Today, the majority of modern organizations understand the importance of data. For startups, this usually means depending on reports produced within the separate software platforms that they use for day-to-day operations. However, sometimes, they encounter a situation where unifying data in a standard or centralized source is advisable. Maintaining and organizing this data properly requires a process called data management.
What is the Meaning of Data Management?
Data management is an IT practice that concentrates on ingesting, preparing, arranging, processing, saving, maintaining, and protecting data throughout the company. Although complete data management is an initiative for almost all businesses, the majority should have a comprehensive data management strategy to guide their work.
Data management involves various functions that broadly focus on making data accessible, perfect, and available. The majority of the needed tasks are performed by data management teams and IT professionals. Business users usually get involved in the process to ensure that data matches their expectations and to help develop usage policies and internal data standards belonging to data governance programs.
This extensive reference to data management additionally describes what it is and offers insight into its various disciplines, good practices, organizational challenges, and the business-level benefits of a profitable data management strategy.
Why is Data Management Important?
Data is a very valuable corporate asset, which is used to make better strategic decisions, increase marketing strategies, reduce costs, and improve business operations to generate revenue. But an absence of data management can burden businesses with inconsistent data silos, data quality challenges, and incompatible data sets. Those issues result in inaccurate findings or limit their capability to run analytics applications and business intelligence (BI).
Data management strategy is a need for businesses that wish to increase both internal and customer-facing components of business operations.
What are the Main Types of Data Management?
Data management makes crucial functions easier and less time-consuming. The following are the major elements that can make perfect data management techniques:
Businesses can use various types of data management based on their special datasets. While small-scale businesses may employ only a few data management methods, multinational companies may need a deeper range of inclusive techniques to best care for their information.
Data integration: By integrating data from different origins into a single format, departments and teams across the business can get rid of data silos and work collectively.
It is the process of merging data from numerous origins into a comprehensive, up-to-date, and perfect dataset for reporting, analysis, and operational purposes. Particular data techniques, including AP-enabled connections, data replication, and synchronization, allow consistent data exchange and let these data work collectively across departments or platforms within the company. Data integration improves decision-making and allows a holistic view of a business’s performance.
Data preparation: Data preparation is the operation of cleaning, converting, and organizing raw data into a compatible format, which repeatedly involves getting rid of duplicates, removing errors, and changing data types. With fruitful data preparation, businesses can make sure that the data is reliable and error-free.
Data pipelines: The majority of data pipelines are executed via extract, load, transform (ELT) or extract, transform, load (ETL) operations, which ease the shifting of data from origin to target systems. Data pipelines remove data incompatibility, automate data transfer, and allow timely data updates for inspecting and reporting.
Data storage: Data storage solutions, including databases, data lakes, and data warehouses, act as repositories for keeping and arranging data that can be linked to business intelligence for data analysis. These storage solutions allow organizations to recover data clearly, handle data solidly, and conduct modern analytics using data analytics solutions and BI.
Data cleansing: Data cleansing includes examining collected data for errors and irregularities and getting it into the preferred format. For instance, a database with telephone numbers in different formats could consist of identical entries that will distort the outcomes if not treated; data cleansing would refer to reformatting all the phone numbers and removing duplicate entries to match a regular standard to confirm the accuracy and credibility of the data.
Data architecture: Data architecture is a visual illustration of the data flow within an organization. What are all the data origins, where all the information is stored, and which devices and applications process it? In-depth answers to these queries help develop a suitable data strategy and find painful areas that could make it difficult to manage and use the data perfectly.
Data modeling: Data modeling is a pictorial illustration of a company’s data, how it flows through the company, and how it connects. The model defines rules for these links and decides how data goes based on those rules.
Data cataloging: A data catalog includes the classification of organizations’ data and contains major metadata, including access controls, data definitions, usage, and lineage. Data catalogs usually involve extra functions that promote data exploration, encourage customized queries, and improve data use.
Best Practices for Perfect Data Management in Business
The following are the best practices for perfect data management:
Developing a proper data management plan: A proper data management plan summarizes the phases included in the data management procedure and assists in ensuring data is gathered, saved, and inspected perfectly and constantly.
Accomplishing data quality control:
Data quality control involves executing procedures and processes to ensure data is perfect, including consistent data audits and checks for instabilities.
Handling metadata
Metadata management is necessary because you can use it for knowing, aggregating, classifying, and organizing data for use.
Consistent maintenance and data cleaning:
Consistent maintenance and data cleaning are integral elements of data management, as they ensure the accuracy of data.
Simplify data integrations
Data usually resides in different silos within a business, making it difficult to inspect and access. Integration and automation tools can help simplify the process of allowing data to flow perfectly between systems, avoiding the necessity of adding data manually.
How does AI affect data management?
