Setting KPIs For Software Development Teams As An Engineering Leader

 It's important to track, measure and assess the performance of your software development teams as an engineering leader. This way, you ensure that you'll come up with the highest quality product. This approach will help your team become more efficient and also help you generate substantial benefits in the long run.

To get the best-intended results from your engineering teams, leaders need to determine some essential KPIs to answer critical questions: How fast is your team moving? And what can you do to improve developer satisfaction and efficiency?

Key performance indicators (KPIs) are just like a map that helps you to determine how far you've come since you started. Having the right KPIs linked to your organizational goals lets you derive your progress within a specific time frame.

You might ask what key performance indicators are and how they can benefit engineering leaders. Please read on to learn every aspect of KPIs. This article will also cover setting KPIs for the software development team as an engineering leader.

What KPIs Are And How To Set Them

KPIs are a set of metrics that allow organizations and businesses to obtain qualifiable measurements over time to accomplish a specific business objective. The prime aim of key performance indicators is to drive engineering teams toward achieving goals and deliver valuable insights to make data-driven decisions for business processes.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/quickerbettertech/2022/11/10/on-crm-what-are-the-most-popular-add-ons-for-crm-applications/?sh=3487e26650f6

Setting KPIs can help engineering teams in the following ways.

• Understand what needs to be improved.

• Define a strict process to ensure consistent progress.

• Minimize the time required to complete a specific development project.

Setting KPIs For Software Development Teams

As a software engineering leader, you can set KPIs in the following ways.

1. Understanding How KPIs Will Be Used

The first step in setting KPIs is understanding how these indicators will be used to track and monitor your team's performance. Additionally, they should be clearly understood by all your team members.

Before setting key performance indicators, discuss the criteria with your teams and equip them with the right tools, knowledge and skills.

2. Linking KPIs To Your Business Goals

Software engineering leaders often adopt vague performance indicators that have no substantial impact. To get the best results, you must link KPIs to your objectives to ensure your team is on the right track.

Additionally, in order to give them the motivation to carry out their duties, your engineering team should also be aware of the organization's main objective.

3. Determining The Effectiveness Of Your Selected KPIs

The next step is identifying your KPIs as "smart" enough to deliver the best outcomes. You can use the SMART formula to check their effectiveness.

• Specific: Your selected KPIs should be focused on a specific objective to help teams develop the finest-quality product.

• Measurable: The performance indicator you select must be measurable and benchmarked against a determined standard.

• Achievable: Your selected KPIs should be well defined and achievable.

• Relevant: As mentioned earlier, your selected KPI should be relevant to your organizational goals.

• Time-Bound: Your key performance indicators should be deliverable and achievable in a set time frame.

4. Auditing KPIs

Make necessary changes depending on your customers' demands and market conditions.

KPI Vs. OKR

Both of these are agile goal-setting methodologies and are recognized for the profitability, productivity and visibility they offer to companies, but they differ from each other in certain aspects.

Objectives and key results (OKRs) help team leaders set, track and measure time-bound business goals. In contrast, KPIs are specific measures of success that allow engineering leaders to track team performance.

They differ in the following ways:

• KPIs evaluate your team's success, whereas the prime aim of OKRs is to facilitate ambitious goal setting and alignment for businesses.

• KPIs are the larger tracking areas with an extended period, while OKRs typically have quarterly cycles.

• KPIs are static performance indicators, but OKRs are meant to be malleable.

Benefits Of KPIs For Software Development Teams

Clear and well-defined key performance indicators can improve the performance of software development teams in the following ways.

Measuring Progress

Implementing the right KPIs can help engineering leaders track the performance and progress of their teams. Performance indicators also help you determine if the processes and policies are working together to improve operations.

Managing Performance

Setting key performance indicators helps teams and individuals maintain accountability and simplify communication, fostering positive performance. KPIs also encourage transparency by tracking and managing the performance of each team member.

Analyzing Trends

Implementing KPIs allows engineering leaders to identify positive and negative performance trends. It helps you spot the areas of work that require improvement rapidly.

Some KPI Examples For Engineering Leaders

As an engineering leader, you should be aware of the following examples of KPIs.

Cycle Time: Cycle time is a significant metric that indicates how quickly code goes from a developer's workstation to production. Identifying these valuable performance indicators helps engineering leaders accelerate time to market by identifying process bottlenecks.

