Think about the last time you made a major decision without consulting any data. It's probably been a while, even if you didn’t know you were using data. Deciding which restaurant to try using Yelp? Selecting a new show to stream? Calling an Uber or Lyft? Data is at the heart of each of those decisions. People leverage data to make informed decisions every day in both our personal and professional lives. Relying on this data, however, requires we can trust the data, which requires the right approach to data management.
As every business becomes a data business, data management is critical to success in any industry. In this post, we'll discuss what it is, why data management is important, the benefits of data management, data management types, and some of the challenges you may face along the way.
What is data management?
Data management is the process of collecting, storing, organizing, and maintaining data so it can be used for various use cases like business intelligence. It involves creating data structures, including data models, to store data in a way that makes data retrieval efficient, as well as ensuring that data remains organized even when it is updated or added.
Data management also includes data security measures such as authentication and authorization to ensure only authorized users can access data. Additionally, data management involves data quality control measures to ensure data accuracy and integrity. Finally, data management includes data analytics tools to help create data analytics strategies. By utilizing data management processes, organizations can better utilize their data, put it into the hands of every employee with self-service business intelligence, and gain valuable insights into their operations.
Why is data management important?
Data management is essential for data-driven organizations as it ensures users can trust their data, and use it for decision making. Here are the top reasons why data management is important.
More efficient data teams
The right data management strategy creates data accuracy, security, and integrity, helping analytics teams organize and leverage data efficiently.
Granular insight into operations
Proper data management processes can help organizations gain greater insights into their operations by providing a structured way to store, organize, and analyze data. This often means pairing a data management strategy with the right data platform such as a cloud data warehouse.
Enhanced security and compliance
Additionally, data management processes are key in keeping data secure from unauthorized access and data leakage. In doing so, companies can limit their risk exposure while more tightly adhering to compliance and regulatory standards.
Trustworthy self-service analytics
Finally, data management allows companies to deploy self-service analytics tools effectively because they can trust the data that employee base decisions on. In doing so, companies can reap the associated benefits such as improved customer experiences, better products and services, and more efficiency operations.
Top 6 benefits of data management
Data management is an essential part of data science, data analytics, and data engineering. Here are the top 6 benefits of data management:
1. Improved data quality
By effectively managing data, organizations can ensure that data errors are minimized, data duplication is avoided, and data integrity is maintained. This leads to data that is more accurate, reliable, and consistent. Measuring the right data quality metrics can help organizations assess the effectiveness of their data management processes and identify areas for improvement
2. Increased data security
By managing data correctly, organizations can ensure they are compliant with data protection laws such as the GDPR and HIPAA. This improves data security by ensuring data is protected from unauthorized access, data breaches and cyber-attacks.
3. Better decision making
By organizing data into meaningful data sets, data managers can create more robust views that make it possible to identify otherwise missed insights, patterns, and anomalies that help inform decisions. This helps organizations make better business decisions that are backed by data-driven insights.
4. Lower costs
Data management strategies can help reduce costs associated with data storage, data processing and data analysis, and optimize cloud data costs. Organizations can also save time by automating data-related tasks such as data cleaning and data transformation.
5. Improved productivity
By managing data efficiently, organizations can improve the productivity of their employees because they can make decisions on trustworthy, reliable information.
6. Faster access to information
By organizing data into data sets then exposing these datasets to business users with augmented analytics, data managers can quickly and easily drive adoption of analytics throughout their organization. This helps the business respond to changing market dynamics, understand shifting customer sentiment, and capitalize on new opportunities.
Overall, data management is an integral part of data science, data analytics and data engineering that plays an important role in helping organizations become more data-driven. By implementing the right data management strategies, especially as part of adopting the modern data stack, organizations can enjoy the many benefits listed above and maximize the value of their data.
5 challenges of data management
Data management involves a range of challenges that data managers must be aware of and address. By addressing these five common challenges below, data managers can help their entire organization tap into the real value of their data.
Challenge 1: Data governance
Data governance refers to the process of setting data standards, policies, and procedures that allow data to be managed efficiently. Without data governance, data can become disorganized, data errors can occur, data duplication can happen, and data security can be compromised.
Challenge 2: Data quality
Poor data quality is a common problem faced by data managers. Data quality issues can include data errors, data duplication, data inconsistency, and data incompleteness. To address these issues, data managers must create data quality checks that detect and correct data errors.
Challenge 3: Data security
Organizations must ensure their data is secure so they remain compliant with data protection laws such as the GDPR and HIPAA. Data managers must create data security policies, data access controls, and data encryption strategies to protect data from unauthorized access, data breaches, and cyberattacks.
Challenge 4: Data integration
Organizations often have data stored in multiple systems that need to be integrated into a single data set for analysis. To successfully do this, data managers must ensure data is properly formatted, mapped, and transformed.
Challenge 5: Data privacy
Organizations must protect data from unauthorized access to comply with data privacy laws such as the GDPR. To do this, data managers must limit data to specific users, implement data encryption techniques, and delete or anonymize data when it is no longer needed.
Types of data management
These are automated systems that move data from one source to another, typically from a data source to data storage. They can be used to create data flows between applications and data stores, allowing for data transformation and analysis along the way. Data pipelines can be used for data cleansing, data integration, real-time event processing, and more.
ELTs (Extract, Load, Transform)
This is a process where data gets extracted from its original source, then loaded into a staging area where it can be manipulated, and then transformed into the desired format and loaded into its destination such as a database or cloud data warehouse.
ETLs (Extract, Transform, Load)
This is a process that involves extracting data from different sources and transforming it into the desired format for loading into a target system.
This enables data users to search and explore data assets, ideate data solutions, and collaborate on data projects.
This is a system used to store large amounts of data from different sources in an organized way. It enables users to easily access, analyze, and interpret stored data.
This is a centralized repository that allows organizations to store large amounts of structured and unstructured data in its native format. This type of storage offers the flexibility to store all types of data so it can be accessed, analyzed, and managed quickly and easily.
Data lakehouses combine data warehouses and data lakes into one platform. By combining the two systems, data lakehouses enable organizations to quickly access data in its raw format and analyze data in a structured environment.
This involves setting rules, policies, and standards for data use in an organization. It is important to ensure data security and compliance with data privacy regulations
This is the practice of protecting data from unauthorized access, modification, or destruction. It involves data encryption, data anonymization, and other measures to protect data from malicious actors.
This is the process of creating data models to represent data sets and data relationships. This enables data professionals to understand the data structure, allowing them to use data more effectively for analysis and reporting.
Easily manage and analyze your data
Data management is a critical part of making the most out of your data, ensuring data is accurate, up to date, and reliable. While there are many benefits to implementing proper data management best practices, there are also some challenges that must be overcome.
With the right data management approach, however, organizations have the confidence they need to empower every business user to explore this data on their own. ThoughtSpot provides businesses with the tools they need to make it possible for everyone to analyze and act on their data. Sign up for a free trial today to see how we can help you make better decisions with your data.