The Role of Blockchain When Buying Bitcoin in 2022

The Role of Blockchain When Buying Bitcoin in 2022

Blockchain is one of the most disruptive technologies of the 21st Century. A growing number of industries are using blockchain to operate more efficiently or boost security.

However, blockchain is still most important in the realm of cryptocurrencies. Bitcoin has become very popular since its inception in 2008 and that is largely due to the power of blockchain. Blockchain has made investing in bitcoin much more secure than ever.

Are you interested in buying bitcoin? You need to understand the general process and the role that blockchain plays in it.

How Does Blockchain Fit into the Process of Buying Bitcoin?

It may seem a complicated task to invest in bitcoin. However, it can be easier with the proper knowledge and breaking it down into steps. You will need to understand the general process, as well as the role that blockchain plays in it.

If you want to trade or invest in bitcoin, you need to have a cryptocurrency service account, secure internet connection, method of payment, and personal identification documents. Initially, things may seem confusing to you, but you will surely not regret buying the bitcoin.

You also need a personal wallet outside in addition to your account with a bitcoin exchange if you want to buy bitcoins. The valid payment methods for the process include credit cards, debit cards, and bank accounts. Also, you can buy bitcoin through P2P exchanges and specialized ATMs that connect directly to the blockchain.

Security and privacy are important factors that you need to consider before buying a bitcoin. You need to get private keys to a public address as it will enable you to authorize transactions on the bitcoin blockchain. The blockchain has made trading bitcoin extremely secure, but you still need to protect your digital coins by securing your wallet and account.

Keeping private keys secret is a must as criminals and scammers can attempt to steal them. You have to be careful while using the balance of a public address. Without further ado, let’s explore how you can buy bitcoin over the blockchain.

1. Choose Crypto Trading Service

Initially, you have to choose a crypto trading venue or service. Some of the well-known trading services that you can choose to buy BTC include payment services, brokerages, and cryptocurrency exchanges. You need to make sure that these are legitimate services that actually connect to the blockchain. Out of all these options, you should go for cryptocurrency exchanges as it has a wide range of features that ensures a great level of convenience.

By signing up for a cryptocurrency exchange, you can sell, buy, and hold cryptocurrency. Usually, bitcoin buyers prefer this option because it allows them to withdraw crypto from their personal wallets safely. As long as it is a service that legitimately connects to the blockchain, the transactions themselves will be highly secure. However, it is still important to make sure that the bitcoins stored on the exchanges will be secured as well.

There are different types of cryptocurrency exchanges. Some exchanges allow users to not provide personal information while some need it. The exchanges that enable users to stay anonymous are decentralized and operate autonomously. It means they do not have any central point of control. Such systems can serve the unbanked population of the world. Refugees or people living with no banking structure can use anonymous exchanges that can help them enter the mainstream economy. The blockchain has given them the opportunity to use a much more efficient currency.

2. Connect your Exchange to Payment Method

Once you have chosen the exchange, you need to connect the exchange to the payment option. You need to ensure that you have all the required documents to do that. It includes a driving license picture or social security card that depends on the type of exchange that you choose. You may also have to provide information about your employer and source of income. The exact process depends on your region where you live and the laws that apply there. The procedure is almost the same as setting up a brokerage account.

Once the exchange verifies your identity, you can connect to the payment option and will be ready to start placing transactions over the blockchain. You can directly connect to your bank account, debit, or credit card in most exchanges. Although, it is not a good idea to buy bitcoin through a credit card because price volatility can inflate the overall purchasing coin cost. Bank can also ask you some questions, so you have to be prepared. You should check in advance to ensure that your bank allows deposits at the exchange you have chosen. The deposit fees of banks through debit, bank account, and credit cards can vary.

3. Place the Order

After choosing the exchange and connection payment option, you are all set to buy bitcoin. In the recent past, cryptocurrency exchanges have become mainstream as they have grown massively in terms of features and liquidity. The cryptocurrency exchanges have reached a point where they have the same features as stock brokerage. The blockchain is so efficient at processing these transactions that bitcoins can be purchased nearly instantaneously.

Crypto exchanges offer many order types and methods to invest. Apart from different types of orders, exchanges also allow clients to set up recurring investments. For example, Coinbase enables users to set recurring purchases on a daily, weekly, and monthly basis.

4. Safe Storage

The blockchain has made the process of buying, selling and transferring bitcoins very secure. However, there are security issues that it can’t address, because it only plays a role during the transfer process. Namely, the blockchain can’t protect coins in your wallet. You have to have a secure storage option.

Bitcoin wallets allow you to keep your digital assets secure. If you have cryptocurrency outside of the exchange, then your personal wallet ensures that only you have access to the private key to control your funds. It also enables you to store funds from the exchange and also mitigates the risk of getting your exchange hacked. In this way, it ensures security, so you do not lose your funds.

Most of the exchanges offer wallets for their users, but security is not their major preference. You should not use such type of exchanges wallet that does not guarantee security. Some wallets have more storage features as compared to others, so you should take a look at each of them. You can consider choosing from different types of bitcoin wallets. The hot wallets are known as online wallets, while cold wallets are known as hardware or paper wallets.

You can access hot wallets on internet-connected devices, including phones, tablets, and computers. It can cause vulnerability because these wallets produce the private keys to your coins on devices that you use. Although using a hot wallet is a convenient option that allows you to make transactions efficiently and quickly with your assets.

Also, it allows you to store your private key on internet-connected devices. On the other hand, cold wallets are not connected to the internet, so there is less risk involved in it. You may consider these wallets as hardware or offline wallets. These wallets store your private keys on anything which is not connected to the internet.

According to experts, cold wallets are a more secure option for storing bitcoin or any other cryptocurrencies. You can generate these wallets by using certain websites that provide you with private and public keys.

The Blockchain Plays a Crucial Role in the Process of Buying and Selling Bitcoins

The blockchain has a very important role in the process of buying bitcoin. Trading and buying bitcoin has become one of the major trends that have completely transformed many people’s lives, due to the benefits the blockchain has provided. It provides you with a great opportunity to attain financial freedom and change your lifestyle. But you need to have complete awareness before buying the bitcoin to avoid any risks of losing the funds.

