 Plus is a pretty great performer for the Rs. 15,999 price it’s coming in at. The phone has decent hardware for the price, and if you look at it that way, it’s basically a Redmi Note 5 Pro with stock Android. It performs pretty much the same way as the Redmi Note 5 Pro does, however, in some places, especially in games like PUBG Mobile, I feel like Xiaomi’s offering performs slightly better, though not really noticeably so. It’s more or less a personal choice at this point. If you want a metal build, gesture navigation, and performance that won’t let you down, the Redmi Note 5 Pro is the one for you; however, if you want stock Android, decent hardware, and performance that is definitely pretty great, the Nokia 6.1 Plus is the phone to go with.

Buy the Nokia 6.1 Plus from Flipkart (Rs. 15,999)

Case Study: Managing Social Media Marketing In The Real World

How Vision Express developed their social media marketing strategy

In this case study, we talk to Kate Webb, the Online Marketing Manager at Vision Express. Kate talks about how they have developed a strategy and manage social media marketing across the Vision Express teams. Thanks for sharing your experiences, Kate!

The response to the growing popularity of social media

How big an impact has the increase in popularity of social media with consumers had on Vision Express?

It’s had quite an impact in terms of time and resource, especially in the early days.

As a company we’re relatively new to social media, we’ve only been active for just over eighteen months. We spent a lot of time during the first 3-6 months listening, watching and learning what consumers were saying about our brand/looking for from our brand, in order to decide on how we should communicate, and where – which platforms.

During this time we have seen both our follower/fan numbers grow, but more importantly the engagement with our customers is increasing and we feel that our customers are really starting to converse with us as a brand.

Since being involved in social media, we have seen an increase in the number of customers who mention us directly, or seek us out, rather than simply mentioning our brand name in passing conversation. To us this is an important development in building our customer relationship.

At Vision Express our social media activities are based on engaging with our existing customer base, we want to improve on relationships, or continue offline relationships, with our customers, online. In the optical industry we have a long purchase cycle, on average our customers come back to us every 2 years, so it is a long period during which to maintain our social media relationships.

“We have found that for probably about 1-2%, of our customer base, social media is their main point of contact with us. The type of communication varies between the different social media platforms, for example we find that Twitter is more of a customer service tool, whilst Facebook is a fun and engaging platform, suitable for promotional outreach”.

There is still progress to be made, especially since social media grows and platforms are developed/changed, but we’re confident we’re on the right track to providing the same high level of service, that our customers get in our stores, online.

Social media strategy

What do you see as the key parts of a social media strategy that require management?

I find that too often businesses think that social media is just about posting messages about the company on Twitter or Facebook, or getting an agency in to handle everything for them. But the key to making social media work, for me, is to have a strong strategy behind it, and to manage that strategy.

For me the key areas of focus in this strategy should be:

Brand/Business persona: I feel it’s key to define a persona or personality for your business and to identify how you want to position your brand on social media, is the brand/business fun/funky, calm/serious, sensitive/nurturing or brash/loud? You need flexibility to evolve this over time as your relationship with customers grows.

Goals/Objectives: It is important to ensure that your social media objectives or goals are aligned with that of your organisation. What is it that you want to achieve via social media. For Vision Express, our 3 critical goals are to:

• Add value and service to our online customers, via informative dialogue, responsive customer service and feedback. This also works as a 2-way path, in that we then pass onto our store network all/any feedback we have received from our online customers.

• Engage with our online customers, and build relationships with them. In order to do this effectively, we are working towards a one customer view database, which will enable us to match social media activity to in-store activity by our customers, thus enabling us to provide a tailored approach in our conversations.

• Build brand awareness and consumer knowledge about our service offering. We want our customers to understand our company, and to recognise our values, ethics and personality, online & offline.

Which Platforms?: There are hundreds of social media platforms that we could all be involved in, so it’s key to identify which platforms support your business objectives, and which ones you are going to get involved with. Otherwise resources and communication will simply be spread too thinly.

Analytics/Results: Be this sentiment or engagement levels, reporting on results/analytics needs to be regular, managed and analysed in order to adapt future strategy.

Prioritising different opportunities from social

How should a company assess the relevance of different social media opportunities to prioritise their focus?

