Data Analytics and ChainSys dataZense

Data Analytics and ChainSys 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.


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.

Data Analytics

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
6 Important Implementations of Data Science in Business

6 Important Implementations of Data Science in Business

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.