Getting professional assistance from bitcoin or crypto experts can make the bitcoin buying process easier for you. It will also help you know about the different aspects that can be beneficial for you in the long term.

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The Importance of Implementing a Sensible Data Collection Strategy

The Importance of Implementing a Sensible Data Collection Strategy

Data has become one of the most valuable assets to modern organizations. Some companies are still in denial of its importance, but their hesitance to embrace it is subsiding. One poll found that 36% of companies rate big data as “crucial” to their success.

However, many companies still struggle to formulate lasting data strategies. One of the biggest problems is that they don’t have reliable data collection approaches.

Data Collection is Vital to Companies Trying to Make the Most of Big Data

Data refers to all the information accumulated about a certain topic. Smart organizations know how to leverage data to solve problems and improve efficiency.

In the world of business, data collection is very important. Quality data can be used to solve many different types of problems that may occur, such as improving financial efficiency, identifying the best customers to target with their marketing campaigns and exploring new ways to improve productivity. Therefore, data must be collected, vetted for quality and preserved for future use. However, data does not just collect itself. It has has to be gathered efficiently and legally.

The data collection process should be carried out methodically. Different data collection processes might be ideal for different types of data. Some common data collection methods are shared below.


When it comes to data collection, nothing beats a good old-fashioned survey. Surveys can come either in a physical or digital form. In a business, conducting a survey is very easy. A survey will allow customers to share their opinions on the services they receive. Therefore, a business can now use this useful data to improve its efficiency and bolster customer engagement.

Interviews and Focus groups

Interviews and focus groups can also be effective for gathering data. This process involves assembling a small group of people and asking them questions about a topic. You are looking to collect data about their sentiments around a particular brand or product.

After a new product or service is released, it can be difficult to understand the public’s perception about it. Focus groups or interviews are modern data collection approaches to get a better perspective on their views. They provide valuable feedback from users, which is collected as data. This data is in turn saved and used to solve problems relating to the product or service.

Online forms

Data collection forms are a great way to collect useful data in any business. A data collection form is a cheap but effective way to get data about customers. Another benefit is that customers can be asked to fill in these forms when they want to purchase the product or service. Most of these forms are usually available online for people that want to register for a service. Data collection forms are very useful when an existing customer needs to receive information about a new product or feature. There is also a data collection forms app that helps to make this easier.

Social media or websites

In the age of digital media, the vast majority of businesses require social media platforms or websites to run their business efficiently. There are actually a multitude of benefits of social media. While social media is very useful for advertising new products or services, that is not all that it is good for. Companies can use social media to field complaints made by people using their services or their competition to improve their business models. Besides, this is a good way to gather data without necessarily paying for it.

The Importance of Data Collection in Business

A business that wants to strive must have an effective data collection strategy. Let’s see why data collection is key to business development below:


In a business, a lot of important decisions have to be made on a regular basis. Many of these decisions are influenced by data that has been shared from users. Properly executed data collection processes help provide useful information that can be used to make good decisions.

Detect problems

When a business is facing a challenge, no matter how visible it might be to the decision-makers within the company, it is usually going to be reflected in their data. Data includes complaints and comments made by customers. As the data is collected, it shows the areas where the business must make improvements. This is the first step in fixing the problem.

Develop strategies

Every business requires a strategy that would be used to achieve the business’s goals. What many businesses fail to understand is that data about customers can be incredibly valuable for articulating strategies on bolstering customers satisfaction. Quality data shows what the customers want, and this will decide what strategy to use.

Saves time

A great data collection strategy saves time because you would otherwise need to waste a lot of effort by executing poorly thought-out, ad hoc data collection strategies. By collecting and storing data, you will never waste your time finding some data. As part of the data collection definition on Simplilearn, saving time is one of the crucial importance of collating data for a business.


Data collection has become a very important process, because data is vital to the decision-making process in many companies. Every business needs a methodical approach to collecting and storing valuable data. This will surely improve the efficiency of the business.

Data collection is the new normal. Data scientists or analysts are highly sought-after by tech companies. It is almost impossible to do without data these days. No wonder, a lot of companies spend millions to collate data. Finally, plan an effective data collection that will suit your business. You can get data collection forms on pareto to collate information.

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Google Cloud Data Heroes Series: Meet Antonio, a Data Engineer from Lima, Peru

Google Cloud Data Heroes Series: Meet Antonio, a Data Engineer from Lima, Peru

Google Cloud Data Heroes is a series where we share stories of the everyday heroes who use our data analytics tools to do incredible things. Like any good superhero tale, we explore our Google Cloud Data Heroes’ origin stories, how they moved from data chaos to a data-driven environment, what projects and challenges they are overcoming now, and how they give back to the community.

In this month’s edition, we’re pleased to introduce Antonio! Antonio is from Lima, Peru and works as a full time Lead Data Engineer at Intercorp Retail and a Co-founder of Datapath. He’s also a part time data teacher, data writer, and all around data enthusiast. Outside of his allegiance to data, Antonio is a big fan of the Marvel world and will take any chance to read original comic books and collect Marvel souvenirs. He’s also an avid traveler and enjoys the experience of reliving family memories through travel. Antonio is proudly pictured here atop a mountain in Cayna, Peru, where all of his grandparents lived.

When were you introduced to Google Cloud and how did it impact your career? 

In 2016, I applied for a Big Data diploma at the Universidad Complutense del Madrid, where I had my first experience with cloud. That diploma opened my eyes to a new world of technology and allowed me to get my first job as a Data Engineer at Banco de Crédito del Perú (BCP), the largest bank and the largest supplier of integrated financial services in Perú and the first company in Peru using Big Data technologies. At BCP, I developed pipelines using Apache Hadoop, Apache Spark and Apache Hive in an on-premise platform. 

In 2018, while I was teaching Big Data classes at the Universidad Nacional de Ingeniería, I realized that topics like deploying a cluster in a traditional PC were difficult for my students to learn without their own hands-on experience. At the time, only Google Cloud offered free credits, which was fantastic for my students because they could start learning and using cloud tools without worrying about costs.

