Why Data Teams Must Lead Business Strategy 

Why Data Teams Must Lead Business Strategy 

Why Data Teams Must Lead Business Strategy

 

There’s a silent revolution happening in many organisations – one that will define whether they thrive or fall behind in 2025.  

For decades, data teams sat on the periphery. 

They were asked to report on performance, fix broken dashboards, and serve up stats when requested.  

But the business world demands more than retrospective reporting.  

In an economy shped by uncertainty, complexity, and disruption, data can no longer be confined to the back office.  

To compete – and to innovate – data teams but be central to the conversations about where the business is heading – not just where it has been. 

The Evolving Role of Data Teams 

Historically, the remit of data teams was narrow: extract, clean, visualise.  

The product was a report or dashboard handed off to business leaders to (hopefully) make the right decisions. 

But in 2025, that model no longer delivers the value or agility that businesses need.  

We’re now seeing a transformation in how modern data teams operate:  

  • From support function to value creators – shifting from reactive to proactive work. 
  • From siloed experts to embedded partners – working alongside commercial, operational, and product teams.  
  • From insight generators to strategic enablers – helping shape the decisions that drive growth. 

This is not simply a shift in tooling or structure – it’s a transformation in mindset.  

Data is now viewed as a product, not a project.  

And data professionals are becoming co-owners of business outcomes, not just report providers.  

It’s no longer enough to ‘inform’ strategy. Data teams now have the mandate to shape it.  

Why Data Talent Belongs at the Strategic Table

Too often, strategic decisions are made without the full picture.  

Assumptions, intuition, and legacy thinking creep into the boardroom – not because of malice, but because data voices weren’t in the room early enough. 

Thats a missed opportunity.  

Because when data professionals are included from the outset, they bring: 

  • A full-spectrum view across customers, operations finance, and product. 
  • Evidence-based thinking that reduces bias and rigor. 
  • Scenario modelling that helps forecast outcomes, not just track them. 
  • Systems-level insight that connects dots others don’t see. 

And most importantly, they ask better questions.  

We’ve seen firsthand how this changes the direction of major initiatives.  

From whether to enter a new market, to which customer segments to prioritise, to how to price a product – strategic choices are simply stronger when grounded in data.  

But this isn’t just about inviting data leaders into meetings. It’s about empowering them to contribute, challenge, and co-create strategy.

Real-World Examples of Data-Led Innovation

Across sectors, data teams are becoming the unexpected drivers of innovation and strategic clarity.  

Here are just a few examples: 

Food Manufacuring – Transforming Financial Reporting

Our client faced challenges in consolidating financial and sales data across multiple business entities.  

We were brought in to design and implement a centralised data solution that would streamline reporting, improve data accuracy, and establish a self-service reporting framework. 

Want more infromation about how we transformed out clients’ financial reporting?

Read the case study.

Financial Services – Building a Robust Data Strategy

Our client needed a comprehensive data strategy, governance framework, and innovation solutions to empower their teams with data-driven decision making.  

Is building an innovative data strategy in your 2025 plan? Learn more about how we helped our client.

Read the case study.

Skills and Behaviours That Matter in 2024 

For data teams to play this elevated role, technical ability alone isn’t enough.  

The most valuable data professional in 2025 are those who can: 

  • Translate complexity into clarity – making insights accessible to non-technical audiences. 
  • Build trust with stakeholders – showing commercial empathy and reliability. 
  • Act like product owners – treating data solutions as long-term assets 
  • Thinking like strategists – understand the ‘why’ behind the business direction 
  • Collaborate across boundaries – engage with sales, operations, marketing, and more.  

Organisations must now invest in upskilling and culture change to enable these capabilities. Without this shift even, even the best tools and platforms will under-deliver.  

If you don’t have the capacity for all of this within your organisation. You could get some external help. 

Check out this blog post to guide your decisions. 

What This Means for Data Leaders and Business Executives 

If you lead a data team, this is your moment to lean in.  

Strop waiting for permission to join the conversation. (Just send your exec’s this blog post, it’ll do the hard work for you!) 

Show your value by aligning with the business’ most urgent priorities – frame your work in terms of impact, not just output. 

