Software Engineering Insights

With the exponential growth of automation in organization architecture and processes, data quality becomes the key differentiator behind a successful sales strategy. The accuracy and quality of CRM data become pivotal for the sales team, be it for creating customer personas or generating revenue – the success of your sales strategies depends on data quality.

It is no surprise that sales leaders are striving to gain the most out of CRM data. But, without continuous maintenance of data, it can decay and become 'bad data' which can create multiple hindrances in the effective plan of action/ sales strategies. The bad data in your CRM refers to the 'wrong data’ which indicates missing, outdated, and incomplete data points.

Statistics show that customer data decays at a rapid rate of 30% and costs the average business 15% to 25% of revenue annually therefore, it is essential to cleanse and enrich your data sets in CRM. In fact, according to IBM, bad data costs US businesses $3 trillion every year. This mind-boggling factor is why data quality improvement should be every organization's primary concern.

Given that number, poor data can easily cripple your sales productivity, forecasting, and ultimately your business growth and revenue. On the other hand, high-quality data empowers sales reps to make decisions that will positively impact the success of an organization, improve its practices, and increase profits.

If bad data isn't detected and fixed on time, it can create many hurdles for your sales team. It will hinder the outbound sales process and waste a lot of time and effort. As a result, it will cause operational inefficiencies and unnecessary costs, and also impact your customer experience.

This blog will uncover how bad data affects businesses and why we should enrich it.

The operational and infrastructural overhead involved in running and maintaining cloud-based solutions is often massive. There is considerable effort and expenditure in the construction and scaling of storage systems for tons of user metadata and other in-house files. Dropbox, the file hosting pioneer, is a success story about how cloud costs can be minimized by intelligently managing your infrastructure.

The proliferation of digital transformation has made system reliability an essential component of any organization's bottom line. These rapid changes have become a new benchmark for success and efficient incident response is key in sustaining site reliability through these changes.

Increasing a website's visibility in search engine results is the goal of search engine optimization (SEO) for SERPs. Increasing your medical website's authority and credibility is a key part of medical SEO.

The automation of time-consuming tasks involved in marketing campaigns using Customer Relationship Management (CRM) software is an emerging trend. In particular, there has been an increase in the adoption of Salesforce for marketing automation because the resultant benefits far outweigh the associated costs and efforts involved in its integration.

With the continuous development in DevOps practice and its unparalleled benefits, now every organization wants to embed it in their software development lifecycle to make the deployment of software even faster and more efficient.

As software development gets complex, we see cross-functional teams collaborating to enable faster product delivery, optimize cost, manage infrastructure, and for several business benefits. An example of such a joint effort is ‘Cloud FinOps’ wherein finance, engineering, technology, and business teams aim at cloud financial management. 

Technology is embedded in every aspect of our lives. We rely on it for almost everything, especially to communicate with others. Simply, take the example of Siri or Alexa that we use in our day-to-day lives. Ever wondered how these virtual assistants understand and interpret our commands? How do they pick up terms and carry out the tasks according to our instructions? For example, when we ask Alexa to set an alarm for us.

Rolling out a new software system, infrastructure, or technology solution for an industry such as healthcare or personal finance has critical end-user outcomes. Technology Readiness Levels (TRL) refers to a robust scale often used by pioneering software organizations throughout their development efforts.

PyTorch and TensorFlow are two of the most widely relied-upon frameworks for Deep Learning (DL) research and industry implementation. While both these DL development frameworks haven't been around too long, they have made a significant mark in AI/ML research and innovation.  However, when it comes to which between PyTorch and TensorFlow is the better framework, there has not been a clear consensus.