The growing complexity of the modern enterprise is reflected in its fragmented data landscape. As data comes from many sources like social media, website activity, corporate finances, and internal records, companies struggle to bring information together for effective Data Analytics.
However, insights acquired from data can help in multiple business and industry functions, right from understanding customers’ feedback about a new product in the market to drug discovery. But before it can be used for creating reports and running analytics that drives effective decision-making, data must first be aggregated throughout the network and normalized in a single location through Data Consolidation.
Table of Contents
- Understanding of Databases
- An idea about Integration
What is Data Consolidation?
Data Consolidation merges data from many systems into a Data Warehouse, which can then be used for Business Analytics to generate strategic and operational insights. The Data Consolidation framework involves data sources, ETL (extract, transform and load) data pipeline, and storage destination. With effective Data Consolidation, users can have the entire required data in a single repository to simplify Data Extraction and insights generation when needed.
The concept of Data Consolidation ensures that all the necessary data is present at a location, which can help enterprises get a holistic overview of their business operations through Data Analysis.
Rather than combing information from many different data touchpoints or databases, Data Consolidation helps organizations simplify access to data, and discover and evaluate trends. The Data Consolidation design also ensures the elimination of redundancies and data errors.
In addition, by making data more accessible, Data Consolidation also decimates the data silos. With the inflation of data volume, the number of storage sites and repositories expands. Accessing and analyzing data becomes more challenging if these storage sites are isolated.
There is also a possibility that the existing data infrastructure may not handle new technological additions or setup changes due to Data Legislation updates. To overcome these challenges, businesses centralize their data in a single system, thereby increasing flexibility and enabling cross-domain collaboration.
Why Data Consolidation is Important?
Data Consolidation helps businesses break information barriers and data silos to make data more accessible. By consolidating data in one integrated place, companies can have better control over their data assets. The ETL process used for Data Consolidation ensures high-speed analytics as the data is already in a pre-processed format.
The availability of high-quality Data Analytics not only assists in improved decision-making but also reduces operational costs. The importance of Data Consolidation can be explained through the following points:
Since organizations eliminate data redundancy with Data Consolidation, they eliminate the need for several databases that holds the same data for different business functions. As a result, this reduces the costs associated with data storage for insights generating.
Apart from optimizing storage cost, Data Consolidation ensures data integrity, which leads to consistent insights across the departments. Reliable data assist in generating uniform insights that help decision-makers avoid making ineffective decisions, which in turn, reduces operational costs.
Controlling the data flow is a strenuous task if you have to individually apply policies. The changing business requirements demand constant modification in the data access control.
Continuously making amendments in data management for different data sources can open up gaps in data security. However, with Data Consolidation, organizations can eliminate flaws in security policy as it requires data management from a single location. Data Consolidation simplifies data administrators’ work for compliance with the data protection laws of different countries.
If data is organized properly, consolidated customer data may be instrumental in developing a Customer Satisfaction Index, which allows a business enterprise to track performance and create KPIs for each department. It not only enhances decision-making within their teams but also improves customer experience by monitoring audience activities and offering a personalized experience.
A fully managed No-code Data Pipeline platform like Hevo helps you integrate and load data from 100+ different sources to a destination of your choice in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line.
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- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the pipelines you set up. You need to edit the properties of the event object received in the transform method as a parameter to carry out the transformation. Hevo also offers drag-and-drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
- Connectors: Hevo supports 100+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources like Google Analytics, that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
What are the Key Techniques of Data Consolidation?
The most often used Data Consolidation approaches are as follows:
ETL stands for “Extract, Transform, and Load”. As the name suggests, the process involves pulling data from several sources, mapping or modifying, and loading information into a Data Warehouse.
There are many ways to perform ETL:
Hand Coding: This manual process requires Data Engineers to build a script to consolidate data from predetermined sources. However, increasing data sources and business functions makes it difficult to manually write code for several Data Pipelines. As a result, Hand Coding is suitable for a smaller pool of data collection with just a couple of sources.
Here are a few key ETL Tools:
- Open-Source Software: It enables companies to integrate and consolidate data at a lower cost with greater flexibility. However, this demands a higher level of coding skill and, in most cases, more labor since organizations have to customize open-source software according to varying needs.
