by | | Data Integration, Data Migration, dataZap, dataZen, dataZense
In this Data-driven business world, Data is like gold whether it is in Structured form or Unstructured form. Structured data is information that has a set format and is simple to obtain and comprehend. Unstructured Data is the type of data that does not fit into a predefined or traditional format. Unstructured data includes everything from emails, social media posts, and customer feedback to images, videos, and audio recordings generated by individuals/customers. Almost 80% of businesses believe that between 50% and 90% of their data is unstructured, however, this does not indicate that the data is useless. Unstructured data contains valuable insights that can help organizations make better decisions, improve customer satisfaction, drive innovation, and gain a competitive advantage.
Let’s understand it by taking an example – Social media help organizations to understand the trends, customers’ reviews, and their emotions with a brand, and their satisfaction level while analyzing sensor data can help brands to optimize their business strategies.
If you want to make your unstructured data ready to use, Data Management is the only choice. Managing Unstructured Data is not an easy task because it generates a large volume of data that is difficult to store, manage, and analyze. Security measures are also required to protect the confidential information of individuals. Unstructured data can be of varying quality and may contain errors or inconsistencies. For example, text data may contain spelling errors or typos, while images may be of varying quality or resolution.
Managing unstructured data can be a challenging task, but there are solutions and tools available to help:
Data Extraction can be Aided by Data Mining Tools: Data Mining tools are successful to extract valuable information from Unstructured data and you can use that information later on. These tools are useful to analyze customer feedback, social media posts, and emails to identify patterns and trends. On the basis of customer buying behavior, patterns, and trends, these tools can help you to predict future demands/outcomes. Unstructured data analysis can assist you in focusing on the areas that require improvement and helping to make the appropriate judgments.
Data Storage in the Cloud: Large amounts of unstructured data can be managed by enterprises using a scalable and affordable option called cloud storage. To store and manage unstructured data, there are numerous incredible Cloud storage options available, like Amazon S3, Microsoft Azure Blob Storage, and Google Cloud Storage. Yet, due to scale and security concerns, several businesses also favor storing their data on-site. Ultimately, It relies on the needs of businesses.
Data Visualization Tools: Unstructured data can be difficult to work with, but visualization tools can help simplify complex data by presenting it in a more understandable format. A graphical display of data can captivate the viewer and provide a clear image of insights that can aid in more effective decision-making.
Data Lakes: Data Lakes are cost-effective solutions to store, manage and analyze a large amount of Unstructured Data in its original format. Data lakes enable data to be stored and accessed without having to be transformed into a specific structure or format, making it simple to integrate with existing data.
Text Analytics Tools: Unstructured Data comes in different formats such as images, videos, audio, and text. Text analytics tools are aimed at analyzing textual data such as emails, social media posts, and customer feedback. The primary goal of these tools is to extract useful information from text format. Natural language processing (NLP) is used in these tools to extract insights and trends from unstructured data.
There are various incredible tools with their own USP that you can use to manage Unstructured Data:
MonkeyLearn – MonkeyLearn is a Text Analysis platform with Machine Learning to automate business workflows and save hours of manual data processing.
MongoDB – MongoDB is a next-generation database that helps businesses transform their industries by harnessing the power of data.
Apache Spark – Apache Spark is an open-source unified analytics engine for large-scale data processing. This multi-language engine is for executing data engineering, data science, and machine learning on single-node machines or clusters.
Hadoop – Hadoop is an open-source software framework that facilitates the distributed storage of data across clusters of computers.
Amazon S3 – Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance.
Managed data is easy to access and use, you can find out the right information at the right time and it leads you to deliver better results. Unstructured Data Management tools help you to monitor your customers’ every move and provide real-time insights. You can track your customer’s preferences, understand their needs, and relationships with your brands, and deliver better services to them.
by | | Data Integration, Data Migration, dataZap
Companies currently obtain data from several business source systems, and businesses of all sizes collect and store enormous amounts of data. however, organizing and interpreting this data can be challenging. only if the data is stored in a single repository it would be easy to access the data. To store in a single repository the data must be extracted from different sources, data must be transformed into a unified view and finally, the data is loaded into the database. In this blog, we will understand what ETL is, why it is necessary, the best practices to gain maximum efficiency, its types, and its benefits.
ETL stands for Extract, Transform, and Load. In simple words, the data is extracted from various source systems, transformed, and then loaded into the Data Warehouse system through the ETL process.
