Describing Enterprise Artificial Intelligence: Platform as a Service With Current AI Infrastructure

If I will talk about the Enterprise AI, then it is hard to think of an application that doesn’t use a database. If you see from mobile to web to the desktop, every modern application relies on some of a database. Some apps use flat files while others rely on memory or NoSQL database.

If I will talk about the traditional enterprise applications, then they interact with large database clusters running Microsoft SQL, Oracle etc. The fact is that every application needs it.

Like databases, Artificial Intelligence (AI) is moving towards becoming a core component of modern applications. In the coming months, almost every application that we use will depend on some form of AI.

Enterprise AI simulates the cognitive functions of the human mind — learning, reasoning, perception, planning, problem solving and self-correction — with computer systems. Enterprise AI is part of a range of business applications, such as expert systems, speech recognition and image recognition.

Figure 1

1. Start Consuming Artificial Intelligence APIs

This approach is the least disruptive way of getting started with AI. Many existing applications can turn intelligent through the integration with language understanding, image pattern recognition, text to speech, speech to text, natural language processing, and video search API.

Let’s look at a concrete example of analysing the customer sentiment in a customer product requirement demo session. Almost all the Customer calls to the service team are recorded for random sampling.

A supervisor routinely listens to the calls to assess the quality and the overall satisfaction level of customers. But this analysis is done only on a small subset of all the calls received by the customers to the service team. This use case is an excellent candidate for AI APIs. Each recorded call can be first converted into text, which is then sent to a sentiment analysis API, which will ultimately return a score that directly represents the customer satisfaction level.

The best thing is that the process only takes a few seconds for analysing each call, which means that the supervisor now has visibility into the quality of all the calls in near real-time. This approach enables the company to quickly escalate incidents to tackle unhappy customers and rude customer service agents.From CRM to finance to manufacturing domains, customers will tremendously benefit from the integration of AI. There are multiple AI platforms and API providers like (With link):

• Amazon AI Services (https://docs.aws.amazon.com/aws-technical-content/latest/aws-overview/artificial-intelligence-services.html)
• Google Cloud ML Services (https://cloud.google.com/ml-engine/reference/)
• IBM Watson Services (https://console.bluemix.net/catalog/?category=ai)
• Microsoft Cognitive Services (https://docs.microsoft.com/en-us/azure/cognitive-services/)
• Clarifai (https://clarifai.com/developer/guide/)

2. Build and Deploy custom AI models in the Cloud

While consuming APIs is a great start for AI, it is often limiting for enterprises.

We have seen the benefits of Integrating Artificial Intelligence with applications, customers will be ready to take it to the next level.

This step includes acquiring data from a variety of existing sources and implementing a custom machine learning model. It requires creating data processing pipelines, identifying the right algorithms, training and testing machine learning models and finally deploying them in production.

Similar to Platform as a Service that takes the code and scales it in the production environment, Machine learning as a service offerings take the data and expose the final model as an API endpoint. The benefit of this deployment pattern lies in making use of the cloud infrastructure for training and testing the models. Customers will be able to spin up infrastructure powered by advanced hardware configuration based on GPUs and FPGAs.

Platforms that offers Machine Learning as a Service:

• Amazon ML (https://docs.aws.amazon.com/machine-learning/index.html#lang/en_us)
• Azure ML Studio (https://docs.microsoft.com/en-us/azure/machine-learning/)
• Google Cloud ML Engine (https://cloud.google.com/ml-engine/docs/)
• Bonsai AppNexus (https://wiki.appnexus.com/display/api/Welcome)
• BigML (https://bigml.com/api)

3. Run Open Source AI Platforms On-Premises

The final step in AI-enabling applications is to invest in the infrastructure and teams required to generate and run the models locally. This is for enterprise applications with a high degree of customization and for those customers who need to comply with policies related to data confidentiality.

If ML as a Service (MLaaS) is similar to PaaS, and running AI infrastructure locally then it is comparable to a Private Cloud. Customers need to invest in modern hardware based on SSDs and GPUs designed for parallel processing of data. They also need expert data scientists who can build highly customized models based on open source frameworks. The biggest advantage of this approach is that everything runs in-house. From data acquisition to real-time analytics, the entire pipeline stays close to the applications. But the flipside is in the OPEX and the need for experienced data scientists.

