Salesforce-BigdataSalesforce has long been one of the tops among the marketing and CRM software. Back in 2017, post the Dreamforce Conference, the makers of Salesforce had brought in some significant changes to the platform. This amended a lot of innovative changes to the face of this marketing platform and got a new avatar, which is Salesforce DX.

There are quite a lot of new concepts embedded on to Salesforce DX in terms of source control management, continuous integration, and scripting, which all now become an integral part of the developer networks of Salesforce. This has paved the way to a new way to development, deployment, and upgrading the data management and e-commerce apps. Salesforce DX enables source-driven app development, continuous code promotion from the source, and automation of testing. Before 2017, these avenues were foreign to the developers.

The aim of Salesforce DX

According to the Salesforce expert Wade Wegner “The primary objective of Salesforce DX is to fully externalize metadata and the source of Salesforce environment for users.” One can test configurations, test data, and metadata now from the source code itself, which plays a significant role in shaping the company profile. It goes beyond the concept of just big data. More and more enterprises are looking for a customizable app which can fully understand the customer interaction with appropriate management of micro-services. With DX, Salesforce is now becoming more diversified.

The importance of Heroku flow in DX

The latest DX version of Salesforce works on Heroku platform. All the apps hosted on this CRM may use this platform. The users of DX get a very luxurious experience in terms of continuous integration and easily up-gradable tools which DX bring with it. Salesforce DX largely depends on Heroku Flow in terms of its functioning. The major four components which help machine learning, as well as big data, are as below. While three out of these are out there for years, the fourth one was launched in 2016 only, which remains new for many of the users now.

  1. Heroku pipelines:

It is actually an innovative way of organizing the Heroku apps which all share the same codebase. Users will find an easy way of reviewing, developing, and producing a unique environment, which provides excellent support, visualization, and management for continuous delivery. This, in turn, creates a visual platform which helps manage the enormous volume of data flowing in each minute.

  1. Review Apps:

This component of the Heroku Flow enables the users to discuss, decide, and propose the changes they want to merge into the common code base. For those apps which are connected to the GitHub or Git, Heroku may run temporary automated tests on some unique URLs based on the open pull requests.

  1. Github integration:

Github integration will let the users connect the report to the Heroku apps. One can do this manually or use automation methodologies when you are working on the Salesforce DX platform. As outlined at Flosum.com, each deployment will show you the actual difference between the current release and the immediate previous commit. You may access the Activity Tab on the dashboard of the Heroku app and then check performance.

  1. Heroku CI

As mentioned above, this is the latest addition to the Heroku Flow suite. It is available in the latest Salesforce DX release. CI or Continuous Integration will offer a better integration approach by using any third-party tools like Jenkins, which the users are already familiar with. This will complement the automated test running against the current code. This is one of the most important parts of any big data management environment which demands instant analysis as enabled by Salesforce DX.

Now, let’s know the approach of data preparation for predictive analysis, which is another essential need in big data analytics environments.

Preparing data for the predictive analysis

Predictive analysis is something which helps to improve the effectiveness of organizations in order to successfully drive the business with the help of big data, statistical analysis, and machine learning techniques in Salesforce DX environment. A few things big data analysts should know in terms of handling data are as discussed below.

1. Understanding the predictive analysis objective

One needs to have a clear objective at the first point to go ahead with the predictive analysis model. There are many objectives to set risk management, revenue forecast, financial modeling, fraud management, marketing management, operational strategies, etc. In order to measure and ensure the success of your model, it is imperative to set the objectives.

2. Identifying issues

Predictive analysis is focused on understanding the problems of an organization. The results from the predictive analysis are used to revamp the operational model for the managers and workers to solve the issues which hinder the objective.

3. Determining the process

This is exploring the opportunities for improvement. The data scientists need to assess each and every process for the needs for amendments in order to execute the results of a proper model.

4. Identifying performance metrics

Measuring the performance is the key to ensure the success of an organization. An ideal performance metrics will yield the outcome as the measure of quantities for the improvement of organizational goals. In the case of metric shows that any action taken isn’t beneficial, then a different approach could be taken in order to fulfill the needs.

5. Selecting data and preparing it for modeling

Data selection and preparation needs a fair understanding of the business objectives and target modeling. The three primary types of data available are:

  • Demographic
  • Behavioral, and

It is critical to do the proper preparation of data for the analysis in the correct format. It should be trained and streamlined based on the previous data and to do this, the need should be cleaned up. The variables also should be well-defined, and multiple datasets could be merged according to the needs.

6. Model developmental methodology

This approach is to structure and plan the process of developing and implementing an analytical data system in any organization. There are various development methodologies as:

  • Agile development
  • Dynamic systems development
  • Feature-driven developmental model
  • Rapid application development
  • Systems development life cycle etc.

All these methods help in minimizing the risks by developing software with maximum perfection as the working teams evaluate the projected priorities.

Apart from these; one should also take care of the need for random data sampling, adequate data governance, implementation of adequate models, and effectively building and deploying the model. Salesforce DX could act as a one-stop solution for the big data management needs to leverage its benefit fully into successful data-driven business intelligence.

--

Subscribe to our Newsletter

Get The Free Collection of 60+ Big Data & Data Science Cheat Sheets. Stay up-to-date with the latest Big Data news.