Introduction to data management
In today’s world, data is everywhere – whether it’s the purchases you make online or all the data your phone collects about your habits.
Managing all that data effectively is critical for businesses and organisations of all kinds. They do this by creating clear data policies, keeping sensitive information secure, and making sure data flows smoothly between different systems.
That’s where data management comes in. This guide will walk you through what data management is, how it works, and how you can step into this exciting and growing field.
Defining data management
In simple terms, data management is all about how we collect, store, organise, and maintain data so it stays accurate, easy to access, and secure.
For businesses, it means making better decisions, running things more smoothly, and staying on top of regulations. Without proper data management, companies can run into legal trouble or miss out on opportunities simply because their data wasn’t handled well.
How does data management work?
Data management follows a structured process that keeps data organised throughout its lifecycle – from the moment it’s created to when it’s archived or deleted. Here’s a simple breakdown of the key stages.
The data management life cycle
Think of data like a product. Effective data management involves several steps to ensure it stays useful and secure:
1. Data collection
Data comes from all kinds of places – customer transactions, surveys, or even visits to a website.
2. Data storage
Once created, data needs somewhere safe to live. This could be in a data warehouse, the cloud, or even a data lake.
3. Data usage
Data is processed and analysed, to help businesses make decisions.
4. Data sharing
Data is shared between departments or systems, but only with authorised users, to comply with data privacy rules.
Data cataloguing
Data is organised and documented in a way that makes it easy to find and understand, helping users navigate large amounts of data more efficiently.
5. Data archiving
Older data that’s still important but not used every day is securely archived for long-term storage.
6. Data disposal
When data is no longer useful, it’s securely deleted to avoid storage costs and potential security risks.
What are data management principles?
When it comes to handling data, there are a few golden rules – or principles – that make sure data is useful, secure, and accurate. A good data management process includes the following principles:
Accuracy
Data needs to be right from the start. If you’re working with wrong or outdated information, it can lead to poor decisions or mistakes. Regularly checking and cleaning data makes sure it stays reliable.
Integrity
This is all about keeping data trustworthy and consistent. You don’t want data getting accidentally altered as it moves between systems or departments.
Security
Securing data from breaches or unauthorised access is a top priority. Companies tend to use encryption and secure access controls to keep sensitive data safe, especially with cyberattacks and enterprise data breaches on the rise.
Accessibility
Data should be easy to get to for those who need it, without jumping through too many hoops. But it also needs to be tightly controlled, so only the right people can access data when they need to.
Governance
Governance is like setting the rules of the game – who can use the data, how it should be managed, and what checks are in place. It's important for keeping everything consistent and following regulations.
Quality
High-quality data is data you can trust – complete, consistent, and reliable. Regularly reviewing and cleaning your data keeps it relevant and helps avoid clutter and inaccuracies.
Transparency
You need to know where your data comes from, how it’s being used, and who can see it. This is especially important in industries like healthcare or finance, where data privacy is really important to customers.
Lifecycle management
Data doesn’t stay useful forever. Managing its lifecycle means deciding when it’s time to store, archive, or securely delete it, reducing costs and risks as you do this. Data management technologies are often used to streamline this process.
Compliance
Every organisation has to follow rules for how they handle data, whether it's data privacy (like the General Data Protection Regulation (GDPR)) or industry regulations.
Usability
Even if data is accurate, it has to be easy to understand and work with. Good usability means data is organised in a way that makes sense and helps teams get their work done faster.
Data management frameworks
A data management framework is a structure that helps businesses handle data in a way that’s efficient, secure, and well-organised. It lays out the policies, processes, and tools needed to keep everything in order.
As well as the key principles above, here are some additional elements that make up a strong data management framework:
Data architecture: organises how data is stored and accessed, whether in data warehouses, data lakes, or other systems.
Data integration: connects data from different sources so that the organisation has a unified view of its information, often using a data management system.
Master data management (MDM) overview
MDM ensures that an organisation has a single, reliable source of truth for its most important data. Having a single source of truth helps keep all departments on the same page, which is important when companies need to make key decisions. Without MDM, different teams could end up working with conflicting data assets, leading to mistakes and inefficiencies.
Data management vs other disciplines
It’s easy to mix up data management with other fields that also work with data, but each has its own specific role and focus.
Data management vs. data architecture
Data architecture designs the structure that data flows through – it’s like building the blueprint for how data is organised. Data management looks after the data itself, ensuring it’s handled properly within that structure.
Data management vs. data engineering
Data engineering is about building the data management systems and pipelines that allow data to move and be processed. Data management comes in after, ensuring the data flowing through those systems is accurate, secure, and easy to access.
Data management vs. data science
Data scientists dig deep into data, often using AI and machine learning to help them find insights. Data management makes sure the data is stored, organised, and accessible so that data scientists can work their magic.
Data analytics vs. data management
Data analytics is all about spotting patterns and insights in data to help businesses make smarter decisions. Data management, though, is more about getting the data organised, accurate, and ready to be analysed in the first place.
Data governance vs. data management
Data governance is all about setting the rules – creating policies and procedures to make sure data is used properly and ethically. Data management is more hands-on, focusing on how data is stored, maintained, and used on a day-to-day basis.