Customers implementing the AI infrastructure use one of the below open source platforms for Machine Learning and Deep Learning:

• MXNet (https://mxnet.incubator.apache.org/api/python/index.html)
• Microsoft Cognitive Toolkit (https://docs.microsoft.com/en-us/cognitive-toolkit/)
• Tensorflow (https://www.tensorflow.org/api_docs/)
• Theano (http://deeplearning.net/software/theano/library/index.html)

If you want to get started with AI, explore the APIs first before moving to the next step. For developers, the hosted MLaaS offerings may be a good start.Artificial Intelligence is evolving to become a core building block of contemporary applications. AI is all set to become as common as databases. It’s time for organizations to create the roadmap for building intelligent applications.

AI Data evolutions like Data Processing and Neural Networks.

Now in present time we are feeding loads of data to the computer, so the computer will learn about Deep learning technologies and the reason for behind this to take AI Initiative.

Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem. Neural networks learn by example to solve complex signal processing and pattern recognition problems, including speech-to-text transcription, handwriting recognition and facial recognition.

Data processing is, generally, “the collection and manipulation of items of data to produce meaningful information.” In this sense it can be considered a subset of information processing, “the change (processing) of information in any manner detectable by an observer.”

AI data processing is the need for high-quality data. While data quality has always been important, it’s arguably more vital than ever with AI initiatives.

In 2017, research firm Gartner described a few of the myths related to AI. One is that AI is a single entity that companies can buy. In reality, it’s a collection of technologies used in applications, systems and solutions to add specific functional capabilities, and it requires a robust AI infrastructure.

Another myth is that every organization needs an AI strategy or a chief AI officer. The fact is, although enterprise AI technologies will become pervasive and increase in capabilities in the near future, companies should focus on business results that these technologies can enhance.

Conclusion

In, the Conclusion I would say everyone has to use the Artificial Intelligence Applications and as we found that many existing applications can turn intelligent through the integration with language understanding, image pattern recognition, text to speech, speech to text, NLP and video search API.

As we have seen earlier a supervisor can do the Random Sampling at the time of Customer Product Requirement demo session. So, what supervisor wants to listen in the last about the customer satisfaction and how better we can use the AI APIs to convert the call into text for the sentiment analysis. As, I have mentioned above in this use case we can definitely find out the score directly what is the satisfaction level of customer.

So, with the help of AI APIs we can easily understand the customer problems and give a better product to them for their use.

As, I have mentioned above for the same that we have to build and deploy the custom AI models in the cloud and why it is more useful and beneficial for the customers.

So, if I will define why is it more useful and beneficial as we have seen above the AI APIs will take to the customer to the next level. AI APIs will acquires data from variety of sources and help in to create data processing pipelines and identifying the right algorithm and deploy in production environment.

So, in the last I would say use the AI APIs applications to reduce the pain of customer and help them to reduce the complexity and make the process more effective with the help of AI APIs.

Odata and REST APIs - A Comparison

Are you curious about what is REST and OData? Then, this article is for you. In it, you will find an explanation of what is REST, how it is applied to the construction of RESTful APIs; what is OData and how it is used to build web services, and the differences between the two.

Brief comparison

The table below summarizes the main similarities and differences between REST APIs and OData Services.

Table1: REST and OData comparison

As the table shows, both technologies follow REST principles, although OData is more relaxed in that, if there is a good reason to avoid its use, it lets you do so.

Thus the OData Protocol is more comprehensive than the REST model, as in addition to the use of the REST principles, it provides a way to describe both the data and the data model.

So, let’s go deep into the details by explaining each of the concepts mentioned in the above table.

What is REST

Representational State Transfer, or better known as REST is a software architecture style, which defines a set of principles useful for creating efficient web services.

These principles were described by Roy Fielding in his doctoral dissertation written in 2000. As he stated during an interview:

“…That process honed my model down to a core set of principles, properties, and constraints that are now called REST”

In his dissertation Fielding identified six aspects of HTTP and HTML that made these technologies successful and efficient. These concepts or principles are:

1. Client–server
2. Stateless
3. Cacheable
4. Layered system
5. Code on demand (optional)
6. Uniform interface
• Identification of resources
• Manipulation of resources through these representations
• Self-descriptive messages
• Hypermedia as the engine of application state (HATEOAS)

The HTTP protocol is an example of a system that implements the principles of REST.

