Table of Contents
Introduction
1. Impact of FaaS in Software Development Practices
2. Challenges and Considerations
3. The Future of Serverless Architecture

Introduction

Serverless architecture, or FaaS (Function as a Service), is one of the most groundbreaking technologies in software development as it promises to reshape how applications are built, deployed, and scaled. This shift from traditional server-based models towards a more streamlined strategy where developers can focus exclusively on writing code without getting overwhelmed by managing infrastructure. 

However, to further understand the heart of the matter, let’s dive into the world of serverless computing and examine the significant impact, real-world examples, and future it has on software architecture.

1. Impact of FaaS in Software Development Practices

The adoption of serverless architecture is more than just a technological shift but also has the potential to change the organizational culture within the software developer (SDE) team. The new-age serverless architecture encourages a microservices approach where applications are broken down into smaller segments that can further independent functionalities as it is deployed to develop and scale independently, promoting flexibility, agility, and easier supervision compared to rigid architectures. 

For CIOs, serverless architecture is one of the best options, as it is quite scalable and cost-efficient when compared to traditional server-based models. The traditional models were based on peak loads, often leading to underutilizing or wasting more resources during off-peak times. On the other hand, we have serverless computing that scales seamlessly by estimating the number of incoming requests, further ensuring optimal resource allocation and cost-effectiveness. 

Another significant impact that serverless architecture can control is a reduction in operational complexity. By removing server management, focusing purely on serverless architecture allows developers to concentrate more on writing code and less on addressing infrastructure maintenance tasks. This enables SDE teams to innovate quicker and repeatedly iterate, driving business dexterity and responsiveness.

2. Challenges and Considerations

Despite its benefits, serverless architecture comes with a baggage of challenges and considerations for SDE teams.

One of the most notable challenges in this list is ‘vendor lock-in’, which allows each cloud provider to offer their own set of serverless services and proprietary tools; however, before implementing serverless architecture into the workplace, developers need to carefully evaluate the trade-offs between vendor-specific characteristics and the portability of their applications across diverse platforms. 

The problem with debugging and monitoring distributed serverless applications remains constant. Developers often use tools for logging, tracing, and debugging serverless functions; however, if not carefully planned and integrated, there will be issues with the visibility issues with the application performance and behavior.

3. The Future of Serverless Architecture

The future of writing codes is likely to evolve, but the SDE team will continue to adopt serverless architecture. 

Serverless architecture has the potential to evolve in the coming years, benefiting cloud providers by expanding their horizons, exploring the usefulness of serverless computing, and addressing current limitations. With more advancements lined up, serverless frameworks and tools will entrust the SDE teams to build more refined and resilient applications. 

To sum up, serverless architecture represents a trend in the future of coding, offering scalability, dexterity, and cost-efficiency for modern software development. As enterprises are embracing serverless computing, SDE teams will play a vital role in leveraging capabilities to drive innovation and drive digital transformation.

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Table of contents

Introduction
1. Codiga
2. Amazon CodeWhisper
3. GitHub Copilot
4. Tabnine
5. AskCodi
Conclusion

Introduction

Programming is the backbone of modern software development, as this aids in driving creation when creating innovative applications and systems that eventually power the digital world. However, the coding process can be complicated and challenging, but thankfully, with the development of modern software, software developers (DEV) can easily navigate intricate syntax, troubleshoot errors, and manage large codebases. Therefore, with the introduction of AI, coding assistants have emerged as valuable partners that can change the programming game forever and enhance the coding experience for DEV. 

In today’s AITechPark article, we will explore the top five AI Coding Assistant tools, especially those made for developers, to streamline every process and boost workflow. 

1. Codiga

The first AI coding assistant tool on our list is Codiga, which focuses on elevating the coding experience by providing intelligent support, code optimizations, and autocomplete suggestions that enable developers to write codes more efficiently. The tool analyzes code for potential errors and vulnerabilities, allowing users to identify and fix the problem before it is set out for production. Codiga excels at code refactoring as it supports more than 12 programming languages, including PHP, C++, Java, and Python, enhancing performance and readability. Regarding pricing, Codiga offers free individual plans, while the paid subscription starts at $14 per month.

