What is Model Based Systems Engineering (MBSE)? A practical explainer for modern engineering 

Engineering domains like defence, automotive, manufacturing and critical infrastructure have always dealt with complexity. But today that reality is compounded by volatility. One seemingly small change can ripple across an entire architecture: a single component going end of life forces updates to requirements, interfaces and test plans or single regulatory change means revisiting assumptions and evidence across multiple teams.  

Traditional, document heavy engineering methods simply weren’t designed for this pace, scale and level of interdependence. Big static specifications, linear stage gated processes and manual drafting and review cycles are slow, siloed and paperwork driven; they just can’t keep up with environments that depend on fast iteration, shared data, and real-time collaboration. 

Model Based Systems Engineering (MBSE) offers a more coherent way forward. It makes models, rather than documents, the primary way of understanding how a system is put together and how it behaves under change. And while it’s often discussed in abstract terms, its value is practical: clearer decisions, fewer surprises and systems that can evolve with the world around them. 

Understanding Model Based Systems Engineering 

Traditional systems engineering spreads knowledge across separate artefacts: requirements lists, design specifications, interface control documents, test plans and more. Each serves a real purpose, but together they create a fragmented picture that engineers must mentally stitch together. 

MBSE brings this information into a single system model. Instead of navigating isolated, and typically manual, documents, engineers work with a visual, traceable representation of requirements, behaviours, structures and constraints across the system’s lifecycle: from concept and design through to operation and decommissioning. 

This connected view enables teams to: 

• Simulate and validate designs before physical implementation 
• Understand the implications of a change across the whole system or system-of-systems 
• Maintain traceability between requirements, design and testing as the system evolves 
• Accommodate iterative and Agile delivery without losing architectural coherence 
• Establish a strong foundation for digital twins and digital continuity 

In short, MBSE replaces a fragmented understanding with a coherent one. By shifting the focus from assembling information to analysing the system as a dynamic whole, it makes decisions clearer and enables swifter action. 

MBSE vs. Enterprise Architecture – what’s the difference? 

As an approach, MBSE is often mentioned alongside or confused with Enterprise Architecture (EA) because both use models to bring structure to a changing, interconnected world. They sit on a continuum, but they don’t do the same job. 

Enterprise Architecture works at the organisational level, the so-called ‘30,000ft view’. It defines the capabilities the business needs, the processes that support them, the information that flows between them and the technology principles that keep everything aligned. EA sets the strategic intent and the architectural constraints within which engineered systems must operate. 

Model Based Systems Engineering works at the system level and, critically, does so visually. It uses graphical models to capture requirements, behaviour, structure and constraints so engineers can see how a system works, how its parts interact and how changes flow across the architecture. MBSE can represent a single engineered system or a “system of systems”, depending on the scale of the environment.  

In plain engineering terms: 

• EA defines the environment: capabilities, context, constraints.
• MBSE defines the system: behaviour, architecture, verification.

EA sets the intent; MBSE delivers the model‑based technical design that realises that intent. So, even when a “system of systems” MBSE model approaches EA in scope, it’s still serving a different purpose. Both disciplines tackle the same operational pressures but address them from different vantage points. 

Model Based Systems Engineering in practice 

In practice, MBSE means working from a dynamic system model that brings together the elements that matter most in complex engineering environments. Typically visualised in a dashboard, it provides a traceable, queryable representation of the system as a single point of truth, containing: 

• Requirements
• Behaviours and interactions
• System structure and architecture
• Constraints and dependencies
• Lifecycle considerations from concept to decommissioning

The shift from documents to models isn’t cosmetic. Documents age; models evolve. Documents sit in silos; models connect disciplines. Documents tell you what the system was; models show you what the system is — and what it could be as it adapts to new constraints, technologies or missions. 

Most organisations use modelling languages such as SysML and tools like Cameo, Rhapsody or Enterprise Architect. SysML remains the most widely used, giving teams a standardised way to express structure, behaviour and constraints across complex systems. But the tools are only the enablers. The real value lies in the clarity, consistency and shared understanding that modelling brings. 

The operational benefits – why MBSE matters in modern engineering

 MBSE gives teams a coherent view of how a system behaves and how change in one area affects others and, fundamentally, a more honest representation of how systems behave in the real world. That shift enables: 

• Earlier validation and simulation
• Clearer communication across disciplines
• Faster impact analysis
• Stronger traceability between requirements, design and testing
• Enhanced collaboration across teams and suppliers
• Scalability for managing large, multicomponent or “system of systems” architectures

This is why MBSE has become particularly relevant in sectors where systems are large, long-lived and safety or mission critical.  

In defence and aerospace, it supports mission level traceability, interoperability across suppliers and stronger evidence for certification. In automotive, it helps integrate mechanical, electrical and software design in increasingly software defined vehicles. And in digital and critical infrastructure, it provides a way to map dependencies, model resilience and design for long-term adaptability. The common theme being MBSE provides the clarity needed to make confident decisions. 

What good MBSE delivery looks like in practice 

Successful MBSE programmes have less to do with tools and more to do with delivery behaviours. The organisations that get the most value tend to share a few consistent patterns: 

• Models are treated as living artefacts. They evolve as understanding deepens, rather than being produced once and filed away. 
• Iteration is normal. Teams model early, test assumptions quickly and refine as they learn, instead of waiting for a single ‘big reveal’. 
• Commercial and governance frameworks allow change. MBSE only works when contracts, schedules and decision gates accept that things will evolve. 
• Practitioners lead the work. Systems engineers, architects and domain specialists shape the model, ensuring it reflects real world behaviour rather than abstract theory. 
• Collaboration is built in. Modelling becomes a shared activity across disciplines, not something done in isolation by a single specialist. 

These principles also shape how CACI deliver MBSE.  

