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Modernes Rechenzentrum mit Server-Racks und Netzwerkkabeln, Sinnbild für stabile Datenbankoperationen, IT-Infrastruktur und sichere Systemlandschaften im Finanzumfeld.

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Modernes Rechenzentrum mit Server-Racks und Netzwerkkabeln

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In financial IT, database operations are not merely an infrastructure issue. They are business-critical. When systems are unavailable, replications fail, or migrations stall, this creates direct risks to operations, compliance, and service quality.
It was precisely in this context that ServiceFactum supported a leading IT service provider for the financial sector in stabilizing and scaling its database operations. The task: to reliably operate more than 250 database instances, reduce operational risks, and simultaneously relieve the internal teams.

How ServiceFactum Stabilized Business-Critical Database Operations in a Regulated Environment

The existing environment was complex. More than 250 database instances based on Oracle, MySQL, and PostgreSQL had to be operated reliably within a highly critical infrastructure. At the same time, operational challenges arose repeatedly.
These included replication issues, storage bottlenecks, performance problems, and failed migrations. Many processes were heavily manual. This slowed down the resolution of issues and made it difficult to implement changes during ongoing operations. At the same time, internal teams were under constant pressure to comply with regulatory requirements and ensure the stability of the systems.
In such a situation, it is not enough to simply add individual specialists on an ad hoc basis. What is crucial is a model with clear responsibilities, coordinated processes, and reliable operational management.

The Challenge

ServiceFactum took responsibility for second-level database operations and stabilizing the environment. Instead of providing individual specialists in isolation, ServiceFactum orchestrated a multi-vendor setup of experienced database experts within a uniformly managed delivery model.
For the end customer, this meant: centralized control, clear priorities, SLA monitoring, and less coordination effort on the customer’s part.
The focus was on four areas:
<ol>
<li>Stability in day-to-day operations 
Recurring error patterns were systematically analyzed and reduced—including replication issues, performance bottlenecks, and critical storage issues.</li>
<li>Faster incident response 
Thanks to clear responsibilities, structured processes, and experienced second-level support, incidents could be addressed and resolved more quickly.</li>
<li>Controlled changes and maintenance 
Backups, log management, monitoring, and maintenance processes were made more stable and repeatable. This allowed infrastructure changes to be implemented faster and with less operational disruption.</li>
<li>Automation and compliance by design 
PostgreSQL configurations were automated using Ansible. At the same time, secure monitoring structures with TLS and MFA were implemented to meet the high regulatory requirements in the financial sector.</li>
</ol>

The Approach

<blockquote>Even in a highly regulated environment, we don’t just provide individual specialists; we bring structure, control, and reliability to operational delivery.
This transforms traditional staff augmentation into a controlled, SLA-driven setup that reduces risks and noticeably eases the burden on internal teams.</blockquote>
Bernd Wandt, CEO and Onshore Delivery Manager at ServiceFactum
By partnering with ServiceFactum, the major financial firm was able to significantly stabilize its operational database operations. Risks in the highly critical infrastructure were reduced, recurring error patterns were contained, and incident processes were accelerated.
SLA compliance improved thanks to faster response and resolution times. Internal teams were relieved of operational firefighting and were able to focus more on strategic tasks.
At the same time, a scalable delivery model was created that combines regulatory requirements, security, and cost efficiency. Compared to purely onshore setups, ServiceFactum enabled cost efficiencies of 40 to 60 percent, while maintaining full operational control through onshore governance.

The result

This case study clearly demonstrates that in regulated IT environments, stable database operations require more than just technical expertise. What matters most are clear lines of responsibility, structured processes, and operational management that balances security and speed.
ServiceFactum has established precisely this foundation for a major financial services company in Germany. Through a managed team of experts, in-depth technical expertise, and a clear focus on stability, database operations were sustainably improved.
Do you want to stabilize critical database operations and relieve the burden on internal IT teams? ServiceFactum supports you with onshore governance, experienced nearshore experts, and SLA-driven delivery.

Conclusion

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Große, spiegelnde Buchstaben mit dem Wort ‚EPIC‘ stehen auf einer Wiese vor Bäumen und einer historischen Steinmauer unter blauem Himmel mit großen Wolken.

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EPIC Typografie

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How do you perfectly structure Epics, User Stories, and Tasks in a nearshore setup? Discover best practices for agile projects without communication barriers.

Epic, User Story & Task in Nearshore Teams: Best Practices

In modern software projects, a clear structure of requirements is crucial for success. Especially in a nearshore setup, a well-organized breakdown of Epic and User Story is essential to enable efficient cross-location collaboration and to avoid misunderstandings. This blog post explains what these concepts are, why they are relevant as strategic requirement units, and how they can be successfully applied within a nearshore team using best practices.