2. Amazon CodeWhisper

Amazon’s CodeWhisperer provides real-time suggestions for developers, allowing full functions tailored to existing code that will help streamline their coding experience and make it easier to encounter unfamiliar APIs. The platform is identified as an open-source suggestion providing information related to the projects, such as URLs and licenses for easy reviewing of codes and their attributions. Beyond code suggestions, Amazon CodeWhisper facilitates comment completions for increased documentation. The tool supports popular programming languages such as Python, Java, and JavaScript and seamlessly integrates with IDEs such as Visual Studio Code, AWS Cloud9, and PyCharm. Amazon CodeWhisper is free for individual users, but the professional plan starts at $19 per month.

3. GitHub Copilot

GitHub Copilot is one of the most popular and collaborative projects by GitHub, OpenAI, and Microsoft that AI coding assistants widely embrace. The tool offers numerous features such as code recommendations, auto-completion, documentation understandings, and multi-language support, and Copilot aids in setting high standards for AI coding assistance. GitHub Copilot can also be integrated with popular IDEs and code editors through plugins to make the development process smoother. However, GitHub Copilot often generates codes based on the patterns that it has learned, producing similar or identical code segments and learning to code duplication across projects. The pricing of Copilot for individuals begins at $10 per month per user, while the business plan starts at $19 per month per user.

4. Tabnine

Tabnine is a versatile AI-driven coding assistant made for DEV to enhance their coding experience. The tool has built-in intelligent code completion, error detection, refactoring assistance, and automatic code documentation that helps in writing efficient and high-quality code. Tabnine also supports numerous programming languages and IDEs, such as VS Code, IntelliJ, and Android Studio, and is SOC-2 compliant, which not only ensures code security but also aids developers in customizing different coding styles and languages to further predict and suggest content-specific codes. Tabnine’s basic plan is completely free to use; users can enjoy features such as basic AI code completions and AI-powered chat using multiple models; however, the pro plan offers $12 per user per month.

5. AskCodi

AskCodi is considered a valuable AI coding assistant for DEV that is designed to streamline the coding process by eliminating unnecessary tasks and increasing productivity. The tool supports a range of programming languages such as JavaScript, MySQL, Kotlin, and C++ and is compatible with multiple IDEs like VS Code, PyCharm, and IntelliJ IDEA. AskCodi excels in generating code, answering queries, and further providing code suggestions that aid you within your IDE, and features a unique Codi Workbook. The membership package costs $9.99 a month and features generate code in different programming languages, including Python, Java, TypeScript, Rust, Ruby, Kotlin, and Scala.

Conclusion

With the advancement in technology, AI coding assistant tools play an essential role in the software development industry, especially with continuous learning and improvement. These tools have the potential to reshape the coding experience and foster innovation and code excellence. Hence, by embracing the above AI Coding Assistants’ software, developers and programmers can overcome the challenges related to coding and enhance their skills to create high-quality software solutions.

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Table of contents
Introduction
1. Getting Started with Platform Engineering
2. The Top Three Platform Engineering Tools You Should Consider in 2024
2.1. Crossplane
2.2. Port
2.3. ArgoCD
Conclusion

Introduction

Platform engineering is considered a practice built up on DevOps guides that assist in improving each development team’s compliance, costs, security, and business processes, eventually helping to improve developer experiences and self-service within a secure, governed framework. 

Lately, there has been quite a buzz about the permanent implementation of platform engineering in the IT industry. According to a recent report by Gartner, it is estimated that more than 80% of engineering organizations will have a crew dedicated to platform engineering by 2026, where these teams will focus on building an internal developer platform. This also implies that regardless of the business domain, these platforms by nature will help in achieving high business scale and reduce the time it takes to deliver business value. 

In today’s exclusive AI TechPark article, we will help IT developers understand the need for platform engineering along with the top three trending tools they can use for an easy business operation. 

1. Getting Started with Platform Engineering

Platform engineering is not for every company; for instance, in fledgling startups, where every individual does a bit of everything, this guide doesn’t come in handy. On the other hand, for companies that have two or more app teams where duplicate efforts are observed, platform engineering is the best option to tackle that toil, allowing the developers to think outside the box.