Having clear objectives and a clear strategy will help. Enabling you, on a case by case basis, to identify what social media opportunities work for which promotion/aspect of the business.

It’s important for any business/brand not to spread their actions/activities too thinly, identify where the majority of your customers are and focus on engaging well with your customers on a few platforms.

As well as identifying which platforms to be active on, it’s important to also understand to what extent you work with these platforms, does your business need/require interactive apps or games? Or is simple communication the key to your social media engagement.

If through doing these engagement activities we acquire customers, then great, but this isn’t our primary focus.

Tackling social media listening

Listen, listen and listen some more. Social media isn’t about who shouts the loudest, it’s about engaging in conversation with your customers/prospective customers and about keeping them informed.

There are some free tools which you can use at the very beginning, such as Tweetdeck or Hootsuite, but bear in mind these are often limited to either 1 platform, or to scheduling outreach messages only.

If you are really serious about social media, and I think companies need to be these days, you need to enlist a social media monitoring platform, which will enable you to listen to what consumers are saying about your brand across micromedia (Twitter/Facebook), blogs and forums.

You won’t be able to respond to all consumer mentions, due to forum rules, but you can at least listen and feed this back into the business, so you can modify activities, or continue doing popular ones!

Start small, don’t overstretch your resources, and be realistic about the amount of time/resource and money social media can take up.

A few key things to remember are once you start talking, you need to continue the commitment to maintain the conversations, and ensure you gain inter-company awareness, there is nothing worse than talking to a customer via Twitter, and then having them go into store to be presented with “We’re on Twitter? I didn’t know that”.

You will also need to get to know your customers, the ideal solution here is to integrate social media activities into your core customer database, so you have one customer view, but this can take time, money and resource. In the interim, the better social media monitoring tools these days are offering engagement platforms, which allow you to add notes and assign tasks, so you can build up a reasonable knowledge of your social media customers.

How to manage social media?

Where do you think the responsibilities for managing social media marketing in a company should lie? How is it managed at Vision Express?

By spending our first 3 to 6 months listening to what our customers were saying about our brand/looking for from our brand, we managed to identify that our social media activities needed to be part of the whole business, not just an ‘add-on’ to our marketing activities.

It is important that social media activities have management ‘buy-in’ in any business. It needs to be integrated into core business activities if it is going to work properly.

To integrate these activities into different departments correctly requires management support, the management structure need to understand why/how/who social media impacts on and affects both internally and within our customer base.

As a result, so far, we’ve integrated social media into a couple of key departments within the business, with the Online Marketing team as social media ‘owners’, in that we will identify the next strategic steps, bring in agency support, provide understanding of new developments and report on analytics and progress.

To have social media as purely a marketing tool/activity, will restrict a business in providing the right level of customer care, and will lead to sporadic/untimely and unfocused outreach.

Getting Started With Julia – A High Level, High Performance Language For Computing

Learning new tools and techniques in data science is sort of like running on treadmill – you have to run continuously to stay on top of it. The minute you stop, you start falling behind.

As part of this learning, I continuously look out for new developments happening in new tools and techniques. It was in this desire to continuously learn that I came across Julia about a year back. It was in very early stages then – it still is!

But, there is something special about Julia, which makes it a compelling tool to learn for all future data scientists. So, I thought to write a few articles on it. This is first of these articles, which provides the motivation to learn Julia, its installation, current packages available and ways to become part of Julia community.

What is Julia?

Julia is a high-level, high-performance dynamic programming language for technical computing, with easy to write syntax. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

Why another programming language?

C compiled by gcc 4.8.2, taking best timing from all optimization levels (-O0 through -O3). C, Fortran and Julia use OpenBLAS v0.2.12. The Python implementations of rand_mat_stat and rand_mat_mul use NumPy (v1.8.2) functions; the rest are pure Python implementations.

A Summary of Features in Julia

Some of the important features to highlight from data science capabilities are:

A more comprehensive list of features can be accessed here

Installation of Julia

Now that you might be raring to give Julia a try for all the promises made above, let me quickly walk through various options to test drive your new sedan (which has sports car like acceleration):

Option 1: Try Juliabox in browser – The simplest of option – no setup required. Just go to Juliabox, sign in using Google (sorry, if you don’t have a Google account – try the next version) and your instance is ready to fire.

