Modern data management is a multi-faceted discipline that covers data governance, integration, architecture, quality, security and many more crucial topics to unlock the value of data for organizations.
With so many factors to consider, it’s no wonder businesses struggle with modernizing their data processes.
However, it’s important you manage each cog in the machine carefully. With the help of more innovative technologies, such as automation and data models, your business can streamline your data management processes and avoid any data pitfalls.
Why is data management so important?
Successful data management fuels successful businesses. Here are just some benefits of data management:
- Overall security. The peace of mind that sensitive information is protected against internal errors or hacking.
- Team alignment. Knowing your business and technical teams are aligned and follow the same data language is essential for reducing data errors.
- A streamlined data flow. Using the right data tools in order to manage, analyze and share data correctly will reduce operating time and expenses.
- Deeper customer and business insights. The better your data is managed, the better the quality of business insights you’ll have.
- AI and machine learning innovation. Effective data management ensures your business is equipped to handle future developments in technology.
What is modern data management?
To define it simply, modern data management connects data with advancements in technology to identify opportunities and insights. It enables businesses to make faster and better decisions, ultimately helping them to streamline traditional data processes for a competitive advantage.
With the new digital landscape becoming increasingly complex, effective data management is now a key challenge for the modern business.
Modern data management involves creating a holistic, innovative approach to tackling data challenges, such as:
- Master data management (MDM). This uses effective technology to ensure the coordination of higher-level data elements, such as classifications of people, places and things.
- Data governance. As data volume increases, so do your data governance and privacy responsibilities. Effective data governance allows you to remain in control of your IT operations and keep your sensitive data secure.
- Big data management. This embraces tools, such as AI and analytics, to discover insights through very large volumes of data that you cannot detect through singular computing methods.
- Data quality. Data quality metrics reveal the standard and reliability of your business data. Ultimately, the better quality of data you have, the more reliable your insights and processes will be.
- Data warehousing. The electronic storage of your data in a secure, reliable, easy-to -access space, ready for analytics.
- Data security. All data handled must be secure at every level of modern data management. Digital transformation has brought with it a lot more security concerns, such as hacking and accidental deletion.
Each of these practices work together to form your data ecosystem. All of them require stringent data management to ensure you don’t compromise the quality, security or existence of your data.
It seems like a big task, but there’s a simple solution.
In order to solve your unique data management challenges efficiently across your data pipelines, you’ll need to embrace modern technology. Automating processes and using tools that turn data models into runnable code will help to streamline your overall management processes.
What makes it modern?
Advancements in technology, such as cloud computing, big data and machine learning are shifting data management needs. More data coming through from multiple sources - mobile devices, social media, video, sensors, text, operational and transactional systems – and better access to analytics, means an opportunity to do something bigger with your data.
The ‘modern’ data management era involves adopting next generation tools, analytics and storage. This way, you can ask more questions and solve more problems by increasing your organization’s data agility.
Organizations today face a number of new challenges in the form of changes in data regulations, repurposing data to put it good use, and changes in data storage. Businesses need to approach these challenges by adopting the right habits and processes to tie their data management plan together.
These ‘modern’ data management habits include:
- Critical business intelligence. Crucial to remaining competitive. In order to take advantage of the advancements in data, your business needs to adopt a more data-oriented culture to build and equip your team with analytical thinkers.
- Automated integration. As your business grows, data integration becomes a key part of effective data management. It involves combining data from different sources and providing a unified view of that data to IT technicians. However, manual data integration can be both time-consuming and error-prone. So, we recommend an automated solution for businesses working with data at scale.
- Quality control. Ensures the data you’re using to grow your business is fit for purpose. Automatic data quality processes are designed to spot errors and label them, making the correction process simpler.
- A cleaner data architecture. The increasing use (and abuse) of personal data puts data privacy high on the agenda of any modern data management structure. Breaching GDPR and other regulations, such as CCPA and HIPAA comes with big fines and even bigger reputational risks. You can meet your risk and compliance requirements more easily with automated processes, a better data culture and a cleaner architecture.
Unlocking the true value of modern data management is a very powerful tool. Ultimately, with the help of automation and streamlined processes, you can simplify modern data management challenges.
Modern data management for a modern business
Data management plays a critical role in determining your company’s success. It allows your teams to follow a clean, structured framework for collecting, storing and archiving your data. Without this stringent management process in place, you could risk losing your data and failing to meet external data regulations.
In this fast-paced, data-driven world, you should be championing a more modern data management approach. With the volume of data rising and regulations becoming stricter, you need to be proactive rather than reactive.
We hope you’ve found this article useful. If you have any questions surrounding modern data management, please contact us for a chat.