The best way to start the platform engineering journey in your organization is to have a conversation with the team of engineers, allowing them to understand and survey bottlenecks and developer frustrations, further advising the use of platform engineering that embeds and pairs programming within application teams.

During the process of building an application, developers need to question the size of the requirements, patterns, and trends needed in the app, the bottlenecks, and many more. However, it doesn’t end here, as to further comprehend the application, they require multiple testing and opinion polls by their internal customers; developers are also required to document every minute detail and change on the platform to encourage self-service and independence in the long run. 

Therefore, whether it is infrastructure provisioning, code pipelines, monitoring, or container management, the self-service platform will be a guide to hiding these complexities and providing developers with the necessary tools and applications. 

2. The Top Three Platform Engineering Tools You Should Consider in 2024

In this section, we will introduce you to the top three tools that every platform engineer should try in 2024 to perform routine tasks without being time-consuming and with zero human errors. 

2.1. Crossplane

Navigating the intricate landscape of Kubernetes infrastructure, Crossplane is one of the best platform engineering tools that securely builds a control plane with its tailored and unique needs without writing tricky distributed systems code. Crossplane is a master orchestrator that extends beyond container management, as its reliability and security are inherent to Kubernetes. 

2.2. Port

Port emerges as an indispensable asset of platform engineering, offering DevOps teams a centralized platform for orchestrating applications and infrastructure with unparalleled precision and control. The platform has a unique blend of oversight and flexibility that allows IT managers to maintain standards and best practices to streamline the business process effectively and efficiently. 

2.3. ArgoCD

Argo CD, a Kubernetes-native marvel, has redefined the landscape of modern application deployment. It offers a meticulous orchestration of deployment processes, ensuring that the applications are not just deployed but thriving and in sync with the demands of the tech world. The platform empowers developers to take full command, seamlessly managing both the intricate web of infrastructure configurations and the pulsating lifeline of application updates, all within a single, unified system.

Conclusion

Platform engineering is considered the optimal suite of tools that aids in orchestrating a symphony of tools that align with developers’ unique operational needs and aspirations while also keeping cost, skillset compatibility, feature sets, and user interface design in consideration.

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Hardly a week goes by without news of a ‘technical issue’ or outage. Sainsbury’s, Marks & Spencer and Tesco Bank are just some of the well-known brands that have experienced tech meltdowns in recent weeks. 

We’ve come to expect IT crashes as a part of life, but if handled poorly, they can snowball into a major crisis, tarnishing a company’s reputation, eroding consumer trust and resulting in lost sales. 

Here are five safeguards brand owners can put in place to protect their sites and minimise fallout from an IT crash.

Get your comms in order

Tech glitches are much easier to address – and fix when everyone’s in the loop, from your software developers to your marketing team to your customer base. Making your users aware of problems is paramount. 

All too often, a bad situation can quickly get worse when details of an outage spread like wildfire across social media (and for some unfortunate brands, this could be the first time they are made aware of the problem). Don’t bury your head in the sand. Be proactive and acknowledge the problem; email subscribers or post a message on social media, reassuring users that you are dealing with the problem and outlining expected timeframes for resolution. 

It’s also a great opportunity for your brand to establish or reinforce their ‘personality’ – using a heartfelt apology or humour, for example, if that tone is appropriate. But don’t promise what you can’t deliver – make sure that timeframes and assurances can be met. This is particularly important if there has been a glitch that could affect customer data.

Test, test and test again

We’ve come across many brands that don’t have a back-up structure in place or rollback code, where a system can revert to its previous state. While there’s no standard way to build a system, in our experience, the lack of robust software and website testing often comes down to cost, particularly with smaller brands and start-ups. It can also be due to not having regular access to a software development team or thinking an outage will never happen. Trust me, it can. 

Legacy brands have the resources and teams in place as their sites require more complex and continual updates, but we’ve had to battle with such clients when it comes to improving the testing phase.

Don’t underestimate this – testing is vital to ensure your website works. Typically, this involves a build phase, a testing phase and a quality assurance (QA) check, which aims to identify any issues across the entire build. Too often, testing and QA are the first things to go when brands want to cut costs. It could be an expensive mistake.           