Extract:
Data extraction from several sources is the initial stage of the ETL process. These sources can include databases, files, web services, and other data sources. In this step, the data is collected from the source system and transferred to a staging area where it is stored temporarily, The staging area makes it possible to combine data at various times so as not to stress data sources and is very useful when there are issues loading data into the centralized database it gives you the option to go back in time as needed and resumed as needed.
Transform:
The next step in the ETL process is to transform the data into a usable format. This is an important step because different sources of data can have different formats, structures, and data types. The data is cleaned, verified, and formatted into a usable form in this step. The transformation may involve eliminating duplicate data, removing unimportant material, and reformatting data. The accuracy, consistency, and usability of the data are all ensured by this crucial phase.
Load:
The final step in the ETL process is to load the transformed data into a data warehouse. Once the data is loaded into the data warehouse it is made available for reporting, analysis, and other business intelligence purposes.
What Creates the need for ETL?
ETL is significant because it offers a means of transforming unusable data into useful information. Working with raw data can be challenging since it is frequently inconsistent, short, or erroneous. ETL makes data easier to examine and utilize for business intelligence and analytics by converting it into a format that can be used.
Some Best Practices for ETL:
Types of ETL Tools:
Open source ETL:
Open-source tools are typically free to use, and businesses with limited IT resources are attracted to them as they provide greater adaptability and customization because the source code can be changed. An expanded user base and developer base provide constant support in the tool’s development
Cloud-based ETL:
With cloud ETL, both the data sources from which businesses import their data and the target data warehouses are entirely online and enable users to build and monitor automated ETL data pipelines through a single user interface.
Enterprise Software ETL:
Commercial ETL software systems are sold and supported by many software firms. Since they have been around the longest, their adoption and functioning have tend to mature the greatest. All of these solutions have access to most relational databases and come with graphical user interfaces for building and executing ETL pipelines.
Batch processing ETL:
Batch processing prepares and processes data in batch files. Batch processing has usually been applied for less urgent workloads, including monthly or annual reports but modern batch processing, however, can be extremely quick, making data accessible in a matter of hours, minutes, or even a few seconds.
Benefits
In conclusion, the ETL process is essential for businesses that want to make data-driven decisions. It involves extracting data from multiple sources, transforming it into a usable format, and loading it into a central repository. By automating this process with the help of ETL tools, businesses can significantly improve their data management capabilities and gain a competitive advantage in their industry.
by | | Data Integration, Data Migration, dataZap
Organizations are producing more and more data everyday, which has propelled the use of Big Data technologies. In today’s online business realm, data is the crucial factor companies rely on for high-end data analytics and decision-making.
What is Big Data?
According to Oracle – “Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.”
Here are some mind-boggling facts about big data.
- The big data market is expected to grow to $103 billion by 2027. – Statista
- Data quality costs the US economy up to $3.1 trillion yearly. – HBR
- 2% of businesses are investing in big data and AI. – Bloomberg
- 95% of businesses say they need to manage unstructured data. – Forbes
- Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes. – Statista
From the above statistics you can see that companies are willing to spend a tremendous amounts of time and money on Big Data to get valuable insights that can help enhance customer experience. But The quality of data and timely availability of data is crucial for any big data investment to succeed. This is where data integration plays a vital role.
Data Integration
Data integration combines data collected from various platforms to increase its value for your company. It enables your staff to collaborate more effectively and provide more for your clients. You cannot access the data collected in one system in another without data integration.
What are the business benefits of Data Integration?
Here are 4 benefits based on the projects we have worked on with various clients.
Increase the ROI of your CRM
Our client a top construction company in Minneapolis had acquired Procore (A cloud solution for construction project software) which they wanted to integrate with Oracle EBS. The master data was maintained in Oracle and needed to integrate data like Employee (team members), Vendors, Cost Issues, Projects and Commitments (contracts) with Procore to perform Change Events, which was the core module used by the business. There was compatibility issues between Procore and Oracle due to which the team had to manually enter data into both softwares which led to duplications.
With dataZap we were able to solve all the above mentioned problems and saw an increase in data quality of 30% and saved close to $100,000 annually.
Clean data is the backbone of your organization
Clean data is the basis for analytics and the decisions that management take. Ideally companies want their data to be clean but that is not the case. One of our clients faced a similar problem where their master data was not clean. They wanted a solution to first clean their data and ensure the data stays clean.
Enter Chainsys, we first used dataZen our Master Data management tool and then introduced dataZap which comes with prevalidation to ensure only clean data in uploaded into the master data.