Measuring cycle time in engineering departments can also help teams innovate faster and improve the sense of ownership.

Project Timeline: This is another valuable key performance indicator that helps you identify how work focuses and volume is modified over time. It also allows leaders to determine how their teams perform compared to market trends.

The Bottom Line

Key performance indicators are essential metrics to track, manage and analyze the performance of software development teams. As an engineering leader, you are responsible for ensuring that the KPI you select is relevant, achievable and measurable in the specific time frame.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource:https://www.forbes.com/sites/forbestechcouncil/2023/05/01/setting-kpis-for-software-development-teams-as-an-engineering-leader/?sh=111f1ac27cef

Five Common Mistakes To Avoid After CRM Go-Live

 I recently came back from an Alaskan cruise. This was a completely new experience for me since I have never been on a cruise before. With beautiful glaciers to climb, rivers to raft and wildlife to watch, Alaska impressed me with its raw, unspoiled beauty. Putting aside whether I am a cruise person or not, one thing I found interesting is that every evening when we walked back into our room, there was a brochure on the bed with everything we needed to know about the following day.

How To Set And Manage Key Performance Indicators For Software Engineering Teams

 Organizations use key performance indicators (KPIs) to measure their performance and progress toward specific goals. In software engineering, KPIs can measure the performance and productivity of software engineering teams. Setting and managing KPIs can be challenging for software engineering leaders, as they need to ensure that the metrics they choose are relevant, measurable and actionable.

The Role Of KPIs In Product Software Security

 Key performance indicators (KPIs) can be used in application security testing to measure the effectiveness of security testing and provide insight into the security posture of an application. Their purpose is to provide visibility into the effectiveness of an organization's application security testing program and to help identify areas for improvement. In a recent IDC survey (paywall) of mid-sized to large-sized software organizations, DevSecOps decision-makers identified the following as their top three KPIs for product security:

1. Vulnerability statistics

2. Compliance time and cost

3. Software build failures and delays

Let's consider each of these in more detail.

On CRM: Do You Really Need A CRM Consultant? Really?

 Do you need an outside consultant to help you implement a CRM system for your business? The answer is: it depends.

How Corporate Leaders Can Help Software Developers Reach Their Full Potential

There’s an image many have of software developers—they sit in a dark room and code endlessly. But unfortunately, more often than not, developers don’t have the leeway to code in an efficient, uninterrupted manner.

What startup founders need to know about software development

 Agility is a startup’s competitive edge against mature businesses. Startups are generally more responsive to emerging customer demands. They can react faster than established businesses because those established businesses usually have longer decision chains.

However, agility requires intelligent allocation of available resources. Startups pivot many times along the way to a market-fit product. They must be prepared for rapid and cost-effective changes.

Startups should think about how they apply specific practices necessary to create a flexible growth strategy, accurately estimate the time and resources needed, keep effective processes up and running, and maintain enough room for necessary pivots. Let’s keep Otherwise, it is not allowed to create a marketable product. Some decisions (eg, related to product structure) can reduce agility. These decisions are not conducive to the latter view.

In order to move forward effectively, first time startups should keep the following details in their mind.

1. Beware of fixed price terms

A fixed price agreement provides the startup with a sense of cost control. Startups can know in advance how much the idea will cost and plan for the expenses.

However, the fixed price may reduce the flexibility of the team. Once the team has agreed on the scope and cost, changes are possible only on a new agreement. As a result, the conversation restarts every time a startup comes up with an improvement. The team should estimate the new scope taking time and resources. Working on a fixed-price basis can slow down development every time project requirements change – and they change constantly.

The time-and-material model is a better option for startups. The development team can flexibly change to new priorities without needing to agree on new terms.

2. Reduce Where Possible—But Wisely

A dependable team invests efforts in providing accurate estimation of the cost of startup development. However, the estimate may exceed the expected budget as a result of the large project scope and the risks posed by the many unknowns.

Discuss the results and determine what you can reduce without compromising on product quality. The following points are included:

• Cutting all except key product features.

• Using frameworks and ready-made modules.

• Application for basic design.

• Explaining project details with the help of presentations, clickable prototypes, demos or proofs of concept.