Be alert

Do an audit of your site at least once a year with penetration (PEN) testing, where you look for any vulnerabilities in any of your systems. It’s not a guarantee that your site is absolutely secure and glitch-free, but as a brand, at the very least, you will have tried to identify any potential issues and protect your site and stored data.  Being proactive with system security alongside testing and QA reduces the risk of outages drastically.

Alongside PEN and code testing, you need to know when systems go down. There is nothing worse than a customer notifying you that your website or platform doesn’t work. 

Setting up monitors for your systems to notify you is the first step of your action to an outage. Depending on your user, you can even make these publicly accessible, like Slack (https://slack-status.com/), and other platforms so your users are aware of this issue as it happens.     

Consider your site structure

It is possible to limit outages to specific parts of a site, but it will depend on how your website or platform is built and whether different parts of it are hosted on separate services, for example.  This approach could help contain the fallout from an outage. Take X, formerly Twitter: its likes and tweets are kept separate where microservices are used for each, so if ‘likes’ were to go down, tweets would still be visible. We would advise this type of structure for brands that would benefit from such an approach. A microservices set up would benefit anyone that’s creating a platform for users need to complete things in, such as banks, ecommerce but not needed for things like marketing websites and ‘brochure’ websites.

Post-crash follow ups

The level of testing required and the number of times this is needed will depend on the size of the company, its user base and its product.  It is also essential to take tech developments into account, all of which can impact even the most robust of sites. We recommend PEN tests once a year, but above all, be vigilant, take customers seriously and respect their data. 

If you’ve suffered an outage, there’s nowhere to hide. Being proactive rather than reactive shows that you care, which can make a big difference to your reputation.

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Table of Contents
Introduction
1. Amazon Web Services Certified Developer Associate
2. Certified Software Development Professional (CSDP)
3. Microsoft Certified: Azure Solutions Architect Expert (ASAE)
4. Certified Secure Software Lifecycle Professional (CSSLP)
5. Certified Agile Leadership (CAL)
Conclusion

Introduction

The digitized world relies heavily on computer-driven processes, and the demand for innovative software products and solutions is all-time high. Organizations and institutions are constantly reshaping their digital structure by investing in software tools and programs to enhance their productivity, streamline business operations, and ensure seamless communication. Therefore, the need to understand the countless opportunities this field can provide will be a major career for software developers. However, to add more credibility to the profession, software engineer certifications are needed that will help you get skilled, grow your knowledge, attain a higher salary, and advance your career.

In today’s exclusive AITech Park, we will explore the top five best software engineering certifications of 2024 that software developers can pursue to gain knowledge about the current trends in software development and also brush up their skills. 

1. Amazon Web Services Certified Developer Associate

The first software engineering certification course on our list is from Amazon Web Services (AWS). The AWS Certified Developer Associate (AWS CDA) certification is used to teach software engineers how to create and deploy cloud-based web apps. Candidates who enroll in this program are required to know how to write applications using an API, AWS, command-line interface (CLI), and software development kits (SDK). The software engineers need to have at least two years of experience working with apps built on AWS before they take this course. 

2. Certified Software Development Professional (CSDP)

The CSDP offered by the famous IEEE Computer Society focuses on upskilling experienced software developers with new technologies. The course validates a candidate’s proficiency in software engineering principles and practices that surround the entire software development lifecycle. Through this course, candidates need to display their knowledge of software requirements, configuration management, engineering management, engineering processes, and tools. The CSDP aims for professionals with a minimum of two years of experience and a postgraduate degree to get this certification.

3. Microsoft Certified: Azure Solutions Architect Expert (ASAE) 

The ASAE certification validates software engineers’s expertise in designing, testing, and building cloud applications and services for the Microsoft Azure website. This course is customized for candidates with at least one year of experience as a software engineer, as this certification requires expertness in Azure SDKs, data storage options, data connections, APIs, app authentication and authorization, debugging, performance tuning, and monitoring. 

