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Data management encompasses an umbrella of functions that work collaboratively to ensure data accuracy, availability and accessibility within corporate systems.
While much of this work falls to IT teams and data managers, business users are frequently involved in certain parts of the process to ensure data meets their requirements and adheres to corporate policies regarding its usage.
This comprehensive guide on data management explains its definition in-depth, its scope across disciplines and best practices for managing it, obstacles organizations encounter concerning effective strategies, benefits of an effective data strategy vs costs associated with implementation and more.
Also available here is an overview of techniques and tools commonly used for data management and any current trends that have emerged over time.
Data Management Is Important
Data has become an indispensable asset to companies, aiding them to make informed business decisions, improve marketing campaigns, reduce costs and optimize operations, ultimately increasing revenue and profit.
But without proper data management practices, organizations risk facing data silos that don't interact, inconsistent quality data sets that hinder using business intelligence software properly or even lead to incorrect findings altogether.
Companies today face increasing data management responsibilities as more regulations such as GDPR or California Consumer Privacy Act take effect while collecting larger volumes and more diverse datasets two hallmarks of Big Data systems implemented across many organizations today that require competent data management practices to operate effectively.
Such environments can prove extremely challenging without proper oversight from data managers.
Data Management Functions Are Classified Into Different Types
Separate disciplines are involved in data management, from storage and processing, and governance of how the information will be formatted and utilized by operational and analytic systems.
When dealing with large volumes of information, developing a data architecture should usually be the initial step - this provides a roadmap for overseeing the deployment of databases, platforms and technologies tailored specifically for individual applications.
Databases are a primary platform for corporate data storage, providing easy updates, access and management. They're utilized by both systems of transaction processing that produce operational information like sales orders or customer records, as well as warehouses that consolidate multiple sets of business systems' information into one location.
Database administration is an essential data management function. Once databases are constructed, their performance must be constantly evaluated to guarantee acceptable response times for user inquiries that access their contents.
Other administrative duties may include:
- Configuring.
- Installing and upgrading them.
- Performing data backup and recovery.
- Applying security patches or software upgrades.
Database Management Systems are essential tools in the deployment and administration of databases. As software applications, they act as interfaces between database users, DBAs (database administrators), end-users applications requesting access, file systems or cloud object storage; alternative storage methods which offer less formal solutions may offer greater freedom when it comes to type/format of information stored but may be unsuitable solutions for transactions.
Here are other fundamental disciplines involved with data management:
- Data modeling is the process of illustrating relationships between data elements and how they flow through systems.
- Data integration is the process of combining data from multiple sources to be used for analytical and operational purposes.
- Data governance is the process of establishing policies and procedures that ensure consistency in data across an organization.
- Data quality management is a process that aims at fixing data inconsistencies and errors.
- Master data management is a system that creates an integrated data set about customers, products and other things.
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Tools And Techniques For Data Management
Data management can involve a wide variety of tools, technologies and techniques. There are several options for managing data.
Relational Database Management Systems: Relational database management systems (RDBMSs) are among the most prevalent DBMSs; data is organized in rows and columns within these systems, with primary keys used to link related records from multiple tables together without creating duplicate entries.
SQL programming language is commonly employed when building relational databases; due to their rigid model for structured transactional data and ACID properties, they've become the go-to database choice when dealing with transaction processing applications.
As data workloads have changed over time, other forms of database management systems (DBMSs) have emerged to meet them.
Most NoSQL databases exist now as storage solutions - meaning they do not impose rigid data model and schema requirements and thus store semi-structured and unstructured information such as clickstream data from websites like Youtube or sensor logs from network servers and applications.
NoSQL is available in four different types:
- Document databases are data structures that look like documents.
- Key-value databases are key-value pairs that combine unique keys with associated values.
- Wide-column shops with large tables and a lot of columns.
- The graph database connects related data in a graphical format.
NoSQL can often be mistakenly named, although not dependent upon SQL for its data storage needs and supporting some elements that fall within its purview.
But that shouldn't prevent us from understanding its promise; in any event, ACID compliance has always been one of its hallmarks of success, and noSQL offers this as one way forward.
Other database options and DBMSs that may interest analytics users include columnar databases designed for analytics applications; hierarchical databases run on mainframe computers before NoSQL systems were introduced; databases may also be deployed as cloud or on-premise systems, with many vendors also offering cloud-based managed database services that facilitate deployment, configuration, and administration for users.
Big Data Management: NoSQL databases are the go-to choice for big data deployment due to their ability to store various information efficiently.
Open source technologies often used in building big data environments are Hadoop (a distributed processing framework that utilizes filesystem running across clusters), Spark engine, HBase database and Kafka/Flink/Storm stream processing platforms, as well as object storage such as Amazon Simple Storage Service for building these environments.
Data Lakes And Warehouses: Data lakes and warehouses are two repositories commonly used to manage analytics.
