Data Services

Data Platforms

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Modern data platforms play a crucial role in helping organisations to make decisions, deliver core services, evolve their strategies and achieve their growth targets. As such, it's essential for any forward-thinking organisation to invest in a data infrastructure that will serve their needs, both now and in the future.

At Methods Analytics, we specialise in building secure, high-performing cloud data platforms for organisations from all sectors. Our team of experts understands your context, use cases and constraints, enabling us to focus on efficiently building only what you really need.

We have the practical experience to work through the complexity of your legacy data estate, the nuance of your user requirements, and a deep understanding of the specialist governance considerations for particularly sensitive data. Our collaborative approach will give your team practical experience to get the most out of your data platform from the start.

To learn more about how we can build your cloud data platform and address your data storage requirements, get in touch with us today. We will advise you on how to implement a platform that will deliver lasting value as the beating heart of your data transformation efforts.

Our Data Platforms service

In the realm of data management, one size doesn't necessarily fit all. Every organisation has unique needs, goals and constraints, requiring a tailored approach when implementing new data tools. We understand this, and that's why we offer a comprehensive suite of data platform services designed to meet your specific needs.

Take a look at the key data platform services we offer to help you harness the power of your data and transform it into actionable insights:

Building cloud-ready data platforms

We specialise in building secure, performant cloud data platforms, ready for integration with modern AI tools. Our customer data platforms are designed to handle the complexity of your legacy data estate, the nuance of your user requirements, and the specialist governance considerations for particularly sensitive data. We focus on building only what you really need and doing so efficiently, saving you time and resources.

Delivering data warehouse health checks

As your data grows in volume and complexity, maintaining service levels can become a challenge. Our data warehouse health check service is designed to help you overcome this challenge. We can help you reduce data ingestion and processing times; ensure your database and data models are optimised for reporting and analytics; review security and data retention to ensure adherence to GDPR; and revise access to data to deliver a better user experience.

Setting up data lakes

Data lakes provide a flexible, scalable solution for storing and analysing vast amounts of raw data. We can help you set up AWS-powered data lakes that can handle the scale, agility and flexibility required to combine different data and analytics approaches. This allows you to gain deeper insights than with traditional data silos and data warehouses.

No matter what your data needs are, we'll leverage our practical experience and deep understanding of data management to provide you with a data platform service that truly meets your needs. Whether you're just starting your journey to the cloud, or are looking to expand the capabilities of your well-established DevOps teams, we'll integrate with your processes and build your cloud data platform at a pace that works for you.

What is a data platform?

A data platform is a sophisticated, technology-enabled environment that provides an integrated and unified view of an organisation's data. It is the foundation upon which data from various sources is consolidated and managed. But what does this mean in practical terms?

In the simplest sense, a data platform is a place where data is collected, stored, processed and analysed. However, in the context of modern business operations and the digital age, a data platform is much more than just a storage facility for data. It's a dynamic ecosystem that enables data to be transformed into actionable insights, driving strategic decision-making and providing a competitive edge.

A data platform is designed to handle a wide variety of structured and unstructured data types. This includes structured formats such as databases, semi-structured data types like XML files, and unstructured data including text files or social media posts. A modern data platform needs to provide the infrastructure necessary to store, process and analyse this data at scale, enabling businesses to derive meaningful insights from the reams of information available to them.

But a data platform is not just about technology. It also involves the processes, methodologies and strategies used to manage and use data effectively. This includes data governance, data quality management, data integration, data security and more.

In essence, a data platform is the brain of your data strategy. It should be designed to learn the context of the world around you, ready to help you make better decisions. It's the key to unlocking the full potential of your data, turning raw data into valuable insights that can drive business growth and innovation.

How can we optimise your data warehouse?

A data warehouse is a crucial component of your overall data platform infrastructure, acting as a central repository for all your structured data. However, as the volume and complexity of data increase, maintaining service levels and managing costs can become challenging. We offer a comprehensive data warehouse optimisation service to help you overcome these challenges in the following ways:

Reducing data ingestion and processing times

The first step in optimising your data warehouse is to reduce data ingestion and processing times. This involves streamlining the processes used to extract, transform, and load (ETL) data into your data warehouse. Our team of experts can help identify bottlenecks in your ETL processes and implement solutions to speed up data ingestion and processing.