4. Certified Secure Software Lifecycle Professional (CSSLP) 

The CSSLP certification provides detailed knowledge about cybersecurity and regulatory compliance to software engineers. The certification courses help candidates understand the best practices for the authentication, authorization, and auditing of personal data. It is designed to equip candidates with the necessary tools to protect data in the cloud and ensure that organizations comply with industry-specific regulations. The CSSLP exam evaluates candidates’ knowledge of data security throughout the software development lifecycle.

5. Certified Agile Leadership (CAL)

The Certified Agile Leadership (CAL) program is one of the most acclaimed courses for software developers to gain a better understanding of the agile software development methodology. Candidates who complete this course will be able to lead software development teams that use agile approaches in their software development processes. This course is composed of three categories: CAL Essentials, CAL for Teams, and CAL for Organizations, which software developers need to come across to complete the certification.

Conclusion

Choosing the right certification course as a software developer is a strategic step that can signify enhancing your skills and market values. Therefore, before selecting any certification course, you need to think about a professional development plan that will guide you in the right direction.

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The dawn of the digital age brought forth a range of technological advancements, reshaping industries and redefining norms. In the realm of software engineering, generative AI coding assistants, including tools like GitHub Copilot and Tabnine, epitomise this wave. Drawing from the impact of foundational models like OpenAI’s GPT and Anthopic’s Claude, these tools interpret natural language inputs to suggest and generate code snippets, amplifying developer productivity. Notably, GitHub Copilot now underpins a staggering 46% of coding tasks, enhancing coding speed by an impressive 55%.

A study from McKinsey emphasised that software development stands as one of the best ways to achieve organisational efficiency with generative AI. Yet, the overarching question remains: How can generative AI go beyond mere code generation to elevate the software development life cycle?

Code better, not just faster

According to a recent survey from Stack Overflow, 70% of developers are either harnessing AI tools or gearing up to integrate them in the imminent future. Yet, while tools like GitHub Copilot and Replit’s Ghostwriter are predominantly centred on development and testing, there are several ways that generative AI could be used to enhance developer’s workflows. 

Among the various stages of the Software Development Life Cycle, code optimisation is one that is often overlooked. Yet, when embedded within the Continuous Integration and Continuous Deployment processes, it becomes the point wherein code is developed to peak performance. It’s the point at which code isn’t just moulded to function but to excel, to minimise latency and to amplify user experiences.

However, the benchmarks for code performance are continuously being changed, particularly in a landscape dominated by AI. But what exactly is driving this? 

Cost of compute and profitability: Software is eating the world. Even the allure of modern vehicles often lies in digital features like parking assistance and IoT connectivity. Yet, the attraction of generative AI coding assistants comes at a price. A16Z’s report underscores this, with cloud spending often taking 75-80% of revenue for software firms. Clearly, efficient code is not merely a technical goal but a financial necessity, as it can significantly cut cloud costs and boost profit margins for organisations.

Speed, Scale and Customer Experience: In the business world where milliseconds matter, code optimisation is the linchpin. From high-frequency trading to autonomous vehicle decision-making, performance is king. However, the advent of Generative AI and LLMs brings a new dimension to the speed challenge. Despite their benefits, the extensive processing times associated with LLMs can pose a significant hurdle for real-time and edge applications, particularly as the number of users and applications continues to grow.

Energy efficiency and ESG: Amidst the rapid expansion of generative AI, the emphasis on Environmental, Social, and Governance factors is intensifying, making energy-efficient code an urgent priority. To put this into perspective, the training of GPT-3 is estimated to have consumed 1,287 MWh of energy, resulting in emissions of over 550 tons of carbon dioxide equivalent. This is comparable to one person making 550 round trips between New York and San Francisco – and that’s before the model is even launched to consumers.

The environmental impact doesn’t stop at the training phase. For instance, integrating LLMs into search engines could potentially lead to a fivefold increase in computing power, resulting in substantial carbon emissions. Efficient code is important in curbing emissions while still enabling businesses to get the most out of AI. 

The challenges of code optimisation

Navigating the complexities of code optimisation is far from straightforward, and it is often littered with challenges. One of the major challenges among these is the scarcity of accomplished performance engineers, a niche segment of professionals that require salaries upwards of £500k in London, which may be a significant hurdle for many organisations. 