If you’re a business or technology executive, ask yourself:  

  • Are your data teams seen as strategic partners – or service desks? 
  • Do they have content on your long-term goals and access to decision-makers? 
  • Are you investing in the skills they need to step up? 

Because the truth is simple: you won’t succeed in digital transformation, AI adoption, or customer experience without your data teams leading from the front.  

Conclusion: From Reporting to Reimaging the Business 

 

The gap between strategy and data is narrowing – and that’s a good thing!  

In 2025, the most successful organisations will be those where data is not an afterthought, but a strategic compass.  

This means building data teams with the confidence, skills, and mandate to lead.  

It means creating space in the boardroom for evidence, experimentation, and curiosity. 

And it means recognising that the future isn’t just data-driven: it’s data-led.  

Download our 2025 Guide to Data Innovation 

Discover how forward-thinking organisations are embedding data into business strategy – and how your teams can lead the way.  

3 Warning Signs You’re Falling Behind in Data Innovation

3 Warning Signs You’re Falling Behind in Data Innovation

3 Warning Signs You’re Falling Behind in Data Innovation

 

Data innovation is no longer a future priority – it is a necessity. 

Companies using real-time insights, automation, and AI are gaining a serious edge: cutting costs, improving customer loyalty, and innovating faster.  

Those that dont are quietly losing ground, often without realising it until performance gaps are too far gone and become irreversible.  

Recognising the early signs of falling behind can be the difference between thriving and struggling in a data-driven economy. 

Here are three critical warning signs to watch out for – and clear actions you can take to regain momentum.  

Want to assess your company’s readiness?

Download our Data Innovation Toolkit!

With actionable checklists – this will help you take the first steps to innovation.

1. You’re Missing Out on Emerging Data Trends 

Falling behind often starts with missing key shifts in how data is used to drive business decisions. 

Warning Signs:

  • Leadership teams rely heavily on quarterly and yearly historical reports instead of real-time dashboards. 
  • Theres little to no investments in upcoming technologies like AI, machine learning, or predictive analytics. 
  • Data remains fragmented and siloed across departments, with no unified view of customers, operations, or market conditions.  

Why it matters:

When companies’ base decisions on old data they are effectively guessing.  

Meanwhile, competitors using real-time analytics can predict customer behaviour, optimise supply chains and adapt pricing dynamically.  

This leads to faster growth, higher margins, and stronger brand loyalty.  

Practical Next Steps:

Run a simple audit: How often does your leadership make decisions based on data that’s less than 24-hours old?  

2. You’re Ignoring the Power of Real-Time Insights, AI, and Automation 

Another major red flag: failing to tap into the capabilities of real-time data, AI, and automation. 

The risks include:

  • Slower Reaction Time: Market trends and customer needs evolve daily, not quarterly.  
  • Higher Operational Costs: Manual processes eat up employee time and budget. 
  • Customer Churn: Customers expect fast, personalised experiences – those who lag lost business to more agile competitors. 
  • Employee Frustration: Talented employees get frustrated when bogged down with outdated, manual processes. 

The deeper issue:

Many businesses underestimate the hidden cost of delays.  

Every month you wait to implement automation or AI isn’t just a missed opportunity – it’s a growing competitive disadvantage.  

According to McKinsey, companies that fully integrate AI across operations see profit margin increases of up to 25% compared to industry peers.  

Why some delay – and why that’s dangerous:

Some organisations fear the perceived complexity or costs of AI and automation. But waiting often means having to leapfrog two or three generations of competitors later – a much harder and riskier move.  

Practical next steps:

Identify one manual, repetitive process today (such as reporting or lead scoring) that could be automated quickly using a no-code or low-code tool. 

3. You’re Treating Data as a ‘Back Office’ Function, Not a Strategic Asset 

Finally, if data is siloed within IT or treated purely as a compliance necessity, your company is missing its biggest strategic lever.  

Warning signs:

  • Data projects are seen as technical tasks, not business enablers.  
  • The executive team discussed ‘IT updated’ but not ‘data-driven growth strategies.’ 
  • Business teams and technical teams rarely collaborate on customer experience or innovation. 

Want to understand the warning signs in more details?

Download Your 2025 Guide to Data Innovation 

Why it matters:

Data isn’t just a record of what happened. When used properly, it can reveal what will happen next. And how to act ahead of competitors.  