Operational excellence and improved competitiveness
Companies will have data in multiple formats and in different places. It also will have large amounts of transactions happening. In many companies there is a time delay in integrating data from various sources. Such problem was faced a client of ours who is a leading lens manufacturer. They have Point-Of-Sale (POS) solutions in 78 locations with 10,000 transactions happening everyday. It was taking them 24 to 48 hrs to to integrate all this data into a single source.
dataZap was implemented as an enterprise-wide integration platform that can ingest and store this business knowledge to enable the integrations. The client saw immediate results where
- Overall processing time reduced by 75% leading to real-time integration.
- Inbuilt business process validations that increased data quality by 20%
- Inbuilt Error Handling and Reprocess the failure records
- Automated Data Integration without manual intervention, which reduced dual entry and errors
Improved decision making
The reason for companies to spend a lot of money on big data and analytics to make the right decisions. But to make the right decisions they need quality data. One can regularly reconcile their master data but new data coming in will reduce the accuracy of any analytics program setup in the organization. One of our clients was doing a complete digital transformation moving from on premise to Oracle cloud. Businesses wanted all their current analytical reporting to continue without any hindrance and invest in a technology that will cater to their future needs.
Chainsys implemented dataZap and took a target for Data quality at 99% clean. Quality processes were instituted to achieve the same. Our data quality engine ensured total profiling and validation of all the data. The profiling process was repeated until data quality reached 99%.
Next step to ensure your big data project is a success
Now you know the reason “why data integration is crucial”. Do you want to learn more about the particular advantages of data integration for your company?
Get in touch
by | | Data Integration, Data Migration, dataZap, Master Data Management
Data is information such as numbers and facts that are used to analyze and contribute to decision-making. It is considered to be a precious asset for organizations today, but it can also be a dangerous asset when it is managed in the wrong way. The way of managing and governing data may lead to a huge success or massive breakdown for the organization. Data is like a child, and its future solely depends on how it is nurtured. Data Governance and Data Management act as parental figures to data. In this blog, we will discuss in detail the difference between data governance and data management, and how dataZen, a part of the smart data platform offered by ChainSys helps to leverage data to its fullest potential.
Understanding Data Governance
Data governance refers to the set of policies, procedures, and standards that guide the management of data assets. It manages the actions and processes people must follow. It also monitors the creation of data dictionaries to make sure everyone has an understanding of the data and ensures that various departments across the organization use the data in a consistent way.
- Policies and Standards: Establishing guidelines for data usage, security, and compliance.
- Data Stewardship: Assigning roles and responsibilities for data oversight.
- Data Quality Management: ensuring the reliability, consistency, and accuracy of data.
- Compliance and Security: Ensuring data practices comply with legal and regulatory requirements.
- Data Catalog: Providing a comprehensive inventory of data assets and their metadata.
Why is Data Governance Necessary?
Many organizations today are expanding quickly, and every day, systems perform a huge number of transactions and generate enormous volumes of new data. There is always a possibility of physically or digitally entering wrong or duplicate data, which can result in a big data failure while decision-making. With the use of dataZen for data governance we can avoid these situations because its goal is to ensure that data is accurate, complete, and secure, and also verify whether it meets the needs of the organization. dataZen takes control of the overall management of data assets within an organization by defining the rules and regulations around data access, usage, and sharing.
Understanding Data Management
Data management refers to the processes and tools that are used to acquire, store, organize, maintain, and analyze data. Data Management ensures that the data is accurate and consistent, and available for use when needed. It also ensures that an organization is using the most updated form of data available.
- Data Integration: Combining data from different sources into a unified view.
- Data Storage: Efficiently storing data in databases, data warehouses, or data lakes.
- Data Security: Protecting data from unauthorized access and breaches.
- Data Archiving: Preserving data for long-term storage and future reference.
- Data Migration: Moving data between systems, applications, or storage environments.
Why is Data Management Necessary?
To develop effective business strategies every organization completely depends on data. An organization’s progress is significantly influenced by relevant, accurate, and usable data. It can become useless if not well managed. But dataZen for data management can guarantee the accuracy, availability, and accessibility of data to be processed and analyzed, therefore helping in making better-educated business decisions and gaining an in-depth understanding of customer behavior, trends, and patterns. To get the most out of the data they have access to, it has become crucial for enterprises to adopt data management. The benefits of dataZen for data management are listed below
The Relationship Between Data Governance and Data Management
To get the most useful business insights from data, data governance, and data management must be used in tandem. Without data governance, data management is like a building without an architectural plan. Data governance, on the other hand, is just paperwork without management.