Our teams work iteratively, use models to drive shared understanding and keep architectures traceable as requirements evolve. We focus on the behaviours that make MBSE effective, clarity, adaptability and practitioner led modelling – because these consistently help programmes navigate complexity and make better decisions. 

Why MBSE is becoming essential 

 Recent research finds the number and intensity of system level dependencies is rising across every major engineering domain, increasing the likelihood that local failures propagate far beyond their point of origin. The PanIberian blackout in April 2025 made this clear: the energy disturbance cascaded across two national grids, disrupting transport, healthcare and communications within minutes.  

In this context, MBSE becomes a core competency rather than a niche specialism. But its value depends on how it is delivered, and by who.  

A strong MBSE approach provides clarity, traceability and better decisions. It reduces risk. It helps engineering systems evolve with the environment. And in sectors where the stakes are high like defence, automotive, aerospace and critical infrastructure, that combination is not optional, it’s foundational — and increasingly essential if organisations are to stay ahead of the rising fragility built into the systems they depend on. 

To find out how CACI can help your organisation build the resilience needed to operate effectively in an increasingly volatile, interconnected engineering environment, get in touch with our experts today. 

FAQs about Model Based Systems Engineering (MBSE)

What does “model-based” actually mean in Model Based Systems Engineering (MBSE)?

In Model Based Systems Engineering (MBSE), “model-based” means that system information is stored in a structured, machine-readable model rather than free-text documents. This allows relationships, dependencies and constraints to be queried, analysed and validated automatically instead of being inferred manually.

Is Model Based Systems Engineering only suitable for large or complex systems?

No. While MBSE is most visible in large, complex programmes, it can also be valuable for smaller systems where change is frequent or assurance requirements are high. Even lightweight models can reduce ambiguity, improve communication and prevent rework as designs evolve.

How does MBSE support verification and validation activities?

MBSE enables verification and validation by explicitly linking system behaviours and constraints to verification criteria within the model. This allows teams to assess test coverage, identify gaps early and maintain alignment between design intent and evidence as the system changes.

What skills are required to work effectively with Model Based Systems Engineering?

Effective MBSE requires a combination of systems thinking, domain expertise and modelling literacy. While familiarity with languages such as SysML is useful, the most important skills are the ability to reason about system behaviour, understand trade-offs and communicate across disciplines using models as a shared reference.

How does Model Based Systems Engineering improve decision-making?

MBSE improves decision-making by making assumptions, dependencies and impacts explicit. Engineers and stakeholders can explore “what-if” scenarios, assess trade-offs and understand consequences before changes are committed, reducing the risk of late-stage surprises.

Can Model Based Systems Engineering be applied to legacy systems?

Yes. MBSE can be introduced incrementally to legacy environments by modelling critical parts of an existing system rather than attempting a full re-engineering effort. This approach helps organisations gain insight into dependencies, constraints and risks without disrupting ongoing operations.

How does MBSE fit with safety, regulatory and assurance frameworks?

MBSE supports safety and regulatory assurance by providing a structured way to demonstrate traceability from requirements through design to verification evidence. This can simplify audits, improve confidence in compliance claims and reduce the effort required to respond to regulatory change.

What are common misconceptions about Model Based Systems Engineering?

A common misconception is that MBSE is primarily a tooling or documentation exercise. In practice, its effectiveness depends on how models are used to support collaboration, learning and decision-making — not on the level of detail or the sophistication of the tools alone. 

When it comes to strengthening your network security posture, doing so is no longer a nice-to-have, but a strategic necessity. The notion of strengthening your network may sound time-intensive and lengthy, however, there are some immediate changes that can lead to quick wins. In this blog, we uncover four key steps IT leaders can take to strengthen network security posture and immediate quick wins that can be achieved upon doing so.  

Four steps to strengthen your network security posture

Security is no longer optional. These four foundational actions will help you reduce risk and build resilience: 

1. Adopt zero trust principles

Zero trust means “never trust, always verify.” Every user and device inside or outside the network must be authenticated and authorised. This approach limits the impact of breaches and is now recommended by the NCSC and leading global providers.  

• Implement strong authentication for all users and devices.  
• Segment networks to limit lateral movement.  
• Continuously monitor for unusual behaviour.  

2. Automate detection and response

Manual processes cannot keep pace with modern threats. Automation can reduce response times by up to 40%, demonstrating its ability to help defenders stay ahead. 

• Use AI-driven tools for threat detection and alert triage.  
• Automate patching, backup, and incident response workflows.
• Regularly test and updated automated playbooks.

3. Operational load

With many IT teams stretched thin, managed network services allow organisations to focus on strategy while experts handle day-to-day operations, monitoring and compliance. 

• Consider managed firewall, detection and response and vulnerability management services.  
• Ensure providers offer transparent reporting and clear SLAs.

4. Secure hybrid work

With two-thirds of UK employees working remotely at least part-time, endpoint protection and secure remote access are essential.  

• Enforce multi-factor authentication for all remote access.  
• Protect endpoints with up-to-date security software and policies.
• Educate staff on secure working practices. 

Quick wins: Immediate actions UK IT leaders should take 

Not every improvement requires a major investment or a long-term project. The following actions can quickly reduce risk and strengthen your security posture:  

Enable multi-factor authentication (MFA) 

Multi-factor authentication (MFA) is one of the most effective ways to prevent account compromise, blocking the majority of phishing and credential stuffing attacks.  

• Enforce MFA for all users, not just administrators.  
• Use app-based or hardware tokens for stronger protection. 
• Regularly review and test MFA coverage.  

Read NCSC guidance on MFA  

Patch the basics consistently and quickly

Most breaches exploit known vulnerabilities. Even delays in patching of a few days can be costly.  

