How to Use Data Platform Services for Growth

Leveraging information and turning it into actionable insights is essential for achieving business milestones quickly, enhancing competitiveness, and boosting growth. However, according to VMware research, 70% of businesses continue to struggle to harness the value of their data.

Issues such as poor data quality and consistency, the inability to scale, and inadequate data management and governance are all barriers preventing businesses from utilising data effectively.

Here is where data platforms come in. Data platform services simplify system integration and data access, allowing for more flexible and secure data management. They reduce data redundancy, eliminate the costs of multiple data storage and boost innovation and collaboration.

In this article, we’ll explore how modern data platform services, including features like scalability, real-time processing, and sophisticated analytics, can enhance data architecture and enable quicker, smarter business decisions to drive growth.

The Role of Data in Business Growth

According to Foundry’s 2025 report, data is the third top tech initiative driving CIO’s IT investments, after AI and security. The convergence of a strong data platform integrated with powerful AI solutions can lead to a wealth of opportunities, enhancing productivity, innovation, and overall operational efficiency. Yet, these technologies can only work with a forward-thinking, multi-layered approach that combines clear regulations with cloud security and employee education.

For instance, AI data ingestion often blends personal information, sensitive IP addresses, or business-critical insights. Thus, compliance with data privacy regulations and strict governance policies becomes non-negotiable features that must be implemented at the very beginning of your AI-integrated data platform project.

Flexible and scalable cloud-native storage solutions are ideal for AI and virtually unlimited data storage. However, many businesses don’t realise the inherent potential risks of cloud computing, stemming from issues such as lack of visibility, security vulnerabilities, and misconfigurations.

To mitigate these risks, it’s vital to adopt robust cloud security strategies to protect your data against emerging threats, minimise the risk and impact of data breaches, and enhance overall cyber security posture.

Verizon’s 2025 Data Breach Investigation Report shows that 60% of data breaches involve a human element. With AI tools, the danger of employees inadvertently exposing or misusing sensitive data is higher, above all in environments where there aren’t stringent data privacy measures in place. Therefore, it is essential to arm your workforce with the training and tools that they need to protect the business against data mishandling.

By adapting to these challenges, you can achieve the perfect balance between leveraging AI and optimising data platforms and processes, fast tracking that competitive edge, and, ultimately, long-term success.

What Are Data Platform Services?

Data platform services enable end-to-end data management for the entire data lifecycle of all the information your business generates and collects (e.g., structured and unstructured data) in one place. They’re the core layers that enable the operation of data platforms like:

• Enterprise data platforms (EDPs), tailored for analytical reporting
• Big data platforms (BDPs), ideal for data mining, predictive modelling and real-time analytics
• Customer data platforms (CDPs), designed to provide deep insights into customer behaviour and preferences

Data platform services can vary depending on the platform chosen, existing infrastructure, and specific business needs. Nevertheless, the main foundational concepts of data platform services include:

• Data storage. Scalable solutions such as cloud-based data lakes and warehouses where you store your data.
• Data processing. Software libraries or engines for large-scale data processing, in batches or real-time, like Hadoop and Kafka.
• Data analytics. Business intelligence (BI), advanced analytics, and machine learning tools for research and analysis, namely Tableau and PowerBI.
• Security. The security controls that protect sensitive information from attacks, data breaches, and unauthorised access. It encompasses security measures such as encryption, access control, and auditing.
• Governance. It comprises all of the policies and procedures to guarantee that the information is consistent, trustworthy, and used in compliance with standards, privacy and security regulations such as the EU General Data Protection Regulation (GDPR).

All these components work together to create a flexible, scalable, and comprehensive platform. As core layers, they enable businesses to gain greater control over data, leverage it for data mining, and even create diverse data storage to improve resilience against natural disasters or cyberattacks.

Data Management Platforms

Pick the Right Type to Capitalise on the Value of Your Data

Depending on industry-specific needs and the data they handle, businesses can manage data on-site, through a cloud provider, or using a combination of both (i.e., hybrid approach).

On-premises data platform solutions give you full control over your system and data layers. Your company manages all the infrastructure, including support and customisation activities. Consequently, this type of data platform requires significant upfront costs for installation and maintenance. Despite being hard to scale, they’re the perfect option for firms with rigorous data privacy, security and reliability requirements (e.g., financial services and healthcare companies) or for those looking to avoid compatibility issues with external systems.

Cloud-based data platform services are increasingly popular among businesses seeking scalable, flexible, affordable and low-maintenance solutions. Everything comes out of the box, and you only pay for what you use – no large upfront investments or long-term contracts. However, 61% of professionals polled by our long-standing partner Fortinet consider security and compliance as barriers to cloud adoption. Data is stored externally on third-party servers, so your business doesn’t always have control over it. This may not be suitable for businesses in heavily regulated industries requiring a high level of data privacy and security.

Hybrid solutions are the best choice for companies striving to maintain control of on-premises hardware while benefiting from the scalability and cost savings offered by cloud data centres. They enable you to handle peak server demands while retaining security and compliance with data regulations. Hybrid platforms also allow you to store and transform data into insights wherever you choose, whilst using the same tools across all environments.

How Data Platforms Work With AI to Unlock Growth

In the past, businesses relied heavily on BI dashboards providing reports and insights based on historical information stored in data warehouses. However, these dashboards lacked the speed required to adapt to rapidly changing trends and markets.

With the advent of data platforms, users can now access real-time data faster. Yet, Oracle reports that data scientists still spend 39% of their time preparing data before it can be used.

