High-Volume, High-Velocity Data Collection: 10 Key Best Practices

Today, businesses are inundated with vast amounts of structured, unstructured, and real-time data from various sources, amounting to 2.5 quintillion bytes generated every day. This data is crucial for gaining a comprehensive view of operations, customer interactions, and market trends. It helps businesses stay competitive, make informed decisions, solve problems, understand customer needs, improve processes, and ultimately drive growth. According to a report by HFS Research, 85% of businesses recognise data as a cornerstone of success, yet only a third are satisfied with their data quality. 

However, while data is absolutely crucial to businesses, managing such vast amounts of data – and ensuring high-velocity collection – can be a real challenge. It’s not just about gathering data; it’s about making sure it’s usable and scalable. So, what are the best practices to manage all this data? Let’s find out. 

#1 Ensuring Data Quality 

Ensuring data quality typically involves implementing processes and tools to validate, clean, and maintain data accuracy and consistency. This includes data validation rules, regular data audits, and automated cleansing tools. 

Why is data quality so important? Data quality is what helps businesses make informed decisions, improve customer satisfaction, and keep operations running smoothly. If data quality is poor, it can lead to flawed analyses, bad decisions, and missed opportunities. 

When it comes to data quality, businesses usually face a few common challenges: like data entry errors, duplicate records, or outdated information. These issues can lead to increased operational costs and frustrated customers. These challenges can be managed with robust data governance practices. For example, setting clear data ownership and accountability, alongside regular data audits and validation checks, can help rectify issues promptly. Take Starbucks, for example: the beverage giant uses data quality practices to enhance its customer loyalty programme. By ensuring accurate customer data, Starbucks is able to send personalised offers and recommendations, which ultimately boosts customer engagement and sales. 

#2 Creating a Clear Data Strategy 

A clear data strategy helps make sure that all data-related activities are aligned with business goals, making decision-making and operational efficiency a lot easier. To start with, businesses need to define clear objectives for their data collection – the type of data they need, how they will collect it, and how it will be used. Businesses also need to put data governance frameworks in place to ensure that data quality, security, and compliance are managed from the very beginning. 

Usually, when developing a data strategy, businesses will find that their objectives aren’t clear, communication between departments is poor, or data practices are inconsistent. To overcome these hurdles, they need to create a cross-functional team to develop and manage the data strategy. Moreover, they need to review and update it regularly to stay on top of changing business needs and new technologies. 

For example, Procter & Gamble (P&G), developed a comprehensive data strategy called Supply Chain 3.0. This strategy involved digital transformation, geotagged deliveries, AI-powered ordering, and sustainability practices. By investing in automation, real-time data collection, and advanced analytics, they streamlined operations, as a result of which they achieved a 10% reduction in inventory costs and improved product availability. 

#3 Build a Comprehensive Data Catalogue 

A data catalogue is helpful because it ensures that data is easily accessible and understood by all stakeholders. This, in turn, helps improve data usage and collaboration across the business. 

To create and maintain a data catalogue, businesses need to organise and manage all their data assets. This includes tagging data sources, describing data types, and documenting data lineage. Maintaining an up-to-date catalogue can be time-consuming, and getting buy-in from all departments can be difficult. But, using automated tools to keep things updated can make the process more efficient. It’s also crucial to provide training to ensure all team members understand its value.  

For example, the European Data Portal acts as a central hub for public data across EU member states. It organises and catalogues data from sectors like health, environment, and economy, making it easier for users to find and use the information. By standardising and centralising data, it reduces duplication and ensures that high-quality data is easily accessible. This approach supports better decision-making and innovation by providing reliable and diverse data sources. The portal is an important tool for promoting transparency and enabling efficient data use across Europe. 

#4 Use Scalable Infrastructure 

Scalable infrastructure is vital for ensuring that a business’s data collection and processing capabilities can grow alongside its business needs. Without it, they risk bottlenecks that could slow things down and impact efficiency. 

If businesses are managing large volumes of data, cloud computing platforms like AWS, Azure, and Google Cloud can offer flexible, scalable resources that grow with their needs. These platforms provide services like data storage, databases, and virtual machines that can easily be scaled up or down depending on demand. 