Some decisions that can reduce the bottom line include giving up essential activities (eg, DevOps or QA), expanding the scope of a fixed-price project – as opposed to a previous agreement – and including one with fewer project hours. Including offering third-party estimates.

3. Hire an expert team and invest in collaboration

To reduce development time, it makes sense for non-technical startup founders to bet on expertise. This may mean hiring someone with a technical management background as the CTO and then building a team with their help. It may also mean hiring a salesperson team with proven experience in launching startup products, including a project manager and a business analyst.

Both options have their advantages and disadvantages, but in any case active participation in software development is necessary. To stay on track, startups must regularly discuss their plans and priorities with the engineering team. In turn, the team of professionals can suggest optimal implementation for what is learned from the marketing study.

The right expert should be able to explain the terms and concepts of software development in simple terms if you ask them to.

4. Focus on Product Architecture

Two factors influence product architecture: the feature list and the number of (future) users.

At first, the initial idea would change several times until the startup found the right formula. You’ll add new features, update existing features, and remove irrelevant ones to test market interest. You need a flexible architecture to manage changes effectively, thus avoiding major changes.

Second, the flexible architecture lets you maintain the best balance between maintenance cost and user experience. On the one hand, spare capacity is expensive. You need to spend less on infrastructure when there are only a few users. On the other hand, when popularity grows rapidly you need to scale rapidly.

Founders will need to ensure that the team has an architecture in place that can enable both keeping costs reasonable today and supporting future growth plans.

5. Build Using a Popular Tech Stack

The popularity of the technology is another concern in software development apart from the experience of the developers working with it. Choose widely popular languages, frameworks and libraries when all other things are equal. Evaluate the following parameters:

• Availability of launched projects similar to yours.

• Regularity of updates.

• A large, vibrant community around the technology.

• Support of a corporation or a foundation.

These parameters can help ensure that the technology is ready for long-term use. It will likely be available, stable and secure in the future.

Another reason for using extensive techniques is ease of replacement. Startups using an unpopular technology face the risks of increased vendor dependency in the case of outsourcing or a higher bus factor in the case of internal development.

6. Take security challenges seriously

Cyber ​​criminals target startups of any size. Potential targets include produced source code, software infrastructure and perimeters, project participants and their equipment, and end users’ accounts.

Startups can only feel secure when they implement strict security policies for internal processes (including software development workflow and data exchange between team members), storage and processing of user data, and compliance with data protection laws.

Designing a secure software architecture is essential. Check source code and infrastructure for vulnerabilities and close them quickly. Make sure team members have relevant permissions and can only access the information needed for the job. Educate users on how to protect against phishing. Freeze suspicious and hacked accounts immediately to prevent large scale attacks.

Final thoughts

Running a digital startup for the first time requires the founder’s concentration on activities they may not have done before. While a seasoned startup can launch faster, a first-time startup can also create a market-fit product within a reasonable amount of time. This is possible when your startup is agile, invests in aspects that enable long-term improvements and incorporates the expertise that engineers bring to your project.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://biz.crast.net/what-startup-founders-need-to-know-about-software-development/

What QA Teams Should Know About Machine Learning For Software Testing

 Talk to any industry insider, and they’ll tell you that the landscape of software testing is undergoing a paradigm shift that’s rendering many existing practices inadequate. The pace of software delivery is unrecognizable from only a few years ago as tech companies release products at breakneck speed, driving quality assurance (QA) teams to expand their toolkit in order to remain competitive.

On CRM: The Inconvenient Truth About Salesforce

 Earlier this month Salesforce, the undisputed leading customer relationship management software provider in the world, announced that it was laying off 10 percent of its workforce - more than 7,350 employees - and closing some offices.

Achieving Next-Level Value From AI By Focusing On The Operational Side Of Machine Learning

Technology research firm Gartner, Inc. has estimated that 85% of artificial intelligence (AI) and machine learning (ML) projects fail to produce a return for the business. The reasons often cited for the high failure rate include poor scope definition, bad training data, organizational inertia, lack of process change, mission creep and insufficient experimentation.

To this list, I would add another reason that I have seen many organizations struggle to achieve value from their AI projects. Companies often have invested heavily in building data science teams to create innovative ML models. However, they have failed to adopt the mindset, team, processes and tools necessary to efficiently and safely put those models into a production environment where they can actually deliver value.