Companies treating data as a strategic asset outperform their peers because they:  

  • Design products based on predictive customer insights.  
  • Deliver proactive, personalised services.  
  • Optimise operations before inefficiencies become visible.  

Practical next steps:

Review your leadership meeting agendas: how often is data-driven opportunity or innovation discussed at the board level, beyond just compliance?  

 

How to Get Back on Track: Simple, Strategic Fixes 

You don’t need a sweeping digital transformation to regain your footing.  

Start small, think strategically, and build momentum. 

Here’s how:

  • Create a Data Innovation Roadmap: Map short-term wins alongside long-term goals. Focus on initiatives that drive revenue, efficiency, or customer experience improvements.  
  • Launch a Real-Time Insights Pilot: Choose a customer-facing department (e.g., sales, customer service) to start using real-time dashboards and see measurable impact quickly. 
  • Automation Where It Hurts Most: Prioritise high-friction processes that slow down employees or customers – automate these first to unlock faster productivity gains. 
  • Appoint a Data Champion or SME: Identify someone responsible for embedding data-driven thinking across departments – not just IT. Give them authority, budget, and clear KPIs.  
  • Bring in External Expertise: Working with data innovation partners can fast-track your progress, avoid pitfalls, and provide access to cutting-edge tools and frameworks.   

Need help with bringing in external expertise?

Check out our blog post, 10 Tips for Choosing the Right Data Consultancy, to gain more insights.

Conclusion

Companies rarely realise they are falling behind in data innovation until the damage is visible – lost customers, slower growth, missed market opportunities. 

The good news: spotting the warning signs early gives you options.  

By acting now, you can close the gap, outperform slower competitors, and build a future-ready business.  

Ask yourself: 

  • Are your decisions driven by live, real-time data? 
  • Are you leveraging AI and automation to improve speed and personalisation? 
  • Is data innovation a priority for leadership – or an afterthought?  

Get started today. Book your Data Innovation Session!

In this 30-minute session, we’ll explore how you can drive efficiency, reduce costs, and uncover new growth opportunities by leveraging data in smarter ways.

Siloed to Smart: How to Unlock ROI from Your Data Investments 

Siloed to Smart: How to Unlock ROI from Your Data Investments 

Siloed to Smart: How to Unlock ROI From Your Data Investments

 

If your teams can’t see the same numbers, how can they make the right decisions?  

Many leaders within Financial Services and Manufacturing are facing the same frustrating reality: despite years of investment in digital systems and data tools, they still struggle to get a clear actionable picture of what’s really happening across the business. 

Why? Because their data is trapped in solos.  

But there’s some good news! Breaking down these barriers is not only achievable – it’s a proven way to unlock real, measurable Return on Investment.  

The High Costs of Siloed Data 

When departments can’t share or interpret data effectively, inefficiencies pile up fast.  

This results in: 

  • Duplicate work across functions because nobody has the visibility of what’s already been done. 
  • Delayed decision as leadership waits on reports pulled manually from disconnected systems.  
  • Missed opportunities because customer, production, or financial insights are fragmented or outdated. 
  • Increase compliance risk, especially in Financial Services, where governance and audit trails are critical.  

 

Why Traditional Data Projects Fall Short

Despite the pressure to ‘become data-driven’, most organisations fall into the same traps: 

  • Technology first thinking: Investing in platforms without a clear business use case. 
  • Confused ownership: IT implements the tools, but departments don’t align on how to use them. 
  • Chasing quick wins: Focusing on dashboards or reporting tools instead of building a connected, scalable strategy.  

In both Manufacuring and Financial Services, this often results in beautiful visualisations of incomplete or low-quality data. 

Which means wrong answers, faster.  

From Siloed to Smart: A Better Way Forward

To truly unlock ROI from data, business leaders need a new approach. One that prioritises outcomes, collaboration, and long-term value.  

1. Align Data to Business Goals

Start with the outcomes: 

  • Do you want faster forecasting? 
  • Better customer segmentation? 
  • More efficient production planning? 

This alignment ensures your data strategy supports core business KPIs from day one.

2. Build an Integration Strategy 

Focus on connecting the systems that matter most.  