What is a REST API

A REST API is simply an Application Programming Interface that uses the above mentioned principles. Usually, they use XML or JSON to communicate, although the REST architecture doesn’t ask anything specific in this regard. Even more, REST doesn’t demand any particular format, and accepts any format that can be used via Content negotiation.

Figure 1: REST API

What is OData

OData or Open Data protocol is an application-level protocol that describes a way for interacting with data using RESTful services. At present it is in version 4.0. This latest version was standardized by OASIS and approved as an ISO/IEC International Standard.

Initially created by Microsoft in 2007, it was later released under the Microsoft Open Specification Promise, and thus made available to all.

The protocol is based on two main components: 

1. Industry standards: HTTP, XML, Atom and JSON.
2. REST-based architecture: HTTP protocol.

However, the specification states that:

1. OData must “follow REST principles unless there is a good and specific reason not to”.
2. OData Services MUST support the ATOM encoding.
3. OData services SHOULD support a JSON encoding.

Figure 2 shows the structure of an OData compliant web service. It rests on HTML technology, which somehow resolves the problem of being REST-based. In addition, it must include Atom encoding, which is based on the XML technology, and also adhere to the OData’s data handling specifications. Since version 4.0, OData also handles JSON.

Figure 2: an OData compliant construct

OData uses Atom or JSON to define the data schema, and it provides two important models for the management of data. They are: 

1. Entity Data Model or EDM: it is an abstract data model that MUST be used to describe the data exposed by the service.
2. Service Model: it consist of two static resources and a set of dynamic resources. The static resources provide a way to ask a service about its data model, and the dynamic ones offer methods to manage the data model.

The two static resources are: 

1. The metadata document, which describes the data model.
2. The service document, which lists all of the top-level entity sets exposed by the service.

An important feature of the OData protocol is its support for a versioning scheme that enables services to be backward compatible.

In addition, there are several libraries that can be used to facilitate building and consuming OData-based services. Among them, Restier is the main library for the .NET framework, and Apache Olingo is the most important for the Java platform.

Conclusion

In this article we have seen what REST means, how it is used in RESTful APIs and in the OData Protocol. In addition, we have described the OData protocol’s main components.

We have concluded that REST is part of OData, and also more general. Thus, OData can be considered a safer guide to creating RESTful services.

Read more related articles at our blog series

How Integration platform is driving the 4th industrial revolution in 2019

The first Industrial revolution paved way for a new energy source – steam, which pushed industries towards mechanization. It helped industries to improve productivity.

The Second Industrial revolution introduced electricity and other innovations in infrastructure. It created new industries and opportunities to thrive on mass production lines.

When we arrived at the Third revolution during the late twentieth century, information technology was the major change agents towards the automation of production processes.

And here we are now, the next big thing in the history of the industrial revolution – The Fourth one. It is getting much attention and hailed as important a milestone as the first one. Let’s have a look at why is that?

Fourth Industrial Revolution (4IR) is all about connectedness and how we can embed the technology into our societies and industries. All emerging technology breakthroughs will speed up the digitization of industries. Cloud technologies and 5G will be the front runners of this. A few examples of emerging fields are Robotics, Artificial Intelligence, The Internet of Things (IoT), where such innovation is happening.

4IR or Industry 4.0 is not about inventing technology for technology’s sake. The diagram below explains how 4IR is giving way to enhance connectedness and productivity and some of the social issues.

4th Industrial Revolution

  

How Integration platform will be driving the revolution

This is the age of disruptive technology. It is creating a level playing field for businesses with innovative and agile business models. Whether it is an enterprise and small business, constant innovation and integration of the business processes has become evident.

Platform with No-code/ Low-code capabilities

With No-code and Low-code development platforms, businesses can develop a software application without the need to write code. These platforms use visual modeling approach with pre-built functional components available in the platform library. The developers can just select the components and add to the workflow in line with the application. This helps developers to achieve the results with greater efficiency.