AI solves this problem. It gives you the freedom to generate insights from multiple data sources on the fly. Leveraging predictive analytics models allows you to forecast long-term trends, tailor products and services and increase operational efficiency.

Unlike conventional BI models that rely on human-driven decision-making, AI models leverage innovative data processing solutions to help you gain valuable insights and actionable intelligence, providing a comprehensive understanding of your data landscape that drives smarter and faster decisions and proactive risk management.

Key Considerations When Implementing a Data Platform

With the rise of AI and ML, alongside the constant flow of data from various sources, batch processing systems that extract, transform and load data chunks before they can be actionable are no longer enough.

Besides, AI-enabled applications require real-time access to high-quality data to take action. In the same way, businesses seeking to gain a competitive advantage and react to changes in market trends and customer behaviour need the information as it’s generated.

Data platforms go beyond traditional batch data processing. They empower you to handle and act on data as is created, paving the way for true, real-time decision-making. But a data platform implementation can be a daunting task that requires a few fundamental considerations:

1. Ensure the alignment between data strategy and overall business objectives. Start by clearly defining your business objectives. Involve key stakeholders across departments. Assess your current data landscape by including your data governance and data management processes. Continue with identifying data requirements and priorities. Define Key Performance Indicators (KPIs) to drive continuous improvement. This way, you will be able to implement a scalable and flexible platform that supports and adds value to your business’ critical initiatives.
2. Factor governance and compliance early. Data governance and security are essential for both compliance and data quality. However, many businesses relegate them at the end of the data platform implementation project. Include a few key governance guidelines early in the process. Your business will progress faster and reach the next milestone much quicker. The key to unlocking real value from AI lies in finding the balance between fixing what matters (i.e., the data foundations) and delivering immediate business value.
3. Choose the right technology for your needs. As we’ve learned, there are several storage technologies available, from cloud-based to on-site and hybrid. Each comes with its pros and cons. Nevertheless, businesses flooded with data striving to unlock their full potential with AI can’t do it with traditional solutions. AI models are hungry for data. This makes cloud-native solutions with powerful data transformation capabilities paramount.
4. Avoid common pitfalls. Poor data quality, fragmented data silos and lack of ownership across teams are some of the most prevalent issues that may arise when implementing a data platform. They can lead to misleading insights, duplicate efforts, inconsistent data and inefficiencies. You can overcome these challenges, by implementing a comprehensive data platform roadmap that links back to key business use cases.
5. Involve the right data stakeholders from the outset. Your data platform implementation success relies on the support of the right stakeholders. However, simply sharing the information with them isn’t enough. Once you have the right people in the room, turn urgency into impact. Drive focused and valuable conversations around AI by including governance, regulations, and costs in the discussion. Get regular feedback to ensure the data platform aligns with business needs and user expectations, facilitating adoption across the business.

Data Platforms: Sector-Specific Insights

Data platforms aren’t one-size-fits-all. Their application varies widely across different sectors. There are several ways data management platforms can give businesses a competitive advantage by enabling them to go beyond KPIs. So, let’s explore how tailored data platforms can align with specific business goals:

• Streamline manufacturing service operations with AI. According to McKinsey, by late 2024, 78% of businesses polled were using AI at least in one business function. In the manufacturing sector, optimising operations and reducing costs is exceptionally challenging. Acora built a unified data platform that leveraged data from IoT devices to obtain real-time insights, predictive maintenance and anticipate future demands. The streamlined processes led to significant cost savings, reduced operational downtime and optimised resource allocation.
• Reduce retailers’ reliance on skilled data personnel and consolidate customer service communications. Data interpretation doesn’t have to be complicated. Incorporating AI tools that can understand schemas, link data points and automate complex connections empowers a broader range of employees to leverage data insights for decision-making. It allows them to personalise customer journeys across channels and implement real-time campaigns without relying on expensive and sometimes hard-to-find specialist data personnel. Acora leveraged the AI-powered data platform model to help a retailer streamline customer service communications. The project led to faster enquiry handling and tailored responses, ultimately delivering a clear and measurable impact on the bottom line.
• Drive productivity improvements in customer services. Data platform services play a vital role in sectors such as human resources (HR) and customer services. Companies can analyse CRM data, HR systems and sales insights to make informed decisions on office space usage and resource allocation. For example, a business may decide to relocate the customer service, marketing and sales departments to the same floor to enhance collaboration and reduce conflicts.

The Future of Data Platforms and Growth

With new data challenges, the focus is shifting to data platforms shaped by AI and the evolution of analytics.

Businesses that embrace these trends and proactively improve their models will gain a competitive edge. In the future, AI use cases will likely continue developing, with a focus on cost reduction and revenue generation while aligning closely with core business offerings. Companies that harmonise their data strategies with these initiatives will experience true business value from AI.

By capitalising tools such as Data Vogon and Fuzzy Matcher for data platform design, businesses can create safer, more powerful, streamlined systems that unlock new opportunities and enable rapid decision-making and responses to market changes.

So, implement data platforms, but also make sure to update and adapt them regularly to keep pace with technological advancements and shifting market dynamics. Invest in training, skills and infrastructure.

Conclusion

A well-planned data strategy will empower you to implement a data platform that enables real-time insights, enhances operational efficiencies and fosters innovation. Acora has the expertise to help you assess the maturity of your current strategy and successfully implement a modern data platform that lays the foundations, enabling you to industrialise AI. Embrace the future of data and boundless growth.

Ready to kick start the conversations with the right people? Request an Art of the Possible session today to dive into your very own use cases and explore how you can make rapid progress towards your next big milestone.