This could mean linking ERP to your CRM, integrating production data with demand forecasting or merging transactional data with risk models.

Want a roadmap for getting started?

Download our Data Innovation Toolkit!

This is your step-by-step guide to unlocking business value through smarter data.

3. Create Cross-Functional Data Ownership 

Break the cycle of IT-only responsibility.  

Empower departments with data stewards – business-side champions who understand the context and value of the data they manage.  

4. Make Data Accessible (with Governance)  

Accessibility doesn’t mean a free-for-all.  

It means the right people can access the right data at the right time, with controls in place to ensure compliance and trust.  

What Smart, Integrated Data Looks Like

In Manufacturing:

A leading organisation partnered with Engaging Data to configure and install a robust Data Warehouse solution. 

Leveraging market-leading software, Engaging Data provided expert consultancy to ensure a high-performing and sustainable data infrastructure. 

Learn more and read the case study.

In Financial Services:

A leading organisation partnered with Engaging Data to optimize their data infrastructure and drive data innovation. 

Since 2022, Engaging Data has worked closely with the client’s business teams, providing expert knowledge and championing best practices for data utilisation across the organisation. 

Learn more and read the case study.

Measuring the ROI of Integrated Data 

Here’s what to look for when tracking ROI from integration: 

  • Efficiency Gains: Fewer manual reports, less duplication of effort. 
  • Revenue Growth: Faster time to insights, smarter product and pricing decisions. 
  • Risk Reduction: Improve compliance, lower error rate, better audit trails.  

You can also track: 

  • Percentage of reports generated automatically 
  • Reduction in time-to-insight 
  • Increase in trusted / validated data sources.  

How to Get Started 

Start Small, Think Big:

Look for 2-3 systems where integration would bring immediate value. 

For example: CRM + ERP or planning + finance. 

Get Stakeholder Buy-In: 

This isn’t just an IT project – loop in finance, ops, and compliance leaders early.  

Bring in Outside Perspective: 

A trusted data partner can help you identify hidden opportunities and avoid expensive mistakes. 

We can help you with that! 

Learn the 10 Tips for Choosing the Right Data Consultancy for You. 

Your Data Can – and Should – Deliver More 

Data investment should not be a cost center. But they shouldn’t be a barren wasteland either.  

When integrated and aligned with business strategy, they become one of the most powerful levers for growth, agility, and risk management.  

If you’re ready to move from siloed systems to smart decision, let’s talk. 

Book Your Data Innovation Session!

Book Your Data Innovation Session!

In this 30-minute session, we’ll explore how you can drive efficiency, reduce costs, and uncover new growth opportunities by leveraging data in smarter ways.

Data Transformation vs. Data Migration: Which is Right for Your Business?

Data Transformation vs. Data Migration: Which is Right for Your Business?

Data Transformation vs. Data Migration: Which is Right For Your Business? 

The High-Stakes Choice to Become a Data-Driven Organisation 

If you’re leading a business wanting to become truly data-driven, you’re probably facing a pressing question: How do we modernise our data infrastructure without disrupting operations to become data-driven and drive innovation? 

With AI, real-time analytics, and regulatory demands evolving at pace, the wrong approach to data modernisation and innovation can lead to inefficiencies, compliance risks, and wasted investment.  

Yet, too often, we see businesses rush into a ‘lift-and-shit, migration or an overcomplicated transformation project without a clear business case.  

So, which approach is right for your business?  

Let’s get into it!  

Learn more about how innovate your organisation using data initiatives.

Check out this blog post

The Fundamental Difference Between Data Migration and Data Transformation

Data Migration: Moving Data from One System to Another  

What is it? The process of transferring data from one system to another with minimal structural changes. 

Example: A Financial Services firm moving from an on-perm legacy database to a cloud-based infrastructure for better scalability. 

Why business choose migration: 

  • Cost effective compared to full-scale transformation. 
  • Faster implementation with minimal business disruption. 
  • Necessary for system upgrades or moving from outdated platforms. 

Common Pitfall: Many businesses assume migration alone is enough.  

But moving messy, unstructured data into a new system does not solve inefficiencies. It simply relocates the problem.

Data Transformation: Refining and Enhancing Data for Better Business Insights 

What is it? Restructuring, cleansing, and optimising data to improve quality, usability, and decision-making. 