1) Introduction

2) Epic, User Story and Task: Definition and Hierarchy in a Nearshore Context  

An Epic describes a major business requirement or strategic goal within a software project and can be specifically applied in nearshore projects. Due to its scope, it is broken down into several User Stories, as it cannot be implemented in a single sprint.
<ul>
<li>Focus: Strategic objectives, long-term product development, and efficient collaboration within the nearshore team</li>
<li>Scope / Size: Very large; includes multiple User Stories and spans several sprints (weeks to months), often across teams and locations</li>
<li>Responsibility: Product Owner or Product Manager, with close alignment with nearshore team leads</li>
<li>Example: “Introduction of a new mobile app dashboard in collaboration with a nearshore team to accelerate development”</li>
</ul>

A User Story describes a specific requirement from the user’s perspective and focuses on delivering direct value. It is small enough to be implemented within a single sprint and is derived from an Epic.
<ul>
<li>Focus: User-centric value and clearly understandable, actionable requirements. This is especially important for smooth collaboration in a nearshore setup</li>
<li>Scope / Size: Medium; implementable within one sprint and part of an Epic</li>
<li>Responsibility: Product Owner in close alignment with the nearshore development team</li>
<li>Example: “As a user, I want to register so that I can access the platform, with clearly defined acceptance criteria for the nearshore team.”</li>
</ul>

A task is the smallest unit of work in an agile project and describes a specific technical or operational step for implementing a User Story, particularly in nearshore setups, to ensure tasks are handled clearly and independently.
<ul>
<li>Focus: Clearly and unambiguously defined implementation steps to ensure efficient execution within the nearshore team, with a strong emphasis on clarity and minimal coordination effort</li>
<li>Scope / Size: Very small; typically a few hours to a few days of work</li>
<li>Responsibility: Nearshore development team</li>
<li>Example: “Implement the registration API, including clearly defined interfaces and acceptance criteria to ensure smooth implementation within the nearshore team.”</li>
</ul>

Epic and User Story are of high importance in nearshore setups, as they provide a structured framework for collaboration and align teams toward shared objectives. By breaking down complex requirements into smaller, actionable units, transparency is increased and efficient, iterative delivery across different locations and time zones is enabled.
In the following, it is explained why this structure is particularly important in a nearshore context and what concrete benefits it provides in practice:
<ul>
<li>Strategic context and clarity (Epic) through big-picture alignment and roadmap management</li>
<li>Improved communication and understanding (User Story) through a focus on user value, clearly defined requirements, and well-defined acceptance criteria</li>
<li>Enables actionable execution and transparency (Task) by breaking down User Story into concrete steps, enabling progress tracking and early identification of blockers</li>
<li>Optimized collaboration through efficient sprints with short iterations and timely feedback</li>
<li>Linking strategy, implementation, and measurable benefits by structuring Epic and User Story and deriving requirements from strategic goals</li>
</ul>

3) The Importance and Benefits of Epic User Story in a Nearshore Context

To optimize collaboration in a nearshore context, Epic and User Story should be applied consistently and intentionally, following these principles:
<ul>
<li>Clearly and understandably define Epic so that the “why” is understood by the nearshore team without additional coordination effort</li>
<li>Write User Story from the user’s perspective and define them with clear acceptance criteria to reduce clarification requests across different locations</li>
<li>Keep Task small, concrete, and actionable so that the nearshore team can work independently without blockers</li>
<li>Conduct regular refinement sessions between onshore and nearshore teams to clarify misunderstandings early and avoid rework</li>
<li>Use a common language and standardized tools (e.g., Jira) to ensure transparency across all locations</li>
</ul>
These principles reduce coordination effort between sites, accelerate delivery, and improve  overall delivery quality in nearshore setups.

4) Best Practices for Efficient Work with Epic and User Story in a Nearshore Team

Epic and User Story form the central foundation for structured and efficient requirement implementation in a nearshore context. The Epic defines the strategic framework and ensures alignment toward common goals, while the User Story translates these goals into clear, user-centric, and actionable requirements. This creates a shared understanding of scope and priorities that can be maintained even without continuous direct communication, significantly simplifying collaboration.
The consistent application of this structure is particularly important in nearshore environments, as it helps reduce challenges caused by geographical distance, different working hours, and limited synchronous communication. Combined with best practices such as regular refinement sessions and standardized tools, coordination effort within nearshore teams is significantly reduced, while delivery becomes more efficient, predictable, and consistently high in quality.

5) Conclusion

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Detaillierte stilisierte 3D-CGI-Illustration, die Konzepte für E-Commerce-Wachstum und Marketing-Analyse darstellt. Ein blauer Einkaufskorb steht neben einer braunen Papiertüte. Schwebende Symbole umfassen eine Zielscheibe mit Pfeil (Targeting), eine leuchtende Glühbirne (Ideen), ein Prozentzeichen (Rabatte) und ein Kreisdiagramm. Im Hintergrund zeigt ein Bildschirm eine Liniengrafik mit einem aufsteigenden Trend und ein Balkendiagramm, was auf erfolgreiche Verkaufsdaten hindeutet.

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E-Commerce Wachstum und Marketing-Analyse 3D Illustration

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As data platforms grow, so does operational complexity
The retail sector generates vast amounts of data every day: sales figures, store data, product information, customer data, financial metrics, and operational KPIs. For this data to inform reliable decisions, it must be consistent, up-to-date, and of high quality.
This is exactly where the collaboration between Takko Fashion and ServiceFactum came into play. Takko Fashion used a central data platform based on Azure Databricks to support international retail analytics and reporting processes. The challenge: As data volumes increased and analytics needs grew, maintaining stable platform operations became increasingly demanding.

How ServiceFactum Stabilized Takko Fashion’s Retail Analytics Platform Using Azure Databricks

As is common in many mature enterprise environments, the data came from a variety of sources: ERP systems, CRM systems, SAP, Salesforce, and other internal applications. This heterogeneous environment led to several problems:
Different data structures made harmonization difficult. Unstable interfaces and changing schemas caused recurring pipeline errors. At the same time, data quality issues such as duplicates or faulty currency logic undermined confidence in reports and dashboards.
For the business units, this meant that data was not always available on time, analyses had to be checked or corrected, and business intelligence teams lost valuable time. At the same time, it was clear that the growing demand for analytics could not simply be met by hiring more internal IT specialists.