• Maintain an up-to-date inventory of all assets, including cloud workloads and remote endpoints. 
• Apply critical patches within 14 days, as recommended by the NCSC.  
•  Automate patch deployment and monitor for failures.  

Back up critical data securely and test your restores

Ransomware is only effective if you cannot recover your data. Secure, tested backups are essential.  

• Use immutable, offsite or cloud-based backups.  
• Regularly test restores to ensure data integrity.  
• Protect backup credentials with MFA and restrict access.

Review firewall rules and access controls

Firewall policies can become cluttered over time with unused or overly permissive rules, creating hidden vulnerabilities.  

• Schedule regular firewall reviews to remove unused or risky rules.  
• Align policies with current business needs.  
• Use automated tools to analyse policies for overlaps and compliance gaps.   

Run a tabletop incident response exercise 

Plans are only effective if teams can execute them under pressure. Tabletop exercises simulate real-world incidents, allowing teams to rehearse roles and identify gaps.  

• Involve both technical and business stakeholders.  
• Use realistic scenarios tailored to your organisation.
• Capture lessons learned and update your incident response plan.  

See NCSC’s guidance on incident response exercises 

How CACI can help enhance your network security

CACI has helped UK businesses protect their networks for decades. From network security to data centre solutions and IT consulting, our expertise delivers secure-by-design architectures, automation, and incident readiness for robust network security.  

Download our 2026 Network Security Survival Guide today to learn more about how your organisation can set its network environments up for success. 

Cloud innovation trends: Why optimisation must come first

In the race to modernise, many businesses make a critical mistake: innovating before optimising their cloud infrastructure. It’s an easy trap to fall into – new technologies promise speed, agility and competitive advantage. However, without a solid foundation, those promises can quickly unravel.

So, what difference will optimisation make to cloud innovation? How do complex hybrid environments affect optimisation and what are the repercussions of innovating too soon?

Why optimisation should come first

Cloud optimisation isn’t just a technical exercise – it’s a strategic imperative. Before you invest in AI-driven tools, advanced analytics or multi-cloud deployments, you need to ensure your existing environment is efficient, secure and cost-effective. Otherwise, innovation becomes a gamble rather than a growth driver.

How the complexity of hybrid environments affects optimisation

Modern IT landscapes are rarely simple. Most organisations operate in hybrid environments, combining:

• Cloud-native workloads
• Semi-native applications
• Containerised services
• Legacy systems migrated via IaaS.

This mix introduces complexity that can quietly erode ROI and performance. Without optimisation, you risk inefficiencies that undermine every future initiative.

Common pitfalls of innovating too soon

When businesses rush to innovate without first optimising, they often encounter:

Duplicated workloads

Hybrid setups frequently lead to duplication of environments or services, especially when containerised and legacy systems overlap with cloud-native tools. This consumes bandwidth and burdens IT and DevOps teams with managing multiple versions of the same workload.

Latency issues

Poor workload distribution across cloud environments increases latency, slowing response times and masking compliance or security issues. For customer-facing applications, this can directly impact user experience and brand reputation.

Security saps

Unoptimised containerised and legacy workloads are vulnerable to governance and compliance risks. Differences in data storage and flow between environments complicate tracking, while unresolved legacy issues can carry over post-migration.

Mounting costs

With up to 30% of cloud spend wasted, inefficiencies inflate monitoring and security costs, draining budgets that could fund innovation.

Why this matters now

Cloud strategies are under pressure to deliver more – faster, cheaper and greener. Without optimisation, businesses risk inefficiency, higher costs and vulnerabilities that stall progress. In an industry where every second counts, building on shaky ground isn’t just risky, it’s expensive.

How to get started

Before chasing the next big trend in cloud innovation, take time to:

• Audit your current architecture: Maintain visibility by understand what’s running, where and why.
• Identify duplicated workloads and inefficiencies: Determine whether any services or resources are the cause behind draining budgets.
• Align resources with business priorities: Ensure any spending on cloud innovation drives value for the business.
• Implement governance and security best practices: Establishing best practices early on will ensure that innovation is scaled effectively.

This foundation ensures innovation is sustainable, not just a short-term fix.

The CACI approach: Building a cloud that enables innovation

Ready to build a cloud foundation that enables innovation?

Don’t leave your cloud strategy to chance. Our specialist cloud architects and optimisation experts have helped leading organisations modernise, streamline and unlock innovation without compromise. Contact us today to start your cloud optimisation journey.

In today’s digital-first world, organisations are sitting on mountains of data — but what happens when that data is poorly organised? 

Across industries, we regularly see brands struggling with data that is fragmented, duplicated, inconsistent or stored in disconnected silos. Instead of unlocking valuable insights, teams find themselves lost in a maze of spreadsheets, dashboards and conflicting reports.

The result? A dataset that’s hard to use, impossible to interpret and offers little value to the business. 

The challenge: data without direction 

One of the most common challenges we uncover in our Digital Analytics work is disorganised data. Whether it stems from legacy systems, ungoverned tracking implementations, or unclear data ownership, the impact is always the same: 

• Time wasted trying to piece together insights 
• Poor decision-making based on unreliable or incomplete data 
• Low confidence across teams in the outputs of digital reporting 
• Missed opportunities to personalise experiences and optimise performance 

The irony is that most brands already have access to the data they need — they just can’t make sense of it. 

Build a data foundation that drives growth 

Modern marketing ecosystems generate data across dozens of platforms — web analytics, CRM, media, social, app, customer service and more. Without a clear data strategy and strong governance, it’s easy for chaos to take root. 

What starts as a few inconsistent naming conventions in your analytics platform quickly evolves into larger problems: 

• Metrics that don’t align across teams 
• Broken tracking disrupting customer journey analysis 
• Difficulties with attribution and ROI measurement 
• Paralysis when trying to prioritise digital investments 

The truth is, data disorder doesn’t just affect your analysts — it affects leadership decision-making, marketing effectiveness, and ultimately, the customer experience. 