Option 2 – Use an IDE – Juno seems to be the best IDE available right now. Sadly, JuliaStudio is no longer supported. The best way to install it is to download the combo package from Julia site itself.

Option 3 – Using Command line – If you are the hardcore programmer, who can’t think of a programming language without a command line, don’t worry! There is an option for you as well. You can download the package here.

Option 4 – Using iJulia notebooks – If you are a Python explorer and have used iPython for your interactive data exploration – here is an awesome news. iJulia notebooks are equally awesome and carry over similar interface. In order to install iJulia, you need to install iPython first, then install Julia 0.3 or later. Next start Julia and add package “IJulia” and start using it. You can find more details here.

A few important packages

There are a total of 610 packages on Julia as on date (9th July 2023). If you filter out packages for which tests have failed or which have not been tested, you are only left with 381 packages. Among these I have filtered out the ones related to data science and have more than 15 stars. That leaves us with the following packages:

Package Description Version Stars

BackpropNeuralNet A neural network in Julia 0.0.3 18


Bokeh Bindings for Julia 0.1.0 26


Restricted Boltzmann Machines in Julia 0.1.0 19


Calculus functions in Julia 0.1.8 46


A Julia package for data clustering 0.4.0 33


A julia package for disciplined convex programming. 0.0.6 108


Utilities for calling C++ from Julia 0.1.0 18

DataArrays Data structures that allow missing values 0.2.16 21


library for working with tabular data in Julia 0.6.7 206


Metaprogramming tools for DataFrames 0.0.1 33


Julia implementation of Data structures 0.3.10 52


Decision Tree Classifier and Regressor 0.3.8 36


A package for evaluating distances(metrics) between vectors. 0.2.0 21


A package for probability distributions & associated functions. 0.7.4 101


Filter design, periodograms, window functions, and other digital signal processing functionality 0.0.8 32


Functional and and persistent data structures for Julia 0.1.2 34


Crafty statistical graphics for Julia. 0.3.13 684


A lightweight framework for writing genetic algorithms in Julia 0.0.3 86


Generalized linear models in Julia 0.4.6 78


Wrapper for fitting Lasso/ElasticNet GLM models using glmnet 0.0.4 23


Working with graphs in Julia 0.5.5 90


Saving and loading Julia variables 0.4.18 65


Hypothesis tests for Julia 0.2.9 16


An image library for Julia 0.4.39 73


Modeling language for Mathematical Programming (linear, mixed-integer, conic, nonlinear) 0.9.2 162


Julia Machine Learning library 0.0.3 37


Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia 0.4.11 44


Markdown parsing for Julia 0.3.0 21


Advanced Pattern Matching for Julia 0.1.3 29


A Julia package for fitting (statistical) mixed-effects models 0.3.22 41


A set of functions to support the development of machine learning algorithms 0.5.1 41


Deep Learning framework for Julia 0.0.8 297


A Julia package for multivariate statistics & data analysis (e.g. dimension reduction) 0.2.1 21


Package to call the NLopt nonlinear-optimization library from the Julia language 0.2.1 31


Julia OpenStreetMap Package 0.8.1 20


Optimization functions for Julia 0.4.2 116


Heterogeneous ensemble learning for Julia. 0.0.5 27


A Julia framework for probabilistic graphical models. 0.0.1 25


Package to call Python functions from the Julia language 0.8.1 183


Embedded R within Julia 0.2.1 16


Julia package for loading many of the data sets available in R 0.1.2 34


Algorithms for regression (e.g. linear / logistic regression) 0.3.2 17


Julia-to-R interface 0.0.12 47


Basic statistics for Julia 0.6.15 57

StreamStats Compute statistics over data streams in pure Julia 0.0.2 27

TimeSeries Time series toolkit for Julia 0.5.10 37

P.S. There is a lot of development happening on the language and the libraries. So this can change very quickly.

A few things to note:

Gadfly looks to be the most popular package. This might well be because it is being used as a showcase library across all the products in the ecosystem

The core data science libraries look more evolved than some of the other libraries. Mocha for DeepLearning, Orchestra for optimization, DataFrames or distributions are all on more evolved version comparatively

How to install & use a package?

