Data Management: How Businesses Do It?
Modern companies struggle to access and manage the enterprise data present in the systems. Without a comprehensive data management strategy, organizations won’t be able to harness data effectively for data analysis.
Decision-makers can stay ahead of the competition utilizing their data ecosystems, but various research suggests that there are hindrances to this. According to 2018 Global Data Benchmark Report by Experian, US organizations consider 33% of their customer data to be inaccurate. An article by Business Leader states that nearly 85% of businesses operate with between 10–40% bad records. Poor data quality affects employee productivity, data analytics and business intelligence.
Enterprise data management (EDM) is a set of methods and practices focused on data accuracy, quality, security, availability, and good data governance.
Enterprise data of a business is the aggregate of all the digital information contained in all the systems. This comprises structured data in spreadsheets and relational databases, or unstructured data. Different forms of data can be listed as:
- Operational data which are customer orders, transaction records, internal labour statistics, data in billing and accounting systems.
- Network alerts and logs used by cybersecurity teams and application developers to manage IT.
- Strategic data from CRM (customer relationship management) systems, sales reporting, market trend and opportunity analyses.
- Application data such as sensor data for IoT businesses, GPS data for logistics or transportation companies, weather data for news organizations, or content for social media applications.
The Main Components of Enterprise Data Management
Components of data management boost data awareness and a focus on data’s true value more than its volume. Here, we have explained some of the terms related to Enterprise Data Management.
Data Integration is the process of consolidating data from multiple sources into a centralised repository making data accessible and valuable.
Data integration can result in:
- Improved collaboration and unification of systems
- Time savings
- Reduction of errors and rework
- Valuable data used to make business decisions
Master data management (MDM)
MDM is a process to ensure an organization works with the real value of data. MDM makes integrated data accessible for applications and analytics. MDM tools are used to remove duplicates, sum up records for reporting, and data modelling.
Data Governance ensures that people in an organization are entrusted with proper data responsibilities. It includes a set of disciplines that determine the progress of an MDM program.
- Data Governance creates a structure to streamline the flow of information.
- It protects the privacy of users.
- It complies to rules and regulation.
- Data Governance promotes ethical responsibility.
Data quality management
Data Quality Management includes activities on addressing latent problems related to data quality. It comprises of:
- Data cleansing
- Data enrichment
- Data integrity checks
- Quality assurance
Data Stewardship is the processes focused on execution and operationalization. It controls the lifecycle of data, ensuring consistency with the data governance plan. It assures that the data is linked with other data assets, and in line with data quality, compliance, and security. It includes:
- Defining and maintaining data models
- Documenting data
- Cleansing data
- Defining rules and policies
A data warehouse is storage for current and historical data from different sources which may be on-premises or cloud-based. A data warehouse is a fundamental element of data analytics architecture. It serves as a platform for data analysis, business intelligence, and data mining. Popular data warehouses are Snowflake, Google Bigquery and Amazon Redshift.
ETL (Extract, Transform, Load) or ELT are processes used by a data pipeline to replicate data from a source into a destination such as a data warehouse or data lake. These processes move data to a central host that is optimized for data analytics.
What is Your Data Management Strategy?
The flow of data depends on integral data management across ingestion, storage, transformation, reporting, and analytics layers. The business needs of an enterprise highly impact the Data Management strategy. However, as a simple guide, try answering the following questions while designing an EDM:
- How do you collect the data for analysis? Businesses generate extensive data, but selecting the most relevant subset for analytics or business intelligence can be challenging. A modern ETL/ELT tool can transfer rich data and to a data warehouse at a minimal cost.
- How to consolidate different data sources? A data pipeline is a technology of extracting data from various systems and make it analysis-ready. A company should wisely choose a data pipeline according to their needs.
- How to store rich data? A data warehouse is the most common and useful repository for storing raw data.
- How to promote data exploration? A data analyst uses statistical programming, data visualization, or business intelligence tools to derive the true value of the data.
How ETL is An Important Part of The Process?
Businesses with powerful Data Management policies, procedures, and tools stay ahead of competition with accurate, high-quality and secure data. These benefits in accurate and timely data analysis, business intelligence, increased employee productivity and new revenue opportunities due to reliable insights.
An ETL tool is an essential part of the data management ecosystem making the process of moving data from sources to destinations simple, quick and effective. Daton is a no-code ETL tool that would seamlessly extract all the relevant data from popular data sources then consolidate and store it in the data warehouse of your choice for more effective data analysis. The best part is that it is the cheapest data pipeline available in the market.