Mastering data in the digital age: Best practices for data management in 2023

Unlock the value of your data in 2023 by implementing an effective data management system. Choose the right platform and reap the benefits.

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The quality of your data determines your ability to scale, compete, and thrive in today’s digital ecosystem. That’s why more and more companies plan to invest in data management systems every year. According to MarketsandMarkets, the enterprise data management market is expected to grow from $77.9 billion in 2020 to $122.9 billion by 2025.

With so many disparate data sources to unite, no one can get away with Excel sheets and manual uploads anymore. Automated data management systems are critical to increase data quality, quicken time-to-market across all your workflows, and make more effective decisions. This all leads to increases in your bottom line, happier DataOps teams, better security, and more personalized customer experiences.

Here’s everything you need to know about data management to get started.

What is data management?

Data management encompasses all the practices, architectural techniques, and tools used to manage an organization’s data lifecycle. That’s data management from creation and collection to storage, transformation, and security.

In an enterprise context, data management enables your business to control, protect, deliver, and enhance the value of your datasets. This leads to better decision-making while also improving operational efficiency, aiding in regulatory compliance, and boosting customer experiences. 

Here’s a look at the benefits that lead more businesses to invest in data management systems every year. 

Main benefits of investing in data management

Improved decision-making: With accurate, high-quality, and accessible data, your whole business can make more informed decisions. This applies to strategic decisions (like entering a new market), tactical decisions (like optimizing marketing campaigns), and operational decisions (like managing inventory levels).

Increased workflow efficiency: Effective data management and data automation leads to more efficient processes. Your employees can spend less time searching for and cleaning up data, and more time on tasks that add value to the organization. 

Better compliance and risk management: Good data management practices can help ensure compliance with various data-related regulations (like GDPR or HIPAA). Risks related to data breaches or data loss are also reduced.

Enhanced data security: A robust data management system will include strong security measures to protect against data breaches, theft, or loss.

Improved customer experiences: By effectively managing customer data, businesses can better understand and meet the needs of their customers, leading to improved customer satisfaction and loyalty.

Cost savings: Although there is a cost to implement data management systems and practices, they lead to savings in the long run by reducing errors, improving efficiency, and preventing data breaches.

Better data quality: Effective data management includes processes for ensuring data quality, which leads to more accurate analytics, better decision-making, and improved business processes.

Data management isn’t easy—your ten biggest challenges

Data management comes with a number of challenges, especially as the volume, variety, and velocity of data continue to increase. These include data security issues, maintaining data quality, complying with regulations, and managing the sheer volume of data. 

Integrating data from disparate sources is also extremely complex. On top of that, making sure the right data is accessible to the right people at the right time is a big problem to solve. Here are some of the biggest challenges in enterprise data management. 

  1. Data quality: Ensuring that data is accurate, consistent, complete, and up-to-date sometimes feels impossible. This is especially true when data is coming from multiple sources. Poor data quality bleeds money, leads to incorrect business decisions, and results in the loss of trust in data. 
  1. Data integration and transformation: Combining data from various sources into a unified view is complex and time-consuming. Data might come in many different formats and schemas, moving across a variety of different systems and locations.
  1. Data security: Protecting data from breaches, loss, and unauthorized access is a constant challenge. As cyber threats continue to evolve (AI has some business downsides), maintaining data security requires reliable systems and DataOps practices.
  1. Data privacy: Complying with data privacy regulations—like the General Data Protection Regulation (GDPR) in the EU or the California Consumer Privacy Act (CCPA) in the US—can be messy and expensive.
  1. Data volume: The sheer volume of data that you’re responsible for continues to rise exponentially. Storing and processing large amounts of data requires significant resources and deep infrastructure.
  1. Data velocity: The speed at which data is created, processed, and changed (its “velocity”) keeps accelerating, too. Real-time or near-real-time data processing requires robust systems.
  1. Data variety: Managing the variety of data—from structured data like database entries to unstructured data like text or images—requires diverse methods and constant attention.
  1. Data governance: Gaining buy-in for data governance initiatives, or dealing with issues of data ownership and responsibility, are some of the hardest parts of managing your data.
  1. Data silos: These isolated pockets of data prevent a unified view of data and can lead to inefficiencies and inconsistencies.
  1. Skills gap: Finally, there can often be a skills gap in organizations when it comes to data management. The need for professionals with expertise in areas like data science, data engineering, data governance, and data security is growing faster than the supply, leading to a talent shortage in many areas.