In 2019, I wanted a change in my career and left on-prem technologies to specialize in cloud technologies. After many hours of study and practice, I got the Associate Cloud Engineer certification at almost the same time I applied for a Data Engineer position at Intercorp, where I would need to use GCP data products. This new job pushed me to build my knowledge and skills on GCP and matched what I was looking for. Months later, I obtained the Professional Data Engineer certification. That certification, combined with good performance at work, allowed me to get a promotion to Data Architect in 2021. In 2022, I have started in the role of Lead Data Engineer.

How have you given back to your community with your Google Cloud learnings?

To give back to the community, once a year, I organize a day-long conference called Data Day at Universidad Nacional Mayor de San Marcos where I talk about data trends, give advice to college students, and call for more people to find careers in cloud. I encourage anyone willing to learn and I have received positive comments from people from India and Latin America. Another way I give back is by writing articles sharing my work experiences and publishing them on sites like Towards Data Science, Airflow Community and the Google Cloud Community Blog. 

Can you highlight one of your favorite projects you’ve done with GCP’s data products?

At Intercorp Retail, the digital marketing team wanted to increase online sales by giving recommendations to users. This required the Data & Analytics team to build a solution to publish product recommendations related to an item a customer is viewing on a web page. To achieve this, we built an architecture that looks like the following diagram.

We had several challenges. First, finding a backend that supports millions of requests per month. So after some research, we decided to go with Cloud Run because of the ease of development and deployment. 

The second decision was to define a database for the backend. Since we needed a database that responds in milliseconds, we chose Firestore.

Finally, we needed to record all the requests made to our API to identify any errors or bad responses. In this scenario, Pub/Sub and Dataflow allowed us to do it in a simple way without worrying about scaling. 

After two months, we were ready to see it on a real website (see below). 

For future technical improvements we’re considering using Apigee as our API proxy to gather all the requests and take them to the correct endpoint. Cloud Build will be our alternative to our deployment process.

What’s next for you and what do you hope people will take away from your data hero story? 

Thanks to the savings that I’ve collected while working in the past five years, I recently bought a house in Alabama. For me, this was a big challenge because I have only lived and worked outside of the United States. In the future, I hope to combine my data knowledge with the real estate world and build a startup to facilitate the home buying process for Latin American people.

I’ll also focus on gaining more hands-on experience in data products, and giving back to my community through articles and soon, videos. I dream one day to present a successful case of my work in a big conference like the Google Cloud Next.

If you are reading this and you are interested in the world of data and cloud, you just need an internet connection and some invested effort to kickstart your career. Even if you are starting from scratch and are from a developing country like me, believe that it is possible to be successful. Enjoy the journey and you’ll meet fantastic people along the way. Keep learning just like you have to exercise to keep yourself in shape. Finally, if there is anything that I could help you with just send me a message and I would be happy to give you any advice.

Begin your own Data Hero journey

Ready to embark on your Google Cloud data adventure? Begin your own hero’s journey with GCP’s recommended learning path where you can achieve badges and certifications along the way. Join the Cloud Innovators program today to stay up to date on more data practitioner tips, tricks, and events.

If you think you have a good Data Hero story worth sharing, please let us know! We’d love to feature you in our series as well.

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Notified team gets smart on MLOps through Advanced Solutions Lab for Machine Learning

Notified team gets smart on MLOps through Advanced Solutions Lab for Machine Learning

Editor’s note: Notified, one of the world’s largest newswire distribution networks, launched a public relations workbench that uses artificial intelligence to help customers pinpoint relevant journalists and expand media coverage. Here’s how they worked with Google Cloud and the Advanced Solutions Lab to train their team on Machine Learning Operations (MLOps).

At Notified, we provide a global newswire service for customers to share their press releases and increase media exposure. Our customers can also search our database of journalists and influencers to discover writers who are likely to write relevant stories about their business. To enhance our offering, we wanted to use artificial intelligence (AI) and natural language processing (NLP) to uncover new journalists, articles, and topics—ultimately helping our customers widen their outreach. 

While our team has expertise in data engineering, product development, and software engineering, this was the first time we deployed an NLP API to be applied to other products. The deployment was new territory, so we needed a solid handle on MLOps to ensure a super responsive experience for our customers. That meant nailing down the process—from ingesting data, to building machine learning (ML) pipelines, and finally deploying an API so our product team could connect their continuous integration/continuous delivery (CI/CD) pipelines. 

First, I asked around to see how other companies solved this MLOps learning gap. But even at digital-first organizations, the problem hadn’t been addressed in a unified fashion. They may have used tools to support their MLOps, but I couldn’t find a program that trained data scientists and data engineers on the deployment process.

Teaming up with Google Cloud to tailor an MLOps curriculum

Seeing that disconnect, I envisioned a one-week MLOps hackathon to ramp up my team. I reached out to Google Cloud to see if we could collaborate on an immersive MLOps training. As an AI pioneer, I knew Google would have ML engineers from Advanced Solutions Lab (ASL) who could coach my team to help us build amazing NLP APIs. ASL already had a fully built, deep-dive curriculum on MLOps, so we worked together to tailor our courses and feature a real-world business scenario that would provide my team with the insights they needed for their jobs. That final step of utilization, including deployment and monitoring, was crucial. I didn’t want to just build a predictive model that no one can use. 

ASL really understood my vision for the hackathon and the outcomes I wanted for my team. They never said it couldn’t be done, we collaborated on a way to build on the existing curriculum, add a pre-training component, and complete it with a hackathon. The process was really smooth because ASL had the MLOps expertise I needed, they understood what I wanted, and they knew the constraints of the format. They were able to flag areas that were likely too intensive for a one-week course, and quickly provided design modules we hadn’t thought to cover. They really were a true part of our team.. 

In the end—just four months after our initial conversation—we launched our five-week MLOps program. The end product went far beyond my initial hackathon vision to deliver exactly what I wanted, and more.

Starting off with the basics: Pre-work

There was so much we wanted to cover in this curriculum that it made sense to have a prerequisite learning plan ahead of our MLOps deep dive training with the ASL team. Through a two-week module, we focused on the basics of data engineering pipelines and ramped up on KubeFlow—an ML toolkit for Kubernetes—as well as NLP and BigQuery, a highly scalable data warehouse on Google Cloud. 