Information management vs. data management
Information management looks at the bigger picture – it’s not just about the data itself but also the context that makes it meaningful. Data management plays a key role here, focusing on how raw data is collected, stored, and maintained as part of the overall process.
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Exploring data management tools
Data management tools exist to help organisations streamline their data processes. Some of the most commonly used tools include:
Selecting data management software
Choosing the right data management software to organise, store and process diverse data sets efficiently is really important. Some popular options include database management systems like MySQL or Microsoft SQL Server. There are also cloud-based solutions like Google Cloud or Amazon Web Services (AWS).
Today, many modern data management solutions also integrate AI and machine learning, which offer advanced features like predictive analytics and automated insights, helping users spot trends and make faster decisions to improve their business processes.
When picking your software tools, think about the size of your data, how fast you need access to it, and the level of security you need. For smaller businesses, a simple cloud-based solution might do the trick, while bigger companies may need more advanced tools that can handle big data and use AI for deeper insights.
Exploring data management jobs
From financial services to retail, companies need skilled professionals to handle their ever-growing amounts of data. Whether you're just starting out or looking for a change, working in data management can offer great prospects, including job security, competitive pay, and room for growth.
Is data management a good career?
Data management isn’t just a good career – it’s a booming one. In the UK, the demand for people with specialist data skills far exceeds supply.
A government report highlighted that fewer than 10,000 data specialists graduate each year, while the need is much greater (Data Science Skills in the UK Workforce, 2023). With potentially 178,000 unfilled data specialist roles in the UK alone, there’s a real opportunity here for anyone considering a career in this field.
Career paths in data management and data manager salaries*
Data manager – £39,042:
Makes sure data is collected, stored, and used correctly across the organisation, keeping everything accurate and secure.
Data architect – £34,758:
Designs how data is organised and stored, building the systems that support the business’s needs.
Database administrator – £45,789:
Looks after the databases, making sure data is safe and easy for authorised users to access.
Data governance specialist – £46,601:
Creates and enforces policies to make sure data is used properly and follows legal and industry standards.
Head of data – £72,056:
Leads the overall data strategy, managing the data teams and making sure data aligns with the organisation’s goals.
*Salary information from uk.indeed.com and correct as of October 2024.
What is a data manager?
A data manager is the person in charge of making sure a company’s data is collected, stored, and used properly. They play a key role in keeping the data accurate, secure, and easy to access, which is essential for any business that relies on data.
What does a data manager do?
A data manager develops strategies for managing data, works with teams to implement data policies, and ensures that data complies with regulations. They may face a range of data management challenges, from ensuring data security to making sure that relevant data is always available for business decision-making. They also need to stay on top of evolving data management capabilities to keep systems running smoothly.
Roles and responsibilities of a data manager
A data manager is responsible for overseeing an organisation’s data and ensuring that it's properly handled. Their role typically includes developing data management strategies, ensuring regulatory compliance, and working with other teams to solve data management challenges.
Create and maintain a data management plan.
Ensure data is accurate, secure, and accessible to the right people.
Oversee database deployments, ensuring databases are set up correctly and run smoothly.
Keep data compliant with privacy regulations and industry standards.
Work with teams across the organisation to solve data issues and improve data processes.
Monitor and manage data quality by implementing data cleaning and validation procedures.
Oversee the storage, archiving, and secure disposal of data.
Developing a data management plan
One of the first things a data manager does is create a data management plan. This plan lays out how data will be collected, stored, and shared, and includes the steps for keeping it safe and secure. Having a solid plan is key to staying compliant with regulations and making sure the data stays accurate and reliable.
Data management consulting
If you enjoy working independently, data management consulting might be a great choice for you. Consultants are brought in to help businesses create and put data strategies in place, pick the right tools, and make sure their data is well-managed.
Why consider data management consulting?
Flexibility: work on different projects, in different industries, and set your own schedule.
High demand: as businesses rely more and more on data, they need experts to guide them.
Variety: every company’s data needs are different, so no two projects are the same.
Data management courses and training
As data becomes more important across industries, studying data management can open up a wide range of career opportunities. There are many paths to get into this field, depending on your goals and experience level. You could start with short courses on specific tools or topics like data governance, data security, or big data analytics. For a more comprehensive approach, pursuing a degree, such as an MSc in Data Management, will equip you with advanced skills to manage, organise, and protect data in a professional setting.
Why study a data management degree?
A data management degree teaches you how to handle vast amounts of data effectively, ensuring it’s accurate, secure, and useful for business decisions. With the growing demand for data specialists, especially in industries like finance, healthcare, and technology, this degree can give you the edge in a rapidly expanding field.
Learn data management with LIBF
At LIBF, you can study MSc Data Management 100% online, offering you the flexibility to balance your studies with work or other commitments. Our immersive learning platform gives you access to personalised support, including tutors, study coaches, and an AI study assistant, ensuring you have the help you need whenever you need it.
Our Master's in Data Management covers key topics like data governance, data warehousing, big data technologies, and cyber security.
You’ll also have the opportunity to customise your learning with electives in areas such as Master Data Management (MDM), data mining, data query languages, and AI prompting – preparing you to tackle the challenges of managing data in today’s fast-paced, data-driven world.
MSc Data Management