This approach also makes the progress visible to all level of people in the business. Also saves businesses a lot of money as they do not need to maintain the stand-up environment and infrastructure. The platforms pretty much offer all in the package. Let’s take a look at how No-code and Low-code development capabilities work;

No-code and Low-code

No-code development capabilities

No-code development capabilities provide the business users to solve basic functional problems between the departments with simple solutions. These platforms will be more suitable to the SMBs to solve their integration challenges without hiring a professional developer as there is no need to write a code to implement such applications.

Low-code development capabilities

Low-code capabilities cater to Enterprises that have technology governance requirements. It uses a more synchronized approach with scalable architectures and flexibility in terms of on-prem or cloud implementation. Low-code platforms also extend the capabilities with the use of open APIs. It supports complex integrations using comprehensive components in the platform library and ability to incorporate all the innovative next-generation technologies and third-party services available via open source.

This platform can create sophisticated applications for enterprises that encompasses various departments and domains. It offers better control to the developers for quality testing and performance tools which results in high productivity and speedy deployment.

API based ecosystem

With the choices and capabilities, the integration platform offers, there is no denying the power of APIs that helps these integrations possible.

API-Integration

As businesses grow bigger, requirements also grow wider. Enterprises need applications to work across departments and domains. They need to incorporate next-gen technologies to increase productivity. Then there is regulatory compliance and technology governance.

Also, there are challenges of modernizing the legacy systems to co-exist with Cloud infrastructure.

APIs act as an intermediary between on-prem and cloud, so all the other applications remain neutral to the platform they are installed on and maintain access to the data and other services as they move to the cloud.

Integration Platforms and APIs work hand in hand to help businesses integrate applications and manage data with faster deployment and higher efficiency. It saves the organization time, money, and needless to say, the errors generating from the complex coding. These platforms can drive the revolution by speeding up the productivity to many folds.

Handling EDI using Cloud Workflow

Electronic Data Interchange or EDI has existed since the early 70s. Today, many IT giants, such as IBM, are still using it.  One of the many reasons is its practicality, as this data format offers a way to transfer commercial documents in an easy and fast manner.

In this article, you will learn about the benefits of EDI, and how to create a collective digital model using EDI files and DBSync’s Cloud Workflow.

Benefits of EDI

EDI has been in the market for a long time. One of the reasons is its benefits.

“EDI continues to prove its major business value by lowering costs, improving speed, accuracy and business efficiency. The greatest EDI benefits often come at the strategic business level.”

EDI Basics

EDI’s most important benefits can be summarized as:

Table 1: Benefits of EDI

These benefits can be capitalized in many business cases. One of the most important of them is Supply Chain Integration.

Use Case: Supply Chain Integration

Today businesses operate in a multi-enterprise environment. An important aspect of this ecosystem is the value added through the integration of partners and customer communities.

Collaborative digital models can deliver great things, such as supplying manufacturing lead times, product availability data, demand forecasts from end customers, and more.

This data availability could, for example, allow you to consider precise inventory levels across the supply chain, based on demand data, prices and market calculations.

Thus, a B2B digital chain could benefit from EDI, and made possible with DBSync Cloud Workflow: EDI being used to describe the documents, and Cloud Workflow to execute the document transfer via FTP servers.

Once the EDI file has been transferred, a third application, usually called an EDI convertor,  is necessary in order to interpret the code contained in the EDI file, and transform it into a proper document.

Figure 2: Supply Chain Integration

How Cloud Workflow Manages the Document Transfer

There are two different operations with an EDI document. First, an EDI file can be transferred between different points, via, for example,  FTP servers. Second, the EDI document needs to be parsed and transformed into the actual document.

Cloud Workflow can easily transfer text documents stored in an FTP server to another data source, including another FTP server. The files are handled via a separator, in a similar manner to CSV files.

This feature allows Cloud Workflow to transfer EDI files. Although EDI files are defined by many different standards, such as ANSI, EDIFAC and TRADACOM, the common characteristic is that they can be transferred via a separator.

However, it must be noted that Cloud Workflow doesn’t parses the EDI document. It only transfers the text file containing the EDI code.

Figure 3: EDI transferred via Cloud Workflow

In order to understand how Cloud Workflow transfers EDI documents between FTP servers, let’s take a look at the next figure, which shows the basics of an ANSI EDI document definition.