That said, managing costs and ensuring data security in the cloud can be tricky. If businesses are not careful, cloud services can become expensive, and data security is always a concern when sensitive information is stored off-site. To mitigate these risks, businesses need to optimise their resource usage by monitoring and adjusting their infrastructure based on actual needs. They can also use cost management tools provided by cloud platforms to keep expenses in check. Lastly, but most importantly, it’s crucial to implement strong security measures like encryption, access controls, and regular security audits. 

Spotify, for instance, leverages AWS’s scalable infrastructure to manage its massive streaming data. By using services like S3 for storage and EC2 for computing, Spotify can adjust its resources dynamically based on demand. This elasticity ensures smooth streaming during peak times and makes data analytics for recommendations run more efficiently. The company also employs advanced load balancing and data replication strategies to maintain high availability and minimise downtime. 

#5 Implement Real-Time Data Processing 

Real-time data processing is a game-changer for businesses because it provides immediate insights and enables quick actions, which ultimately improves operational efficiency and decision-making. By using tools like Apache Kafka and Apache Flink, businesses can handle high-velocity data and process it as it’s generated. 

That being said, real-time processing does come with its own set of challenges, particularly when it comes to ensuring data consistency and managing costs. This can be tackled with strong data synchronisation techniques and careful resource allocation to balance performance and cost. 

The European Central Bank (ECB) uses real-time data processing to monitor financial transactions and ensure compliance with regulations. They use advanced technologies like machine learning and big data analytics to analyse transactions as they happen. This enables the ECB to detect anomalies, prevent fraud, and maintain financial stability. By processing data in real-time, the ECB can make timely decisions and respond to issues quickly. 

#6 Prioritise Data Security 

Data security is a non-negotiable priority, especially when it comes to maintaining customer trust, complying with regulations like GDPR, and avoiding costly data breaches. Strong security measures include encryption, setting up access controls to restrict who can view or edit data, and performing regular security audits to identify vulnerabilities. 

However, staying ahead of evolving cyber threats and managing compliance across various regulations can be difficult. Cybercriminals are always finding new ways to exploit vulnerabilities, and keeping up with the latest security trends can feel like a full-time job. To stay on top of this, businesses need to invest in advanced security solutions – such as AI-driven threat detection – and regularly review and update their security policies. 

The European Medicines Agency (EMA) is a good example of how to take data security seriously. They use strong encryption methods to protect sensitive healthcare data, enforce strict access controls to ensure only authorised personnel can access information, and conduct regular audits to identify and address vulnerabilities. This holistic approach helps them maintain compliance and safeguard stakeholder trust. 

#7 Leverage Data Integration Tools 

Data integration is essential if businesses want to have a comprehensive and unified view of all the data within their organisation. Such a holistic approach enables better analysis, supports informed decision-making, and helps with strategic planning. 

Businesses can use data integration tools like Apache NiFi, Talend, and Informatica to bring together data from various sources and formats. These tools help ingest, transform, and combine data seamlessly, making it available for analysis and decision-making. 

Having said that, integrating data from different sources can be complex. The challenges typically involve ensuring data quality during integration, managing different data formats, and handling data at varying speeds. To mitigate these issues, businesses need to implement strong data validation and transformation processes. Automating the data integration process can reduce human errors and boost efficiency. It’s also important to regularly monitor and audit the data to ensure it stays accurate and reliable. 

For example, the European Environment Agency (EEA) uses data integration tools to merge environmental data from a variety of sources, such as air quality sensors, water monitoring stations, and satellite imagery. By integrating this diverse data, the EEA can offer comprehensive insights into environmental conditions across Europe. This integrated data helps shape effective policies on pollution, climate change, and resource management. 

#8 Invest in Advanced Analytics 

Advanced analytics helps businesses spot trends, predict outcomes, and detect issues early. It allows businesses to be more proactive rather than reactive, improving decision-making and operational efficiency. It also enables businesses to personalise customer experiences and optimise resource management. 

Investing in advanced analytics typically involves adopting technologies like machine learning and AI. These tools analyse large datasets to identify patterns and generate insights. For instance, machine learning algorithms can predict future trends based on historical data, while AI can automate complex decision-making processes. 

Having said that, implementing advanced analytics can be challenging. It requires specialised skills and significant investment in computing resources. To overcome this, businesses can provide training and resources to upskill existing teams. Partnering with third-party vendors can also help by providing access to cutting-edge tools and expertise without the need for extensive internal development. 