How CACI brings clarity to digital data 

At CACI, we specialise in bringing structure, clarity and control to digital data ecosystems. Our Digital Analytics consultants work with brands to audit their current set-up, streamline tracking implementation, and align measurement frameworks to real business goals. 

Our proven approach: 

• Uncovers data issues at source, not just the symptoms 
• Builds a trusted foundation for consistent, accurate insight 
• Enables cross-channel visibility with a single source of truth 
• Empowers teams with dashboards and tools they can trust and use 

We go beyond just fixing the data — we design ecosystems that scale with your business, support smarter decisions, and create a foundation for advanced analytics, personalisation and experimentation. 

Take control of your digital data 

If your organisation is struggling with messy, misaligned or underperforming data, it’s time to take back control. Poorly organised data isn’t just a technical issue — it’s a barrier to growth. 

Let’s turn your data into a strategic asset. 

Use our Digital Analytics Self-Assessment Checklist to evaluate your current capabilities and uncover opportunities for growth. It’s a practical first step toward unlocking the full potential of your digital strategy.

As a trusted partner for organisations seeking to implement and evolve complex, bespoke CRM solutions using Microsoft Dynamics, CACI’s work with a luxury vehicle manufacturer conglomerate over the past decade has exemplified our ability to deliver high-impact, continuously evolving platforms that support critical functions.

In this blog, we’ll uncover the challenges that this manufacturer faced in the absence of a bespoke CRM solution and the benefits they’ve been able to realise once integrating this new solution with CACI’s support.  

Understanding the power of CRM solutions

The luxury vehicle manufacturer’s Key Account Management team is responsible for managing fleet sales, accounting for approximately 40% of their global vehicle sales, equating to billions of euros annually. Fleet sales involve a highly complex and customisable product (vehicles) sold under constantly shifting market conditions, regulatory environments and customer-specific pricing structures. With this in mind, the manufacturer needed a CRM solution that could: 

• Handle intricate pricing logic and discount structures 
• Integrate with multiple internal and external data sources 
• Adapt rapidly to changes in product offerings, market conditions and geographies 
• Scale across brands and European markets 
• Provide a single source of truth for pricing and account management. 

The difference CRM solutions would make 

To achieve this, CACI built and continues to maintain a bespoke pricing and account management tool on Microsoft Dynamics Customer Experience (CE). This solution includes: 

• Custom APIs and integrations with the manufacturer’s internal systems and external data feeds 
• Bespoke code to support complex pricing logic and real-time quote generation 
• Advanced data management to consolidate and process information from diverse sources 
• Continuous customisation and improvement, ensuring the platform evolves with the manufacturer’s needs 
• Migration to Microsoft Azure, modernising the infrastructure for scalability and performance. 

In doing so, the manufacturer has enhanced: 

• Speed and accuracy: The tool enables the generation of accurate, real-time pricing for complex fleet deals, reducing turnaround time and improving customer satisfaction 
• Revenue protection: By ensuring pricing precision and agility, they maintain a competitive edge against industry rivals 
• Scalability: The platform has been successfully rolled out across multiple European markets and adapted for use with other brands 
• Strategic partnership: CACI’s decade-long collaboration with this manufacturer reflects a deep understanding of their business and a shared commitment to innovation. 

Why this matters to other organisations & how CACI can help 

CACI’s Microsoft Dynamics capability is ideally suited for organisations that: 

• Sell complex, customisable products (e.g. in automotive, logistics, FMCG or retail) 
• Operate in dynamic markets with evolving pricing, regulatory or customer requirements 
• Require deep integration with existing systems and data sources 
• Need a continuously evolving CRM platform rather than a static, off-the-shelf solution. 

While CACI’s expertise is strongest in the Customer Experience module, we also support other Dynamics modules (excluding Finance, which typically requires accounting SMEs). Our strength lies in delivering bespoke, high-performance solutions that go far beyond basic configuration, leveraging our back-end development expertise in .NET, Java, and Azure to build platforms that drive real business value. 

If your organisation is grappling with complex sales processes, diverse customer needs or fragmented data systems, CACI’s approach to Microsoft Dynamics could be the transformative solution you need. Our work with this luxury vehicle manufacturer demonstrates how a tailored, continuously evolving CRM platform can become a strategic asset, driving revenue, efficiency and competitive advantage. 

Get in touch with our expert team at CACI to explore how a bespoke solution can drive efficiency, scalability and competitive advantage for your organisation.

What is Marketing Mix Modelling (MMM)?

Marketing mix modelling (MMM) is a statistical tool that helps organisations understand and quantify the impact of marketing activities on consumers’ behaviours, sales, return on investment (ROI) and more. It breaks down an organisation’s performance by channel, incorporating various types of data to evaluate effectiveness and determine which marketing activities are most heavily influencing the organisation’s business outcomes, which we explore further in our blog on marketing mix modelling.  

Based on a series of steps, MMM begins with data collection of marketing variables, followed by an analysis of the data collected to identify relationships or patterns and building a customised model to showcase actions and results. Finally, scenario testing can be conducted to gauge possible outcomes, leveraging the results to optimise marketing strategies and bolster decision-making. 

What is multi-touch attribution (MTA)?

Multi-touch attribution values each customer touchpoint leading to conversion, with its goal being to decipher the marketing channels or campaigns that should be credited with the conversion. The intention of this is to measure the effectiveness of each channel or touchpoint so that marketers are aware of where they should focus efforts and resources and allocate future spend in the most effective ways possible to enhance customer acquisition efforts.  