AI for data management supports best practices of data management. You have to keep monitoring data quality to ensure everyone has a good understanding of their responsibilities and develop strong structures like data supply chains. Moreover, AI can increase business productivity and allow businesses to make proper use of data. Let’s discuss further how AI impacts data management.
Data extraction
Traditional tools face difficulties in extracting data from unstructured data sources, including images, text, and PDFs. Earlier, these tools were dependent on templates, where you could fetch data from documents that referred to the same template. However, AI has ignored the necessity of uniformity in templates. AI-enabled data extraction tools make use of natural language processing to know the fields an organization has to extract. For instance, if an organization is interested in fetching customer details from purchase orders or invoices, it will have to indicate the fields, and the tool will fetch them.
Data mapping
Soon after extracting the data, it is mapped from the origin to the target destination. Soon, codeless data mapping tools emerged that let data experts visualize and perform data mapping with a drag-and-drop. Now, AI will take care of the whole data mapping process.
Artificial intelligence has allowed the automatic identification of data origins, relationships, and attributes. ML algorithms inspect current data to find connections, and as a result, reduce effort and time.
Data cataloging
Earlier, businesses faced difficulties in tracking the location of their important data across their systems. Data cataloging, which categorizes and monitors data, has become a protector, but managing these catalogs is not an easy task. With AI, it is easy to automate the search via different data repositories and produce these catalogs with less human effort. It can monitor data lineage, displaying the origin of data, who has touched it, identifying its present location, and how it’s modified.
Data quality
Organizations are good at producing loads of data, but maintaining clean data is a major concern. According to IBM’s reports, the yearly cost of dirty data to U.S. businesses is $3.1 trillion. Even with the best data management software, the challenge is real. But here, AI is the effective solution provider.
AI algorithms are experts at scanning datasets for irregularities and errors and fixing such issues immediately. No need to worry in case you find the missing data. Generative AI for data management can find those gaps and fill them with approximate entries with higher accuracy.
Data analysis
AI plays an incredible role in data analysis. With the progress in AI and GPT, natural language processing has become an integral part of data analysis. This has made AI filter through text-based data from origins like customer reviews, business documents, and social networks. AI can use clustering algorithms to group identical data and make trends transparent.
AI has improved traditional techniques, including regression analysis and decision trees. ML models can create complicated decision trees when dealing with multidimensional and large datasets.
Data management challenges and solutions
- Problem: Poor quality and inaccurate data
With the development in business, the huge quantities of data present fresh problems in maintaining data quality, tracking the complete data cycle, and producing value from data. Additionally, simply collecting and classifying data doesn’t offer any value; you have to be able to process it for useful insights.
One of the reasons for these data management problems is human errors and system glitches due to the huge volume of data being managed.
Solution:
Giving high priority to data relevancy and data simplification methods can help minimize the possibility of errors. Implement cloud-oriented, extendable data storage solutions to meet developing data volumes.
2. Problem: Increasing data silos
Without a properly designed data architecture, the higher volume of unstructured, semi-structured, and structured data spread across various systems or departments within a firm can result in data silos that are not easy to combine. That leads to data repetition, making it complicated to ensure data quality.
Solution:
Encourage a culture of collaboration by highlighting the significance of sharing data across teams and departments. Invest in tools and technology that allow ideal integration of various data sources, creating an effortlessly accessible and united data repository.
3. Problem: Duplicate data
Duplicating data is unavoidable due to various siloed systems, which are usually seen in corporate travel.
Solution: Identify possible variations and trigger suitable action.
Based on your data’s structure, there is a possibility of an order of variations that can be pointed out. The suitable data management platform will be capable of identifying these variations and automatically triggering reformatory actions.
4. Problem: Underused data
Your organization may have data analysis tools, but without a clear and transparent dashboard that responds to the relevant questions and offers suitable insights to the relevant people, the data will be underused.
Solution:
You can find numerous solutions to this problem. The first one is to ensure you have genuine, simple-to-use tools in place. You can identify different tools that offer visual reports to those individuals who will use the data and enable analysis and queries in a simple space.
Apart from simple-to-use reporting tools, you should plan to provide support or training to your data management platform. Participants in the business intelligence process should get platform training and have simple, solid access to support to ask inquiries and help troubleshoot as required.
5. Problem: Invalid data
It is one of the commonly seen problems in data management. Data analyses are better if the data is going into them. Whereas, in the majority of scenarios, a huge portion of this data may be considered manually. This indicates that the data is prone to user error.
Solution:
The best solution to this problem is to follow good data processes. Hence, expectations are roles that have to be neatly defined.
6. Problem: Inexperienced resources
There is a desperate need for data management experts who are available for quick hire. These professionals are normally paid higher because they are crucial in organizations that have to keep up strict data protection management.
Solution:
New technology businesses will think it is costly to train new employees. Companies should keep these employees when they acquire the necessary skills.
What are the Four Major Types of Data Management Systems?