Optimising database and data models for reporting and analytics

The structure of your database and data models can have a significant impact on the performance of your data warehouse. We can help ensure your database and data models are optimised for reporting and analytics, enabling faster query performance and more efficient data analysis.

Reviewing security and data retention to ensure adherence to GDPR

Data security and compliance are critical considerations for any data warehouse. We can help review your data security measures and data retention policies to ensure they adhere to GDPR and other relevant regulations. This not only helps protect your data, but also reduces the risk of non-compliance penalties.

Revising access to data to deliver a better user experience

We can help you manage access to data, ensuring that users can easily access the data they need, when they need it, without compromising data security. By improving data accessibility, we can help your team make more effective use of your data warehouse.

By taking advantage of our data warehouse optimisation service, you can get the most out of your enterprise data platform. Whether you're looking to improve performance, enhance security or improve user experience, we have the expertise and experience to help.

What is a data lake, and what are its advantages?

A data lake is a storage repository that can hold a vast amount of raw data in its native format until it is needed. Unlike a hierarchical data warehouse, which stores data in files or folders, a data lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data, and that smaller set of data can then be analysed to help answer the question.

Choosing the data lake approach for your customer data platform delivers a number of key advantages for your data strategy:

  • Handling a variety of data: data lakes allow you to store diverse data types - structured, semi-structured or unstructured - and you don't need to define the data type or schema until the data is needed.
  • Scalability: data lakes are designed to provide high scalability, making it easy to store and manage vast amounts of data. Data lakes built on cloud platforms can be scaled up or down quickly to accommodate changes in data volume.
  • Cost-effective: data lakes are often more cost-effective than traditional data warehousing options, especially when dealing with large volumes of data. They can store raw data at a lower cost, while still providing the necessary tools and capabilities for powerful data analysis.
  • Real-time processing: data lakes allow for real-time data processing, which is crucial for applications that require immediate insights, such as fraud detection or process monitoring.
  • Advanced analytics: with data lakes, your raw data is readily available for advanced analytics, including machine learning and predictive analytics. This can lead to more accurate insights and better decision-making.
  • Data democratisation: data lakes make data accessible to different users across your organisation. Whether it's data scientists, analysts or business users, they can use this data to extract valuable insights tailored to their specific needs.

In this way, data lakes provide an intuitive environment for the processing and preparation of diverse data types, making it easy to use machine learning and data science methods directly using this raw data. This will give you the opportunity to generate new understanding about your business and customer base.

We can help you integrate data warehouses into the data lake model, ensuring that the data warehouse receives the output from the data lake, and that data consistency is maintained across the two data environments. We'll make this process as straightforward and seamless as possible, helping you unlock the full benefits of the data lake.

What is needed for a modern data platform?

A modern data platform is more than just a place to store data. It's a comprehensive solution that enables organisations to harness the power of their data and transform it into actionable insights

Here are the key components of what a modern data platform looks like, and what it needs to be effective:

  • Scalable data storage: a modern data platform needs to be able to handle the volume, variety and velocity of today's data. This requires scalable data storage solutions that can grow with your data needs.
  • Data integration: data comes in many forms and from many sources. A modern cloud data platform needs robust data integration capabilities to bring all this data together in a unified, consistent manner.
  • Data quality management: poor data quality can undermine the value of your data platform. Effective data quality management tools and processes are essential to ensure the accuracy, consistency and reliability of your data.
  • Data security and compliance: with the increasing prevalence of data breaches and the growing importance of data privacy regulations, security and compliance should be a core part of your data management strategy. A modern data platform needs robust security measures and compliance capabilities to protect your data and maintain trust with your customers.
  • Advanced data analytics: the true value of a data platform lies in its ability to transform raw data into actionable insights. This requires the use of sophisticated data analytics platforms, with capabilities including data mining, predictive analytics and machine learning.
  • Real-time processing: in today's fast-paced business environment, real-time data processing is a must. A modern data platform needs the ability to process data in real time, enabling you to respond to changes and make decisions as quickly as possible.
  • Data governance: effective data governance ensures that data is used and managed in a way that meets your organisation's policies, as well as regulatory requirements. It includes aspects like data stewardship, data lineage and data cataloguing.
  • User-friendly interface: data platform modernisation should not come at the expense of accessibility for users of all skill levels, from data scientists to business analysts. Achieving this requires a user-friendly interface that makes it easy to access, analyse and visualise data.
  • Cloud-ready: with the increasing shift towards cloud computing, a modern data platform needs to be cloud-ready. This not only provides scalability and cost-efficiency, but also enables access to data from anywhere, at any time.