Traditional data warehouses use relational databases or columns as repositories of analytics information collected from multiple operational systems before being prepared for analysis by business analysts to examine sales, inventory levels and KPIs. Data lakes also exist as storage options that facilitate analytics efforts; their usage primarily is for enterprise reports, allowing business analysts to look deeper at sales figures than with traditional database reporting platforms alone.
Data warehouses are comprehensive collections of information drawn from all business systems within an organization, making up one of two types of warehousing options - data marts or subset stores are smaller versions that provide subsets of an organization's data tailored specifically for departments or groups of users, while one deployment method uses existing stores as sources to create multiple marts; another approach builds data marts first before populating storages with content.
Data lakes provide large data pools for advanced analytics, predictive modeling and machine learning applications.
Data lakes were originally built using Hadoop clusters; however, S3 and cloud object storage are increasingly being utilized as well as deployment onto NoSQL databases in distributed environments containing multiple platforms containing raw or processed data ingested through feeds from data lakes by analysts who prepare it before entering it into analysis pipelines.
A data lake house is an alternative way to store and process analytical data that combines features from data lakes, data warehouses, and flexible storage solutions into one 'lakehouse' format.
Its name alludes to this hybrid form's combined advantages over its two counterparts - data lakes and warehouses - for analytical data storage needs.
Data Integration: Extract, Transform and Load (ETL), where data are taken from their source system and transformed to fit an industry-standard format before finally being loaded onto an integrated database or another target system, is the most frequently employed data integration technique.
Data integration platforms also support several other integration techniques, including ELT - an alternative ETL technique that leaves the data unchanged when loaded onto its target platform; ELT is often employed when managing big data systems or lakes.
ETL and ELT processes are scheduled batch integrations; real-time integration can be accomplished using change data capture (which applies changes made to databases into an archive or data repository) and streaming data integration - two processes that seamlessly ingest real-time streams - by data management teams.
Another real-time method used by data virtualization provides users with virtual representations of information rather than physically loading it into warehouses.
Data Modeling: Data modelers create conceptual, logical and physical data models to represent data sets in a visual form visually.
They then align these models and workflows with business requirements such as transaction processing or analytics - for instance, by employing entity relationship diagrams (ERDs), mappings or schemas as modeling techniques - frequently updating these models when new sources or information requirements emerge or requirements change requiring updates on models used previously created.
MDM, Data Governance, and Quality: Data Governance is an organizational practice. Software products are designed to assist with this task; however, their usage is optional.
Professional data managers usually oversee these programs with input from an executive council that collaborates to make collective decisions regarding corporate standards and common data definitions.
Data Stewardship is another key element of governance that involves monitoring data sets and ensuring users adhere to applicable policies.
Depending on an organization's size and scope, a data steward may serve in either a part-time or full-time capacity; both business operations departments and IT can have data stewards on staff.
Data quality improvement efforts are closely associated with efforts undertaken for data governance. Successful data governance depends upon high data quality.
Metrics that track improvements to an organization's data are an indispensable way to demonstrate the worthiness of governance programs; here are a few data quality techniques supported by the software:
- Data profiling is the scanning of data to find outliers that may be errors;
- Data cleansing, also called a data scrubber, fixes data errors by modifying or deleting bad data.
- Data validation is the process of checking data for quality against pre-set rules.
MDM (Master Data Management) is closely connected to data governance, quality management and master data administration.
Yet, MDM programs remain relatively rare within companies due to being complex and only appropriate for large enterprises. MDM creates what is known as golden records - central databases of master data that feed into analytical systems for consistent reporting and analytics across an enterprise; also, an MDM hub pushes updates back out if desired.
Data observability, an emerging process in data management, can support quality initiatives and governance efforts by offering an overall picture of how data is performing within an organization.
Adopted from IT systems observability techniques, it monitors data streams in an attempt to spot problems as soon as they arise; tools designed specifically for data observability enable automated monitoring/alerting procedures and root cause analysis as well as planning/prioritizing of problem resolution work.
Read More: SQL and NoSQL solutions to their clients.
They are Both Great but Which One Should You Choose?
Data Management Best Practices
Here are a few best practices to help an organization maintain a successful data management process.
Data Governance And Quality Should Be Top Priorities: A strong data governance program can significantly contribute to effective data management, particularly in organizations with a distributed environment that includes a variety of systems.
Data quality must also be a priority. But in both situations, the IT and data management teams cannot do it all alone. Users and business executives must also be included to ensure that their data requirements are met and problems with data quality are not perpetuated.
Data modeling projects are no different.
Choose Your Data Management Platform Wisely: The variety of data sources and platforms available today requires careful consideration when designing architectures and selecting technologies.
Data managers and IT professionals must ensure that the systems they deploy are suitable for their intended use and provide the analytics and processing information the organization needs.
Ensure You Can Meet The Business And User Requirements Now And In The Future: Data environments don't remain static.
New data sources and data sets are constantly being added. Business needs also change. Data management needs to be flexible enough to meet changing business requirements. Data teams, for example, need to collaborate closely with users to build and update data pipelines to ensure the data is always up to date.