Data integration
Data from different sources and systems is consolidated into a single repository, like a data lake or data warehouse, removing data silos. Particular data techniques, including data synchronization, API-enabled connections, and replication, allow uninterrupted data exchange and let these data function mutually across departments or platforms within a business.
Data preparation
The primary step is to ensure authenticated, clean, and complete data sets. Data preparation contains 6 phases that profile, cleanse, convert, and validate data:
- Data collection:- Collect suitable data from different sources such as data lakes, operational systems, and warehouses.
- Data profiling and discovery: Inspect data quality and find any connections and interrelationships between data points. Data profiling can point out quality issues, including instabilities and missing values.
- Data cleansing: Fix errors pointed out in the earlier step.
- Data transformation: New sets of data might not be suitable for your present schema. Changing data from one structure into another for data warehousing, and the preparation of data for reporting and analysis.
- Data validation: Inspect the data for regularity, authenticity, and completeness.
Data pipelines
The majority of the data pipelines are implemented via ETL (extract, transform, load) or ELT (extract, load, transform) processes, which ease the transfer of data from origin to destination systems.
Data catalogs
They can store and arrange data according to back-end information, which is referred to as metadata. A data catalog ensures crucial information is explorable so you can identify it rapidly.
Benefits of Data Management in Business
Below are some business benefits of data management:
- Increase workforce productivity via self-service data access
- Increase profitability and revenue with valid AI models
- Increase flexibility with a unified view of data across the enterprise
- Supports simplified workflows and increases operational efficiency
- Increase customer experience by linking authentic data to produce customized engagement
- Develop a multi-channel product experience across all platforms
- Supports data exchange with external partners and inside a company to improve teamwork
- Increases strategic planning
How can Data Quality be Improved?
Achieving the best data quality includes a structured technique that encompasses different processes. Below are determined steps that ensure the data is reliable, valid, and matched with the needs and objectives of an organization.
8 data quality steps are explained below:
- Point out data requirements
Pointing out data requirements includes understanding the particular data requirements of your business. This involves identifying what kinds of data are important for your operations, strategic goals, and decision-making.
This step includes:
- Stakeholder collaboration: Be in touch with stakeholders from several departments to collect data on the kinds of needs for their activities.
- Business goals: Meet the goals of your business with the data needs.
- Data sources: Find possible origins of data, both external and internal, that could fulfill your needs. This might involve industry reports, customer databases, and transaction records.
2. Describe data quality metrics
Describing data quality measures means setting up definable criteria that decide whether your data matches expected standards. These measures act as benchmarks for measuring data quality. Consider:
- Reliability metric: Indicate what comprises valid data. For example, in a customer database, reliability could indicate properly spelled names and recent contact information.
- Perfectness metric: Describe the degree of perfectness needed for each data field. For instance, perfect consumer profiles might require email addresses, names, phone numbers, and addresses.
- Stability metric: Establish steady predictions across sets of data. This could contain standard units or date formats of measurement.
- Timeliness metric: Define standards for your recent data. For time-bound data, like real-time updates, stock prices might be important.
3. Data profiling and evaluation
Data profiling and evaluation include a detailed investigation of your data to understand its quality and characteristics. This step involves:
- Data allotment analysis: Understanding the allotment of data values within columns helps find anomalies that could specify errors.
- Data type inspection: Find the data types of every field to ensure valid formatting and the right representation of the data.
- Perfectness check: Inspect your dataset’s missing values. This helps you measure the degree of perfection and categorize data enrichment activities.
- Format identification: Identify formats and occurrences in your data. This can display errors that require correction.
4. Data cleaning and enrichment
5. Implementing data verification
6. Set up data governance
7. Track and evaluate data quality
8. Consistent improvement
Why is Express Analytics a Reliable Data Management Service Provider?
Express Analytics is a data management services provider with a track record of offering excellent data management offerings personalized to your business needs. Our data management consulting services allow you to develop a powerful strategy to match the objectives of your business and extract useful business insights. Being a data management services company, we ensure a clear roadmap to increase the value of your data.
Express Analytics offers crucial data management services, such as data integration and auditing, data analytics, data visualization, data warehousing, and data verification.
What Differentiates Data Management Services from Data Analytics or Business Intelligence Services?
Data analytics, business intelligence services, and data management services each have clear roles within the data lifecycle:
- Data analytics services: They include inspecting data to display trends and meaningful insights. Data analytics converts unstructured data into valuable information using mathematical and statistical techniques, helping with strategy development.
- Business intelligence services: Business intelligence offers reporting mechanisms, platforms, and tools for visualizing data trends. These services connect the gap between decision-making and data analysis by displaying data in accessible formats like reports and dashboards.
- Data management services: They include tasks like data gathering, storage, organizing, maintaining data quality, and security. Data management services develop a genuine data foundation, acting as the platform for the next stages.
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