Getting back in the classroom: MLOps training

After the prerequisite learning was completed, we transitioned into five days of live, virtual training on advanced MLOps with the ASL team. This was a super loaded program, but the instructors were amazing. For this component, we needed to center on real-world use cases that could connect back to our newswire service, making the learning outcomes actionable for our team. We wanted to be extremely mindful of data governance and security so we designed a customized lab based on public datasets. 

Taking a breather and asking questions: Office hours

After nearly three weeks, our team members needed a few days off to absorb all the new information and process everything they had learned. There was a risk of going into the hackathon and being burnt out. Office hours solved that. We gave everyone three days to review what they had learned and get into the right headspace to ace the hackathon. 

Diving in: Hackathon and deployment

Finally, the hackathon was a chance for our team to implement what they had learned, drill down on our use cases, and actually build a proof of concept–or best-case scenario— working model. Our data scientists built an entity extraction API and a topics API using Natural Language AI to target articles housed in our BigQuery environment. On the data engineering side, we built a pipeline by loading data into BigQuery. We also developed a dashboard that tracks pipeline performance metrics such as records processed and key attribute counts.

For our DevOps genius, Donovan Orn, the hackathon was where everything started to click. “After the intensive, instructor-led training, I understood the different stages of MLOps and continuous training, and was ready to start implementing,” Orn said. “The hackathon made a huge difference in my ability to implement MLOps and gave me the opportunity to build a proof of concept. ASL was totally on point with their instruction and, since the training, my team has put a hackathon project into production.”

Informing OSU curriculum with a new approach to teaching MLOps 

The program was such a success that I plan to use the same framework to shape the MLOps curriculum at Oklahoma State University (OSU) where I’m a corporate advisory board member. The format we developed with ASL will inform the way we teach MLOps to students so they can learn the MLOps interactions between data scientists and data engineers that many organizations rely on today. Our OSU students will practice MLOps through real-world scenarios so they can solve actual business problems. And the best part is ASL will lead a tech talk on Vertex AI to help our students put it into practice.

Turning our hackathon exercise into a customer-ready service

In the end, both my team and Notified customers have benefited from this curriculum. Not only did the team improve their MLOps skills, but they also created two APIs that have already gone into production and significantly augmented the offering we’re delivering to customers. 

We’ve doubled the number of related articles we’re able to identify and we’re discovering thousands of new journalists or influencers every month. For our customers, that means they can cast a much wider net to share their stories and grow their media coverage. Up next is our API that will pinpoint more reporters and influencers to add to our database of curated journalists.

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Get value from data quickly with Informatica Data Loader for BigQuery

Get value from data quickly with Informatica Data Loader for BigQuery

If data is currency in today’s digital environment, then organizations should waste no time in making sure every business user has fast access to data-driven insights.  

Informatica and Google Cloud are working together to make it happen. We’re excited to share that Informatica will provide a free service on Google Cloud called Informatica Data Loader for Google BigQuery, which accelerates data uploads and keeps data flowing so that people can get to the insights and answers they need faster. The company made the announcement at Informatica World, on May 24, 2022, describing Informatica Data Loader as a tool to mitigate lengthy data upload times and associated high costs —  challenges that are only growing as organizations ingest more data from more sources.

Maintaining a healthy data pipeline from multiple platforms, applications, services, and other sources requires more work as the number of sources grows. But with Informatica Data Loader, companies can quickly ingest data for free from over 30 common sources into their Google BigQuery cloud data warehouse while Informatica technology automates pipeline ingestion on the back end. This shortens time-to-value for data projects from what could be weeks or months to just minutes, and it frees people up for more strategic data work. 

The Informatica Data Loader empowers Google Cloud customers to: 

Centralize disparate data sources on BigQuery for better visibility into data resources and faster delivery to whoever needs the dataQuickly load data into BigQuery in only three steps, with zero setup, zero code, and zero costOperationalize data pipelines with the power, performance, and scale of Informatica’s Intelligent Data Management Cloud (IDMC) at no costReduce maintenance resource requirements by eliminating the need to fix broken pipelines and keep up with changing APIsAllow non-technical users across the organization to easily access, manage, and analyze data

Informatica partnership streamlines data transformation

This isn’t the first time Google Cloud has partnered with Informatica to help customers get the most value from their data. Google Cloud-validated connectors from Informatica help customers streamline data transformations and quickly move data from any SaaS application, on-premises database, or big data source into Google BigQuery. ​​Our partnership has helped hundreds of customers on Google Cloud.

“Data is fundamental to digital transformation, and we partner closely with Informatica to make it very easy for enterprises to bring their data from across platforms and environments into the cloud,” said Gerrit Kazmaier, VP and GM of Databases, Data Analytics, and Business Intelligence at Google Cloud. “The launch of Informatica Data Loader will further simplify the path for customers to bring data into BigQuery for analysis, and accelerate their data-driven business transformations.”

According to Informatica, Data Loader is the industry’s first zero-cost, zero-code, zero-DevOps, zero-infrastructure-required cloud data management SaaS offering for departmental users. Google Cloud customers can access Informatica Data Loader directly from the Google BigQuery console and ingest data from dozens of common sources, including MongoDB, ServiceNow, Oracle SQL Server, NetSuite, Microsoft SharePoint, and more. 

The Informatica IDMC solution is available in the Google Cloud Marketplace, but Informatica is making Informatica Data Loader available to all Google BigQuery customers, whether they use IDMC or not. Informatica Data Loader shares a common AI-powered metadata intelligence and automation layer with IDMC, but companies can subscribe to each use case individually. 

“Expanding our strategic partnership with Google Cloud beyond enterprise cloud data management to offer free, fast, and frictionless  data loading to all Google customers represents a new chapter in our partnership and brings the power of IDMC to everyone,” said Jitesh Ghai, Informatica’s Chief Product Officer. “With the launch of Informatica Data Loader for Google BigQuery, we are enabling every organization to put the power of their data in the hands of business users so they can move from data ingestion to insights at a speed never before possible.”

Learn more about the Informatica Data Loader for Google BigQuery here.