An EDI document starts with a “Start of transaction” indicator, and ends with an “End of transaction” definition. In the middle, the contents of the document are defined.

In EDI terms, a single document is called a “transaction set” or “message”. A document can be something like an invoice, a purchase order or another commercial document. A transaction set is comprised of data elements, segments and envelopes.

Data elements are the individual items of the document, and are contained in segments, separated by a data element separator. In the example shown in the figure the separator is a star or *.

Finally, a document is stored in an envelope, which can contain one or more documents. Envelopes are then transferred between sites.

By using this EDI file structure, Cloud Workflow can transfer EDI documents located in FTP servers in a similar way it transfers CSV files.

Figure 4: Structure of an EDI document

Wrapping Up

We have seen that DBSync’s Cloud Workflow can easily transfer EDI documents stored in FTP servers. The next step is to convert the EDI code into a proper file.

This feature opens immense possibilities. One very important option is its use in Supply Chain Integration, where a chain of FTP servers could serve as a link between different points in the supply chain.

The opportunities opened by this collective digital model are many: from production based on customer data, to inventory planning, reduction of lead times, and more.

Would you like to learn more about Cloud Workflow and its powerful features? Visit our website at https://www.mydbsync.com/, where you can try our app for free.

A 10 Minute Guide To Hybrid Integration Platform (HIP)

Are you interested to know what the newest thing in integration is? The answer to your question is Hybrid Integration Platform, better known as HIP. This integration approach is fast becoming the new wave in integration.

By 2022, Gartner predicts at least 65% of large organizations will have implemented an HIP to power their digital transformation

This article is a step-by-step explanation of what HIP is, why you should consider it, what will you need to implement it, and how to choose the right implementation approach according to your business reality.

So, let’s start by explaining what HIP is.

What is HIP?

A Hybrid Integration Platform or HIP is a framework for system integration that was developed by Gartner, and considers the following four dimensions.

1. Constituents: are stakeholders in the integration project.
2. Endpoints: are where the systems to be integrated reside.
3. Integration models: are the entities to be integrated.
4. Deployment models: are the different connection environments.

Now, let’s see each dimension in detail.

Figure 1: An HIP example

Constituents

One of the most important characteristics of HIP is the inclusion of new stakeholders, who are now involved in integration. Together, they join forces by forming a “facilitation team” that assists and performs the integration task. This team is composed of the following roles:

1. Integration specialists: people with technical knowledge involved in the integration project.
2. Citizen integrators: people that participate in the integration project, but are not technically strong. For example, business unit managers, people from marketing, etc. This group participation will increase in the future.
3. “ad hoc” integrators: people who participate in the project without a specific role.
4. Smart machines: their importance is increasing, and will increase even more with the advances in IT. Typical examples are delivery trucks, production machines and pool cars.

Endpoints

The endpoints define where the systems reside. HIP innovates, when compared with other integration approaches, in that it basically includes all possible hosting methods. These are:

• On premises devices: includes systems that are available on site. An example is custom made legacy systems.
• Cloud: includes systems that are available through the internet, usually as a service. A typical example is Salesforce.
• Mobile: includes systems that are available on mobile platforms, such as Android.
• IoT: includes systems used to manage things, such as security cameras and automobiles. With the advance of IoT in recent years, systems integration will increasingly consider including these elements.

Integration models

An important aspect of HIP is that it considers not only the integration of data, but a vast range of entities. These are:

1. Applications: applications that need communication with each other.
2. Data: data available on databases and generated by different systems.
3. B2B: systems across different businesses.
4. Processes: business and operation processes.

Deployment models

Similarly, HIP connects systems residing on different deployment settings. The main ones are:

1.  
1. On-premises: systems residing on site.
2. Cloud: systems hosted on the cloud.
3. Hybrid: systems on the cloud and on site.
4. Embedded: systems embedded in IoT devices.

Now that we know what HIP is about, we can move to the next important question: why you must consider HIP in order to survive in the present competitive market?

Why HIP? Breaking the boundaries

In today’s world of hyper-competition, businesses need a powerful competitive edge to survive. In order to have it, businesses cannot fall behind in the quality of their software systems. They need the best software available, development speed in custom software, and data sharing among different systems.

