In the fast-paced realm of modern regulated business, data reigns supreme as the ultimate driver for success. And where there’s business critical data, there must be proper data management.
Enter EDM. And before you start tapping your feet, we’re not talking about Electronic Dance Music. We’re instead talking about Enterprise Data Management; an essential discipline that empowers businesses to harmonise, govern, and leverage their data to make informed business decisions.
Data is pictured by many as something quite abstract – just ones and zeroes floating around in computers, and because data is often used as an umbrella term for all information, it can mean different things to different people.
But data, in the context of what will be discussed in this blog, can be defined as a collection of raw facts, statistics, or information that comes from a number of sources; customer, supplier and internal interactions, sales transactions, market research, website analytics, and more.
The type of data an organisation manages can differ greatly depending on the industry it operates in. In Financial Services for example, the data will likely include records of financial transactions, details of accounts, balances, and information about clients such as personal details and financial history.
Whereas a Government agency may manage economic data, educational data, citizen data like demographics, and legal and regulatory data like legislation or legal records.
What is Data Management?
At its core, data management is the systematic process of collecting, organising, storing, adding context to, and protecting that collection of facts, statistics, and information throughout its entire lifecycle. This comprehensive approach makes sure that data is accurate, accessible, secure, and relevant to an organisation’s needs.
But what is the difference between general data management, and data management at an enterprise level?
What is Enterprise Data Management?
The way we see it, data management on a smaller scale involves the implementation of practices within individual departments or teams to ensure that data is accurate, consistent, and accessible for their specific needs. It focuses on maintaining data quality and ensuring data is available to support the day-to-day operations and decision-making at a smaller scale within the organisation.
On the other hand, data management at an enterprise level takes a more holistic and strategic approach. It involves coordinating and harmonising data management efforts across the entire organisation, transcending individual departments and business units. EDM aims to create a unified and consistent data framework that spans various functions, systems, and processes within the enterprise.
The primary goal of data management at an enterprise level is to ensure that data is aligned with the organisation’s overall objectives, supports cross-functional collaboration, and drives better decision making across the entire business ecosystem.
Why Data Management is Important
Data and data management’s importance cannot be overstated in today’s digital age. Here are a few key reasons why they are so crucial to any organisation:
Data-driven Decision Making
Data forms the bedrock of well-informed decision making. Through data analysis and interpretation, organisations can gain insights into customer behaviour, market trends, operational efficiency, and more. Data-driven decision making empowers businesses and governments to make strategic choices based on real insights and evidence, rather than guess-work.
Improved Efficiency and Productivity
Effective data management streamlines processes and workflows to reduce inefficiencies. With more organised and accessible data, employees can spend less time searching for information, allowing them to focus on the work that really matters in advancing your business goals, and increasing overall productivity.
Risk Mitigation and Compliance
Data management helps mitigate potential risks and vulnerabilities within an organisation. By maintaining data accuracy and security, businesses can comply with regulatory requirements and safeguard sensitive information from breaches.
Companies that leverage data effectively can identify opportunities, optimise processes, and create innovative products or services tailored to specific customer needs, outperforming their competitors.
Planning and Forecasting
By analysing historical data and data trends, organisations can think long-term by anticipating future demand, planning for growth, and making informed investment decisions. A data management platform that can offer visualisations of your data through reports and graphs can really enable less data-savvy users spot trends and pull valuable insights.
Enterprise Data Management Challenges
Enterprise Data Management comes with its fair share of challenges, below we’ve listed some of the most common ones that we encounter through conversations with current and prospective clients:
An organisation’s data is often scattered across various departments and systems. These systems either struggle to, or cannot, communicate with each other without a complex network of connectors and integrations. This leaves data from different departments, teams, and systems left isolated and only functional in its specific domain. The resultant data silos hinder collaboration and prevent a holistic view of the organisation’s information.
Integrating modern data management practices with existing legacy systems can be difficult, as older, less flexible systems cannot easily adapt to new technologies and processes. Plus, they often feed into the creation of data silos (mentioned above), as these outdated systems are difficult to integrate with, and connect to, other systems or repositories.
Data Quality and Integrity
Ensuring data accuracy, consistency, and reliability across different systems and sources can be challenging, and poor data quality can lead to incorrect insights and misjudged decisions. It’s key to develop a robust data governance framework that includes data quality standards, data ownership, and accountability for data accuracy to help overcome this hurdle.
Giving Context to Data
Well-contextualised data that is properly categorised aids in the discovery and reusability of information across various functions, and reduces the time spent searching for what’s relevant. However, providing context can be challenging; data often originates from diverse sources and can come with a lack of standardisation. Plus, the context of data can evolve over time, so it’s important to keep context up to date as business requirements change. Organisations can overcome this challenge by implementing sound data governance and metadata management practices.