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7 Things Data-Driven Healthcare Providers Must Consider with ePCRs

7 Things Data-Driven Healthcare Providers Must Consider with ePCRs

Big data is changing the future of the healthcare industry. Healthcare providers are projected to spend over $58 billion on big data analytics by 2028.

Healthcare organizations benefit from collecting greater amounts of data on their patients and service partners. However, data management is just as important. Smart healthcare providers are using ePCRs to facilitate data management.

ePCRs Help Healthcare Providers Manage Patient Data

Since they were widely introduced, Electronic Patient Care Reports (ePCR) systems have proven to be one of the most vital assets available to emergency medical service (EMS) providers. These systems have dramatically cut down the amount of paperwork for EMTs, paramedics, and other EMS personnel. More importantly, they have empowered EMS operations to save more lives with fewer resources by better organizing patient data.

However, most ePCR software have been around for decades and require an update. To date, some software developers specializing in ePCR for ambulance services have come up with many innovative updates, following the larger trends of big data, secure cloud hosting, and customizations, to name a few. If it’s been a while since your organization acquired its current ePCR solution, an upgrade is likely going to give you substantial benefits.

That said, not all ePCRs for ambulance services are made equal. To truly benefit from an upgrade, it’s worth considering these things before upgrading your system with an ePCR to manage patient data better. This is a way for emergency services to use data to improve patient care.

1.) NEMSIS and HIPAA compliance

The healthcare sector has to take data security and privacy very seriously. All ePCR software sold in the US needs to be compliant with the National Emergency Medical Services Information System (NEMSIS) and Health Insurance Portability and Accountability Act (HIPAA). Additionally, the vendor maintaining the software should have a good track record of maintaining compliance with both federal and state requirements. This will help your organization avoid potential legal issues related to the handling of sensitive patient data.

2.) Appropriate Customization Levels

Earlier generations of ePCR software for ambulance services still had plenty of kinks to work out, particularly in their customization features. For one thing, it was often too easy or too difficult to create data fields, with either situation causing all kinds of problems from errors and difficulty completing ePCRs to fraud and false claims. Thankfully, this problem has been solved with newer ePCR solutions, such as the one offered by Traumasoft. Whichever solution you do choose, it’s important that it could be customized to meet your organization’s needs while still maintaining the required standards for data security.

3.) Scalability

To address all issues with scalability, EMS providers should consider ePCR solutions hosted on cloud servers. Cloud-hosted solutions not only eliminate the need to maintain onsite servers and a large IT team, but they also tend to be less expensive to use and offer superior uptimes compared to most traditional locally-hosted solutions. Cloud hosting also simplifies operations for EMS organizations that are located in multiple geographic areas.

4.) Requires Minimal Training to Use

Given the lifesaving mission of EMS, onboarding users to a new system should not be something that uses up too much time and resources. If you’re going to choose a new ePCR solution, a functional one that’s easier to use and requires less training is usually a better pick than a fully-featured one that gives users a hard time.

5.) Vendor Reputation

Before going through with any big purchase, it’s important to check the vendor’s reputation. It’s no different when you buy ePCR systems or any other kind of essential software. Doing your homework helps you understand the level of service you can expect should something go wrong or should you desire any customization. And, as mentioned earlier, learning more about the vendor can also give you an idea of how well they maintain legal requirements and standards.

6.) Enables Seamless Data Standardization

Ideally, data documentation and formats should be standard throughout an organization. In practice, this is not always possible because of the specific needs of different departments of EMS organizations. Unfortunately, this situation can lead to a lot of accuracy problems when consolidating data for reports or data.

Thankfully, current ePCR solutions enable ambulance crews, back-office workers, and other stakeholders to easily draw data from one system. This removes the need for manual data wrangling and vastly improves data transparency and accuracy. Additional time savings could also be gained, thanks to the straightforward automation this standardization allows.

7.) UX Is Optimized for Field Conditions

One of the issues EMS providers had with first-generation ePCR solutions was the poor user experience (UX) for ambulance crews. Paramedics and EMTs work under very different conditions from personnel working in the station or back office. Given the practical constraints faced by ambulance crews, the relatively crude UX design of older software often-times led to frustration with ePCR systems and its underutilization.

Current systems that have addressed most of these problems are thus better suited for the common challenges faced by EMS workers. The improved UX also allows the organization to reap the full benefits of its ePCR solutions.

ePCRs Are Crucial for Managing Data in Healthcare Organizations

Vendors focused on ePCR for ambulance services now understand the needs of EMS providers better than they did a decade ago. This is an important part of using big data for healthcare providers. As a result, modern ambulance ePCR solutions are easier to use, more resistant to fraudulent activity, and enable a wider degree of synergy within the organization and with insurance providers. All of these translate to very real benefits for ambulance crews, healthcare stakeholders, and the communities served by EMS organizations.

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QA Teams Need All-in-One Data Analytics Platforms for Testing

QA Teams Need All-in-One Data Analytics Platforms for Testing

Data analytics is an invaluable part of the modern product development process. Companies are using big data for a variety of purposes. One of the most essential benefits is with the QA process.

Advances in data analytics have raised the bar with QA standards. Companies need to invest in higher quality data analytics solutions to make the most of their QA methodologies. This entails creating robust, all-in-one solutions, rather than relying on a fragmented set of big data tools.

All-in-One Big Data Platforms Are Key to Successful QA Systems

QA engineers use a variety of big data tools in their work. All of these solutions are designed to make work more efficient, but in practice very often there is confusion. In this case, it is expected that each new tool will complicate things. This is not entirely true.

A high-quality testing platform easily integrates with all the data analytics and optimization solutions that QA teams use in their work and simplifies testing process, collects all reporting and analytics in one place, can significantly improve team productivity, and speeds up the release. Let’s figure out what a testing platform is and why it is beneficial to use it. This is an important part of big data testing for teams.

Why is a testing platform a necessity for Agile-teams? Hint: It’s all about release speed

QA teams need a data analytics platform that would help them work effectively in a number of areas:

Run simple automated tests.Writing scripts and running complex automated tests.Data reporting.Deep data analysis.Communication with developers, as well as with management.