In order to achieve this, technology ecosystems are converging more and more towards using a common data source, which is administered in an efficient and focused manner, according to the priority needs of the businesses.

In these ecosystems, operational, transactional and analytical apps feed from and contribute to the same pool of data sources. For example, apps such as Salesforce, which are used by the Marketing and Sales department, draw data from the same pool that BI and analytics do.

This unified data source results not only out of the need to reduce data silos, but also from the necessity for updated information that prompts the right business decisions and thus, increases in sales and profits.

  

Figure 2: Apps connected to a unified data source

Now that you are aware of the importance of HIP, let’s analyze how to implement it. In order to do this, first we need to consider what factors are important when deciding on an HIP approach.

3 Implementation key factors

The main points to consider when implementing an integration implementation are:

1. Business priorities: functional and timing requirements, considering the better ROI for your business. Based on these priorities, integration should be considered as a “product” that drives business value.
2. Available skills: certainly a very important aspect, as integration specialists are a scarce resource.
3. Allocated budget: particularly important for the small and medium enterprise.

Figure 3:  Implementation factors

How to implement HIP

There are three main ways to implement HIP. How to choose among them depends mainly on the size and level of homogeneity / heterogeneity of your business.

The first one is Centralized. It includes a single implementation and a centralized governance. The implementation is done by a centralized team. Thus, it is best for small to medium enterprises that, due to their size, are highly homogeneous.

The second implementation method is Shared. This method also has a single implementation and a centralized governance. However, it is implemented by several teams, usually located at the different business’ subsidiaries. Therefore, it is ideal for large and global organizations that are homogeneous in nature.

Finally, there is the Federated model. It involves multiple HIP implementations, a federated governance (from which its name derives), and it is implemented by several teams. Thus, it is the characteristic implementation model for large, global and heterogeneous businesses.

Table 1: Implementation approaches

DBSync and HIP

HIP is the future in integration, and DBSync is already there. Our three main products are: Cloud Workflow, CDM and Replication.

Cloud Workflow lets you connect two or more apps via connectors. A typical example would be reading data from Salesforce and feeding it into a database or another app.

CDM and Replication are two powerful tools that help you create snapshots of your Salesforce data and replicate it. There is an on-premises version and a recently released cloud version.

There are several characteristics that define our tools as hybrid. First of all, their user friendliness that basically creates an environment that does the job for you. Secondly, the capabilities to work both, on the cloud and on-premises. And finally, the capacity to create custom connectors and thus, to basically connect anywhere.

The benefits of these tools are legion: reducing admins’ and developers’ wasted time, reducing data duplication, reducing storage needs, creating continuous workflows that can run from lead to sale, coordinating data sources for Analytics and AI, and many more.

Table 2: DBSync main connectors

Conclusion

There are many reasons to understand what Hybrid Integration Platform is about. In this article, you have seen some key concepts that define HIP, and which can also help you analyze how you can apply this approach.

Ready to learn more? Check out our website at http://www.mydbsync.com/ which features many more knowledge resources, and try hybrid integration with our product for free.

9 Netsuite Benefits For fast-growing companies

What is NetSuite – The Leading Cloud Solution

NetSuite is widely regarded as the first cloud computing software company, founded in 1999. And nowadays is the leading cloud solution on the market, offering consolidated ERP, CRM and e-commerce features.

As a cloud-based software, it enables companies to manage important business processes in a unified system. It is highly efficient thanks to its modular approach and cost-effective because it doesn’t involve hardware, upfront license fees or maintenance fees and it’s easy to set up.

These are only a few of the benefits of NetSuite for growing companies, and in this article we’ll dig deeper and expound the most important for accelerated growth.

Benefits of the Cloud

But before tackling NetSuite, it makes sense to understand why its underlying technology is so great for businesses.

Everyday, more companies are turning to the cloud when looking for a solution to track their financials, manage inventory, improve their customer relationship management (CRM) system and even implement e-commerce stores.

The cloud technology gains adepts every moment and is a technological trend that grows stronger among small and mid-size companies due to its lower cost and among larger enterprises because of its high power and efficiency.