Compounding this is the time-intensive and iterative nature of optimisation, necessitating a cyclical process of code refinement, testing, and analysis that even the best of engineers find daunting – especially within expansive codebases that make a comprehensive view difficult to attain. 

Further to this, there are resource limitations that exist in the process. Large codebases require significant human resources for improvement; a codebase with a million lines of code could require up to 70 top developers, across several stages from testing to backend orchestration, further extending the optimisation timeline. 

These challenges also come with a cost. In 2020, the Cost of Poor Software Quality in the US alone exceeded the $2.08 trillion mark. This underscores the urgent need for innovative strategies in code optimisation. 

This staggering figure includes expenditures on rework, lost productivity, and customer dissatisfaction resulting from subpar code. Addressing this trillion-dollar problem demands a new approach to code optimisation.

Already we are seeing instances of AI being used to supercharge code optimisation to overcome these challenges. AI can automatically identify inefficiencies, generate enhanced code versions, recommend optimal code changes and more. This could be one way that businesses can turn this potential performance headache into a genuine competitive advantage. 

The bottom line

As we continue through this new phase of digital transformation, it’s clear that just creating code faster, even though it’s a breakthrough, doesn’t solve all the complex issues in software development. Generative AI has certainly changed how we code, making things faster and more efficient. But if businesses want to make the most of what AI offers, they need to use more than just code generation tools.

One area often ignored is performance optimisation. This important step is about making code work better, not just getting it done. It’s about saving money and protecting the environment, and that takes more than just an AI that suggests code. Companies need advanced tools and plans that are designed for today’s tech needs.

The cost of not doing this is huge. Poor software quality can lead to spending billions of dollars. This fact alone shows why we need better solutions right now. The way forward for companies is to combine code generation tools with other AI developer tools that accelerate coding while enhancing both performance and efficiency. By adopting this strategy, companies can really benefit from their AI technology, keeping them competitive and responsible as they navigate through the digital world.

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Table of Contents

Introduction
1. The Era of the Data Renaissance
2. Top 4 Data Trends of 2024
2.1. Data Democratization
2.2. Data Unification
2.3. Data-as-a-Service
2.4. Data Governance
Growing Emphasis on Data

Introduction

2023 was a terrific year in the IT industry, but 2024 is set to bring some exciting and groundbreaking developments that will help IT professionals and data scientists develop innovative software and tools to strive in the competitive landscape. 

The most recent technological advancement in the data landscape is quite commendable. In 2024, IT enterprises will be heavily impacted, as data is the new oil that can transform any business and reshape the traditional process of analyzing, visualizing, and making data-driven decisions. 

As IT enterprises grapple with the data deluge, they often find themselves at an intersection of technological innovation, ethical considerations, and the need for actionable solutions. 

In today’s exclusive AI Tech Park article, we will focus on gearing up IT professionals and data scientists to understand the data trends they can expect in 2024.

1. The Era of the Data Renaissance 

The phrase “data is the new oil” was stated in 2006 by British data scientist Clive Humby. The one big difference between data and oil is that oil is a nonrenewable energy, and data can be renewed and reused in an infinite number of ways. 

Three decades ago, one of the main challenges that IT enterprises faced was the scarcity of data. However, with time, the main challenge for most IT businesses was having a plethora of data. 

With such a volume of data, enterprises struggle with how to use the data, where to implement it, when they need it, and most importantly, how to store it. The traditional database management systems (DMS) failed to tackle the new data sets, which made data professionals realize the importance of cloud storage, which is efficient in handling numerous types of data and quite cost-efficient compared to DMS. 

As we stand at the crossroads of a data renaissance, the year 2024 heralds an important role in the data analytic landscape, where data analytics is no longer a tool for data-driven decision-making but a driving force to push greater efficiency, innovation, real-time data insights, responsible AI, reinforce security, and more.

However, IT professionals and data scientists need to address the challenges and considerations of imposing data privacy, skill development, and ethical dilemmas to stay compliant with this evolving regulatory landscape. 