Through multi-touch attribution, a more comprehensive view into customer journeys can be gained, enabling organisations to create better strategies or optimise their ad spend in line with market shifts. The ability to see how each touchpoint impacts a sale is what allows organisations to dissect customer journeys and allocate budgets accordingly.

What are the differences between multi-touch attribution (MTA) vs marketing mix modelling (MMM)?

Aggregated versus disaggregated data

Aggregated data is statistical data used in MMM that is grouped into channels, regions or times to assess trends in terms of how channels contribute to sales. Disaggregated data, on the other hand, is behavioural data that is used in MTA to gain the most detailed insights possible at user or individual level.  

Organisations require aggregate information for visibility into external trends that may be affecting marketing efforts and conversions. In comparison, the precise level of detail available through disaggregated data is critical in MTA as it is required for assigning multiple touchpoints within a customer journey.

Objective and impact assessment

MTA uses trackable customer interactions to understand the importance of each touchpoint. As a result, one of the most substantial differences between these two is their objective. MTA focuses on the impact of specific, individual touch points and their sale or conversions impact, whereas MMM focuses on the overall impact of your marketing mix and how that combination influences sales or other outcomes.

Choosing the right approach for your company

MMM’s main goal is to help organisations deduce overall business outcomes and MTA helps organisations understand the contributions of individual touchpoints to conversions or actions. MMM includes both online and offline channels, whereas MTA only includes digital channels that track individual user behaviours. 

While MTA may not be easy to implement due to ever-changing customer journeys paired with uniting all touchpoints across various devices, channels and platforms, it does enable flexibility and offers a more granular understanding of what does and does not work within marketing initiatives. This flexibility and granularity equips organisations with insights that allow for informed, data-driven decision-making for digital marketing campaigns.

When to use multi-touch attribution modelling (MTA)

Multi-touch attribution has become a staple for organisations requiring tactical insights and are focused on short-term optimisation by measuring and quantifying the impact that their digital marketing campaigns are having. The visibility that multi-touch attribution modelling provides into the success of touchpoints across a customer’s journey is unparalleled. #

This insight is critical for organisations to consider amidst consumers’ increasing wariness of marketing messaging. Through this, the right audiences and their respective marketing preferences can be identified across channels, enabling customised messaging to be created and the right consumers on the right channels at the right times to be reached. 

Maximising ROI can also be made possible through multi-attribution modelling by engaging with consumers in fewer though more frequent and impactful marketing messages that ultimately shorten sales cycles. 

When to use marketing mix modelling (MMM)

Marketing mix modelling should be used when needing to understand the combined impact of advertising spending, promotions, pricing and distribution channels. It can be particularly impactful for organisations that are well-established and have a plethora of data over the course of many years to work with.

From media activities to external variables including macroeconomic factors and competitors’ activities and internal variables like product distribution, product changes and price changes, countless categories can be monitored for organisations to analyse data and understand the relationship between sales and these elements. Its [immunity to the everchanging privacy landscape] is also a key advantage.

How to use both approaches together

Both MTA and marketing mix modelling MMM are key approaches in the realm of marketing analytics. When used together, MMM can offer macro-level views into marketing impact on revenue, while MTA can supply granular insights into the effectiveness of specific marketing channels. Organisations that understand when and how to use both approaches will find themselves transforming their marketing strategies and maximising their ROI.  

Combining these two approaches when building an attribution strategy is often recommended. However, MMM will ultimately be most effective for gaining long-term, strategic insights that can bolster planning and financial outcomes, whereas MTA is best suited for short-term, tactical insights that can enhance day-to-day optimisation, campaigns and decision-making. 

How CACI can help

CACI supports businesses in their delivery of optimised marketing efficiency by:  

• Determining the value and performance of activity through evolved multi-touch & econometric modelling
• Producing results to sustain & increase growth through targeted investment & improved marketing performance
• Delivering improved accuracy, consistency and availability of marketing performance insights
• Enhancing capability by evolving data, technology & process
• Supporting the provision of ongoing strategic & delivery resource.

Find out more about the impact that digital attribution modelling can have on your business by contacting us today. 

Watch a session from our recently event on how to optimise marketing performance through Commercial Mix Modelling.

Sources:

• https://segment.com/academy/advanced-analytics/an-introduction-to-multi-touch-attribution/
• https://funnel.io/blog/mta-vs-mmm#:~:text=Marketing%20mix%20modeling%20(MMM)%20and,effectiveness%20of%20specific%20marketing%20channels
• https://www.marketingevolution.com/marketing-essentials/multi-touch-attribution
• https://segment.com/academy/advanced-analytics/an-introduction-to-multi-touch-attribution/
• https://www.marketingevolution.com/marketing-essentials/multi-touch-attribution

Your website holds something quietly powerful within the realm of fleeting attention spans and constant digital noise: insight. Not just numbers in a dashboard, but real signals about what your audience cares about, how they behave and what they need next. 

For many teams, however, that insight feels just out of reach. You know the data exists, but the tools are clunky, the dashboards are overwhelming and the time to act on it is always in short supply. The result? A platform that feels like it’s underperforming, even when your team is working overtime. 

At CACI, we help teams turn that quiet potential into confident action. We bring clarity to complexity, unlocking the value already sitting in Drupal platforms so that websites become a source of momentum, not maintenance.  

The following are five trends that we’re seeing increasingly more from our clients and how we approach each one as a leading UK Drupal agency. 

Building a modern, AI-ready communications platform

For many organisations, the day-to-day reality of communications can feel more frustrating than inspiring. Content workflows are slow, developer dependencies are high and CRM integrations are patchy at best. These aren’t just technical issues, they’re blockers to growth. 

We tackle this head-on by streamlining Drupal workflows. Through optimised content types, reusable components and design systems, we empower marketing and content teams to self-serve. Our SiteGuardian module helps monitor patches and security, and we look forward to Drupal 11’s roadmap that will include automatic core updates, freeing up your developers’ time to focus on innovation, not firefighting. 