To avoid this trap and achieve greater value from AI, here are four recommendations to help your organization translate your data scientists' amazing algorithms into real business impact.

1. Adopt a software mindset.

ML models are undoubtedly important, but developing ML code is just one part of the AI/ML life cycle. Data collection, feature extraction, data verification, machine resource management and other activities adjacent to the ML code actually consume the bulk of time and resources in the ML life cycle.

To be successful, companies must stop thinking of models as an end on their own. The fact is that a model is just a way to transform data written in the form of a function. In short, the model is just software.

When software engineers think about putting a model into production, their concerns are around how the model handles errors, whether the model will do what it is expected to do, whether it can respond quickly enough and whether it will integrate effectively into the organization's software stack.

Adopting a software mindset means moving away from an "artisanal" approach of handling every model as a one-off toward an "industrial" approach focused on putting the tools and processes in place to get models into production efficiently and effectively.

2. Build an ML platform team.

Since models are software, companies should look to their software organizations when they think about how to structure the ML operations team that will be responsible for bringing models into production.

Where a software organization has product development teams supported by an applications platform team (along with a core group to manage the infrastructure), the AI/ML organization should have data science teams supported by an ML engineering group—along with a team whose mandate is to assemble, manage and monitor the platform that the data science and ML teams use (i.e., an ML platform team staffed with ML platform engineers).

The ML platform engineer is a crossover role—similar to a DevOps position, plus software since they might need to build APIs or support the development of infrastructure patterns, for example. Awareness of data helps because data is so intertwined with ML. The ML platform engineer role also requires strong soft skills, curiosity and a collaborative mindset since they will work with diverse teams across the ML life cycle.

3. Establish end-to-end processes.

When a company is still in the "artisanal" stage of ML and is working with only a few use cases, it can get by with bespoke processes, treating each model as a one-off. However, as it expands the number of models that it's putting into production, it needs to standardize its processes to ensure a high level of confidence in both the processes themselves and in the models that it's putting into production.

This means establishing processes across the entirety of the model life cycle—which can be challenging because of the diverse teams involved throughout the life cycle. For example, different groups or individuals tend to be involved in promoting models from lab to staging and then to prod. As a result, different processes need to be implemented for each stage.

It's worth saying again that processes need to be established across the entire model life cycle. Yes, handoffs need to be defined all the way from experimentation to production. However, a model's life cycle doesn't end when it goes live in production, and procedures should be vetted for monitoring and retraining models as well.

4. Incorporate an operational platform.

Many companies that are successful with AI/ML invariably have a dedicated platform for operationalizing models for a variety of reasons. First and foremost, the computational workloads that a system supports in experimentation or training are very different from the workloads in the operationalizing phase.

In experimentation, the limiting factor is how quickly you can spin up resources independently so that you can use your Scikit-learn or TensorFlow and so on. When you go into the implementation phase, you care about a completely different set of capabilities. Is the platform resilient and high availability? Does it have hooks into Datadog or New Relic?

That's why even companies that have a training platform should consider incorporating an operational platform. As a rule, the ML platform itself should provide "self-service with guardrails," allowing data scientists to quickly and safely deploy models into production. At a minimum, the tools that a high-functioning ML team requires for managing operational AI workloads at scale should include:

• A training platform.

• An operational AI (or serving) platform.

• A data platform.

• DevOps to orchestrate everything.

• A workflow system, which may or may not include a batch prediction platform.

By adopting a software mindset around ML and putting in place the team, processes and tools to safely and efficiently deploy ML models, companies can significantly reduce the time required to put models into production and see value from their research innovations.

Implementing standard end-to-end processes can also improve model governance and prepare teams for upcoming regulations around AI, such as the EU's AI Act and the American Data Privacy and Protection Act (ADPPA).

Finally, these companies can free up their data scientists to develop even more innovative models to deliver intelligent products and services, ultimately increasing AI's value and impact on the business.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2023/01/17/achieving-next-level-value-from-ai-by-focusing-on-the-operational-side-of-machine-learning/?sh=22fd8f682d7e

How Transparent Is Your Software Outsourcing Vendor?