Fragmented systems and unstable data pipelines

ServiceFactum assumed operational responsibility for stabilizing and further developing the data platform. This was not merely a matter of fixing individual bugs. The goal was to establish a robust operational model that would ensure the long-term stability of data pipelines and enable the rapid delivery of new analytics requirements.
To achieve this, ServiceFactum provided a nearshore team of four data engineers, managed through clear onshore governance. This gave Takko Fashion additional engineering capacity without having to build or expand internal teams in the long term.
The focus was on three areas:
<ol>
<li> More stable data pipelines 
Recurring pipeline issues were reduced through proactive monitoring, systematic error analysis, and the optimization of unstable connectors.</li>
<li>Higher data quality 
Using Python, Spark, and SQL, data was cleaned, standardized, and harmonized in Azure Databricks. Duplicates were removed, and critical business logic, such as currency conversions, was corrected.</li>
<li>Reliable analytics datasets 
The prepared data was made available in a way that allowed BI teams and business units to use it reliably in Power BI and other reporting processes.</li>
</ol>

Databricks Operations Instead of Short-Term Firefighting

<blockquote>
We don’t just provide a nearshore team, we manage the delivery process to transform unstable data processes into reliable data operations with clear KPIs, transparency, and increasing operational responsibility.
Our goal is to lay the groundwork, step by step, for stable, scalable operations.
</blockquote>
Bernd Wandt, CEO and Onshore Delivery Manager at ServiceFactum
By partnering with ServiceFactum, Takko Fashion was able to significantly improve the stability of the Databricks platform. Pipeline failures and manual interventions were reduced, while data became more consistent and reliable for reporting and analytics.
Most importantly, confidence in dashboards and business reports was strengthened. Finance, BI, and business units were able to access more robust data and implement new analytics use cases more quickly.
At the same time, a scalable delivery model emerged with clear responsibilities, active management, and measurable results. Compared to teams staffed entirely onshore, the model enabled cost efficiencies of 30 to 50 percent, without additional internal headcount.

Greater trust in data, less operational friction

The Takko Fashion case study demonstrates that modern analytics depends not only on technology, but above all on stable operations, clear processes, and reliable data quality.
With Azure Databricks, a managed nearshore team, and operational responsibility handled by ServiceFactum, Takko Fashion was able to lay the groundwork for faster, more reliable, and scalable retail analytics.
Would you like to stabilize your data platform or implement analytics projects faster? ServiceFactum supports companies with experienced data engineers, clear governance, and a scalable nearshore model.

Robust data platforms are the foundation for scalable retail analytics

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Advanced Near-/Offshoring als ROI-Hebel im Mittelstand: Internationale Teams, lokal gesteuert – kürzere Time-to-Market und höherer Return on Investment.

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How SMEs can finally achieve their tech lift – faster, cheaper and with less risk
German SMEs are in the midst of a technological leap: AI agents, process automation, cloud migration, new digital business models. But many small and medium-sized enterprises (SMEs) are experiencing the same bottleneck: the ideas are there – but implementation fails due to a lack of capacity, skills and speed.
Advanced near/offshore outsourcing is not a ‘cheap developer model’ here, but a strategic lever: for real ROI, faster time-to-market and greater innovative strength, without relinquishing control over critical IT projects.
In this article, we show:
<ul>
<li>Why the tech lift process often slows down growth in SMEs</li>
<li>How advanced near/offshore outsourcing really pays off for SMEs (including numerical examples)</li>
<li>Which governance and delivery models are necessary to ensure that near/offshore outsourcing does not fail</li>
<li>What a German onshore delivery manager and a local prime contractor have to do with it</li>
</ul>

The real ROI of advanced nearshoring in medium-sized businesses

Many medium-sized companies are currently investing heavily in:
<ul>
<li>New digital products</li>
<li>Modern platforms and interfaces</li>
<li>Process automation</li>
<li>AI and data projects</li>
</ul>
We call this approach the tech lift process: the company raises its technology and IT capabilities to a new level in order to remain competitive in the long term.
The 5 hidden obstacles to success in the tech lift process 
In practice, we repeatedly encounter the same obstacles in medium-sized companies:
<ol>
<li>Overloaded core team 
The internal IT and specialist team has to juggle operations, support, minor changes and innovation projects at the same time. The result: constant firefighting instead of focus.</li>
<li>Time-consuming knowledge transfer 
New technologies first have to be laboriously learned. Training, PoCs, trial and error – everything takes time before any real added value is created.</li>
<li>Poor integration of new experts 
External developers are integrated ‘somehow’, processes are unclear, roles are not clearly defined. Friction losses are inevitable.</li>
<li>Short-sighted investment perspective 
Instead of strategic scaling, many decisions are made from a ‘project perspective’: The main thing is that project X gets finished somehow – instead of building up long-term capacity.</li>
<li>Lack of technological vision/roadmap 
There are projects, but no clear target vision: Which systems will be critical in 3–5 years? Where do we want to go technologically? What skills do we need in the core team?</li>
</ol>
The consequences: When the tech lift becomes a predetermined breaking point! The consequences of these blockers are already being felt in many SMEs:
<ul>
<li>Technical debt &amp; fragmented source code</li>
<li>Slowed development cycles &amp; quality problems</li>
<li>Unclear product roadmaps &amp; increasing innovation risk</li>
<li>Longer time-to-market &amp; loss of market share</li>
</ul>
In short: the tech lift process becomes a strategic bottleneck, even though it should actually be a growth driver.

