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
3 Signs Your Company Needs a Data Analytics Solution

3 Signs Your Company Needs a Data Analytics Solution

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!

The 5 Key Types of Data Analytics Every Business Should Know

The 5 Key Types of Data Analytics Every Business Should Know

In this brave new world of big data, it can feel like a challenge to stay on top of all the insights you’re told you need. We’re here to make things a little simpler by breaking down different types of data analytics that your teams will come across. Read on to learn the five key types of data analytics every business should know.


The simplest way to think of descriptive analytics is as statistics. Before you can start deciphering the meaning of your data, you need to collect information. The information you collect will differ based on your goals. For example, if you want to know about sales trends, you might collect sales information from a particular item to learn that it sells far better in summer than in winter.


Diagnostic analytics is all about taking a closer look at your data to find the root causes of things. With diagnostic analytics, you observe data points one by one until you can determine whether that individual piece of data has the effect you’re searching for.


Predictive analytics is self-explanatory—you’re looking for a forecast of things to come. Based on information from the past, what trends will be hot in the coming season or year? Predictive analytics alone can be powerful, but you’ll see the best results when you couple it with prescriptive analytics.


Prescriptive analytics takes predictions and tests out all the options before suggesting the best course of action. Salesforce data management and prescriptive analytics can help you avoid pitfalls while staying on the optimal route to your final destination.


Augmented analytics takes the best aspects of artificial intelligence and machine learning to provide automated data preparation as well as insight discovery. It takes predictive and prescriptive analytics, combines them, and gives you your answers in the snap of your fingers.

Now that you understand these five key types of data analytics every business should know, we hope you have the tools you need to take your business to the next level.

4 Benefits to Utilizing Data Analytics for Your Business

4 Benefits to Utilizing Data Analytics for Your Business

There are so many reasons to invest in data analytics. Whether you’re looking to improve productivity, cost savings, or decision making, data analytics can help. When you want to understand your customer better, choose data analytics. Read on to discover the four benefits of utilizing data analytics for your business.


There are so many day-to-day tasks that could be automated. Let’s consider just one department: human resources. There’s no need for people to manually push buttons to make payroll processing or candidate screening happen. Data processing can be significantly automated, leaving your workforce with more time to spend in other areas. AI doesn’t need to eliminate human jobs, but rather it can be a massive aid to the people doing those jobs.


Intuition is excellent, but intuition paired with statistics leads to more consistent results. Data analytics can go hand-in-hand with your employees to take decision making to a new level. Data is stellar at predicting future trends, so couple your workers’ minds with hard numbers, and you’ll see near-immediate results.

Predictive Modeling

Predictive modeling may feel futuristic, but it’s the same as playing out a scenario in your head. The only difference is the accuracy that data analytics can provide. Financial services can make great use of predictive modeling to quickly assess someone’s creditworthiness or pick out fraud risks. To say that data analytics tools are smart is an understatement!


In the modern era, customers expect a personalized experience. They want emails to address them by name and advertisements to cater to their unique sensibilities. Without data analytics backing your company’s personalization efforts, your customers will be left wanting. With modern analytics, you can easily determine niche interests and appeal to your customers in a way that’s beneficial for both parties. You make the sale, and your customers get the product or service they want!

Now that you know these four benefits to utilizing data analytics for your business, connect with Chain-Sys Corporation to determine which data analytics tools are right for you.