Take our work with Sanctuary, for example, where we connected Drupal Webforms to Salesforce Marketing and Service Cloud. The result? Seamless data flow, reduced manual effort and a platform that supports strategic growth. 

Empowering content teams 

If your website feels like it’s standing still, it probably is. When it comes to today’s digital landscape, that’s not just a missed opportunity, it’s a risk. 

Even for organisations that have already moved on from Drupal 7, the pace of change hasn’t slowed. The expectations of modern users – faster performance, seamless personalisation and inclusive design – are only growing. Platforms that were cutting-edge a few years ago can now feel sluggish, disconnected or difficult to scale. 

Drupal 10, and soon Drupal 11, offer more than just technical upgrades. They represent a shift in mindset from maintenance to momentum. With smart defaults, accessibility built in and a no-code interface, the new Drupal CMS is designed to empower content teams and reduce reliance on developers. For many teams, this isn’t about catching up. It’s about staying ahead and building a platform that’s not only modern and AI-ready, but flexible enough to evolve with your organisation’s needs. 

When digital experience is often the first impression made, standing still isn’t safe, it’s falling behind. This is where leveraging the latest and greatest innovation coming from Drupal will make all the difference. 

Driving real growth 

Your website shouldn’t just be something you maintain, but something that moves you forward. It should help your team work more efficiently, connect more meaningfully and adapt more confidently to what your audience needs next. 

Whether you’re trying to reach new users, simplify how content is created or tailor experiences to different audiences, your platform should be working with you, not against you. Drupal CMS and Drupal AI are designed to do just that. They help you cut through complexity, reduce repetitive tasks and focus on what really matters: creating experiences that feel personal, purposeful and easy to manage. 

We’re preparing for the evolution of Layout Builder into Experience Builder, which will bring in-browser theming and greater intuitiveness. This means faster campaign launches, more creative freedom and an end to the “everything looks the same” problem. 

Along with AI-powered editorial tools, we’re helping teams automate tagging, metadata and translation workflows, scaling content creation without scaling headcount. Default Drupal marketing tools often fall short in today’s fast-paced, multi-channel world. That’s why we partner with Acquia to bring in a full ecosystem of personalisation and MarTech modules, along with custom analytics that enable agile, lead-gen focused marketing. 

The impact? Campaigns that launch faster, reach the right audiences and deliver measurable ROI. 

Delivering personal, meaningful experiences 

In the age of AI overviews and accessibility-first design, traditional SEO isn’t enough. Your content needs to be structured, searchable and inclusive by design. 

At CACI, we optimise Drupal sites for AI-generated summaries, implement structured data and ensure WCAG-compliant accessibility from the ground up. But we don’t stop there. 

When it comes to your on-site search engine, we’re building deep expertise in integrating Typesense with Drupal core, enabling natural language queries and dramatically improving on-site search and filtering. This means users can find what they need faster, with search that understands intent not just keywords. 

Discoverability and accessibility aren’t just technical checkboxes, however: they’re design principles. That’s why we advocate for robust, tokenised design systems that act as a single source of truth across teams and platforms. These systems bring consistency to UI components, streamline development and ensure accessibility is embedded, not retrofitted. 

As CACI puts it, design systems are how you “deliver on critical requirements like accessibility, all while keeping your brand and your teams’ sanity intact”. They connect the work of designers, developers and content teams, enabling you to scale experiences without sacrificing quality or compliance. The result? Better search visibility, more inclusive user journeys and a platform that’s not only easier to manage, but easier to trust. 

Let’s talk 

You don’t need another tool, you need a partner who understands what you’re trying to build and why. At CACI, we work alongside you to unlock the full potential of your Drupal platform. Whether you’re reimagining your site, migrating from Drupal 7, exploring headless solutions or simply trying to make things work better for your team, we’re here to help you move forward with clarity, creativity and confidence. 

Reach out for a quick chat. Through a quick audit, we can take a closer look at your content structure, SEO performance and integration setup, so you can see where you are now and where you could go next. 

Let’s talk about what’s possible. 

Unifying Your Data Story: Speak the Same Language Across Teams and Systems

In the world of digital analytics, there’s a silent struggle happening behind the dashboards: inconsistency. Different teams. Different tools. Different definitions. And the result? A fragmented data story that no one can confidently act on.

This is where data harmonisation comes in. It’s the process of aligning data from disparate sources into a consistent structure, using shared definitions, taxonomies, and metrics. By harmonising data, businesses can eliminate confusion, reduce duplication, and ensure that insights are accurate and actionable across the organisation.

At CACI, we work with brands to bring order, clarity, and alignment to their data foundations—because without consistent taxonomy and standardised metrics, even the most advanced analytics can mislead rather than inform.

The Hidden Cost of Inconsistent Taxonomy & Metrics

When marketing, digital, product, and analytics teams define success differently, it creates confusion. What counts as a “conversion”? How is “engagement” measured? Is “bounce rate” the same across platforms?

Without a common language:When marketing, digital, product, and analytics teams define success differently, it creates confusion. What counts as a “conversion”? How is “engagement” measured? Is “bounce rate” the same across platforms?

Without a common language:

• Reports contradict each other
• Decisions are delayed
• Cross-channel comparison becomes unreliable
• Teams lose trust in the data

In an era where fast, data-led decisions drive competitive advantage, this kind of friction is a blocker to growth.

