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.
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.
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.
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.
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 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.
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.
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
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!
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.
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.
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.
Data is a simple fact of running a business in this day and age. However, you must understand that data is more than facts. What good are a million customer insights if you can’t convert numbers into actionable information? We have the answers! Read on to discover three signs your company needs a data analytics solution.
Tons of Meaningless Data
A dataset consisting of tens of thousands of data points is potentially very useful—and potentially very useless. A pile of numbers is meaningless unless you have a way to parse the data and glean helpful insights into your customers’ habits.
If you have more numbers than you know what to do with, data analytics can help guide you to understanding. This is the single largest benefit to data analytics—it’s a way to make sense of the proofs and figures arranged in columns before you.
Reports Come Too Slowly
Many businesses rely on up-to-the-minute information to stay agile in a competitive space. When you learn about a new trend that could benefit your company, it makes sense to pounce right away so you can capitalize.
Unfortunately, when that information arrives on your desk too late, there’s not much you can do. Data analytics ensures you have the reports you need when you need them.
Old Spreadsheets Aren’t Cutting It
When you scour through the old spreadsheets in your data catalog, you may find them sufficient. However, where Excel once served you well (when you had far fewer fields to fill), you now find that populating an Excel spreadsheet is time-consuming and far from flawless. Data analytics provides an alternative without the possibility of an invalidated report due to a single incorrect formula.
Now that you know these three signs your company needs a data analytics solution, come up with your own solution and start benefitting from new insights. It won’t take long to see results, so set the wheels in motion and enjoy the results!
When you hear people talk about data backups and archival data, you probably hear those terms used interchangeably. Even in tech-savvy companies, people tend to get backup and archival confused. However, these terms refer to entirely different processes, and it’s important to understand what each process brings to the table. Read on to learn the four major differences between data backup and archival.
What Is a Data Backup?
Essentially, a data backup can be thought of as a copy of your data. Most of the time, data backups are created on a set schedule or whenever your original data is modified. This allows you to constantly have the most up-to-date data in your backup, meaning you lose as little progress as possible in the event of a data disaster.
When a backup is made, you keep the original data and your latest backups—older backups are deleted to make room for the new ones. Plenty of devices can be backed up, from phones and computers to large servers. In addition, you can specify the data you want backed up. Some companies want everything in their backup, like the operating system, application files, and data, while others just want the data.
Many hackers and bad actors try holding data for ransom through a ransomware attack. Essentially, they lock your team out of their computers and threaten to delete your data unless you pay them. Companies without a good backup strategy are vulnerable to this type of attack, as all of their data would disappear if it were deleted from their main machines.
A business with a good backup strategy could feasibly ignore the hackers and allow them to delete the data, then update their security and restore their backups.
On top of that, backups are great for dealing with internal problems as well. If a file becomes corrupted or a large portion is deleted, you can recover an older version of that file and get back to work.
What Is a Data Archive?
A data archive, on the other hand, is a copy of data that you make for reference as well as long-term storage. Once the data is archived, you can either keep the data on the original machine or delete it, as a copy of the data is already safe in your archive.
If you’re not sure why you would want an archival copy of data, consider an advertising agency. A skincare company approaches this agency to develop an ad campaign for them—the agency collects information, puts together a campaign, and runs it. Everyone is happy with the outcome, and the skincare company feels as though they got what they needed out of the exchange.
The ad company puts all the information from the campaign into an archive and deletes it from their main computers to clear up space for their next client. A few years later, when the skincare company wants another campaign, they decide to return to the same agency as they have a good relationship.
Instead of spending weeks or months collecting information again, the ad agency can simply restore their archived information and get right back to work without missing a beat.
A data archive can also be used to store legal documents and other inter-office documents that are not regularly needed or to meet information retention requirements for a business or corporation.
Most of the time, data is archived on a last-used basis. If data has not been accessed within a given period of time, or there is data for a project that is no longer active, the information is archived and stored, just in case.
Hopefully, the definitions for each term have clearly distinguished the major difference between archives and backups. However, there are a few additional differences that are important to understand.
At their very core, data backups and data archives are processes designed to solve entirely different problems. While backups are utilized to keep a close copy of your data for quick recovery, archives are designed to protect your inactive data for long-term storage.
A backup allows you to return to normal operations with minimal downtime in the case of a security breach, and archives seek to retain all your company’s inactive data in a safe, cost-effective system.
Because of the nature of each process, backup and archival access are significantly different. With a backup, you want to have quick access to your data. Whether you need to restore information after a data breach or go back to a non-corrupted file, you want to be able to get that done as quickly as possible.
With an archive, however, you won’t even have to think about it. Naturally, a way to save on costs is with an archive system that is a bit slower than your backup.
Disaster recovery and backups go hand in hand. The whole point of a backup is to allow your company to keep trucking through whatever disasters come your way, so full recovery of your system is made quick and easy.
Alternatively, archived data is a bit different. Depending on your archival system, you may find that the best solution is to purchase an identical archival system as a form of redundancy. While you have several backups from various points in time, your archive is one large pool of information. That means if you lose it, it’s very difficult to recover.
Which Is Right for You?
When considering which data management services to secure for your company, it’s natural to want to find a balance between cost savings and total protection. In practice, though, the truth is that most businesses need both backups and archives. They serve different purposes, and having one without the other will only solve half of your problems. With access to both short- and long-term data, you’re much better prepared for whatever happens.
Now that you know the four major differences between data backup and archival, make sure your company has a healthy dose of each. Without both processes working in tandem, you’re left vulnerable in the case of unauthorized access or a data disaster.