Installing and using a package in Julia is dead simple. If you want to install / add a package, simply type this in your programming interface


This will install the package as well as its dependencies.

Once the package is installed, you can load it simply by calling “using”

using Gadfly


The Julia ecosystem:

Julia is supported by a close knit community of developers. Here are a few mailing lists, you can be a part of:

julia-news – for important announcements, such as new releases.

julia-users – discussion around the usage of Julia. New users of Julia can ask their questions here.

julia-stats – special purpose mailing list for discussions related to statistical programming with Julia. Topics of interest include DataFrame support, GLM modeling, and automatic generation of MCMC code for Bayesian models.

julia-opt – discussions related to numerical optimization in julia. This includes Mathematical Programming (linear, mixed-integer, conic, semi-definite, etc.), constrained and unconstrained gradient-based and gradient-free optimization, and related topics.

In addition to these newsletter, you can also look at chúng tôi . The site looks like a developing ecosystem as of now though.

End Notes

I hope that you have got a good overview of this powerful language under development. I was pretty excited when I saw it first and I continue to check this language for new developments closely. In the next articles to come, we will understand the data structured available in Julia, its interface with other languages e.g. Python and solve one of the case studies using Julia to understand its power.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.


Guiding Principles Of Performance Management

Performance management is also a technique for maximizing the efficiency of a workforce. While many believe it is a once-a-year occurrence, this is not the case. It is a never-ending process. It is a strategic plan designed to maximize an individual’s production quality. It effectively matches company objectives with an employee’s productivity when done correctly.

The guiding principles of performance management should be about people and performance, not just the process involved.

Some of the guiding principles of performance management are as follows −

Performance Analysis

In performance management, the reviewer measures the frequency of behavior of the employee and the outputs he/she gives prior to any kind of management changes. Through this analysis, the management measures the current performance, establishes standards, specifies the deficiencies, calculates the value of improvement. The aim of this analysis is to achieve these identifications of potentially high-payoff behaviors and also the outputs that can be improved.


It is critical to maintain openness to ensure employees feel comfortable and involved. Ambiguity in any program breeds distrust and uncertainty. Transparency enables individuals and teams to easily understand how their objectives correspond with the company’s goals. When employees are left alone with limited information, they are more likely to feel under control and pressed.

In order to be an independent employee and be able to take initiatives, every employee must have the necessary information at their disposal. This refers to, for example, transparency about relevant company data, availability of resources to be utilized to increase work efficiency, and company goals. This way can employees take responsibility for goals and synchronize themselves independently.

Set Right Goals

Employee performance should be improved to meet a corporate goal. It is critical to choose the one that inspires and is easily quantifiable. When the objectives seem impossible compared to the current state, this functions as a demotivator. In this manner, performance may be controlled optimally.

Be Specific

Managers should grasp how the company’s aims and objectives relate to individual targets. Any doubt should be avoided when communicating expectations and goals. Keep it concise and easy to comprehend. This encourages employees to focus their efforts on improving their performance by following established standards and enhancing productivity.

Effective Measure

The criteria must be quantifiable for a performance management program to be effective. An employee should be able to understand his/her performance aligning to the program aimed at specific targets.

Proper Communication

A well-designed communication system may significantly improve efficiency and accelerate the whole process. When vital information is provided to individuals, they feel valued. Communication is also critical during times of crisis to maintain staff performance.

Motivation and Feedback

Performance management is a continual activity, just as inspiration must be. Every person needs some motivation. Develop some techniques to encourage them consistently to be driven to meet objectives and strive for greater performance.

When recipients get precise feedback at the appropriate moment, they may take remedial action and adjust their performance. Provide feedback to all of the company’s component entities. Then, individuals, teams, and departments work together to accomplish objectives.

Appropriate Tools and Training

The employees should be supplied with the necessary tools and technology necessary to do their assigned tasks more efficiently and effectively.

Appropriate training that enables the desired output is also critical. They should be capable of doing the assigned tasks while also overcoming everyday obstacles.


Performance management is a constant process of matching individual efforts with company objectives. This results in significant cost savings, accelerating the achievement of the firm’s objectives and a continuous rise in production.

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