Example: A Manufacturing company consolidating supply chain data across multiple sources to create a single source of truth for predictive analytics 

Why businesses choose transformation: 

  • Enables advanced analytics, AI, and automation. 
  • Improves data quality, consistency, and accessibility. 
  • Enhances regulatory compliance and reporting accuracy. 

Common Pitfall: Some businesses over-engineer transformation projects, adding complexity without clear ROI.  

Transformation should be driven by business needs, not just IT capabilities.  

How to Determine Which One Your Business Needs 

To make the right decision, ask yourself: 

  • Are we moving to a new system due to outdated infrastructure or compliance risks? = Migration 
  • Are we struggling with inconsistent, siloed, or poor-quality data that hinders decision-making = Transformation 
  • Do we need to enhance AI, analytics, or automation capabilities? = Transformation 
  • Do we have a short timeline and need minimal disruption? = Migration (but with strategic data governance.  

The most effective businesses don’t choose just one. They combine both. 

Planning on innovating your organisation using data-driven initiatives? 

Download our Data Innovation Toolkit!

Common Mistakes That Lead to Costly Data Projects

For Financial Services: Ignoring regulatory compliance during migration, leads to fines and security risks. 

For Manufactuing: Overlooking real-time data needs, causing inefficiencies in supply chain and production planning. 

For Both: Assuming IT alone should drive the decision instead of aligning with business goals and ROI. 

Additional Challenges: 

  • Underestimating Data Complexity: Legacy systems often contain redundant, outdated, and conflicting data. 
  • Lack of Stakeholder Buy-In: Transformation efforts fail when leadership, IT, and operations teams aren’t aligned.  
  • Failure to Future-Proof: Short-term fixes may lead to long-term inefficiencies if scalability and adaptability aren’t considered.  

The Smart Approach – A Hybrid Strategy 

Many organisations find that neither migration nor transformation alone is enough 

Instead, they require a hybrid approach that strategically balances both. 

Migration ensures that data moves into modern, scalable environments, while transformation enhances its accuracy, usability, and strategic value. 

By integrating both processes, businesses can unlock powerful insights, drive automation, and ensure compliance while maintaining operational stability.  

How to Execute Successfully 

  • Assess Current Data Health: Condust a full audit to identify gaps, inconsistencies, and compliance risks 
  • Define Clear ROI and Business Outcomes: Set measurable goals to justify the investment. 
  • Implement Phased Deployment: Minimise risk by gradually rolling out migration and transformation initiatives. 
  • Partner with Experts Who Understand Business and Technical Strategy: A data consultancy like Engaging Data can guide you through the process (shameless promo, not sorry about it!) 
  • Prioritise Security, Compliance, and Sustainability: Ensure all data initiatives align with regulatory and ESC requirements.  

Learn how our clients successfully implemented a hybrid strategy.

Read out case studies!

Final Words… 

To summarise:  

  • Data migration is essential for moving systems but doesn’t fix poor data quality. 
  • Data transformation improves data usability, analytics, and complies by requires clear objectives.  
  • A hybrid approach – combining migration and transformation – ensures maximum business value. 
  • Avoid common pitfalls like overlooking compliance, failing to engage stakeholders, and underestimating data complexity.  
  • Future-proofing your data strategy now prevents costly issues later. 

Why Act Now?

Waiting means wasted resources, lost insights, and higher costs later.  

A strategic approach to data will define your competitive advantage in 2025 and beyond!  

Book a FREE 30-minute consultation call and take the first steps to innovation through data initiatives.

Big Data and AI World 2025 

Big Data and AI World 2025 

Big Data and AI World 2025 

Guess what?  

We are exhibiting at Big Data and AI World – the premier event for data and AI professions – alongside our long-term partner WhereScape!  

Combining our bespoke consultancy services with WhereScape’s powerful data automation, we are the perfect fit for your data needs!  

Event Details: 

  • Date: Wednesday 12th and Thursday 13th March 2025 
  • Location: ExCeL. London  
  • Booth Number: BD0610 

Visit our booth to explore how our tailored solutions, combined with WhereScape’s data automation software, can transform your data projects into success stories!  