Establishing and maintaining effective data governance policies and processes can be quite complex. Defining roles, responsibilities, and access controls while complying with regulations requires careful planning. But we’ll touch on data governance and compliance later on.
Data Culture and Adoption
Encouraging a data-driven culture and getting employees to adopt data management practices can present a cultural shift for some organisations, and it won’t happen overnight. It’s important to put strategic steps in place to promote data management adoption; leadership support, clear communication, and training and education are just a couple of key steps to move in the right direction.
Data Governance and Compliance
Data governance and compliance, in the context of effective data management, go hand-in-hand. They compliment and reinforce each other to ensure responsible and compliant handling of data within an organisation.
Data governance focuses on the overall management, control, and utilisation of data within an organisation. It involves defining data ownership, establishing quality standards, data access controls, data lifecycle management, and creating processes for data handling and other activities. The main goal of data governance is to ensure that data is reliable, accurate, secure, and in a broader sense; aligned with the organisation’s strategic goals.
Compliance, in the context of data, refers to meeting data protection, privacy, security and retention requirements as mandated by the relevant laws, such a GDPR and HIPAA.
Data governance plays a crucial role in achieving compliance by providing the structure, processes, and accountability necessary to meet regulatory and legal obligations.
Data storage methods, such as data lakes and data warehouses, are designed to accommodate the storage, management, and retrieval of different types of data within an organisation. The differences between the two can be subtle, so we’ll dive into the definitions below.
A data warehouse is a centralised repository that stores structured data collected from various sources across the organisation. Data warehouses are optimised for structured data analysis, enabling complex queries, reporting, and business intelligence tasks. They also typically offer high-performance querying, as they are designed for efficient data retrieval and aggregation.
However, data warehouses are often seen as quite rigid, and adapting to changing data structures or adding new data sources can be challenging. Plus, as the business grows (and the amount of data with it), scaling a data warehouse can be complex and costly, especially for handling large volumes of unstructured data.
A data lake often comes with connotations of an unorganised repository with enormous amounts of unstructured data. But actually, a data lake is a storage repository that can hold vast amounts of unstructured, semi-structured, and structured data. It offers a more flexible and scalable approach to storing data, accommodating a wide range of data types and data in all stages of the refinement process.
Data lakes were developed as a response to the limitations of data warehouses, and are often used to consolidate all of an organisation’s data in a single, centralised location, where it can be saved ‘as is’ without the need to impose a ‘schema’.
Future Trends in Data Management
We can certainly expect the future of data management to be shaped by a number of emerging trends and advancements in technology. Data management’s future will be characterised by the adoption of a more data-driven culture, where organisations harness the power of their data to gain a competitive advantage and make more informed decisions to achieve their strategic goals. And, as technologies continue to evolve, data management practices will continue to adapt to maximise the value of data, and navigate the complexities of the ever-expanding data landscape.
Some of the key future trends in data management include:
Big Data and Analytics
With the exponential growth of data, data management will increasingly focus on harnessing the power of big data through advanced analytics and machine learning. Organisations will likely seek more sophisticated data analysis techniques to gain valuable insights and drive further innovation.
Data Privacy and Security
As data breaches continue to be a significant concern, data management will need to place greater emphasis on enhancing data privacy and security measures. Stricter data protection regulations and the adoption of privacy preserving technologies will be critical for safeguarding sensitive information.
Cloud-Based Data Management
Cloud computing will play an increasingly prominent role in data management, providing scalable and cost-effective solutions for data storage, processing, and analytics. We will see even more organisations leverage cloud platforms to manage vast amounts of data and facilitate real-time access and collaboration.
As mentioned earlier on, legacy systems present one of the fundamental challenges towards an organisation’s data management efforts – now is the most ideal time to migrate to cloud based systems and reap the benefits of a more modern, flexible, and scalable solution.
The CARA Platform unites best of breed data management, content services, business process management, and low code configuration on a single platform as a way to completely transform the way your business operates. Find out about the CARA Platform’s Enterprise Data Management capabilities.
Data management is the systematic process of collecting, organising, storing, and protecting data throughout its entire lifecycle, with the goal of making sure that data is accurate, accessible, secure, and relevant to an organisation’s needs.
Data management at a smaller scale involves practices within individual departments to ensure that data is accurate and accessible for their specific needs. The focus on supporting day-to-day operations at a smaller scale within the organisation.
Enterprise Data Management is more holistic, it involves coordinating data management efforts across the entire organisation. EDM aims to create a unified data framework that transcends individual departments.
- Data-driven Decision Making
- Improved Efficiency and Productivity
- Risk Mitigation and Compliance
- Competitive Advantage
- Planning and Forecasting
- And many more.
Some common challenges behind establishing a data management framework include:
- Data Silos
- Legacy Systems
- Data Quality and Integrity
- Data Governance
- Data Culture and Adoption
- Big Data and Analytics
- Data Privacy and Security
- Cloud-based Data Management