 Over the years and across the countless number of products that I have helped bring to market, the ones that nearly failed were the ones that an organization I worked with at the time used an external software outsourcing vendor. The vendors in question ultimately were selected because an executive's friend referred them or had a piece of the action getting some sort of kickback.

In one particular case, it led to quick agreements that based the relationship on just sheer speculation that the vendor was to be trusted, and thorough vetting was not required. 

The first red flag popped up when the vendors refused our software engineering teams to review detailed resumes. Instead, we received a card stating that the individual had "X" number of years of experience with the exact technology stack that we were using. Because of this, the executive team put pressure on the engineering team to get started right away.

There was not a single test, nor line of code reviewed, nor even questions about code structure and object-oriented programming — not even a problem to solve to see how the engineer approached complex problems. The team grumbled a bit and just focused on the day-to-day while getting the new front end, back end and, supposedly, a tech-lead ready to ramp up on the systems.

The second red flag came when the first group of engineers started onboarding with our teams. It took days of back and forth with the engineers because they were nine hours ahead. It took four to six weeks to start seeing production delivery. The code that was delivered did not pass testing. And a junior developer replaced the senior tech lead.

Staff had to spend more time coaching, teaching and guiding the outsourcing team, which took focus away from critical deliverables and ultimately delayed business objectives by several months. The outcome affected the business, which lost opportunities and frayed existing client relationships. In the end, we all learned a valuable lesson, and we had to have a heart-to-heart talk with the decision-makers concerning all the problems encountered when our teams could not vet the vendor and its candidates thoroughly.

After that, I made sure to review in-depth every single vendor. Yet I still faced enormous challenges when it came to vendor transparency on talent, costs, skills and performance. Other problematic areas I commonly discovered using different vendors was talent assigned to other projects while working on my products, talent rotation and engineers unavailable when meeting critical production deadlines.

To this day, when talking with technology executives, those similar gaps still come up, so I decided to tackle that challenge and push for greater industry transparency.

In leading a software engineering services organization, a strategy I share with business owners, engineering stakeholders and economic buyers is to approach software outsourcing vendors with a vetting plan that focuses on total transparency, talent evaluation, onboarding process strategy, training and retention programs. Furthermore, the vendor needs to be able to understand fully what your needs are. If they don't get it, move on.

So even if you are a small startup or mid-size company, it is useful to have preliminary documentation on the following, if applicable. Based on my experience, here are a few questions to ask about your business before engaging a vendor.

What are your business overview and objectives? 

Spell out what it is that your business is trying to achieve. Also, review the reasons the initiative requires certain technical elements to materialize and maximize ROI.

What does your tech stack look like?

Break down the technology stack you are using or looking to use. This overview guideline is basic and can expand depending on complexity and scope. This includes a full technical stack review, meaning apps and data, utilities, DevOps and business tools. A basic product introduction might entail:

• Agile PM Platform: Documentation (e.g., Jira, Rally, Trello, etc.) 

• Repositories (e.g., GitHub, GitLab, etc..) 

• UX: Adaptive, technical feasibility, asset manipulation

• UI: Performance-optimized

• Front End: Performance/ReST APIs focusing on the presentation layer of architecture schema

• Back End: Schema cocreation/authentication/data management

• iOS: Objective C/Swift/React Native/Flutter

• Android: Android Dev Kit/Java /Kotlin/React Native/Flutter

• QA: Test-driven development/unit testing

• DevOps: Docker/Kubernetes

• Security: Best practices 

• Compliance: Protocols 

• Server Setup: Local, staging, testing, release/production 

What does your architecture overview/workflow/diagram look like? 

1. Presentation Layer: This entails all the components for users to interact with the application (UI stuff too). It is for processing the user's input and returning the accurate response to the user.

2. Service Layer: This serves as a transactional boundary and contains both the application and proprietary infrastructure services.

3. Business Layer: This includes the core business rules and functionality of the application.

4. Data Layer: It's the bottom layer of the application. It communicates with stored user data.

After signing an NDA and presenting the software outsourcing vendor, your business requirements document should cover the following, which you may want the vendor to address:

• Ability to review resumes

• Ability to review preliminary screens from the resumes proposed

• Ability to provide calibration feedback and pivot the search

• Review preliminary technical screen 

• Review technical testing video and actual code of those tests and challenges 

• View resource salary requirements, taxes, benefits costs, insurance and vendor profit (total cost of ownership calculator)

• Review onboarding strategy

• Resource equal replacement contract clause

Bottom Line

Doing your due diligence before embarking on a technical project with software outsourcing vendors is vital to ensure business success. Take time to review all necessary components of such a partnership to ensure goals are understood and processes are outlined across the team from the start of the collaboration.