1. The tech lift process – and why it often fails in medium-sized businesses

Many medium-sized companies initially try to solve the problem in the traditional way:
<ul>
<li>Hiring additional internal developers</li>
<li>Training programmes and certifications</li>
<li>Experimenting with new tools and technologies</li>
<li>Selective support from freelancers</li>
</ul>
The problem: The pressure to digitise is growing faster than the available capacity. 
Added to this are the typical conditions for medium-sized companies in Germany, Austria and Switzerland:
<ul>
<li>IT talent is scarce and expensive</li>
<li>Competitors (including corporations and start-ups) are fishing in the same talent pool</li>
<li>Internal teams are tied up in day-to-day business</li>
<li>Technologies such as cloud, AI, data and automation are uncharted territory for many teams</li>
</ul>
The result: projects are postponed, stretched out or implemented in a ‘minimalist’ manner – and the market moves on.

2. Why traditional in-house approaches are no longer sufficient

This is where software development in the advanced nearshore or offshore model comes into play. It is important to clearly distinguish between classic offshoring and advanced nearshoring and offshoring.
Classic offshoring: 
‘We hire cheaper developers from abroad.’
Advanced nearshoring/offshoring: 
‘We build scalable, integrated delivery teams that increase our innovation and project speed – controlled by professional onshore delivery management.’
The difference: It&#8217;s not just about reducing costs per developer hour, but about accelerating and stabilising the entire tech lift path.
The biggest pain points in competence building that advanced near-/offshoring addresses
Typical challenges in SMEs:
<ul>
<li>Lack of technology experts: senior profiles are hardly available</li>
<li>Budget pressure: classic in-house teams quickly reach their budget limits</li>
<li>Know-how gaps: cloud, AI, automation, integration – lots of new things at once</li>
<li>Dependence on individuals: key people become a risk</li>
<li>Delays: internal teams are consumed by day-to-day business</li>
</ul>
This is exactly where advanced near/offshore outsourcing comes in:
<ul>
<li>International talent pools for senior profiles</li>
<li>Cost advantages per team and project</li>
<li>Faster scaling of capacities</li>
<li>Structured know-how transfer to the core team</li>
<li>Relief for the internal team without loss of control</li>
</ul>

3. Advanced nearshoring/offshoring: More than just saving costs

When it comes to nearshoring or offshoring, many people first think of hourly rates. 
But the real leverage lies in the combination of cost savings, time-to-market and innovation effects.
Let&#8217;s look at a typical project example from a medium-sized company:
Comparison: In-house vs. nearshore project
Scenario: Development of a new digital platform (SME, DACH)
In-house project 
&#8211; Total costs: £921k 
&#8211; Go-live: after 12 months
Nearshore approach with advanced nearshoring 
&#8211; Total costs: £620k 
&#8211; Go-live: after 8 months
At a glance: 
✅ 30% cost reduction 
✅ 3–4 months earlier on the market 
✅ Positive ROI after 3 years through nearshoring
The key point: It&#8217;s not just the absolute savings that count, but the earlier utilisation of:
<ul>
<li>Efficiency potential (e.g. automated processes)</li>
<li>Revenue potential (e.g. new digital services)</li>
<li>Competitive advantages (e.g. better customer experience, shorter turnaround times)</li>
</ul>
Especially in innovation and tech lift projects, every month of delay means: 
❌ lost revenue 
❌ higher opportunity costs 
❌ increasing frustration within the team 
❌ growing gap to the competition
Conclusion: 
Advanced nearshoring/offshoring is not a ‘cost-cutting programme’, but a growth and ROI accelerator.

4. The real ROI of advanced near/offshoring – a practical example

Many companies have also had bad experiences with offshoring: 
&#8211; Language barriers 
&#8211; Time zone issues 
&#8211; Misunderstandings in requirements 
&#8211; Quality issues in the code 
&#8211; High dependence on individuals or freelancers
The reason is almost always the same: a lack of governance and structure in the delivery model.
What SMEs really need: Better delivery models 
SMEs don&#8217;t just need cheaper developers, they need a setup that neatly solves the following questions:
Who is responsible for communication and coordination? 
How is quality measured and controlled (KPI, SLA)? 
How do we ensure that knowledge remains within the company? 
How do we integrate the external team into our processes and tools?
The answer is an advanced near/offshore setup with two central roles:
1) German onshore delivery manager 
The onshore delivery manager is:
<ul>
<li>the bridge between SMEs and the global development team</li>
<li>responsible for communication, governance and quality</li>
<li>on a par with management and specialist departments in terms of expertise and culture</li>
<li>the person who translates, prioritises and controls requirements</li>
</ul>
2) Local partner as prime contractor
The local (near/offshore) partner acts as the prime contractor:
<ul>
<li>a central contractual partner for all external teams</li>
<li>responsible for capacity planning, team composition and skills</li>
<li>guarantees quality, stability and availability</li>
<li>manages the international delivery teams on site</li>
</ul>
This setup significantly reduces typical offshoring risks: 
→ Misunderstandings &amp; cultural conflicts: clear communication channel 
→ Project delays &amp; quality losses: defined KPIs, SLAs, standards 
→ Dependence on individuals: structured teams instead of ‘hero developers’
The result for SMEs: control remains within the company, while speed and scaling are provided by international teams. Knowledge is systematically secured and transferred!