2. Top 4 Data Trends of 2024

The groundbreaking IT trends and the introduction of generative AI (GenAI) have redefined IT professionals’ and data scientists’ interactions with the new data, which drives sustainable growth and allows IT businesses to stay ahead of the competition. These five most trending data trends of 2024 will change the IT landscape in the coming years: 

2.1. Data Democratization

Data democratization has been a growing trend for the past few years, but the increased usage of AI and machine learning (ML) tools has rekindled a new horizon for this trend. With data democratization, every employee in an IT organization will have access to the data to make data-driven decisions for a seamless business process. However, to get full access to data, IT leaders need to provide in-house training on data literacy to familiarize them with the principles and techniques of working with data. 

2.2. Data Unification

It is often observed that teams don’t communicate with each other while working on multiple data sets, which causes data loss. Therefore, to fix this issue, IT enterprises can implement a data unification strategy that combines data from numerous sources into a consistent and trustworthy format. In 2024, data unification will be mostly used in automated solutions, such as cloud computing or software development, helping companies eliminate the risk of data theft. 

2.3. Data-as-a-Service

Data-as-a-service (DaaS) comes in for those IT businesses looking to store and analyze their data in a cloud-based management model. The DaaS model is used to collect, store, and analyze data provided on a subscription basis, where users can access a wide range of unstructured to structured data without physically storing it. DaaS providers such as Snowflake and Databrick are well-known for their data warehousing solutions. DaaS ensures good-quality data while keeping security and privacy standards in mind, making it a cost-effective approach for startups and small businesses.

2.4. Data Governance

Organizations employing GenAI need to comprehend the ethical use of data; for that, they need to stay accountable for transparency, data collection, and data usage. With the rising concern over data breaches and leaks, CIOs and data leaders need to be accountable for reposing AI. They must focus on developing robust regulatory frameworks and industry-wide standards to resolve the ethical challenges.

To learn more about data governance, read: The Value of the Chief Data Officer in the Data Governance Framework

Growing Emphasis on Data

With the growing popularity of data analytics, IT professionals and data professionals are on their toes to understand the data journey. Organizations are willing to embrace this digital transformation with the integration of AI, natural language processing (NLP), augmented analytics, and edge computing, as it offers better opportunities and data-driven outcomes.

However, the organization must keep data security and privacy measures in mind while dealing with generative AI and acquire the right talent to thrive in this dynamic landscape.

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As technology evolves, businesses proactively leveraging new capabilities are better positioned to drive product and service innovations. A key factor in being able to move forward quickly is access to highly skilled technology experts. Even with announced layoffs at big tech firms, it can be difficult for growth-stage companies to find the right professionals needed to meet development timelines. To better understand the impact of tech talent on business success, AITech Park interviewed Ed Szofer. In this exclusive interview, the President and Co-Founder of SenecaGlobal talks about how the company focuses on solving complex technology challenges for clients so they can stay focused on strategic planning and operations.

Below are the interview highlights:

Ed, please introduce yourself to our audience and tell us about your esteemed career so far.

I am the president, CEO and co-founder of SenecaGlobal where I focus on strategic planning, corporate management and operations. Recently, our firm celebrated its 15^th anniversary, during which time we’ve achieved consistent growth and built a reputation for creating quality solutions that solve complex technology challenges for our clients.

I’m a lifelong Chicagoan and am very proud of SenecaGlobal and the other Chicago-based firm I helped build and take public (one of Wall Street’s most successful IPOs in 1996) – Whittman-Hart. After the IPO, WH grew from about $50M to nearly $500M within four years with about 5,000 employees worldwide. 

How did SenecaGlobal come into existence? What are its key features and services?

The idea for SenecaGlobal was borne over dinner in 2007. Our conversation centered around the question: what if we created an IT services firm with a people-first culture that focused on solving complex technology challenges for growing companies? Along with my co-founders Rao Tummalapalli and Swaminathan Mani, we put the culmination of lessons learned during years of IT and leadership experience to create a SenecaGlobal – a firm that enables innovators in underserved mid-market companies to move forward faster and generate ROIs.

Tell us about the solutions offered by SenecaGlobal. What makes the company stand out in the market?

