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, 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 | | dataZense
What is Analytics
Data analytics is the process of examining large and varied data sets to uncover hidden patterns, correlations, and other insights that can be used to inform business decisions. It involves the use of statistical and computational methods, as well as data visualization tools, to make sense of complex data.
The need for Data Lakes and Data Connectors
Today big organizations are straddled with multiple operational ERP systems, by way of acquisitions or mergers, or other means. So to get the overall picture of the organizational performance and to predict (guesstimate) future performance, a consolidated data lake has to be built and dataZense with its connectors/adapters available for multiple ERP Systems becomes more relevant. With data traffic coming from multiple systems traffic cops become essential (Governing the data coming into the lake – Analytical MDM feature of dataZense). The choice for the data lake has also expanded (Microsoft Synapse, data bricks, Delta Lake, Cloudera, Amazon, Snowflake, Mongo DB, Open TSDB, Google Big Query, and others). Now pumping of data into these lakes (data ingestion) is to be done through specialized receiving ports. dataZense has ready-to-use Ingestion adapters.
Improving Data Quality in the Lakes
Within the lake, all the data brought in from various sources is kept initially in a Bronze layer. The data from Bronze is cleaned and transformed in line with the needs of the Business and moved into the Silver layer. From Silver, analytics-ready data is formed in typically data cubes in the Gold layer. Data de-duping, standardization, and some enrichment also happen between Silver and Gold.
Visualization
dataZense provides dashboards most commonly required by CEOs, CFOs, COOs, CISOs, Shop Floor Planners, etc. Several algorithms are available in dataZense to analyse the data. The crux is to find the right analysis method for the objective, for which we set out to collect data. Sometimes we might use it to find a best-fit line, curve, or graph to best describe the spread of data collected. Other times we may resort to “Machine Learning” techniques or algorithms to answer futuristic questions or group (classify, cluster) the data. The dashboards and reports can be distributed within the organization.
Access Control
Now access to the dashboards, reports, and in general the Gold Layer has to be restricted based on the role of the Business User and geographical location within the organization of the Business User. The access controls are well managed from within dataZense. It has the capability to integrate the Analytics System with popular Identity Management Systems such as Oracle’s OIM (Oracle Identity Management) and Okta. Identity Management Systems can provision access information to the Analytics System.
The Gold layer data can be scrambled or masked based on the critical and sensitive nature of the column of data.
Unstructured data
When we are done doing something, the next thing is ready for consideration in the fast-paced Information Technology world. People today want to rein in unstructured data (e-mail, PDF, excel, word, social media, etc.). Are you wondering how Excel could be unstructured? Yes, unstructured, if people put multiple tables of data in the same sheet, horizontally and vertically. dataZense crawlers get tabular data from Word, PDF, Excel, and e-mail and get them over to the data lake. OCR technology is put to use, where needed.
Meta data
So far it has been data, data, and data. What about metadata (Definition, meaning, and interpretation details for data)? We lose metadata information when systems get old and concerned employees to retire (loss of tribal knowledge). We lose metadata information when there is a rapid acquisition of a company and its systems and no proper transfer of information happens (or if many of the acquired entity employees are fired).
Data Catalog
Data Cataloging is an effort where an organization goes about meticulously collecting and documenting usable data stores lying in various legacy and other systems and filling in missing metadata. If done manually it is a very tedious and time-consuming process. dataZense offers crawlers to analyze (examine) data in columns and see what their nature is, and document the same in a Catalog. Built-in workflow allows many data experts to collaborate and provide/refine metadata. The Catalog would be a living reference document, which would mean constant updating by the parties responsible for it. The Catalog can be used by business analysts and data scientists to know the availability of data across the organization and use the data in Analytics and Visualization.
The data catalog also profiles intelligently and arrives at possible relationships among the columns considered for metadata enhancements. This allows us to create a first cut Entity Relationship (ER) diagram.