Looking to hire skilled software developers? Contact TP&P Technology - Leading Software Outsourcing Company in Vietnam Today

Article resource: https://www.forbes.com/sites/forbestechcouncil/2020/07/13/how-transparent-is-your-software-outsourcing-vendor/?sh=200113e6ef2a

Software Development Time Estimation: How Long Should It Take To Develop A Product?

Once you come up with the idea of creating a software product and have proof of concept, you can start thinking about how much it takes to bring it to life. There is much work to be done before your product hits the market, but the question I want to discuss in this article is how much time it will take to develop the software.

Digital Transformation In The Software Industry: Changing The Game With Data And Analytics

Although digital natives, software development companies still benefit from digital transformation processes that enable them to integrate data-driven decision-making into all business areas. As the general landscape becomes more competitive and the need for agility increases, tech leaders need to know how to master data and analytics as critical elements of their organization’s digital transformation.

5 Types of Technology All Entrepreneurs Need Access to in the Digital Age

 Saying that we live in a "digital age" may feel a bit cliche, but there's no denying that the internet and social media have revolutionized the way we interact. Survey data from the Pew Research Center reveals that 81 percent of Americans use the web daily, with 28 percent saying they are online "almost constantly."

Everything You Should Know About Mobile Application Development

The application development service has been increasingly sought after by major brands and reputable companies in the market to boost sales and strengthen customer relationships. It is undeniable that mobile devices have become part of our routine. You must have on your smartphone several applications to order food, shop, and carry out your banking operations, right? 

Top Tips to Choose a Software Development Company in 10 Steps

The global software development market is growing at an unprecedented pace. In 2019, the industry was worth around $457 billion and was estimated to reach $777 billion by 2022.

With such huge growth potential, it’s no wonder that more and more businesses are looking to invest in bespoke software solutions. But with so many options out there, how can you be sure to choose the right software development company for your needs?

Why AI is the only answer to legacy manual software testing

Until recently not feasible, the increasing availability of AI, ML and NLP is not only making test automation smarter, but also more accessible

Every business is caught between a demand to deliver things quickly and make sure they meet quality expectations. It doesn’t matter whether they make furniture, cars, food or software, companies have to meet the demands of customers that want fast access to great products.

Why Data Scientists Should Follow Software Development Standards

As the organisations have started to recognise the value of data, the need for data scientists has seen an exponential rise since then. Unlike traditional methods, crucial decisions among organisations are now mostly data-driven.

Data Engineer vs Data Scientist: What’s the Difference?

The data engineer equips the business with the ability to move data from place to place, known as data pipelines. Data engineers provide data to the data science teams.

The data scientist consumes data provided by the data engineers and interprets it to say something meaningful to decision-makers in the company.

In this article, let’s dive a little deeper into the roles of data engineer and data scientist.

7 Mistakes to Avoid When Hiring IT Talent

With the continuing digital shift in the workforce, competent IT specialists are in higher demand than ever. Unfortunately, many recruiters haven't caught up with the shift in hiring, leading to avoidable mistakes.

Currently, the most significant success barrier most sectors face is a stifling lack of IT talent. According to Gartner, a lack of qualified team members directly halted the adoption of 64% of new technologies.

With the continuing digital shift in the workforce, competent IT specialists are in higher demand than ever, yet there is an ever-widening skills gap. Korn Ferry found that, by 2030, the U.S. could lose out on at least $162 billion in revenue per year if nothing nges.

This means recruiters need to rethink how they scout top talent. There's no room for missteps or blunders. One wrong move can cost a company massive amounts of profit. Unfortunately, many recruiters haven't caught up with the shift in hiring, leading to avoidable mistakes. Let's look at seven instances where recruitment tends to fail.

1. Focusing too much on your own needs

It's easy to become self-absorbed when hiring IT talent. Companies are often so laser-focused on what they need that they rarely pause to think about what their potential candidates might need.