Register now and stop by our booth to understand how our services combined with WhereScape’s software will innovate your business!  

Oh, and we have some pretty cool swag and games – so that is reason enough!  

Register now and stop by our booth to understand how together we can innovate your business!

Oh, and we have some pretty cool swag and games – so that is reason enough!

Implementing CI/CD in Your Data Warehouse: The Future of Data Management

Implementing CI/CD in Your Data Warehouse: The Future of Data Management

Implementing CI/CD in Your Data Warehouse: The Future of Data Management


As businesses continue to evolve, the need for efficient and reliable data management becomes increasingly critical.  

Traditional data warehousing approaches are no longer sufficient to handle the dynamic requirements of modern data environments.  

CI/CD (Continuous Integration/Continuous Delivery) is emerging as a cornerstone for future proofing data warehouses.  

In this blog post, we will explore how CI/CD is shaping the future of data management, offering a robust framework to enhance flexibility, scalability, and reliability in data warehousing 

The Evolution of Data Warehousing 

Historical Perspective on Data Warehousing 

  • Early Days: Data warehousing began as a method to store and analyse large volumes of historical data, primarily for reporting and decision-making purposes.  
  • Traditional Approaches: Relied heavily on batch processing, manual interventions, and periodic updates, leading to delays and inconsistencies. 

Current Trends and Innovations 

  • Real-Time Data Processing: The shift towards real-time data ingestion and processing to support timely decision-making 
  • Cloud Data Warehousing: Adoption of cloud platforms for scalable and cost-effective data storage and processing 
  • Data Lake Integration: Combining data warehouses with data lakes to manage both structured and unstructured data 

The Role of CI/CD in Modern Data Management 

How CI/CD Fit into Contemporary Data Management Strategies 

  • Continuous Integration: Ensures that data changes are integrated regularly and tested thoroughly, reducing errors and improving data quality 
  • Continuous Delivery: Automates the deployment of data updates, enabling rapid and reliable releases of new data models and transformations 

Advantages Over Traditional Methods 

  • Speed and Agility: CI/CD accelerates the development and deployment process, allowing for more responsive data management 
  • Reduced Risks: Automated testing and deployment reduce the risk of errors and ensure consistency 
  • Enhanced Collaboration: Fosters better collaboration between development, operations, and data teams. 

    Preparing for the Future with CI/CD 

    Steps to Future-Proof Your Data Warehouse

    1. Adopt a Version Control System: Using tools like GIT to manage code and data changes effectively. 
    1. Implement Automates Testing: Develop automated tests for data validation, schema changes, and transformation logic 
    1. Configure CI/CD Pipelines: Set up CI/CD pipelines using tools like Jenkins for continuous integration or Octopus Deploy for continuous delivery 
    1. Monitor and Optimise: Continuously monitor the performance of CI/CD pipelines and optimise for efficiency and reliability. 

    Integration with Emerging Technologies: 

    • AI and ML: Incorporate AI and machine learning models into your data workflows for predictive analytics and automated decision-making. 
    • Big Data Technologies: Use big data frameworks to process large volumes of data efficiently.  
    • Serverless Architectures: Leverage serverless computing to scale data processing dynamically based on demand. 

    Flexibility and Scalability Benefits 

    • Elastic Scalability: CI/CD pipelines can be scaled to handle increasing data volumes without compromising performance.  
    • Adaptability: Easily adapt to new data sources, formats, and processing requirements.  

    Expert Predictions on the Future of CI/CD in Data Warehousing:

    • Increased Adoption: More organisations will adopt CI/CD to meet the demands of real-time data processing and analytics. 
    • Integration with Advanced Technologies: CI/CD will increasingly integrate with AI, machine learning, and big data technologies to drive innovation in data management. 
    • Focus on Security: Enhanced security measures will be integrated into CI/CD pipelines to protect sensitive data and ensure compliance. 

    Conclusion

    The future of data management lies in the adoption of CI/CD practices.  

    By implementing CI/CD in your data warehouse, you can achieve greater flexibility, scalability, and reliability, ensuring that your data management processes are equipped to handle the demands of the digital landscape. 

          Ready to modernise your data warehouse with CI/CD?

          Learn more about our CI/CD Package and take the first stop towards a more efficient, reliable, and agile data management process.