CACI’s Approach: Building a Solid Analytical Foundation

Our approach simplifies  complexity and align on the fundamentals. Here’s how:

• Taxonomy Harmonisation – Through collaboration with stakeholders, we design and implement a unified taxonomy that reflects your brand’s goals, customer journey stages, and platform specifics.
• Metric Standardisation – Consistency in metric definitions ensures everyone speaks the same data language. We bring consistency to how key metrics are defined, calculated, and reported—so that insights are trusted, comparisons are accurate, and decisions are aligned across the organisation.
• Governance Frameworks – We help embed processes, guardrails, and tools that ensure ongoing data integrity, even as teams scale and evolve.
• Enablement & Training – A good taxonomy isn’t just a document—it’s a mindset. We deliver practical enablement to ensure adoption and understanding across your organisation.

Why It Matters

When taxonomy and metrics are consistent, analytics becomes a true strategic asset. Brands can compare performance across campaigns, channels, and regions. They can move faster, with confidence. And perhaps most importantly—they can trust the story their data is telling.

In our previous blog in this series, we uncovered the key characteristics of digital twins, their advantages and challenges and what organisations that adopt a digital twin can expect to gain from it. Today, we’ll examine Mood’s unique approach to constructing digital twins and how it can support organisations. 

What is Mood and what approach does it take with digital twins? 

Mood’s platform addresses the challenges of creating digital twins by offering a highly flexible and customisable solution that caters to specific organisational domains. Mood’s approach is centred on three key pillars:   

Agility and flexibility   

Mood enables the creation of agile digital twins that can be rapidly adapted to an organisation’s unique requirements. Whether it’s a specific industry, business model or operational process, Mood’s platform provides the tools needed to build a digital twin that accurately represents the organisation’s domain in the virtual world.   

Integrated data and consistency  

Mood’s platform integrates data from multiple sources, ensuring that the digital twin is truly reflective of the real-world state. This integration is key to maintaining clarity and consistency across the organisation, allowing for more accurate analysis and decision-making.   

Rapid deployment and optimisation 

Mood offers services that accelerate the deployment of digital twins, allowing organisations to start benefiting from their virtual models in a shorter timeframe. Its continuous monitoring and real-time analysis capabilities also enable rapid optimisation of operations, providing a significant competitive advantage.   

Common questions about digital twins 

While simulations and 3D models are static representations often used for specific scenarios or time points, a digital twin is a living, dynamic model that continuously updates based on real-time data. Digital twins provide a more comprehensive and accurate view of the current state of a system and allow for ongoing monitoring, predictive analysis and decision-making, far beyond what static models or simulations offer.  

2. Do digital twins require IoT (Internet of Things) technology?  

While IoT technology is a common and effective way to gather real-time data for digital twins, it is not strictly required. Digital twins can also be built using other data sources, such as enterprise systems, manual inputs and historical data. However, IoT devices enhance the digital twin’s ability to reflect real-time changes where physical assets are critical, making them particularly valuable in dynamic environments.  

3. Are digital twins only applicable to manufacturing and physical assets?  

No, digital twins are not limited to manufacturing or physical assets. They can be applied across a range of industries and domains, including healthcare (e.g., patient monitoring), urban planning (e.g., smart cities), logistics (e.g., supply chain management) and even service-oriented sectors. Any process or system that can benefit from real-time data integration and analysis can potentially utilise a digital twin.  

4.How difficult is it to create and maintain a digital twin?  

The difficulty of creating and maintaining a digital twin depends on the complexity of the system being modelled, the availability and quality of data and the technology stack used. While some digital twins can be complex and resource-intensive to develop, there are also more straightforward and scalable solutions available. With Mood, your digital twin can start small, returning instant value and iteratively scaled based on priority.  Maintaining a digital twin requires ongoing data integration, model updates and regular performance evaluations to ensure it remains accurate and relevant, so a single platform acting as the lynchpin can be hugely beneficial.   

How Mood can help 

and professional services offer a unique solution by providing the flexibility, integration and agility needed to develop and maintain effective digital twins. By leveraging Mood’s capabilities, organisations can achieve a new level of operational clarity and efficiency, ensuring they remain resilient and competitive in the face of ongoing challenges.  

For organisations lacking the confidence to build their own digital twin from scratch, our consultants work directly with our customers to help them, ensuring they have the skills they need moving forward. Contact Mood today to begin your journey towards an agile, data-driven future.  

In our previous blog in this series, we examined a real-life example of where a digital twin helped drive outcomes for an organisation and the overarching importance of digital twins amidst the ever-changing technological landscape. Today, we’ll explore the characteristics comprising digital twins, including their advantages, challenges and what organisations can expect from them. 

What are the key characteristics of a digital twin? 

A digital twin, in its most basic form, is a virtual representation of a physical entity or group of entities, such as the machines and their systems on a manufacturing shop floor.

However, in the context of organisations, digital twins go beyond simply replicating physical assets. They represent the entire organisational structure, including processes, workflows, systems and even human behaviours. Some of the key characteristics of a digital twin include: 

Real-time data integration  

• Dynamic and continuous synchronisation: A digital twin constantly updates its virtual model based on data from its physical counterpart or the processes it represents. This real-time integration allows the twin to accurately reflect the current state of the system, asset or organisation it models.   
• Data sources: It incorporates data from various sources, including IoT sensors, enterprise systems, operational data stores and external data feeds, ensuring a comprehensive and up-to-date virtual representation.   

High fidelity and accuracy

• Detailed and precise representation: A digital twin provides a high-fidelity model that captures the complexities and nuances of its subject. This includes both physical characteristics (e.g. dimensions and materials) and operational parameters (e.g. performance metrics and environmental conditions).   
• Scalability: The accuracy of a digital twin can scale from a single asset (e.g. a machine) to complex systems (e.g. an entire manufacturing plant or organisational process, including its external factors).   

Two-way interaction 

• Bidirectional communication: A digital twin supports two-way communication, allowing not only the updating of the virtual model based on physical world changes, but also enabling the virtual model to influence its real-world counterpart. For instance, adjustments made in the virtual model can be implemented in the real-world system. 
• Predictive and prescriptive capabilities: Beyond mere replication, a digital twin can predict future states and prescribe actions based on simulations, scenario analysis or machine learning algorithms.   