The difference between data management and data governance is
- Data governance is the overall management of data assets within an organization whereas data management refers to the operational activities involved in managing data.
- Data governance involves defining policies, procedures, and standards for how data is collected, stored, processed, and used while data management includes the processes and tools used to collect, store, process, and analyze data
- Data governance ensures that data is consistent, reliable, and trustworthy while data management ensures that data is available and usable for the people who need it
- Data governance verifies the data used is consistent and used across the organization whereas data management verifies that the data is available in the right format, at the right time, and in the right place.
- Data governance includes data dictionaries and data catalogs whereas data management is more concerned with data storage, processing, and exploration.
- dataZen is a master data management tool that enhances data quality and tightens security within the enterprise. It has over 7000+ master data templates, for over 200+ endpoints.
- Proper “System of Record” for master data, provides a Centralized data hub for consolidated reporting and querying of master data.
- It has preconfigured workflows supporting data governance and approval processes, and does data encryption and masking to keep data safe while at rest and in motion. This creates a single source of truth.
In conclusion, Data governance and data management are two distinct aspects of data management. data governance is focused on defining policies and establishing a framework for managing data, data management is focused on the day-to-day operational activities involved in managing data.
Even though both have different characteristics, both play a vital role in the effective management of organizational data, and they complement each other in ensuring that data is managed effectively throughout its lifecycle. With help of dataZen, you can fix fundamental issues with master data management such as duplicates, fragmentation, and inconsistency across systems, and also establishes master data governance rules to define a common data model, and master / transactional data creation using a workflow which creates a huge impact for a data.
by | | Data Integration, Data Migration, dataZap, dataZen, dataZense, Master Data Management
How Data Management Services Can Help Your Business?
To run an organization successfully, every business needs data. Massive amounts of data of various kinds are being gathered and stored by businesses, but managing and analyzing this data could be difficult. In this case, data management services can be useful and also considered to be a crucial component of corporate operations in the current digital era. We’ll explore more about what data management services are and ChainSys, a data management firm, offers a variety of data management services that help your business in this blog.
What are data management services?
Data management services refer to a collection of procedures and tools for gathering, archiving, organizing, securing, and maintaining data across the course of its lifecycle. It includes a broad variety of operations, such as data integration, data governance, data migration, data quality management, data analytics, data security, and data storage.
Let’s take a closer look at various data management services:
The process of merging data from various sources into a single, unified view is known as data integration. dataZap, Chainsys’s integration platform helps you integrate and transform data from any source. No coding is required and high volume integrations of up to 1 million records can be done in an hour. It also keeps data clean by validating & cleansing during data integration.
Data governance refers to the set of policies, procedures, and standards that guide the management of data assets. It manages the actions and processes people must follow. With dataZen it is an easy-to-build workflow with data governance tools and it monitors the creation of data dictionaries to make sure everyone has an understanding of the data, to identify, it collaborates with various departments across the organization to confirm that the data is used consistently across the organization.
Transferring existing historical data to new storagee, a system, or a file format is known as data migration. While the process may sound quite simple, it requires a change in storage, a database, or an application, but ChainSys approaches complex data migration to various ERP and Enterprise Application needs with simplicity and robustness with its ready-to-use 7000+ Data Adapters for Data Extraction, Data Loading, and Data Mappings from Source to target applications.
Data quality management (DQM), a business strategy, aims to enhance the data quality metrics that are most important to an enterprise company, by bringing together the necessary people, procedures, and technologies. ChainSys’s cloud-based dataZen data quality management platform enables businesses to identify inconsistent and erroneous data across their applications and provides you with data cleansing tools as well as the ability to de-duplicate data.
Data analytics refers to a collection of quantitative and qualitative methods for extracting insights from data. With dataZense a holistic data and analytics platform provides rapid results by following the most efficient processes to ensure governance and quality to bring forth a single source of truth for information at all times, thus driving sustainable decision-making across businesses.
Data security is the process of defending your information against unauthorized access or usage that can expose, delete, or corrupt that information. Using dataZense for Data Security, you can avoid internal and external data breaches and have simple sensitive data management by providing comprehensive data security management, data masking, and data scrambling solutions for many applications.
In what ways the above data management services can benefit your business?