Siemens, for example, uses advanced analytics to optimise its manufacturing processes. By analysing data from production lines, Siemens can identify inefficiencies and predict when maintenance is needed. This has led to reduced downtime, improved efficiency, and significant cost savings. Predictive maintenance powered by AI, for instance, has helped Siemens reduce equipment failure rates and boost overall production efficiency. 

#9 Foster a Data-Driven Culture 

A data-driven culture is essential for ensuring that decisions are based on evidence and insights, rather than just gut feel. This approach leads to better outcomes across the board – from more accurate predictions to better understanding of customer needs and improved efficiency. 

Encouraging a data-driven culture means providing teams with the training and resources they need. It also involves making data accessible to everyone who needs it and promoting its use in decision-making. This could include setting up easy-to-use data platforms and ensuring that everyone knows how to leverage data within their specific roles. 

Changing organisational culture isn’t always easy, though. Resistance to change and a lack of data literacy can slow things down. To overcome this, businesses need to lead by example – start using data to guide leadership decisions. They need to provide continuous training and invest in building data literacy across the organisation. And, importantly, they should celebrate data-driven successes to reinforce the importance of using data in decision-making. 

ING Group is a good example of a data-driven culture. The company integrates data into every aspect of its business. They use data to enhance customer service by analysing interactions and feedback to offer personalised solutions. In risk management, ING uses data analytics to identify potential risks and make informed decisions to mitigate them. By making data a core part of its strategy, ING has been able to stay competitive in the financial industry. 

#10 Continuously Monitor & Optimise 

Continuous monitoring and optimisation are key for ensuring that the data collection processes stay relevant and up to date with changing needs and technologies. This helps businesses keep data accurate, relevant, and valuable for decision-making. 

To do this, businesses need to regularly monitor the data collection processes to make sure they’re running efficiently. They can use performance metrics to measure how well things are working and gather feedback from users to identify areas for improvement. Automated monitoring tools can also help streamline this process and give real-time insights. 

In this step, it can be time-consuming for businesses to track all of their data processes, and making changes based on monitoring results can be complex. But using automated monitoring tools – like dashboards and alert systems – to track performance in real time can make the process more efficient. Establishing a feedback loop, where regular reviews and user input inform continuous improvements, can help businesses stay ahead of the curve. 

Deutsche Bank, for example, continuously monitors and optimises its data collection and processing systems to handle large volumes of financial transactions. They use advanced monitoring tools to track system performance, detect issues quickly, and make adjustments as needed. By regularly reviewing processes and incorporating feedback, they ensure the reliability and efficiency of their financial data systems. 

Merit’s Expertise in Data Aggregation & Harvesting Using AI/ML Tools 

Merit’s proprietary AI/ML tools and data collection platforms meticulously gather information from thousands of diverse sources to generate valuable datasets. These datasets undergo meticulous augmentation and enrichment by our skilled data engineers to ensure accuracy, consistency, and structure. Our data solutions cater to a wide array of industries, including healthcare, retail, finance, and construction, allowing us to effectively meet the unique requirements of clients across various sectors. 

Our suite of data services covers various areas: Marketing Data expands audience reach using compliant, ethical data; Retail Data provides fast access to large e-commerce datasets with unmatched scalability; Industry Data Intelligence offers tailored business insights for a competitive edge; News Media Monitoring delivers curated news for actionable insights; Compliance Data tracks global sources for regulatory updates; and Document Data streamlines web document collection and data extraction for efficient processing.

Key Takeaways 

Data Quality is Crucial: Ensuring data accuracy and consistency is vital for better decision-making and customer satisfaction. 

Clear Data Strategy: Aligning data practices with business goals ensures efficiency and helps businesses stay competitive. 

Data Catalogue: A comprehensive data catalogue improves accessibility, collaboration, and decision-making across teams. 

Scalable Infrastructure: Cloud platforms offer flexible, scalable resources to manage large data volumes while keeping costs and security in check. 

Real-Time Data Processing: Real-time insights improve operational efficiency and allow businesses to take immediate action on emerging issues. 

Data Security: Strong data security practices, including encryption and audits, protect against breaches and ensure compliance. 

Data Integration: Integrating data from various sources enables more complete analysis and informed decision-making. 

Advanced Analytics: Machine learning and AI can help identify trends, predict outcomes, and optimise processes. 

Data-Driven Culture: Building a culture that embraces data in decision-making improves efficiency and innovation. 

Continuous Optimisation: Regular monitoring and optimisation ensure that data practices evolve with the business’s needs.