On the bright side, you can overcome these challenges without wasting resources. Take the time to understand all the layers of data management before you choose a platform to handle your data operations. 

Common components of enterprise data management

Each of these types of data management helps your company manage data effectively to make data-driven decisions, reduce risk, save money, and gain a competitive advantage.

Database management systems (DBMS) are software applications used for creating, retrieving, updating, and managing data in a database.

Data warehousing is the consolidation of data from various sources into one comprehensive database. Data warehouses are crucial for data analysis and reporting.

Data integration is the combination of technical and business processes to combine data from disparate sources (including legacy data centers) into meaningful, valuable information.

Data governance encompasses the overall management of a company’s data availability, usability, integrity, and security.

Cloud data management combines data integration, data quality, data replication, data virtualization, and data masking, all optimized for cloud storage environments.

Master data management (MDM) is the process of creating a single, consistent view of all the master data entities in an organization.

Big data management entails managing large volumes of structured and unstructured data, often from diverse sources.

Data quality management is the process of maintaining high-quality data through the ongoing process of checking, correcting, and improving data quality.

Metadata management deals with data about other data, simplifying complex data management processes.

Fully managed cloud data services are platforms that handle the automation of data management off-premise.

Data quality includes keeping data accurate, consistent, and up-to-date. This includes dealing with issues like duplicate data, missing data, and data errors.

Data security includes protecting data from unauthorized access or breaches. This concerns everything from access controls to encryption to security audits at your data center.

Data analytics and business intelligence tools include everything from simple reports to advanced predictive analytics.

Data architecture and storage determine how your data is stored and accessed. It includes the use of databases, data warehouses, data lakes, and other storage technologies.

Data management process overview 

There’s a long list of tasks that turns the data management process into high-quality data for your enterprise. While complex and different in practice at every company, there are common steps in every data management process. 

  1. Data collection and pipelines: At this initial stage, organizations gather data from various sources like internal systems, external databases, IoT devices, social media, and more. The specific data gathered depends on the organization’s unique needs and objectives.
  1. Data transformation: After collecting data, organizations process it to make it usable. This step involves cleansing the data to remove errors, inconsistencies, and duplicates, transforming the data into a format suitable for further analysis or storage, and integrating data from different sources into a unified view.
  1. Data storage and organization: Once processed, organizations store and organize data for future use, typically using a database management system (DBMS) that allows for structured data storage and easy retrieval. Depending on the organization’s specific needs, data may reside in databases, data warehouses, or data lakes.
  1. Data security and privacy: At this stage, organizations implement measures to ensure the security and privacy of their data, protecting it from unauthorized access, data breaches, and loss. They also ensure compliance with data privacy laws and regulations
  1. Data governance: Here, organizations establish the rules, policies, and procedures that guide data management. This includes defining data access rights, classifying and labeling data, and maintaining data quality.
  1. Data analysis: With data collected, processed, stored, and governed, organizations can now analyze it to generate insights. Analysis might range from simple queries and reports to complex predictive analytics and machine learning models aimed at informing decision-making and driving outcomes.
  1. Data archiving and disposal: Over time, organizations identify outdated or now-useless data and archive or dispose of it in a secure and compliant manner. This step ensures efficient use of data storage resources and reduces the security risks posed by unnecessary data.
  1. Continuous monitoring, observability, and improvement: Data management is an ongoing cycle, not a one-time event. So, organizations continuously monitor and improve their data management processes to identify and rectify issues, optimize performance, and adapt to changing needs and circumstances.

By accounting for all these steps, you can make sure your data is reliable, secure, and valuable. But it’s not a one-and-done task. Effective data management is an ongoing process that needs continual observation and improvement. 

It’s also a cross-functional effort that involves collaboration between different parts of the organization—IT, business units, data governance teams, and executive leadership, for example.

The future of data management

As we look to the future, data management is set to become more and more critical. Your company will need to extract actionable insights from your data sets, often in real time. One big trend is moving towards “right data” analytics. This focuses on obtaining and analyzing the most relevant data, rather than simply amassing large quantities of data. 