- Cloud-Based Software: It is used to automate multiple Data Consolidation tasks with speed, scalability, and security. Today, several no-code ETL tools can streamline the ETL process while ensuring data quality.
Irrespective of the methods, the extraction, transformation, and loading processes are still a crucial part of every ETL function. Let’s understand them in the following:
- Extract: Here, raw data — structured or unstructured — is transferred or exported to a staging area from different databases or Data Lakes. Several data filtrations through validation are carried out here to ensure data quality.
- Transform: Several Data Transformations, including Data Cleaning, Data Deduplication, Joining, and Encryption, are carried out to make it suitable for Data Analytics. Transformation is mostly a custom process that is processed based on reporting and analytics requirements of organizations.
- Load: In this phase, once the data is transformed, sorted, cleaned, validated, and prepared, it is moved to the destination data warehouse from the staging area.
ETL is a crucial part of Cloud Migration and Data Integration. It gives data administrators the ability to manage organizations’ data flow as a part of privacy-preserving workflows.
Data Virtualization is the methodology of accessing data from many sources without duplicating or moving it, providing users with a single virtual layer that spans different applications, formats, and physical locations.
Here the data stays in the original location but is virtually retrieved through front-end applications. It only saves metadata and integration logic for virtual views. Data Virtualization is leveraged to simplify access to information effortlessly while preserving the privacy of users’ data.
It is important to note that Data Virtualization is not virtualized Data Storage nor a Logical Data Warehouse either.
Data Virtualization not only simplifies data management but also boosts real-time collaboration. It also reduces duplication and Data Migration costs while eliminating errors that occur during those processes. Further, data governance issues are also alleviated due to centralized access control. If needed, data privacy policies are applied to all data from a single location.
Although Data Virtualization supports better data security and real-time reporting systems that allow users to access various internal databases, it limits data enrichment capabilities for in-depth analysis.
This process involves a storage architecture designed to hold data gleaned from multiple sources. Storing data in a central repository enables analysts to carry out ad-hoc inquiries, generate reports, and uncover business-related insights easily.
Since Data Warehouses consolidate data in a single location, it improves the turnaround time for analysis and report generation. Without a Data Warehouse, analysts would be required to spend a lot of time in Data Wrangling, thereby slackening Data Analysis workflows.
Along with providing speed, Data Warehousing allows organizations to scale business processes rapidly while maintaining their throughput performance.
The superior performance of Data Warehousing has also enabled companies to implement Streaming Analytics for making decisions in real time. Finally, the use of a Data Warehouse promotes the uniformity of data throughout an organization.
A Data Warehouse architecture consists of three tiers:
- Top Tier: Front-End client that presents analysis, and reporting results.
- Middle Tier: Contains analytics engine for accessing and analyzing data.
- Bottom Tier: Database server that loads and stores data.
What are the Challenges of Data Consolidation?
Data Consolidation can benefit organizations, but it comes with its fair share of challenges. Data Consolidation can sometimes lead to permanent loss of data if it is carried out without proper measures like backups.
Even with backups, a lack of adequate planning can lead to data loss; while consolidating data, you might purge information that may be useful later. Such loss of data could be challenging to recover or source lost information from different locations again. Consequently, organizations should be critical about Data Purging while consolidating data into warehouses.
While data loss can be a major shortcoming during Data Consolidation, organizations also have to be critical of the information they are pulling from different sources. Critical information shall be kept at the source location to ensure privacy and security. If you are consolidating data that you are not permitted by the law, it can lead to penalties.
With Big Data becoming a new common asset, businesses are struggling to keep up with the multitude of applications and data sources. When data is consolidated, it makes it easy to run business operations smoothly while also enabling enterprises to have a clearer insight into the collected data.
These insights can empower companies to understand their audience, enhance products and services, and identify ways to reduce operational costs. To summarize, Data Consolidation helps business enterprises maximize return on investment from Big Data.
Extracting complex data from a diverse set of data sources can be a challenging task and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications into your Data Warehouse to be visualized in a BI tool.
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