NetSuite in a Nutshell

According to NetSuite, users can run their company from the point the customer touches the organization right through the journey of ordering, managing, delivering, invoicing and accounting the products. The whole business process is managed within a single system.

An ERP System for Growing Companies

NetSuite works under a subscription model which many small companies find appealing because they can pay per use only. Thanks to this, users can reduce initial investments and reduce the effort on maintaining the software.

Businesses can implement the standard software suite which includes the core functionality for both ERP and CMR. However, companies that require other specific, more advanced modules, can license them on demand.

In general, companies can obtain a bundle of modules specific for their industry, or cherry-pick only the ones they need the most.

9 NetSuite Benefits

1. Richness of features

NetSuite fits well across different industries as well as company sizes. It provides rich functionalities for Manufacturing, Software, Wholesale Distribution, Professional Services, Nonprofits and Retail/Ecommerce companies out of the box. All included in a single ERP product.

2. Pricing approach

Also worth mentioning is the modular approach of NetSuite. Essentially, it enables users to select and license only the modules they need. This empowers the user and reduces the costs.

3. Ease of NetSuite Integration

One of the major benefits of NetSuite is that it provides robust APIs that make possible the integration with other important software application. It can be done directly or through an integration platform (iPaaS). For example, one of the most popular integrations is Salesforce and NetSuite integration because many users want to continue using Salesforce CRM system but also implement NetSuite ERP in their businesses.

4. Improved visibility

Real-time visibility is crucial when it comes to making informed decisions. Data can be accessed instantly and users don’t have to waste time extracting and tying data from various sources. This allows for more accurate and timely reports, which translate into quicker decisions. Additionally, NetSuite pools data across the whole organization, as opposed to silos of data, which leads to more powerful insights.

5. Speed-up growth

Having an integrated software in place makes it possible to expand to multiple locations and establish new sales channels much faster. These are the benefits of unified order and accounting management processes and data.

6. Centralized information about your company

NetSuite offers a software system that cover your whole business. One single, integrated system translates into information flowing seamlessly throughout all departments. This way, employees avoid having to manually input data when sending reports to a different department. Manual input often leads to errors and inaccuracies in the information as well as time spend in low value tasks.

7. Extending processes to the supply chain

Being able to collaborate closely with customers, suppliers and partners is a benefit of an extended organization. It is possible to offer self service portals that make B2B and B2C collaboration better. NetSuite includes timely notifications about partner-specific events that help speed up process cycles and keeps you positioned as a partner of choice.

8. Scalability

Fast-growing companies at some point may decide to go global and NetSuite can be easily scaled to that extent thanks to multiple currencies, multiple languages, multiple subsidiaries and related functions. If a company grows horizontally it can add more users to the subscription rather than undergoing and infrastructure change. And if it expands vertically into new operations, additional modules can be added to the ERP system. In general, business can be assured that the system won’t place any limitations to their growth.

9. Customization and extension

NetSuite is regarded as the most customizable SaaS solution in the world. The high degree of customization is possible with NetSuite’s SuiteFlex, in which the user can set up all the modular implementations with some clicks rather than coding. Also, with help of the industry specific modules you’ll be able to tailor the business processes to meet the particular requirements of your company.

 NetSuite Integration

Netsuite can be integrated with other applications by using an iPaaS solution like DBSync. This platform provides a NetSuite connector that is highly customizable, and has the ability to connect your NetSuite account with many other applications such as Salesforce, SkuVault, and MSCRM. It also lets you connect your NetSuite account to several databases, such as Oracle, MySQL and MSSQL. These apps can be on-premises or available as SaaS.

The NetSuite connector exposes all standard and custom objects available within the NetSuite instance of the connected user through the use of a trigger and one or more rules. The trigger defines the reading components, which are then related to the receiving apps via a rules that contain mappings. These mappings are highly customizable and easily defined by way of a drag and drop mechanism.

Each NetSuite connector instance represents a single NetSuite account, including login credentials. If a user wishes to connect to multiple instances of either a sandbox or production data, he can create a separate connection for each of them, and configure each URL and login credentials accordingly.

Added to this, another nice feature of the DBSync platform is its online updater, which enables customers to automatically acquire all the product’s different features available in new releases.