Comprehensive lifecycle representation

• Lifecycle coverage: A digital twin spans the entire lifecycle of the system, organisation or asset it represents, from design and development through to operation, maintenance and even decommissioning. This ensures that insights can be derived at any stage, supporting continuous improvement and adaptation.   
• Change management: It adapts to changes in the physical environment, evolving over time as the real-world counterpart undergoes modifications, whether in design, operation or environment.   

Simulation and scenario analysis 

• What-if scenarios: A digital twin enables the simulation of various scenarios and potential changes before they are implemented in the physical world. This includes testing new designs, operational strategies or responses to hypothetical events, all within a risk-free virtual environment.
• Optimisation: By analysing different scenarios, the digital twin helps in optimising performance, reducing costs, improving efficiency and enhancing risk mitigation.   

Advanced analytics and machine learning  

• Data-driven insights: A digital twin leverages advanced analytics, including predictive modelling, machine learning and AI to extract meaningful insights from the vast amounts of data it processes. This allows organisations to predict outcomes, prevent failures and optimise operations.     
• Learning capability: The digital twin can “learn” from the data it receives, continuously improving its accuracy and predictive capabilities over time.   

It’s important to note, however, a digital twin can still function effectively and add value without ML and AI, instead relying on real-time data integration, simulation and rule-based systems, until enough data is generated to create ML models.   

Contextual awareness 

• Environment and ecosystem awareness: A digital twin understands the context in which the physical asset, organisation or process operates, including its environment, external influences and interdependencies with other systems, enhancing the relevance and precision of the insights generated.  

Interoperability and integration 

• Seamless integration: Digital twins are designed to integrate seamlessly with other digital systems, tools and platforms within an organisation. This interoperability ensures that the digital twin can act as a central hub for data and insights, interacting with various enterprise systems like ERP, CRM and PLM.   
• Modularity and scalability: The architecture of a digital twin should allow it to be modular, enabling different components to be updated, replaced or scaled independently, which is critical for adapting to evolving organisational needs.   

Visualisation and user interaction 

• User-friendly interface: A digital twin often includes advanced visualisation tools such as 2D & 3D models, dashboards or even augmented reality (AR) interfaces, simplifying users’ interactions and interpretations of the virtual model. The use of these depends on the need, however.   
• Interactive decision support: Users can interact with the digital twin to perform analyses, run simulations and explore different operational strategies, all through an intuitive and accessible interface.   

Security and compliance   

• Data security: Given that a digital twin deals with real-time and potentially sensitive data, robust security measures are a fundamental characteristic. This includes data encryption, secure communication protocols and compliance with industry standards and regulations.   
• Governance and compliance: Digital twins must adhere to governance frameworks and compliance requirements, ensuring that the data and operations they manage meet regulatory and ethical standards.   

What are the advantages of digital twins for organisations? 

Proactive maintenance  

The system sent automatic notifications when machines required attention, whether due to routine maintenance, in response to a negative trend or as a response to an unexpected incident. This minimised downtime and ensured continuous production with a higher utilisation rate. 

Trend analysis 

The digital model tracked stats over time, allowing for trend analysis. This feature was invaluable in predicting when a machine might require more significant intervention or identifying when a production line was consistently underperforming.  

Quality assurance  

By integrating the testing processes into the digital twin, the system provided real-time feedback on the quality of the fire detectors being produced. Engineers could react quickly to any deviations, ensuring that only high-quality products left the facility.    

Enhanced decision-making

Digital twins provide a comprehensive view of organisational operations, enabling decision-makers to visualise the impact of changes before they are implemented. This leads to more informed and strategic decisions, reducing risks and improving outcomes.   

Operational efficiency 

By simulating processes and workflows, organisations can identify inefficiencies and bottlenecks in real-time, allowing for continuous optimisation and therefore improved productivity, reduced costs and agility to change.   

Predictive maintenance and risk management  

Digital twins can predict potential failures or risks by analysing data trends and patterns, minimising downtime, preventing costly disruptions and enhancing resilience.   

Scalability and flexibility 

Organisations can use digital twins to model and test new business strategies, products or services without disrupting existing operations, enabling businesses to innovate and adapt to changing market conditions with minimal risk.   

Employee and resource optimisation  

By simulating human behaviours and interactions within the organisation, digital twins can optimise resource allocation, improve workforce planning and enhance employee engagement.   

What challenges arise when creating digital twins? 

Complexity and customisation  

Developing a digital twin for an organisation is inherently complex due to the need to capture and integrate diverse data sources, processes and systems. Additionally, each organisation has unique requirements, complicating the creation of a one-size-fits-all solution.   

Data integration and quality  

A digital twin’s accuracy and effectiveness depends on the quality and integration of data. Inconsistent, incomplete or siloed data can compromise its ability to provide reliable insights, leading to suboptimal decision-making.   

Scalability of platforms    

Most existing platforms for creating digital twins are rigid and domain-specific, limiting their applicability across different industries or organisational needs and potentially hindering organisations from fully leveraging the potential of digital twins.   

High development costs and time

The process of designing, developing and deploying a digital twin is often time-consuming and expensive. This can be a significant barrier for organisations, particularly those with limited resources.  

How Mood can help 

For organisations lacking the confidence to build their own digital twin from scratch, Mood consultants work directly with customers to equip them with the necessary skills to progress towards an agile, data-driven future. Contact Mood to begin your journey. 

Stay tuned for the next blog in this three-part series, where we’ll explore the unique approach to digital twins offered by Mood and how organisations that leverage Mood’s capabilities can enhance their digital twin experience.