To conclude, data management services can help businesses in many ways, from improving decision-making to reducing costs and gaining a competitive advantage. Whether it’s a small business or a large enterprise struggling to manage data, ChainSys smart data platform would be a great investment as it helps in maximizing the value of data.
by | | Data Integration, Data Migration, dataZap
According to Gartner research, organizations lose approximately $15 million on average per year due to poor data quality. Staying ahead in today’s environment requires access to reliable data at a moment’s notice. Smart businesses invest the time and resources needed for effective data integration, which is an essential part of creating successful analytics programs that drive overall success. Data integration solutions aid in transferring data from the source to the destination, accelerating and streamlining the process.
Finding the right people to complete projects involving data integration can be expensive and challenging. Using many coding languages and frameworks adds another level of complexity. Upskilling resources to get data into and out of source systems becomes more difficult.
Moreover, several businesses manage multiple data marts across various cloud ecosystems. It is difficult to swiftly and simply integrate data across platforms. The solution must integrate all the data, which will make it possible to switch over smoothly to an enterprise-grade platform. Additionally, it gives you access to all the data required for efficient analytics.
Maintenance tasks need to be carried out throughout the data lifetime to keep your data integration current. This entails tracking how your data integration tools are being used, leveraging outside schedulers to conduct your processes, configuring new users, and carrying out upgrades.
All this and more can be done with a data loader. Chainsys’s dataZap is a data migration and integration platform. It requires no code, no DevOps, and no infrastructure. It comes with 9000+ smart data templates and is certified by both Oracle & SAP.
5 ways dataZap can help optimize operations
Listed below are some of the ways that our mass data loader can help your organization reduce maintenance and operational costs.
1. Pay for what you use
You will only have to pay for how much data has been used. You can upload as much data as you need into your data warehouse. But for any integration to be successful you will need to keep track of who is using the data loader and the jobs being executed. dataZap comes with a dashboard that will give you an in-depth analysis of who is using the application and the amount of data that is being processed.
How you benefit – Any project will involve multiple teams and people such as developers, data engineers, analysts, and business teams. With so many people involved you might have to get a subscription for them all. But with the dashboard, you can find out who is effectively using the tool and restrict access to them. This way you can cut down on the costs of subscriptions where the tool is not used.
2. Data validation and reconciliation
Without quality data, any decision that is made cannot be 100% correct. When integrating or updating data, care must be taken to ensure the data is correct and without errors. If done manually human errors are prone to happen. All this can be avoided with the dataZap which comes with prevalidation and data reconciliation.
How you benefit – Any business decisions that the top management makes will have company-wide repercussions. Having clean data uploaded the first time ensures that any analytics or decisions taken will be accurate and make it effective.
3. Visualization and monitoring
To measure the success of any tool you require actionable information. Chainsys’s dataZap has a reporting engine that generates reports on the various adapters’ execution and produces dashboards to understand the actions taken and to be taken. The information in this section will be organized into job name, status, rows processed, start time, and end time. Click on any job to view the work’s attributes, outcomes, and information about the subtask. You will get an error notice if the job fails. On the same page, you may download logs. Users can view the log details without needing additional access to other environments.
How you benefit – In case you have multiple data points that need to be updated in your data mart, you can pause any analytical operations till the data are successfully uploaded. The dashboard will give a notification when all the jobs are completed without the need to individually contact the teams at the site for a status update. Once the data is loaded successfully the analytics program can be initiated automatically.
4. Task scheduling
dataZap data provides the capability to schedule tasks without any external simulators. By scheduling known or repetitive tasks you can free up resources internally and focus on other business priorities.
How you benefit – Following up on our previous example, suppose an analytics operation requires data from various sources and the sources are in different time zones, it is not possible to get the information at the same time. The analytics operation can be scheduled to run when data from all the regions are uploaded successfully and in any time zone. Due to this requirement, the data is regularly loaded and the report is readily available when required.
5. Working in shared environments
Many organizations have shared data and data siloed in multiple environments. Bringing them together into one location is difficult and in many cases costly. dataZap allows working on multiple environments at the same time.
How you benefit – Consider your company is acquiring another company and you want to upload large amounts of data from Salesforce to your system which is SAP. For an analytics need now they also merge multiple data warehouses. Now you can upload all the data into one data warehouse by inviting the other analysts to the dataZap. They can use existing tasks or build their own. This will help reduce costs as you will not need to create new pipelines or go for more third-party integrations.
Get started with dataZap data loader
Are you interested in our data loader into your data integration strategy to help optimize your maintenance and operational costs? Get on a call with our experts for a live demo and how we can specifically help your organization.
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Or you can go through our resources to understand more about dataZap and mass data loader
Mass data loader
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