5. Governance & Delivery: Why near/offshoring often fails – and how to prevent it

Let&#8217;s summarise the perspective from the CEO and CTO&#8217;s point of view.
The most expensive part of an IT project is NOT the developer – it&#8217;s lost time.
Part I – Growing pains in SMEs: 
Tech initiatives get stuck, not because of a lack of ideas, but because of a lack of implementation capacity, gaps in expertise and overloaded core teams.
Part II – Quick ROI through advanced nearshoring: 
Nearshoring/offshoring is worthwhile not because developers are cheaper, but because projects go live earlier, budgets are better utilised and investments pay off faster.
Part III – Tech global, responsibility local: 
Without clearly defined governance, nearshoring/offshoring becomes chaotic. With onshore delivery management and a strong prime contractor, it becomes a scalable model for success.
Overall conclusion
Advanced nearshoring/offshoring is not a tool for reducing personnel costs, but rather the way to make tech lift feasible for SMEs in the first place. It enables SMEs to start faster, think more boldly and still maintain control. This is precisely what is crucial if SMEs in the DACH region do not want to fall behind when it comes to digitalisation.

6. Advanced near/offshoring as a strategic lever – not as a cost trick

A few simple indicators that it might be time for an advanced nearshoring model:
<ol>
<li>Your internal IT team is constantly working at full capacity.</li>
<li>Important digital projects are repeatedly postponed.</li>
<li>You cannot find suitable senior developers locally.</li>
<li>New topics such as AI, cloud computing and integrations remain ‘on the roadmap’ but are not being implemented.</li>
<li>You have already had bad experiences with traditional offshoring or freelancers</li>
</ol>
If you can relate to at least two of these points, it makes sense to consider a structured advanced nearshoring/offshoring setup – with a clear governance model and proven delivery structure.

7. When is advanced nearshoring/offshoring worthwhile for your company?

ServiceFactum supports medium-sized companies in
<ul>
<li>clearly defining their own techlift process</li>
<li>setting up suitable advanced near/offshore models</li>
<li>clearly establish onshore delivery management and prime contractor roles</li>
<li>seamlessly integrate international development teams with existing structures</li>
</ul>
This approach turns innovation potential into actual market success – measurable in shorter development cycles, more stable software quality and earlier time-to-market …
… and a clear, robust ROI.

8. How ServiceFactum supports the tech lift in small and medium-sized enterprises

If you want to know what an advanced near/offshore model could look like for your company, you should start by answering three questions:
<ol>
<li>Which core systems and products are truly critical for our future?</li>
<li>What skills are we currently lacking in our team to develop or further develop these?</li>
<li>How much time do we currently lose each year due to delays in IT and digital projects?</li>
</ol>
With these answers, we can design an initial, robust setup for your tech lift – including a realistic ROI scenario.
Get IT done! Let&#8217;s work together to make your next IT project a success!

Next steps

Question 1: What is advanced nearshoring/offshoring in SMEs? 
Advanced nearshoring/offshoring means that SMEs not only relocate software development to lower-cost countries, but also use structured delivery models, clear governance and onshore delivery management. This creates an integrated team that increases speed and quality without the SME losing control.
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Question 2: How does advanced nearshoring differ from traditional offshoring? 
Traditional offshoring usually focuses on lower hourly rates for individual developers. Advanced nearshoring relies on well-coordinated teams, standardised processes, clear KPIs and German onshore delivery management. The focus is on time-to-market, quality and ROI – not just on cost reduction.
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Question 3: When does advanced nearshoring/offshoring really pay off for SMEs? 
Advanced nearshoring/offshoring is particularly worthwhile if digitalisation projects are regularly postponed, internal IT teams are permanently overloaded or senior profiles are difficult to find at the location. At the latest when it comes to recurring or larger innovation projects with tight budgets and time frames, a structured nearshoring model can bring significant ROI advantages.
<hr data-start="3530" data-end="3533" />
Question 4: How do you calculate the ROI of near/offshoring in SMEs? 
The ROI results not only from lower project costs, but above all from faster time-to-market. In addition to direct development costs, SMEs should also take into account lost revenue, efficiency gains and the earlier utilisation of digital potential. If a project goes live several months earlier, the model can pay off significantly after just a few years.
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Question 5: What are the risks of near/offshoring – and how can they be reduced? 
Typical risks include communication problems, quality deviations, cultural and time zone differences, and dependence on individuals. These can be significantly reduced through a clear governance model, defined KPIs and SLAs, a British onshore delivery manager and a local prime contractor with well-coordinated teams.
<hr data-start="4447" data-end="4450" />
Question 6: What role does an onshore delivery manager play in advanced nearshoring/offshoring? 
The onshore delivery manager is the central bridge between SMEs, specialist departments and the nearshoring/offshoring team. They are responsible for requirements, prioritisation, communication, quality assurance and reporting. This means that control remains in the German-speaking region, while implementation capacity can be scaled internationally.
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Question 7: Is advanced nearshoring also suitable for SMEs in the DACH region? 
Yes, German-speaking SMEs in Germany, Austria and Switzerland in particular benefit from advanced nearshoring. Cultural proximity, small time zone differences and clear governance models allow international development teams to be connected efficiently – without the high cost of building a large in-house IT team.

FAQs about advanced nearshoring

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Big Data – eine große Menge an unstrukturierten Daten.

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Big Data

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Big data has changed organisations in a big way. The digital transformation has put data at the centre of companies of all sizes and industries in both the public and private sectors. 
In this blog post, we describe what exactly big data is, why it is so important for organisations today and what technologies and strategies organisations should use to make better use of big data.

Big Data: What it is, how it works, and what the benefits and challenges are!