A significant role in this matter is played by the fact that more and more companies are implementing Agile and DevOps methodologies. They do this to speed up software development and get to market faster. Integrating testing into these software delivery models requires new QA tools that can be easily integrated into open-source test automation solutions for data engineers and QA specialists.

Thus, the presence of an all-in-one platform that integrates the entire workflow of QA engineers allows you to strengthen the potential of the team, simplify and significantly speed up many processes.

Top crucial capabilities of good testing platform

One place of truth

High quality test automation platform allows QA engineers to plan and complete all the required work. The platform easily integrates with a variety of tools and collects all data in one place.

Orchestration: Set up one synchronous process

Test Orchestration is the setting up of a well-defined sequence of automated test activities. For example, at the end of a development sprint, when the software is ready to be released, there are several steps that take place before the application is made available to users.

Each of these test actions can be individually automated, but manual intervention is still required to run all tests at the right time with the correct input. Orchestration helps link each of the individually automated test activities into one synchronous process.

With orchestration, you can run multiple rounds of testing within a limited amount of time and still achieve the desired level of quality. Test orchestration can be used by commands to perform several use cases:

Comprehensive testing of complex business scenarios that include multiple combinations of APIs and services.Upgrade testing a release candidate against multiple upgrade paths. A certain version of the software may be upgraded from several previous versions. Testing each of the possible update paths helps to avoid a number of problems for users in the future.Special security testing to identify security vulnerabilities in software or vulnerabilities with software dependencies.Testing compliance with predetermined standards and rules.

The right data analytics algorithms help with all of these steps.

Launcher: Don’t worry about the execution environment

A launcher is a predefined configuration associated with a specific test repository that allows you to perform parameterized tests. The test run configuration should be available for any testing frameworks. You can configure presets for your team and run tests in one click.

Today, more and more companies have QA teams. The main responsibility of these teams is to automate the execution of test cases using the programming language. But having automated test suites is only part of what is needed. The most important part is to run these tests regularly to get instant feedback on software quality.

This is not as simple a task as it may seem at first glance.

At a minimum, this requires setting the environment for the programming language in which the tests are written. As the number of automated tests increases, the test suite becomes more time consuming, requiring some kind of scaling for the execution.

Test automation platform allows you not to worry about the execution environment. QA teams can focus on the test automation. Good platforms provide a tool that can be used to execute test suites stored in a Git repository and get comprehensive reporting at the same time.

Analytical capabilities: Take advantage of AI/ML

Test automation platform should provide a centralized place for reporting and automatically collect and analyze data from test launches, as well as report results in real time. A platform integrated into DevOps and CI/CD pipelines can do much more than just provide basic reporting. Test automation platform collects and links information from git-repositories, test launches, and a range of Agile tools to provide deep analytics of test activity such as requirements coverage, release readiness, test flakiness.

Test automation platform provides correct information for employees considering their role on the project. So, QA engineers get the information they need to work on product quality assurance, developers get the information that points to the problem spots in the code, and managers get the analytics and metrics, which allow them to evaluate the effectiveness of work on the product and its readiness for release.

High-quality test automation platform provides data processed with AI-technology. Thus, key decisions such as product release will be made on the basis of data rather than intuition. You can also train AI by yourself to improve the accuracy of its operation and facilitate the identification of failed tests in the future. 

Reporting: Get all information in a few minutes

Creating a high-quality report for the C-level can take a lot of time: you need to collect statistics from various resources, analyze it, identify trends, build charts, and calculate the necessary metrics. Test automation platform will create a high-quality report in a few minutes.

Zebrunner Testing Platform as the optimal solution

Zebrunner Testing Platform is an efficient test automation management solution. The platform significantly accelerates test execution, provides instant reporting and deep analytics, ensures full transparency and availability of test results.

Zebrunner is an advanced solution for scaling test execution, smart and transparent reporting, and QA team management. The tool helps save time and effort and makes automated testing as productive as possible.

There are critical capabilities of the Zebrunner Testing Platform:

Fast test execution and scalable testing environment. Many companies run tests in their own runtime environments with limited scalability. This is time-consuming. If a product, for example, is covered by 1000+ automated tests, their execution can take over 8 hours. As a result, the release is delayed and the project team is overloaded. Zebrunner Selenium Grid helps you run tests in parallel in the cloud or on-premise (locally). You can execute up to 1000 threads in 15 minutes. The team gains time to fix the bugs, and managers can be confident in the release.

Transparent reporting. Preparation of a high-quality report on the results of the tests can take lots of hours. Zebrunner Testing Platform does this automatically. Users can customize numerous widgets and dashboards to fit their metrics and create reports within minutes.

Analytics. Zebrunner helps QA engineers check for failed tests, find their causes, and provides many test artifacts (test logs, screenshots, videos). It is a perfect data analytics tool.

After running automated tests, a lot of time can be spent analyzing the causes of failures. Zebrunner Testing Platform solves this problem by using AI/ML automatic classification of test failure reasons. You can add links to bugs directly to Jira and GitHub so that the developer understands where and what needs to be fixed. Recurring problems can be identified using the test history.

Test process monitoring. Zebrunner Testing Platform gives management access to the QA process 24/7. Managers see release timelines, test coverage, ROI, KPI, so they can easily identify gaps in team productivity and optimize workload.

QA Teams Need the Best Data Analytics Platforms

Data analytics is crucial for QA processes. These companies need to invest in the right data tools to do their jobs successfully.

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6 Ways Data Analytics Can Improve Targeting with LinkedIn Ads

6 Ways Data Analytics Can Improve Targeting with LinkedIn Ads

Big data has become a very important part of modern marketing practices. More companies are using data analytics and AI to optimize their marketing strategies. This is especially true for companies using digital marketing practices, such as social media.

LinkedIn is one of the platforms that helps people use big data to facilitate online marketing. You can use their sophisticated analytics dashboard to improve your marketing strategies. Sprout Social has a blog post on accomplishing this.

However, there are other ways to use big data to get the most of your LinkedIn marketing strategy. One option is to use data analytics to improve your LinkedIn Ads targeting.

Data Analytics Can Improve the Performance of Your LinkedIn Advertising

It is well known that LinkedIn is built on big data. This creates a lot of advantages for its advertisers.

