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How to Use Data as a Service Tools in Your Marketing Analysis

In DaaS (Data as a Service), the network resources, storage, and infrastructure for support get hosted in the cloud.

Published

8 months ago

January 28, 2022

TwinzTech

How to Use Data as a Service Tools in Your Marketing Analysis

New advancements in the cloud computing domain have streamlined cloud-based information administration and storage. It has become accessible due to the DaaS platform.

Companies utilizing this platform can enhance administration, utilization, and assortment of information. DaaS abilities are equal to SaaS (software as a service), which eliminates the requirement for downloading and managing software programs domestically.

In DaaS, the network resources, storage, and infrastructure for support get hosted in the cloud. A desktop as a service provider streams data and applications over a network to the end users’ devices through zero clients; wherein the staff can easily access their desktop via client software with the needed authentication.

1. For Whom are DaaS Tools Suitable?

DaaS is suitable for any business model educated by data. Irrespective of whether you are a product-based company or a promoting company that focuses on enhancing buyer insights, you can gain much from these cloud-based firms.

For example, if a company’s growth relies on aligning well with the preferences of the buyer and product selections, data or information firms can provide crucial insights into enhancing this alignment.

Enterprises that require entry to data-driven analytics for enhancing buyer relations can gain revenue from such a service. A better buyer experience is one of the main advantages of DaaS.

Such a platform can streamline information administration both internally and externally. For enterprises, DaaS makes comprehending and optimizing campaigns and selections seamless.

2. Using DaaS Tools in Marketing Analysis

DaaS tools give companies the capacity to break down distinct datasets generated from Big Data. This data can be gathered from six different categories, including the following.

Search information: It is the data collected from people’s activity on the browser and the search behavior of users.

Web mining: It is the data compiled through mining the internet.

Social media: It’s the data gathered from various actions taken by people on social media, like comments, shares, and check-ins.

Crowdsourcing: This means the information from the public via forums, polls, surveys, and the like.

Transactional: It means the data generated from business practices like logistical, financial, withdrawal insurance claims, flight reservations, and the like.

Mobile: It’s the data compiled from applications and services operating in the background of smartphones.

When marketers collect, interpret, and keep all this kind of data specific to searchers or users, they can undertake more thorough and robust market research.

3. How to Use the Data Received Through DaaS Tools to Enhance Your Marketing Strategies.

With DaaS, you can easily measure how you perform against your competitors. With access to organizational data, the creation of benchmarking reports for gathering information on turnovers, financial, and leadership endeavors and backing all of it with percentile breakdowns are possible.

Data as a Service tool simplifies the process of data standardization, as it brings together analytics and data sources with fresh data visualizations. Enterprises can provide this information to internal users to trigger BI processes.

With DaaS tools, enterprises have access to data marketplaces, where users can exchange various datasets from numerous sources. They can repurpose this data and monetize it for long-term business growth.

You can connect with a desktop as a service provider to get fully managed virtual desktops on your desired cloud platforms according to your enterprise’s needs.

You may need different features in your DaaS tools according to your needs. So, it’s essential to consider the tool’s price, reliability, and flexibility. Only then can you benefit from great market research.

Internet

How do collect and train data for speech projects?

Data collection is the process of gathering, analyzing, and, measuring accurate data from diverse systems to use for business process decision-making, speech projects, and research.

Published

2 days ago

September 28, 2022

TwinzTech

How do collect and train data for speech projects

With technology evolution, we are moving towards machine learning systems that can understand what we say. In our daily lives, we all have encountered many virtual assistants like Alexa, Siri, and others. These virtual assistants often help us in tuning the lights of our homes, finding information on the internet, and even starting a video conference. But do you know how it does that?

To produce results, these virtual assistants use natural language processing to understand the user’s intent. Natural Language Processing technology enables virtual assistants to understand user intent and produce outcomes. Basically, these virtual assistants are applications of automatic speech recognition and are also known as speech recognition software. This software uses machine learning and NLP to analyze and convert human speech data into text.

But, attaining maximum efficiency of these software requires the collection of substantial speech and audio datasets. The purpose of collecting these audio datasets is to have enough sample recordings that can be fed into automatic speech recognition (ASR) software.

Furthermore, these datasets can be used against the speakers using unspecified speech recognition models. And to make ASR software work as intended, speech data collection and audio datasets must be conducted for all target demographics, locations, languages, dialects, and accents.

Artificial Intelligence can be as intelligent as the data given to it. Hence, collecting data for feeding the machine learning model is a must to maximize the effect of ASR. Let’s discuss steps in speech data collection for effective automatic speech recognition training.

Table of Contents

1. Create a Demographic Matrix

For creating a demographic matrix, the enterprise must consider the following information like language, locations, ages, genders, and accents. Along with these, it is a must to note down a variety of information related to environments like busy streets, waiting rooms, offices, and homes. Enterprises can also consider the devices people are using like mobile phones, headsets, and a desktop.

2. Collect and transcribe speech data

To train the speech recognition model, gather speech samples from real humans and take the help of a human transcriptionist to take notes of long and short utterances by following your key demographic matrix. In this way, human is a vital and essential part of building proper audio datasets and labeled speech and further development of applications.

6 Reasons to Transcribe Audio to Text

3. Build a separate test data

Once the text subscription is completed, it’s time to pair the transcribed test with the corresponding audio data and segment them to include one statement in each. Later on, take the segmented pairs and extract a random 20% of the data to form a set for testing.

4. Train the language model

To maximize the effectiveness of the speech recognition model, you can train the language model by adding general additional text that was not additionally recorded. For example in canceling a subscription, you recorded one statement that ‘I want to cancel my subscription, but you can also add texts like “Can I cancel my subscription” or “I want to unsubscribe”. To make it more effective and catchy you can also add expressions and relevant jargon.

5. Measure and Iterate

The last and important step is to evaluate the output of automatic speech recognition software to benchmark its performance. In the next step take the trained model and measure how well it predicts the test set. In case of any gaps and errors, engage your machine learning model in the loop to yield the desired output.

Conclusion

From travel, transportation, media, and entertainment, the use of speech recognition software is evident. We all have been using voice assistants like Alexa and Siri to complete some of our routine tasks. To effectively use this speech recognition software requires proper training in the audio datasets and the use of relevant data for the machine learning model.

Proper execution and the right use of data make sure the speech recognition software going to work efficiently and enterprises can scale them for further upgrades and development. As data and speech recognition go hand in hand, make sure you are using data with the right approach.