Data Lineage
The Data Lineage function of dataZense allows you to track the origin and movement of data throughout the organization. You can see where your data came from, how it has been transformed, and where it has been used. This is especially important for compliance and regulatory purposes.
dataZense Advantage
- Ready connectors/extract adapters from one of the ERPs such as Oracle E-Business Suite, SAP ECC, SAP S/4HANA, Oracle Cloud Applications, Microsoft Dynamics, JDEdwards, Peoplesoft, and others.
- Pre-defined cubes (financial, manufacturing, HR, and other areas) lead to commonly needed dashboards and reports for company executives to make meaningful business decisions.
- Economical Enterprise Data Management (EDM)
- Build and deploy data lakes in platforms of your choice: Microsoft, Amazon, Oracle, Google, Snowflake, databricks, in-house servers, and others
- Data Quality measures
by | | dataZap, dataZen, dataZense, Smart App Builder, Smart BOTS, Smart Data Platform
Computers have been around for a long time, but the general public still doesn’t fully understand how they work. Because of that confusion, there are often many questions surrounding companies that offer computer products and services, especially when those products and services are amorphous. Read on to learn what a software product company like Chain-Sys is.
Software vs. Hardware
The first (and most important) thing to understand is the difference between software and hardware. It’s in this distinction that a lot of confusion around the nature of software comes from. You may think about hardware stores when you hear this term—in that case, hardware refers to things like nails, screws, and cabinet fixtures.
In this case, however, hardware refers to all the physical components required for a computer. This includes the RAM sticks, CPU, video card, hard drive, and more. On the other hand, software is the programs and processes that tell those pieces of hardware what to do. Your web browser, for instance, is a piece of software on your device.
Software: Product or Service?
With that said, many people aren’t sure whether companies that offer software provide a product or a service. That discrepancy is understandable, as products are traditionally known to be physical goods. We believe the best way to look at it is that software companies provide products and services. The design and upkeep of software is the service, while the software itself is a product (albeit a digital one).
Software Company Services
In addition to developing and maintaining software, many software product companies also offer IT services so that you can ask questions about the software. Some software companies may also provide services or products to assist with cloud data management or other processes—it depends on the company.
Now that you know what a software product company like Chain-Sys is, we hope you better understand what we do. If you have any remaining questions, we’re always happy to talk. Contact our team today!
by | | dataZense
In many ways, data is the lifeblood of a company. Through the use of customer data and internal information, a business can set themselves up for success and stand apart from the competition. However, that makes data a valuable thing that untrustworthy individuals want to gain access to. Read on to learn the top three benefits of utilizing data masking.
What Is Data Masking?
Data masking transforms information from sensitive information into a nonsensical, non-sensitive string of characters. For instance, “John Smith, 707 Main Street” may become “asdw g54Gep v35 54Yb tw90f.” This keeps the information safe even if it falls into the wrong hands.
The Customer Element
One benefit of using data masking is that it proves to your customers that you are trustworthy. Customers want to know that their private information will stay secure, and they’re willing to go out of their way to ensure that happens. If you don’t have credentials on your website that make potential customers feel safe, they will not want to do business with you.
The Compliance Element
You must meet certain legal requirements when working with sensitive information. Data masking and protection are not always something a company can utilize, but in many cases, they are legally required to maintain compliance.
Self-Protection
Data masking isn’t only capable of protecting customer information—it also protects your company’s sensitive data. Bad actors can seek out a business’s information in the same way they may look for customer social security numbers and credit card numbers.
When you want the best data protection for your company, come to ChainSys. We’re here to answer any questions you may have and will set you up with top-notch data masking software.
Now that you know the top three benefits of utilizing data masking, you can keep your information and that of your customers out of the hands of bad actors. Even if they gain access to the information, data masking will ensure they won’t be able to understand or use it successfully.
by | | dataZense
Data science is the backbone of modern business. When you look at any of the most successful companies in the world today, you can see example after example of data science implemented to its fullest extent.
But what if you’re not a multi-billion-dollar company—can you still utilize the data science to get ahead? We’re happy to inform you that you can use data science and change the game. Read on to discover important implementations of data science in business.
What Is Data Science?