DataOps is a process that combines DevOps with Agile software development, lean manufacturing and DevOps to develop data pipelines and systems. DataOps brings data managers and users together to automate workflows and improve communication.
Risks And Challenges Of Data Management
Data management processes become increasingly challenging with ever-increasing volumes of semi-structured, unstructured, and structured information.
Without an effective data architecture in place, organizations could create systems that are hard to integrate or coordinate efficiently - making it hard to ensure all platforms utilize identical information.
Data scientists and analysts can still have difficulty accessing and finding relevant information quickly and efficiently in well-designed environments.
This is particularly difficult when data is scattered among multiple databases or systems; many data management teams create catalogs to make these resources readily available - these catalogs include data dictionaries with metadata descriptions, glossaries of business terms used within businesses and historical archives of data usage patterns.
Cloud computing can provide data managers with many advantages but presents them with new challenges. Migrating from on-premise databases to cloud databases is often an extensive and cumbersome process for companies; managing services and cloud-based systems must also be closely watched to make sure costs do not surpass budgeted figures.
Data management teams now make up an increasing portion of corporate workforces, being charged with protecting data assets for use and protecting companies against potential legal liabilities should misuse or breaches occur.
Data managers must adhere to industry regulations and government laws regarding privacy, security and use.
GDPR (the General Data Privacy Regulation of Europe), adopted on May 25, 2018, and the California Consumer Protectivity Act, which became law in September 2018, have made privacy even more of an immediate concern.
They take effect starting January 2020. In November, voters also passed the California Privacy Rights Act, which expanded upon and added provisions of CCPA; its effects went into effect on January 1 2030.
Roles And Tasks In Data Management
Data management requires many tasks and abilities; individual workers in smaller organizations with limited resources may take on multiple responsibilities themselves; for larger organizations, however, data managers typically consist of architects, DBAs and database developers working as part of an organized data team.
A more recent trend involves hiring data warehouse analysts; this role helps manage information stored within data stores by creating analytical models that business users use for data manipulation.
Data management teams often consist of scientists, analysts and engineers assisting in building data pipelines and prepping them for analytics.
While such individuals might belong to different analytics or data science teams, they still manage some aspect of the data themselves - this can be especially important when managing raw data contained within lakes that must be prepared before filtering before analysis can occur.
Application developers can help manage and deploy large data platforms. As these require different skill sets than relational databases, organizations may hire additional workers or train traditional DBAs to handle such platforms effectively.
Data governance managers and guardians are professionals trained in data management who typically form part of an independent data governance team.
Good Data Management Has Many Benefits
The benefits of a well-executed Data Management Strategy can be numerous for organizations.
- Improving operational efficiency and making better decisions can give companies a competitive edge over their competitors.
- Well-managed data allows organizations to be more flexible, allowing them to spot trends in the market better and act more rapidly to seize new opportunities.
- Data management can also help businesses avoid security breaches, privacy concerns, and data collection mistakes that can damage their reputation and add unanticipated costs.
- A solid data management approach can ultimately improve the performance of your business by improving business processes and strategies.
History Of Data Management, Its Evolution And Current Trends
IT professionals were key figures in the initial advancement of data management. Their efforts concentrated on finding solutions to early computers' garbage in/garbage out the problem after discovering they reached incorrect conclusions due to inaccurate or inadequate data.
By the 1960s, mainframe databases with hierarchical structures were readily available - further formalizing the data-management process.
In the early 1970s, relational databases first debuted as part of the data-management ecosystem. By the 1980s, they had taken hold as a core component.
Data warehouses first saw use by early adopters during the mid-1990s before ultimately dominating the data management ecosystem in early 2000. By 2009-2011 relational database software had nearly become ubiquitous technology for managing information.
Spark was first released as a processing engine for big data in 2006. Meanwhile, various NoSQL database systems became widely available at that same time; In contrast, relational databases remain popularly utilized data stores for storage needs; other options like NoSQL database systems and data lakes allow organizations to make a broader range of choices when managing data management needs.
More recently, in 2017, data lake houses have further broadened these possibilities.
All these choices, however, have caused greater complexity to evolve in many data environments and are leading to new processes and technologies to simplify managing them.
A data fabric architecture framework designed to unify assets through automation and reuse also belongs here, as does data mesh technology.
Augmented data management aims to streamline processes Vendors are adding enhanced functionality to data quality, database integration, data cataloging, and data management that utilizes AI and machine-learning technologies to automate repetitive work, identify problems, and suggest possible actions.
Edge computing has created new needs for data management. Organizations increasingly use remote sensors, IoT devices and endpoint devices to process and collect data in edge computing environments.
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Conclusion
Data management should never fall on developers' shoulders. They are responsible for creating applications that generate and process the necessary data; as a developer, you should pay close attention to how your applications handle data within wider workflows for effective data handling in organizations.
As well as considering optimal handling practices when considering your applications as data processing units.