Big data stands for a large amount of structured and unstructured information generated by people and machines. This data is multiplying every day — data in the order of zettabytes produced by computers, mobile devices and electronic sensors. These large and complex data sets cannot be easily managed or analysed using traditional data processing tools.
Big data comes in different formats:
<ul>
<li>Structured data: e.g. transactions and financial documents</li>
<li>Unstructured data: e.g. texts, documents, video, and multimedia files</li>
<li>Semi-structured data: e.g. web server logs and streaming data from sensors</li>
</ul>
Organisations can use this data to make data-driven decisions, improve processes and policies and develop customer-centric products, services and experiences.
The need to manage large amounts of data dates back to the 1960s and 70s, when the first data centres were built and the relational database was developed. This was the starting signal for today&#8217;s big data.

1. Introduction to the topic ‘Big Data’

Traditionally, big data has been recognised by three characteristics: Variety (Variety), Volume (Volume) and Velocity (Velocity), also known as the ‘3 Vs’. In recent years, however, two further characteristics have been added: Value and Veracity, because today data has become capital. These ‘5 V&#8217;s’ as characteristics of Big Data mean the following:
<ul>
<li>Volume: The amount of data — for some companies this can be tens of terabytes of data, for others it can be hundreds of petabytes.</li>
<li>Velocity: The speed at which data is received and (potentially) processed.</li>
<li>Variety: The types of data that are available — structured data, unstructured data, semi-structured data</li>
<li>Veracity: How truthful is data and how much can it be relied upon?</li>
<li>Value: Data has an intrinsic value to the organisation and this value should be discovered. This value can be internal, such as operational processes that can be optimised, or external, such as suggestions for customer profiles that can maximise engagement.</li>
</ul>

<ul>
<li>Better insights: When companies have more data, they can gain better insights for every process.</li>
<li>Decision making (market intelligence): Thanks to deep data insights, companies can make data-driven decisions with more reliable forecasts and predictions.</li>
<li>Improved operational efficiency: Every department can benefit from data at an operational level.</li>
<li>Personalised customer experiences: Customer profiles and associated data can improve customer loyalty and experience.</li>
<li>Cost savings: Using Big Data, organisations can find ways to reduce costs and improve operational efficiency.</li>
</ul>

2. Advantages of Big Data

When data experts start a new project with a big data analysis, they generally follow a five-step process. We call this process the ‘data science workflow’:
<ol>
<li>Identify business questions: The first step in turning data into insights is to define clear goals and questions: What does the business need? What kind of problem are we trying to solve? What type of data is needed? What techniques and methods will we use?</li>
<li>Data collection and storage: Then the data needs to be collected from various resources such as cloud storage or mobile applications and stored in a secure location such as a data warehouse in order to analyse it.</li>
<li>Process data (Processing): Once the data is collected and stored, it needs to be organised properly to get accurate results in analytical queries. Some processing options are batch processing and stream processing.</li>
<li>Data cleansing (cleaning): The next step is to improve the quality of the data in order to achieve a more meaningful result. All data must be correctly formatted to eliminate duplicate or irrelevant data.</li>
<li>Data analysis: Once the data is ready, advanced analytics can turn big data into understandable insight. Some of these big data analytics methods include data mining. Machine learning methods: predictive analytics, deep learning, natural language processing (NLP). Finally, the data is ready for visualisation and communication.</li>
</ol>

3. How does Big Data Analytics work?

The most practical tools used by organisations for big data analytics projects are the following:
<ul>
<li>Tableau: Mainly used for business intelligence and data visualisation to share, analyse and report information.</li>
<li>Microsoft Power BI: It combines business analytics, data visualisation and best practices that help a company make data-driven decisions.</li>
<li>Google Analytics: Is a free web analytics service from Google that provides basic analytics tools and statistics for search engine optimisation (SEO) and marketing. The performance of a website and information about its visitors are monitored and analysed.</li>
<li>Python: Is one of the most popular programming languages for data science and analytics. It offers many libraries for tasks such as data manipulation, cleansing, analysis and visualisation as well as machine learning and automation.</li>
</ul>

4. Best Big Data Analytics Tools

Although big data offers many advantages for companies, there are some points that companies should consider in order to achieve better results. The biggest challenges associated with big data are as follows:
<ul>
<li>Big data volume and complexity in architecture design</li>
<li>High costs for big data projects and infrastructure</li>
<li>Accessibility and slow gain of knowledge</li>
<li>Skills and knowledge shortage</li>
<li>Complex security and compliance</li>
</ul>

5. Challenges with Big Data

Big data plays a crucial role for companies in various industries and offers significant benefits that can increase efficiency. As big data also brings some challenges and evolves over time, we at ServiceFactum suggest some points that can increase the efficiency of big data for organisations in the future.
The following technology trends will have the biggest impact on big data:
<ul>
<li>AI and machine learning analytics: AI and machine learning algorithms will become key to performing analyses and tasks. Automated machine learning tools will be helpful in this area.</li>
<li>Improved storage with increased capacity: Storage options in the cloud are constantly improving. Data lakes and warehouses (either on-premise or in the cloud) are attractive options for storing big data.</li>
<li>Emphasis on governance: Data management and regulation will become more comprehensive as the amount of data increases, requiring more effort to protect and regulate it.</li>
<li>Quantum computing: Quantum computing can also accelerate big data analyses with improved processing power.</li>
</ul>

6. Conclusion

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Ein Teil von Team ServiceFactum ist versammelt. Bernd, Ricarda und Fabian stehen und schauen gemeinsam auf den Bildschirm von Shahrzad. Shahrzad sitzt am PC. Alle lachen dabei.