LinkedIn is essential for any business focusing on B2B outreach or trying to reach decision-makers in other organizations. With a formidable strategy, you can target your ideal audience, convert them, and increase sales. Fortunately, including LinkedIn’s audience targeting options in your marketing strategy helps you optimize your budget and reach potential clients and audiences with higher chances of converting.

Consequently, this article discusses six targeting options on LinkedIn and how you can use them to drive better results in your campaign strategy. You will get a lot more value out of them by using big data to improve your targeting strategies.

What Are the LinkedIn Audience Targeting Options?

Most experienced digital marketers are familiar with different targeting options across several social networks. They also realize that data analytics helps you improve your digital marketing strategies. Although most of these social platforms allow you to target people based on demographics or interests, LinkedIn retargeting options do more. 

This section looks at the six LinkedIn targeting options available, including the following. If you pay close attention to the data in your reporting panel, then you can optimize your ad targeting for optimal conversions.

#1 – Location

LinkedIn makes the location targeting option mandatory for all ads. When using this option, your targeting can be specific or broad. For example, targeting people by city or metropolitan area is specific, while targeting people by state or country is broad.

Furthermore, with the location targeting option, you can reach people based on their IP address or the location listed on their profile. Also, if you are trying to reach people unfamiliar with your business, this targeting option allows the exclusion of locations.

You want to look at the data in your reporting panel. You may be surprised to find some regions convert much better than others, which will help you optimize your campaigns best. Always let your data guide you.

#2 – Company

One of the biggest benefits of the data infrastructure provided by LinkedIn’s marketing platform is that it allows you to target different companies. LinkedIn has extensive data on the companies in its network.

You can actually target your ads to people working at different companies. If you track your conversion data carefully, you will see which companies tend to drive the highest conversion rates.

Unlike Location, Company is an optional targeting method, and it contains several other options. These options include company connections, name, industry, size, and followers. Now, let’s look at these.

Through the company connections targeting option, you can reach any 1st degree employee connections at the companies you select. However, you can only use this option for companies having more than 500 employees.

Company name targets users based on their listed employer. The targeting option focuses on the company employees who maintain LinkedIn pages.

The company industry targets the primary sector where a user is employed, while company size targets users based on the size of the company that employs them. Similar to the company name option, company size is based on the number of employees listed on the company’s LinkedIn page.

Furthermore, the company followers targeting option focuses on people following your LinkedIn page. Therefore, before using this targeting option, you have to link your ads account to your LinkedIn page. Otherwise, your ads will reach both your followers and non-followers.

#3 – Demographics

LinkedIn demographics targeting option has two categories – member gender and member age. Member gender depends on what a user puts on their profile, while member age estimates the user’s profile information.

Data-driven marketers will take the time to review their conversion data to see which genders are most likely to convert. They can also use data mining to take a deeper look at the factors that drive conversions. They might find that women in some regions or age groups convert better than men with similar demographic factors and vice versa. You don’t want to make blind demographic targeting decisions without detailed data to guide you.

#4 – Education

The education targeting option targets users based on their field of study, degrees, and member schools. The field of study category focuses on a member’s degree’s major area of study. This option also targets people based on their selected degrees.

On the other hand, member schools target schools, colleges, or universities where users have completed courses. At the same time, degrees let you target the users based on their achieved rank at a learning institution.

You will also want to use data analytics to get better insights into the behaviors of people with different educational backgrounds. You will be able to make better data-driven decisions about your educational targeting. For example, you might find that STEM majors convert very well, while people with degrees in liberal arts do not. Nuanced insights like this can be derived from data analytics, which is better than just haphazardly targeting anyone with a bachelors degree.

#5 – Job Experience

Like the company targeting option, job experience also has several options for targeting users. Through job experience, you can target users based on their job functions. Please note that the job function is a standardized grouping of the job title a user enters.

Other targeting options under job experience include job seniority, job title, member skills, and years of experience.

Job seniority targets users based on their rank and position in their current position, while job title targets users based on their currently listed title. Fortunately, when users update their profile roles, LinkedIn algorithms group the titles and organize them into standardized ones.

The member skills function targets users with relevant keywords in the skills section indicating their expertise in such areas. This option is broad, and it includes skills entered by members, the skills they mention in their profile text, and the skills inferred based on what they list.

In addition, the years of experience option target users based on their accumulated years of experience. Because experience gaps may affect your ads, LinkedIn excludes them. Furthermore, the platform does not double count overlapping experiences.

#6 – Interests and Traits

The interests and traits targeting option reaches users based on shared group interests and individual member interests and traits. It is important to note that users’ interests and traits depend on their profile information, online engagements, and actions.

Data analytics can also give you more insights into the behavior of people with different traits and interests. You should keep detailed records, since there are so many variables to look at.

Use Data Analytics to Optimize Your LinkedIn Ads

There are several ways of targeting users with LinkedIn ads. Because of the several ad formats available, you can be creative with your ads, creating the ones suiting your target audience.

You don’t want to make these decisions without detailed insights derived from big data. Data analytics technology can make it a lot easier to get a high ROI from your LinkedIn Ads.

Furthermore, the audience targeting options discussed allow you to reach different users with higher chances of conversion. Nevertheless, you have to be strategic, flexible, and learn what works best for your business.

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Big Data Creates Many Great Opportunities With Sales Automation

Big Data Creates Many Great Opportunities With Sales Automation

Big data technology is incredibly important in many aspects of modern business. The sales profession is one of the areas most affected by data. Companies spent $2.8 billion on marketing analytics in 2020 alone. That figure is growing substantially each year.

There are many ways that big data is helping companies improve sales. One of the biggest benefits is that it can help automate many aspects of the sales process.

Big Data is Helping Improve Sales Processes Via Automation

It requires hours upon hours to establish relationships and constantly communicate with consumers to keep them happy. This could involve phone conversations, appointment setting, emails and much more. That’s a significant amount of time spent learning about your customers and their interests, gathering information, collecting data, and checking up. As your lead and client lists become longer, managing your workflow becomes more difficult.