Here are a few of the big future considerations you need to plan for:

  1. “Right Data” analytics: As artificial intelligence (AI) and machine learning (ML) technologies continue to progress, the focus is shifting from “big data” to “right data” analytics. This means emphasizing the most relevant and actionable data, rather than just focusing on large volumes of data​.
  1. Digital experiences and data management: Digitally mature businesses are expected to stand out through their effective data management and data governance strategies. Investment in tools that improve the customer experience and single dashboards that enable data discovery, metadata management, and access to all data will be high on enterprise wish lists. There will also be a trend toward deploying new digital workloads on cloud-native platforms, and the use of a mix of cloud providers’ best-fit capabilities will likely increase​.
  1. Data governance and automation: There is a growing demand for automation in data governance processes, with the migration of data to the cloud necessitating sophisticated data governance strategies. Automated master data management (MDM) tools are expected to become a critical enterprise function. The rising importance of compliance is also pushing organizations to embrace these automated MDM tools. Additionally, the adoption of remote or hybrid working policies is expected to lead to increased traction for self-service (automated) data governance among employees​.
  1. Increased pressure for advanced data management: Enterprises will face increasing pressure to adopt advanced data management strategies. That’s because you need to extract actionable insights from the large volumes of data involved in everyday business operations. This market pressure will force enterprises to make effective data management a top priority.

Overall, the better systems you have in place for data management, the better prepared you are for big sea changes in the digital landscape. Just take generative AI for example. The world of data management has changed forever and completely depends on your data quality. With the right enterprise data management system, you can pivot now and scale later. 

Scalable enterprise data management systems


Snowflake is a cloud-based data warehousing platform that separates compute and storage resources, enabling scalability and performance improvements. It supports structured and semi-structured data and enables simultaneous read and write access, making it suitable for both data warehousing and data lakes.

Amazon Redshift

Amazon Redshift is a fully managed, petabyte-scale data warehouse service by AWS. It’s designed for online analytical processing (OLAP) and business intelligence (BI) applications. It also integrates well with various data loading, reporting, and analytics tools.

Google BigQuery

BigQuery is a web service from Google that’s used for handling and analyzing big data. It’s part of the Google Cloud Platform and allows super-fast SQL queries against append-only tables using the processing power of Google’s infrastructure.

Microsoft Azure SQL Database

Azure SQL Database is a fully managed relational database service provided by Microsoft Azure. It offers the broadest SQL Server engine compatibility and automates updates, provisioning, and backups to reduce management overhead.


MongoDB is a source-available, cross-platform, document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas, making it suitable for managing semi-structured and unstructured data.


Databricks is an analytics platform based on the Apache Spark analytics engine. It provides a unified platform for data engineering, data science, machine learning, and analytics that can be deployed across various cloud platforms.

Apache Hadoop

Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It’s designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Oracle Database

Oracle Database is a multi-model database management system produced and marketed by Oracle Corporation. It supports various database workloads, including online transaction processing, data warehousing, and mixed database workloads.

ActiveBatch by Redwood

ActiveBatch provides DataOps teams and IT professionals with an integrated, cross-platform toolset for automating complex workflows. ActiveBatch supports various types of workloads, from batch processes and microservices to big data and ETL workflows, making it a versatile tool for diverse data management needs.

Seven steps to choose and implement a data management system

Ultimately, the data management system that works for you depends on your unique needs and goals. Choosing the right system is a journey in itself—from research to vendor evaluation. 

To get started, read through this short list to save some time finding the right data management system for you.

  1. Define your goals and requirements. Outline your needs and what you’re hoping to achieve with a data management system.
  1. Research and evaluate options. Investigate different data management systems, keeping in mind your data needs and business goals.
  1. Plan your implementation. Determine how you’ll deploy the data management system and integrate it with your existing infrastructure.
  1. Test your data management system. Test the system with a small amount of data to identify and resolve any issues early in the process.
  1. Train your team. Ensure everyone who will be using the system is adequately trained.
  1. Roll out your data management system. Implement the system gradually across your organization, starting with one team or department and expanding to others.
  1. Monitor and improve. Continually evaluate the data management system’s performance and look for ways to enhance it.

Start your data management journey today

Want to connect and orchestrate data across your entire tech stack? You can build and automate all your data management workflows in half the time without messy scripts. ActiveBatch by Redwood gives you end-to-end workflows that span your entire IT environment.

Our data management system with no-code connectors and low-code REST API adapter connects everything from your customer data platform (CDP) to your marketing automation tools. Schedule a quick demo here.