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Team ServiceFactum schaut auf Shahrzads Bildschirm

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71% of companies agree that they use agile methods frequently. Agile project management methodology is one of the most popular project management methodologies around and has revolutionized the way software teams and companies collaborate, develop and deliver their products today.
In this blog post, we discuss what exactly agile project management is, its differences from traditional project management methods and why most software development teams follow this method. We also explain its principles, methods, values and tools, as well as its key benefits for software teams, products and organizations.

Agile software project management: methods, principles and benefits

Project management can be defined as the activities of planning, organizing, securing, monitoring and managing the resources and work required to achieve specific project objectives effectively &amp; efficiently.
The project management approach used should always be adapted to the needs of the project.

1.1 What is project management?

Traditional or classic project management, known as the waterfall model, focuses on a linear approach in which all project phases are completed in sequential order.
The waterfall model method is not an ideal approach for the development of complex products, as this approach does not allow room for changes in requirements and, as a result, the risk of late product delivery is increased.
This is where the concept of agility comes into play.
In contrast to the waterfall model, the agile project management method is not linear, but is based on teamwork, flexibility and collaboration.
In agile project management, project phases are completed at short intervals. Changes are responded to quickly and the focus is more on implementing customer feedback and regularly reviewing the product.

1.2 Agile method vs. traditional project management method

The Agile Manifesto outlines the core values and principles of agile software development and provides teams with an effective model for successfully implementing the agile project management philosophy to improve their work processes.

2. What is the Agile Manifesto?

<ol>
<li>Individuals and interactions are more important than processes and tools</li>
<li>Functioning software is more important than comprehensive documentation</li>
<li>Collaboration with the customer is more important than contract negotiation</li>
<li>Responding to change is more important than following a plan</li>
</ol>

2.1 The important values in the Agile Manifesto

<ul>
<li>Customer satisfaction has the highest priority.</li>
<li>Changes to requirements — even late in the development process — are welcome.</li>
<li>Delivery of finished work at regular, preferably short intervals.</li>
<li>Putting together a motivated team, with the right environment and support, as well as trust in the team.</li>
<li>Personal communication from outside and within the development team.</li>
<li>The most important measure of progress is the functionality of the software.</li>
</ul>

2.2 Important principles in the Agile Manifesto

<ul>
<li>Increased adaptability: This is because agile teams practice iterative development, where they plan and execute work in small chunks.</li>
<li>Increased customer satisfaction: Agile teams start with the understanding that customer satisfaction is a top priority, and successful agile outcomes emphasize regular interactions with the customer.</li>
<li>Reduced risk: Through regular customer feedback &amp; continuous collection of lessons learned from previous team work as well as feedback in sprint reviews &amp; sprint retrospectives.</li>
<li>Increased business value: By focusing on iterative improvement, the team spends more time on high value work. This leads to faster value creation and the ability to meet market requirements sooner.</li>
</ul>

3. Advantages of agile software development

The Scrum method is an agile framework that supports teams in working together and helps them to complete software projects in true agile principles. It provides a guideline with values, roles and guidelines so that the team can focus on continuous improvement through iterations.
<ul>
<li>Scrum Values: Commitment, Courage, Focus, Openness &amp; Respect</li>
<li>Scrum artifacts: product backlog, sprint backlog &amp; product increment</li>
<li>Scrum events: Sprint; Sprint Planning; Daily Scrum; Sprint Review; Sprint Retrospective</li>
<li>Advantages: Continuous improvement; Fast implementation; Faster innovation; Satisfied customers</li>
</ul>

4. What is Scrum?

<ul>
<li>Product Owner: Manages the product roadmap, prioritizes the sprint backlog, defines the product vision and manages the stakeholders.</li>
<li>Scrum Master: Moderates Scrum events. Acts as a coach for the rest of the team and ensures that the team can work in a focused manner during a sprint.</li>
<li>Developers: Developers create the product and are responsible for programming, testing and troubleshooting the product.</li>
<li>Stakeholders: All other people who have interests in the development of the project. However, these people are not part of the Scrum team and do not have to be explicit managers, subject matter experts or similar.</li>
</ul>

5. Scrum team and stakeholders

<ul>
<li>Jira by Attlasian: Agile teams often use Jira because it is a powerful, flexible tool that supports the core principles of agile methods such as Scrum and Kanban, backlog management, sprint planning and tracking as well as reporting and analytics.</li>
<li>Confluence: Agile teams use Confluence primarily for documentation and collaboration. It complements tools like Jira by providing a central place for teams to create, organize and share information.</li>
<li>ClickUp: Agile teams use ClickUp because it integrates task management, time tracking, goal setting and reporting into a single platform. It also offers customization and flexibility, as well as multiple views for workflows.</li>
</ul>

6. Known Agile & Scrum software project management tools

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Cloud Computing

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Imagine you have a brilliant idea for a software application. But before your vision can become a reality, you face a host of challenges: expensive servers, endless storage space requirements and a dedicated network team. Sounds like a nightmare, right?
Luckily, there&#8217;s the cloud.
But what exactly is the cloud? Simply put, it&#8217;s the magic you need to manage your IT infrastructure over the internet. No more bulky servers in the office, no more stress over limited capacity. The cloud offers you everything you need — flexible, scalable and cost-efficient.
In this blog post, we will guide you through the wonderful world of cloud computing. Find out how you can access your data from anywhere, reduce IT costs and increase productivity. Let&#8217;s explore the different cloud models and their applications that can revolutionize your business processes.