SenecaGlobal is a leader in innovation outsourcing for complex software development, managed services and enterprise resource planning (ERP) solutions. The company helps our clients – many in the HealthTech, SecurityTech, FinTech and manufacturing/logistics industries – develop and bring innovative solutions to market faster. Our agile team consists of world-class innovation technologists and business executives who have deep domain expertise.

What would you say is the factor that drives success and growth wherever you go? What’s the secret behind your success at Whittman-Hart?

The key to success for any business is understanding what your clients need, which can be harder than it sounds. Mid-market companies are where innovative ideas come to life. Big consulting firms prefer to work with larger companies, which is why SenecaGlobal – and likewise when I was at Whittman-Hart – focuses on providing top-notch software development, cloud managed services and NetSuite implementation and managed services to growth-stage companies. We are the engine behind their success which in turn makes us successful.

The world of software development is continuously transforming, especially with modern technologies in the picture. How is SenecaGlobal aligning its offerings with these disruptive technologies?

This is what SenecaGlobal does best. We solve complex technical issues for our clients. To do that, we stay at the forefront of emerging technologies such as artificial intelligence (AI), machine learning and tools that enable secure, efficient Software-as-a-Service (Saas) applications.

What is innovative outsourcing? How does it differ from traditional outsourcing?

Traditional outsourcing often focuses on creating value for clients while maximizing billable hours. We take a different approach. Innovation outsourcing looks at the big picture of what a client is trying to create or update. We provide experts who have business process experience and proactive problem-solving skills to enable our clients to focus on strategic planning and execution. We handle software development, quality testing, cloud management and NetSuite optimization that enables our clients to accelerate their pace of innovation and time to market. Despite recent downsizings at big tech firms, finding the right tech talent continues to be a challenge, which is why many companies turn to SenecaGlobal to get things done.

As a distinguished leader, what advice would you give to budding entrepreneurs and enthusiasts who wish to venture into the industry?

My advice for those starting a new venture is not to get overwhelmed by all the decisions – big and small – they need to make. First and foremost, I encourage them to be very clear on what problem they are trying to solve and what the users of the solution want. Then seek out experts in the areas where you are not well versed to guide decisions about business operations and solution development. Also, if you think you’ve raised enough capital for an operating runway of 18-24 months, think again and double it.

How do you plan to scale SenecaGlobal’s growth strategy in 2023 and beyond? Do you have any plans to expand?

SenecaGlobal continues to grow at a rapid pace. In 2022, we grew revenue from new clients by 400% and 22% overall revenue growth from the previous year. Our recent customer traction is reflective of our market-specific initiatives, top software developer talent and industry experts in SecurityTech, HealthTech, FinTech and ERP.

Our growth strategy is to continue to focus on those markets where our clients rely on us to develop and scale their product innovations, resulting in faster time to market and competitive advantage. As the global economy becomes more complex, our customers consider innovation outsourcing an integral part of their strategy for growth.

How do you spend your downtime? What are the hobbies that you enjoy pursuing?

I’m very close with my immediate and extended family. My wife Diane and I have three adult children and five grandchildren under that age of 5. They keep us very busy. When I’m not on Grandpa duty, you’ll often find me on the links or boating on Lake Michigan. Diane and I (along with friends) love to travel and we usually find a way to fit a few rounds of golf at new and challenging courses.

Ed Szofer

President, Co-founder, and Chief Executive Officer of SenecaGlobal

Edward V. Szofer is the president, chief executive officer and a co-founder of SenecaGlobal, where he leads strategic planning, corporate management and operations. Under his leadership, SenecaGlobal has achieved consistent growth and built a reputation for creating high-quality solutions that solve complex technology challenges for clients. Ed is a proud, lifelong Chicagoan who began his career at Arthur Andersen & Co. (today’s Accenture) and has a long history of developing highly profitable national and global organizations. As president, chief operating officer and a member of the board of directors of Whittman-Hart (NASDAQ:WHIT), he helped take the company public. Within four years, the firm achieved revenues of nearly $500 million with more than 5,000 employees worldwide. He earned a bachelor’s degree in Business Administration from Loyola University in Chicago, Illinois, USA.

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