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NetSuite Reporting Tools – Table of Content

Reporting in NetSuite

NetSuite provides various reporting capabilities that retrieve, analyze, and present real-time business results. Users can run pre-built reports, or they can set their preferences and modify them. Users can also build custom reports in the Report Builder tool. Users who have access to reporting can find this function in the NetSuite Reports tab.

It contains a lot of standard reports with pre-defined data and formatting. It also provides ad-hoc reports in which users can select formatting options. They can set their preferences that change the results of the reports. We also have a Report Snapshot portlet option to view a summary of reports on a dashboard for a selected date or period. Once the reports creation is done, we will get options for printing, emailing, and exporting to .csv, .xls, .doc or .pdf files.

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Report Customization

The most standard and ad hoc reports can be customized using the Report Builder. We can modify the reports to meet specific needs. NetSuite offers a special Financial Report Builder to customize financial statements only. The Report Builder follows a What You See Is What You Get (WYSIWYG) format, which means we can view the changes that we made before saving the report.

To create a report, go to the Report Builder and click on ‘Customize’. Click on the ‘More Customization’ option on the new report definition page. We can edit the default columns, filters, and sorting options applied to the report. We can also choose which users are allowed to access the reports by setting permissions. The customization options also depend on the user’s role, permission, and the report that we select.

Managing Reports

Users can manage custom reports by using the Saved Reports page. This is where users can view all the saved reports. We can even edit, view, delete, or change the ownership of reports. We can perform the following functions on the report.

  • Inline editing of reports – We can select the reports that we want to edit, and it lists the selected reports on the same page.
  • Mass updating reports – This is a unique feature offered by NetSuite, where we can change ownership of a few reports at a time.
  • Mass delete reports – We can select all the reports that we want and delete them at once.
  • Saved reports – All the customed and saved reports can be found under the saved reports section. 

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SuiteAnalytics

It delivers embedded analytics for customers to find out hidden information from data. We can monitor company performance across different departments, subsidiaries, and teams. It provides flexibility to create saved searches and reports, which help to make critical decisions and gain meaningful insight.

Using SuiteAnalytics, we can create workbooks that perform complex data analytics tasks. We can create sophisticated criteria filters by exploring data in real-time visualizations. The workbook also has pivot and chart capabilities with drag and drop functionality. A workbook can be deployed into the NetSuite dashboard as portlets. We can then save, share, or reuse the workbook.

NetSuite also has artificial intelligence and machine learning-based capabilities included within SuitAnalytics, which delivers insights and helps customers make faster and smarter decisions. It can also predict what happens in the future based on historical data. It can also automate routine tasks. It also provides data transparency from the summary level to the transaction level.

Business Intelligence

Since NetSuite offers different services through a unified platform, data will be stored across disparate systems. Analysis of this data will be time-consuming and error-prone. NetSuite Business Intelligence provides built-in, real-time dashboards, reporting, and analysis on data integrated across all processes within the Suite platform. It helps is effective decision-making in a reliable and timely manner.

Users can get real-time visibility on issues, trends, and opportunities. We can drill-down to individual transactions. It is very easy to use, as it does not require programming or technical knowledge. The Business Intelligence platform can be accessed via a web browser and mobile device.

Saved Searches

A saved search is a reusable search definition. When we search for something by applying some filters, we get some results. We can save these search options, like advanced search filters and results display with Saved Searches. If the users have ‘Publish Search’ permission, they can share the search results with others. It provides reporting and tracking, which also helps in decision-making.

Users can save the search before defining the search or after the search is run. We can set a list of recipients, so the search results will be sent automatically through email. These emails with search results can be scheduled at a specific time or can send them once the result is updated. Others can go to the search menu and search for these published searches by title.

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Report Snapshots

Displaying all the reports on a dashboard will be cluttered and difficult to understand for users. So, NetSuite has provided an option to save snapshots called report snapshots. It displays a high-level summary of the selected report results. It also contains the links to the underlying reports. When we don’t have space to show the full report, we can use report snapshots. For instance, we can show the top 5 sales items on the dashboard instead of showing all the items. NetSuite provides both prebuilt and custom snapshots.

Key Performance Indicators

KPIs synthesize raw data in your NetSuite and show the results over time. It helps in breaking down the results of data over time. A KPI dashboard component will show results in a tabular format. We can then drill down into each of these results. We can create custom KPIs through saved searches.

A user can only have one KPI dashboard component on their home screen. We can display the KPI search result as an image through the KPI Meter dashboard component. We can add a maximum of three KPI Meter portlets to a dashboard. NetSuite provides more than 75 KPIs, like revenue trends, sales pipeline trends, etc.

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Workforce Analytics

Workforce analytics helps to monitor, measure, and analyze workforce data. A company can analyze its headcount, hiring, and turnover trends. CFOs can identify talent gaps by monitoring payroll data, tenure, and performance management. HRs and management can make use of these reports to access workforce conditions and plan for employee engagement and retention.

It provides two pages called Headcount Analysis and Turnover Analysis, which shows human resource metrics. It also offers drill-down options to get details on analytics. Both employees and HRs can benefit from tracking and reporting on the company’s benefits plans and costs. It can provide historical analytics, predictive analytics, and prescribed analytics. 

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Conclusion

NetSuite has been increasing its analytical ability by making it more and more powerful. We have covered all the tools that NetSuite provides to create reports. You can use standard reports or easily customize reports according to business needs. It allows analyzing real-time data in any dimension. The NetSuite report layouts can be modified to different presentation formats to highlight critical data. NetSuite also standardizes financial reporting in both internationalization and localization. NetSuite also allows interacting with third-party analytical tools like Orbit analytics, Glew, Sisense, and many more.