Suppose you develop an innovative idea for a software application. To implement it and be successful, you need servers, storage space, development, dedicated network and application security.
Providing all this support can be expensive, time-consuming and risky. 
All of these problems can be easily solved with the help of the cloud.
But what exactly is a cloud? Cloud or cloud computing is the provision of computer services such as servers, storage space, databases and networks over the internet. 
It is a new way of working and managing data that allows you to access your data and applications from anywhere.

1. What is a cloud?

Without the cloud, companies are reliant on on-premise solutions.
They have significant challenges and limitations such as
<ul>
<li>Hardware limitations and higher operating costs;</li>
<li>Dependence on the physical capacity and location of resources;</li>
<li>Downtime, data loss, compatibility and compliance;</li>
<li>Impairment of the quality and reliability of services and products;</li>
</ul>
With the help of cloud computing, companies can benefit from the following advantages
<ul>
<li>Lower IT costs;</li>
<li>Centralised data security;</li>
<li>Scalability;</li>
<li>Flexibility;</li>
<li>Automated updates;</li>
<li>Rapid deployment;</li>
<li>Instant business insights and continuity;</li>
<li>Collaboration and communication;</li>
<li>Increased performance and availability;</li>
</ul>

2. Advantages of cloud computing

Cloud providers offer different types of provision and services.
Cloud service models:
<ul>
<li>Software-as-a-Service (SaaS): e.g. Slaesforce, Slack;</li>
<li>Platform-as-a-Service (PaaS): e.g. Microsoft Azure;</li>
<li>Infrastructure-as-a-Service (IaaS): e.g. Google Compute Engine, OpenStack;</li>
<li>Function-as-a-Service (FaaS): e.g. Google Cloud Functions;</li>
</ul>
Cloud deployment models:
<ul>
<li>Private cloud: In the private cloud, services such as servers, data centre or distributed network are completely reserved for one company.</li>
<li>Public cloud: In this deployment, cloud services are shared by several organisations.</li>
<li>Hybrid cloud: In hybrid cloud deployments, public and private clouds are combined.</li>
<li>Multi cloud: This is a type of cloud deployment in which several public clouds are used.</li>
</ul>

3. Cloud services & deployment models

The best-known providers of cloud computing are Amazon Web Services, Microsoft Azure and Google Cloud. However, the cloud comes in various forms and technologies:
<ul>
<li>Online storage: Microsoft OneDrive, iCloud;</li>
<li>E-mail: iCloud Mail;</li>
<li>Smart watches / fitness trackers: Apple Watch, Fitbit;</li>
<li>Streaming platforms: YouTube, Netflix;</li>
<li>Online word processing: Google Doc;</li>
</ul>

4. Cloud technology & examples

Although cloud computing can have many benefits for businesses and development teams, it can also present some issues and challenges that organisations should be aware of in order to achieve the best outcome.

5. Problems & challenges of cloud computing

Most cloud-based programmes cannot be used if there is no internet connection. Companies need to invest in higher bandwidth options and focus on improving operational efficiency to minimise reliance on network capacity.

5.1 Internet connectivity

As the cloud infrastructure is built, maintained and managed entirely by third parties in remote locations, end users have less control over the function and execution of the services. So before deciding on a cloud provider, you need to ask yourself: What data does the cloud service process? What functions does the cloud service offer? What does the contractual relationship look like?

5.2 Ownership & limited control

Not all cloud solutions are equally secure. This is especially important when it comes to off-site storage of important or private data. Before entrusting critical aspects of your IT or data to third parties, it is important to apply some security guidelines, such as configuring network hardware and installing the latest software updates to close potential security gaps.

5.3 Security & Privacy

The effectiveness of cloud solutions depends heavily on providers offering their customers appropriate services. For example, a sudden outage of a cloud provider can have a significant impact on the business. Organisations need to select providers that implement proactive SaaS monitoring standards in real time.

5.4 Performance problems

Organisations need experts to manage infrastructure effectively. Companies need to hire professionals who specialise in DevOps and cloud engineering as workloads continue to increase and the market experiences a surge in the adoption of new tools.

5.5 Insufficient knowledge

The scarcity and unreliability of individual services is a major challenge for cloud computing. Therefore, organisations must seek additional resources to meet evolving business needs and be assured of the reliability and availability of the cloud provider.

5.6 Reliability & availability

Cloud providers offer a subscription model where you pay for the services you use. However, these costs can accumulate over time. It is better for companies to check their systems regularly and use tools to monitor resource utilisation.

5.7 Ineffective cost management

Cloud computing offers a modern solution for organisations by providing cost-effective, scalable and flexible IT resources. The benefits, such as centralised data security, rapid provisioning and improved collaboration, make it an attractive option, despite challenges such as internet dependency and limited control.
With various models — from private and public to hybrid and multi-cloud, as well as service options such as SaaS, PaaS, IaaS and FaaS — companies can choose the solution that best suits their needs. A thorough examination of security and data protection aspects as well as effective cost management are essential in order to fully utilise the benefits.
Cloud computing is a key driver of digital transformation and increased efficiency in companies.

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KI in der Software-Entwicklung

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In recent years, AI has had a significant impact on many industries, including software development. In this blog post, we explain the impact of AI on software development and how it is already changing the way software is created.

Artificial intelligence in software development

Artificial intelligence (AI) is an area of computer science that focuses on the development of machines that can perform tasks that normally require human intelligence. Examples include understanding natural language, recognizing images and making decisions based on specific circumstances. John McCarthy, the father of AI, defines artificial intelligence as the science and technology of creating intelligent machines.

Knowledge

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Big Data: What it is, how it works, and what the benefits and challenges are!

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