Data science is a lot like all other types of science—scientists create datasets to form hypotheses and test them through experimentation. In addition, scientists perform tests to confirm the quality of their data and put it into a cohesive structure for later analysis.
Data science uses more than scientific principles—it also uses math. Algorithms are essential in a data scientist’s toolkit to facilitate proper collection and analysis, especially with large datasets.
The best algorithms can notice patterns that human eyes wouldn’t usually see while also making data gathering and analyzing more efficient.
Data Science vs. Data Analytics
There are a lot of similarities and connections between data science and data analytics. In truth, you shouldn’t have one without the other. To make a long story short, data scientists learn new ways to model and interpret data using algorithms and predictions. On the other hand, analysts view datasets to find trends that help businesses make more calculated decisions.
1. Customer Insights
Customer insights can tell you nearly everything about the people buying your products or services. You can learn the demographics, habits, and even the goals of your patrons so that you can more effectively market to them in the future.
Perhaps your algorithms highlight that a person consistently visits your online store, puts an item in their cart, and leaves it. When your algorithms catch this, you might have your system send out an email to remind that customer about the item in their cart or even to offer a small incentive to come back.
When that tactic proves successful, you’ve learned something about your customer: they wanted the item the whole time, but something held them back from making the initial purchase. With a little more work in customer insights, perhaps you can determine what kept them from pulling the trigger so you can address that inadequacy for the future.
2. Produce Better Goods and Services
You may think your product is as good as it can get, but customer reviews often tell you otherwise. Of course, your data science platform may need to sift through quite a few reviews before it finds genuinely helpful suggestions, but there is no shame in adapting your products to suit your customers’ needs.
The companies that consistently improve their goods in a free market are often the most successful.
3. Effectively Manage Your Business
Data science is about more than customer-facing factors. You can also leverage data science to perfect your internal management structures. Just as your customers are rich with information about their spending habits, your business is rich with information about its processes.
With the right algorithms, your data scientists can sniff out patterns that indicate what is working and what needs work.
4. Predict the Future
While data science can’t actually predict the future, it can paint an accurate picture of the outcomes of certain decisions. Even without data science, businesses have spent years trying to perfect the art of prediction. When will markets crash, and when will they boom? Is the Internet bubble ever-expanding, or will it pop?
With data science, those predictions can rely more heavily on hard data than ever before. While gut feelings and intuition will always be a part of the business world, data science can hone instinct to drastically reduce risk.
5. Assess and Double-Check Your Decisions
Once you make a decision, the process isn’t over. It’s essential to assess your choices and track how the decisions affect your company’s performance. Even when a choice leads to a negative repercussion, it’s not all bad. You’ll gain valuable information, and your data science platform will have more evidence to understand what went wrong.
6. Detect Anomalies
Datasets are imperfect. When dealing with petabytes of information, it’s impossible to ensure that all of it is flawless. However, data science is excellent at scouring datasets to find anomalies and isolate them.
Fraud detection, for example, is a process made far easier through the implementation of data science. When fraudulent activity occurs, it’s essential to address the issue quickly—data science platforms can detect problems within minutes and alert you so you can take the proper steps.
How Data Science Works in Practice
To analyze the benefits of data science in a real-world environment, all you have to do is look at Walmart. While Walmart may have more pieces of data to work with than your company, what they do with the data may well be the same.
For instance, if you have a physical location, especially one with checkout lanes, you may be able to take a page out of Walmart’s book. Walmart uses its data analytics platform to keep track of the times of day when checkout lanes become overcrowded. This allows the platform to suggest staffing more cashiers during certain hours on certain days.
In this way, a simple staffing change can reduce traffic at the front of your store while also providing a better experience for your customers.
Additionally, Walmart uses data to keep track of the purchasing patterns of its store patrons. Perhaps pizza ingredients sell better on Saturdays, while pickles sell best on Wednesdays. This information can ensure enough items are in stock at the correct times so you don’t miss out on sales.
Now that you know these important implementations of data science in business and real-life examples take your company’s data game to the next level with your own data analytics platform. Training your people and getting the most out of the platform will take some time, but the investment is worth it.