A modern data platform should be seen as a complex ecosystem that brings together a wide range of technologies, tools and processes. It's not just about storing data; it's about making data work for you.

How do you choose a data platform?

Choosing the right data platform for your organisation is a critical decision that can significantly impact your ability to leverage data effectively. In order to make the right decision, you'll need to consider the following factors:

  • Understand your data needs: before you can choose the right data platform, you need to understand your data needs. What types of data will you be working with? How much data do you have, and how fast is it growing? What are your data processing and analysis needs? The answers to these questions can help guide your decision.
  • Consider the scalability: as your organisation grows, your data needs will likely grow as well. Therefore, it's important to choose a data platform that can scale with your needs. This includes not only the ability to handle larger volumes of data but also the ability to process and analyse data more quickly.
  • Evaluate the cost: cost is always a factor when choosing a data platform. This includes not only the upfront cost of the platform itself, but also ongoing costs for maintenance, support and upgrades. Be sure to consider the total cost of ownership when comparing different platforms.
  • Check the security features: data security is a critical concern for any organisation. Make sure the data platform you choose has robust security features to protect your data from threats. This includes encryption, access controls, audit logs and compliance features.
  • Look for advanced analytics capabilities: the real value of a data platform lies in its ability to turn raw data into actionable insights. Look for a platform that offers advanced analytics capabilities, such as predictive analytics, machine learning and artificial intelligence.
  • Ensure it supports data governance: good data governance is essential for ensuring the quality, consistency and security of your data. Choose a platform that supports data governance, including data cataloguing, data lineage and data stewardship.
  • Ease of use: finally, consider the ease of use of the platform. A platform that is easy to use will be more likely to be adopted by your team, which can increase the value you get from your data.

The best data platform for your organisation depends on your specific needs and circumstances. We'll work with you to assess what you need from your data management strategy, and deliver a tailored customer data platform that meets all of your requirements.

How to implement the right data architecture for your needs

Implementing the right data architecture is crucial for managing and utilising data effectively. It involves designing and setting up systems to collect, store and manage information, in alignment with how you use data.

When implementing the right data architecture for your needs, you should consider the following:

  • Understand your business goals: the first step in implementing the right data architecture is to understand your business goals, and what you hope to achieve with your data. This could be anything from improving customer service to making more informed business decisions. Your business goals will guide your data architecture decisions.
  • Identify your data needs: once you understand your business goals, you can identify your data needs. What types of data do you need to collect? How will you use this data? How much data will you be dealing with now and in the future? The answers to these questions should guide your data architecture approach.
  • Choose the right data models: data models define how data is stored and organised. The right data model for you will depend on your data needs; we can advise on which model is best suited for the requirements of your organisation.
  • Implement data governance: properly managing and protecting your data means setting up policies and procedures for data access, quality and security. Implementing data governance is crucial for ensuring the integrity and security of your data.
  • Choose the right tools and technologies: there are many tools and technologies available for implementing data architecture. This includes databases, data warehouses, data lakes and data integration tools. The right tools for you will depend on your data needs and business goals.
  • Test and refine your data architecture: once you've implemented your data architecture, it's important to test it to make sure it's working as expected. This includes testing the performance, security and usability; based on your testing, you can refine and improve your data architecture as needed.

Implementing the right data architecture is not a one-time task. As your business goals and data needs change, you'll need to continually review and update your data architecture to ensure it continues to meet your needs. We can help you with this, establishing a best-in-class infrastructure for data acquisition, processing and analysis, and reviewing its effectiveness on an ongoing basis to deliver lasting improvements.

Tools of specialty

Azure data services

Azure Data Services provides a set of fully managed relational, NoSQL and in-memory databases. These services automate tasks like configuring and managing high availability, disaster recovery, backups and data replication across regions. This automation saves you time and money, allowing you to focus on extracting value from your data rather than managing the infrastructure.