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4. NetSuite for Dummies

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What Are Selection Techniques

Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. The techniques have the ability to choose the relevant variables according to the type of user’s problem. In case any data comes up that is not relevant to the requirement, it tends to slow down the efficiency process of the model and also decrease the accuracy. Therefore, it is very important to have appropriate feature selection techniques for the models in order to have better outcomes and accuracy. 

The main idea of working with selection techniques is to manually extract the relevant settings from the parent set to have high-accuracy model structures.

Feature Selection in Machine learning

The techniques are divided into the category of supervised and unsupervised learning. These two categories are further divided into 4 main methods for selecting the features.

Filter Method :

There are statistical ways for selecting the features using the filter method. The features are selected in the pre-processing stage as there is no learning process involved in this. The aim of this approach is to filter out the unrequired and irrelevant features by using matrices and ranking methods. The most important advantage of using the filter method is that it does not overfit the data.

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Wrapper Method :

In this method, a user makes different combinations that are evaluated or compared with a lot of other possible combinations. In this way, the feature selection is done. A subset of features is selected and the algorithm is trained based on the subset. The output of the algorithm then decides if the features will be added or not. This method is further based on 4 types which are:

  • Forward Selection : This process takes in an empty feature set. It keeps adding a feature to each interaction and checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Backward Elimination : This approach is the complete opposite of the forward selection approach. The process takes in all the features of the algorithm and then keeps removing a feature one by one on each iteration. It checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Exhaustive Feature Selection : It is the most common approach for feature selection as each feature is set as brute-force. The approach aims to try various combinations of features in order to give the best outcome.
  • Recursive Feature Elimination : This method is based on the greedy approach as its features are selected in a smaller amount. An estimator is made to test every set of features designed and thus we get an outcome of the best features.
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Embedded Method :

This is a great method for feature selection as it has the advantages for both filter and wrapper methods collectively. The processing time in the embedded method is very high just like the filter method, however, they provide more accurate outcomes.

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There are a few techniques involved with embedded methods which are:

  • Regularisation : This aims at regularising the feature selection method simply by adding a penalty if the data gets overfitted in the model. The points shrink to a value of 0 and they are eliminated from the dataset. The types of regularizations are L1, L2, L3, etc. 
  • Random Forest Importance : This technique involves a lot of tree-based approaches to select the features for an algorithm. A number of decision trees are involved in this as the ranking of nodes is performed in all the trees to get the results. After filtering out the irrelevant nodes, a subset of the most relevant nodes creates a final selection of features.
Hybrid Method :

This approach takes in features as small-sized samples. The main idea is to select the features using instance learning. The features that correspond to the instances are selected as they are relevant to the algorithm.

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Feature Selection Models

Supervised Model :

This model is defined as the class of machine learning methodologies where the user can train with the help of continuous and well-labelled data. For instance, the data can be historical data where the user wishes to predict whether a customer will take a loan or not. Supervised algorithms tend to train over the well-structured data after the preprocessing and feature characterization of this labelled data. It is further tested on a completely new data point for the prediction of a loan defaulter. The most popular supervised learning algorithms are the k-nearest neighbour algorithm, linear regression algorithm, logistic regression, decision tree, etc.

This is further divided into 2 categories:

  • Regression: The dealing of output variables is done using regressions as it includes graphs, images, etc. For example to determine age, height, etc. 
  • Classification: it helps in classifying different objects such as yellow, orange, wrong or right, etc.
Unsupervised Model

This model is defined as a class of machine learning methodologies where the tasks are performed using the unlabelled data. Clustering is the most popular use case for unsupervised algorithms. It is defined as the process of grouping similar data points together without manual intervention. The most popular unsupervised learning algorithms are k-means, k-medoids, etc. 

This is further divided into 2 categories:

  • Clustering :This means when the machine requires an inherent group while training the data.
  • Association :This category has a set of rules which helps in the identification of massive data. For example, a list of students who could be interested in artificial intelligence as well as machine learning.
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How To Choose a Feature Selection Model

It is very important for machine learning engineers as well as researchers to understand which feature selection model is most suitable for them. The most data types are known by the engineer, the easier it will be for him to choose properly and wisely. This whole concept is based on 4 main approaches which are:

  • Numerical Input, Numerical Output : There are two methods used in this technique which are Pearson’s correlation coefficient and Spearman’s Rank Coefficient.  The numerals are basically used for the prediction of regression models for continuous numerical such as int, float, etc. 
  • Numerical Input, Categorical Output : There are two methods used in this technique which are the ANOVA correlation coefficient, and Kendall’s rank coefficient. The numerals are basically used for the classification of predictive models for continuous numerical such as int, float, etc. 
  • Categorical Input, Numerical Output : This is a case of the prediction of regression models using input based on categories. The process is the same as numerical input, and categorical output but in a reverse fashion. 
  • Categorical Input, Categorical Output : This is a case of classification of predictive models using both categorical inputs as well as outputs. The main approach affiliated with this method is the Chi-squared method. Moreover, information gain can also be used with this technique.

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Conclusion:

The process of selecting features in machine learning is a vast concept and it involves a lot of research to select the best features. However there is no hard and fast rule for making the selection, it all depends on the type of model and its algorithm and how a machine learning engineer wants to pursue it. Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. 

In this article, we have talked about various feature selection methods that use certain algorithms for making the best possible outcomes and why we should make this feature selection method. Along with this, we have talked about how we can finalise the best feature selection model to work with.

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