You can think of it using the "fitting a square peg into a round hole" analogy. Recruiters have to consider more than just the fact that a candidate sounds right on paper. Will they mesh with company culture? Are they looking for non-financial motivators? If something isn't right, don't try to fit the talent into the job opening. There will be others.

2. Hiring for KPIs

Some companies are handing out bonuses for finding and onboarding new talent. While this can be a good incentive, the truth is that it can also be a hiring disaster.

For example, hiring someone to close a KPI often means that the talent gets left behind once the quota is filled. It's sadly common for nobody to take an active role in talent development when KPIs are the primary focus.

3. Overlooking candidate expectations

This is a massive problem that the current hiring market has shone a glaring spotlight on. Previously, when companies believed candidates should be courting them, most felt little need to cater to what the talent might expect or need.

However, in this new market where the talent has more power, it's a mistake to assume candidates will just "take what they're given." Top talent has plenty of options, and they won't simply stay out of need or obligation if their work situation is different than advertised.

In a December 2021 poll by SHRM, 65% of executives said they were "extremely concerned" about their company's ability to recruit new talent.

Part of this problem is that many organizations don't have compelling, accurate employee value propositions. When it comes to IT recruitment, this is a death knell for a business, because there is such a shortage of qualified talent.

4. Over-communicating

Becoming like the clingy friend or significant other in your potential hire's life is the quickest way to destroy any rapport you may have built.

There's a fine line between staying in touch and smothering your talent. Conventional wisdom states that you should constantly be in contact to "keep candidates warm" throughout the process. However, most experts say it's best to stick to a single, meaningful daily reach-out unless there's a specific need for more. Talent won't appreciate a constant stream of calls, texts and emails. Doing this can sour your candidate's perception of the company.

Instead, during the initial contact phases, ask how they want to be contacted (phone, email, text, etc.) and how much information they'd like. This is an easy way to inform your communication strategy and signal that you're thinking of the talent as a person.

5. Using money as the only motivator

Is money a powerful motivator? Absolutely. However, it's not as important in today's hiring market as it once was. Qualified IT specialists can pull substantial salaries anywhere. What else can your company offer?

Get to know your candidates. Find out what's important to them. Do they have longstanding family obligations that require regular time off? Do they like spontaneous travel? Do they want easy access to health or fitness programs?

Understand that most workers aren't looking for their careers to be an integral part of their self-identity. The idea of a "fulfilling career" doesn't hold much weight anymore, so the siren song of a high salary and basic benefits package is no longer enough. Take the time to discover what other things motivate your talent, and see if your company culture meshes with them.

6. Being inflexible

The Covid-19 digital pivot has permanently transformed the way we work. Now that employees understand how to effectively work remotely, most expect employers to deliver a certain level of flexibility, even if they have to return to the office.

There's no place for rigidity in today's hiring market. This applies to how, when and where your talent works, but it also applies to the rest of the recruitment process. Understand that the "rules of recruitment" may not apply in the same way they used to, especially since companies are often trying to attract top talent instead of vice versa.

Decide ahead of time which requirements are negotiable and which aren't. Be open-minded to flex time, telecommuting and hybrid work. HR consulting expert, Rey Ramirez, told CNBC that companies that don't offer flexibility are missing out on up to 70% of qualified candidates, which can be truly disastrous for businesses.

7. Lacking true diversity

It's no secret that diversity in the workplace is critical to corporate success. However, candidates note which organizations are only giving lip service to the cause and which put action behind their words.

This goes deeper than gender and race. For instance, some companies may hire a superficially diverse range of people, but then candidates find they're all Americans with old money connections and Ivy League degrees.

True diversity means looking holistically at a candidate's qualifications, motivations and personality. Talent comes in all forms, and there are far more important traits than a college degree or a specific background.

Companies need all the help they can get in finding the broadest possible talent pool, especially for IT recruitment. There is intense competition across all sectors, and it's no longer simply about who can shell out the biggest salary.

If your company falls victim to any one of these mistakes, there's no way for you to reach your full potential as an organization. Businesses and talent both deserve the very best, but the only way to achieve this is to avoid the mistakes outlined above.

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Article resource: https://www.entrepreneur.com/article/425729