Azure Data Factory

Azure Data Factory is a fully managed, serverless data integration service. It allows us to integrate all of your data, regardless of where it resides. With Azure Data Factory, we can create, schedule and manage data pipelines, transforming your data from disparate sources into meaningful insights.

AWS-powered data lakes

AWS provides robust support for data lakes. AWS-powered data lakes, supported by the unmatched availability of Amazon S3 and Redshift, can handle the scale, agility and flexibility required to combine different data and analytics approaches. This allows us to build and store your data lakes on AWS, enabling you to gain deeper insights than with traditional data silos and data warehouses.

AWS Redshift

AWS Redshift is a fast, fully managed, petabyte-scale data warehouse that makes it simple and cost-effective to analyse all your data using your existing business intelligence tools. It delivers fast query performance by using columnar storage technology and parallel query execution. With Redshift, we can accelerate your time to insights with fast, easy and secure cloud data warehousing at scale.

Our approach

We believe that a successful data platform is built on a deep understanding of your unique needs and context. As such, our approach is designed around four key steps - Discover, Design, Develop and Migrate - to ensure that we deliver a data platform that is secure, efficient and tailored to your specific requirements.

Here's how we do it:

  1. Discover: The first step in our process is to understand your organisation, your data and your goals. We believe that your data platform should be the brain of your organisation, ready to learn the context of the world around you and help you make better decisions.

    Our expert architects and engineers will start building this context by working with your data and information asset owners, documenting your systems, rigorously cataloguing each entity and mapping the data flows into, out of, and within your organisation. Our user researchers will talk to your users, your teams and your executives, capturing use cases for the platform which will help you prioritise your development roadmap.

  2. Design: We believe that the secrets of a successful data platform design are reducing complexity, utilising existing best-practice and continuously validating that we are meeting a user need.
    Complexity can be reduced by creating simple repeatable processes. This starts with the design process itself. We’ll create design tasks that are easy to understand, simple to track, and contain clear instructions for taking them off the page and into build.
  3. Develop: Whether you're getting started on your journey to the cloud, you've just begun implementing Agile into your organisation, or have well-established DevOps teams, we'll integrate with your processes and build your platform at a pace that works for you.

    We’ve designed your platform for the AI-ready future of data, so we build for the long term. This means loosely coupled, best-of-breed data capabilities, with each engineered to be independently scaled or replaced, making maintenance automated and simple. We’ll build interfaces to be easily managed and readily extended, meaning your team can quickly ingest data from new sources as they become available.

  4. Migrate: To start using your new data platform to make better decisions, you’ll need to introduce it to your first phase of data, users and integrations. We offer a comprehensive migration service to get you up and running.

    Our experienced team will migrate your historic data into your new platform and make sure it’s modelled to enable your new capabilities to extract maximum value. We'll ensure your data is handled as if it were our own throughout, in compliance with your policies and in adherence with your governance regime. We’ll onboard your users and work with your other suppliers and partners, to bring them on board. Together, we’ll start rationalising your legacy data services system-by-system, and begin realising savings for your organisation.

    Our approach is collaborative and focused on your needs. We work closely with your team, building a deep understanding of your context, use cases and constraints. This allows us to build a data platform that is not only efficient and secure, but also tailored to your specific needs and ready to deliver value from the start.

Case Studies

Positioning Data at the heart of Innovation - Swindon Borough Council (SBC)

The council employs over 2000 staff who work to prioritise improving infrastructure and housing to support a growing, low-carbon economy; offer education opportunities that lead to the right skills and the right jobs in the right places; ensure clean and safe streets; improve public spaces and local culture; and helps people to help themselves, while always protecting the most vulnerable children and adult. As part of its pledge to compete at the forefront of digital innovation with a commitment to using
technology for positive change, the council wanted to improve organisational data management enabling it to more effectively use its data, lower costs and support new services.

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Disposal Management - Defence Equipment and Support (DE&S)

If an item becomes obsolete, no longer in use or isn’t working, it’s either disposed of or sent to storage. Across DE&S, there was no set process to monitor and manage the disposal risk of these items. It was only being done at team level, tracked over non-interfaced electronic systems, local spreadsheets and paper-based registers.

Systems could not be linked. Confidence in the disposal process could not be recorded or understood.

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