Keeping track of login details, transaction data, openings, salesforce, goods, services, leads, and clients manually is a massive burden to the employees. Many traditional companies continue to gather data mechanically and run their sales operations using spreadsheets.

Every part of the business is affected by technological advancements, from how we present and scout to how we complete transactions. Due to the benefits of automated technologies powered by artificial intelligence and data analytics, sales staff may now concentrate on the most vital aspects of the sales cycle.

Sales automation is a pot of gold for marketing and sales organizations aiming to increase productivity and profitability. Smart companies are using big data to make it possible. They can get a lot of value from it by using big data to understand the sales pipelines and automate many processes. In this article, we will discuss how sales automation works and how to use automation to optimize your sales process.

Use big data to facilitate automation to save time during the sales cycle

When automating a sales process, it’s important to keep an eye on how well the system is working. You’ll inevitably make some poor judgments and allowing irresponsible blunders like emailing unsuitable responses or using the wrong contact name could damage your reputation.

This is where data analytics is going to come in handy. You can carefully review data to see how different aspects of your funnel are performing, which will increase the ROI of your funnel and help you identify ways to improve efficiency.

You must also ensure that your potential buyers don’t feel like communicating with the robot. If it’s clear that the interaction that’s supposed to be personal has been automated, or if you’re not answering a user’s inquiries or worries accordingly, they could lose trust in your brand.

Therefore, strengthen your existing protocols and technologies with automation. It should support what you’re already doing and make it smoother, not take over entirely. There are a lot of data-driven tools that can make this easier. Salesforce combines data analytics and AI to help sales professionals automate many important processes.

Try to incorporate automation throughout your entire sales cycle, from identification and getting familiar with your prospects to how you interact with them via emails, appointments, or phone conversations. Then, leverage lead assessment and selection to reach out to relevant potentials at the perfect time.

Handle your leads efficiently

Lead generation: By automating your lead generation, you’ll never have to worry about keeping your funnel full of fresh leads. You can control every operation by using cold email advertising, social network sales, personalized promotions, etc., rather than investing hours in attracting prospects.

Lead prioritization: After you’ve generated leads, which is typically the first step in the sales cycle, there’s usually a big pile of them. Now, how can you determine who is truly ready to pay for your goods or services? You must develop a framework of the ideal consumer and specify the criteria by which leads will be scored. You can use artificial intelligence-based lead scoring tools to sort them out. They assist salespeople in prioritizing leads and direct their efforts in the right direction.

Lead enrichment: Having more knowledge of the leads at the hunting stage is always beneficial. When it comes to B2B business, considering the prospect’s organization and what it offers, as well as its vendors and online presence, can assist salespeople in interacting with them effectively. They’ll appreciate the fact that you did your homework before pursuing them, and it will help you make a positive impression on potential clients and close more deals.

Close CRM can assist you in prioritizing leads and boosting income. With easy, user-friendly contact filtering, you can increase your reach rate. Create dynamic Smart Views that match relevant lead data, and then automate email and phone workflows around them. Identify your most promising leads, as well as those that have suddenly gone dark and more.

Nurture clients through email

Email lead nurturing means forming a tight connection between a company and its clients via email. There are many email marketing software that enables you to automate your emails. You can use it at any point of the process, and they benefit both parties in a transaction. Businesses have access to a low-cost, simple, and effective technique of reaching out to their users, and customized, well-timed communication makes consumers feel appreciated.

Email lead nurturing is effective not only for purchases but also for driving open rates, reminding prospects about your brand, providing quick results, and giving a platform to educate and engage leads. It’s a no-lose situation.

Thanks to sales automation solutions, creating drip campaigns is as simple as it seems. Usually, when a new entry for an inbound client is formed, an email is sent to greet them and to introduce the suggestion of booking a product demo. Then there’s a time gap to give the possible buyer some space. After that, they receive a persuasive email that’s supposed to push them into the next sales cycle phase.

Assaf Cohen, who runs Solitaire Bliss, a company that makes games for publishers, explains, “Follow up emails are incredibly important. My reponse rate improves with every successive follow up. However, it’s time consuming. Finding what to automate this allows you to do so much more.” 

Close CRM ensures that you stay on top of your pipeline. Take advantage of automatic follow-up reminders, personalized templates, bulk emails, and other features. Send email sequences on a pre-determined timetable over the course of days or weeks. Design unique initiatives for your customers using a range of recommended pre-built features. You can monitor the delivery and reception of your emails, which helps your team assess the performance of each campaign and connect with potential customers.

Automate the scheduling and monitoring of sales calls

This is only useful for people who make a lot of outbound calls, which has become less of a focus for many businesses these days. If you’re still employing telemarketers, this can be beneficial because it eliminates a lot of complications from your operation.

Close’s CRM tool comes with an auto-dialer, but it isn’t necessarily a function that CRMs have. If your CRM doesn’t have an integrated auto-dialer, you can still use call center software that has that feature.

Handle RFP generation

The RFP outlines the project, its objectives, and the funding organization, as well as the tendering and contract conditions. Systems based on natural language processing/generation and robotic workflow control can assist cut the amount of time needed to generate requests for proposals (RFPs) by up to roughly 2/3 while eliminating human error. 

For instance, it can analyze the unanswered questions and recommend solutions in a personalized document that you can automatically send to your lead. This type of technology can improve corporate version control and storage of key RFP material while also speeding up RFP response time and productivity.

Post-sales customer journey optimization

There are many reasons to use big data in marketing. One of the most important benefits is that data analytics can help you get a better understanding of your customers’ experiences. You can use this data to improve the funnel and boost customer satisfaction.

You can employ process automation to redefine the user experience and establish a smooth online purchasing, tracking, and dispute management procedure. Brands can utilize data technology to communicate with clients after the transaction in a lot of formats, for example:

Keep an eye on online reviews: In this era of technology, people do not call or write an email when faced with an issue. They use online comment sites and social networks to express their dissatisfaction and notify others. Furthermore, a single social media message can reach tens of thousands of people before a company can respond. A company’s failure to reply can give the impression that it doesn’t care about its customers. Businesses can use social media monitoring to receive notifications of any online comments, allowing them to respond swiftly and hopefully turn a dissatisfied consumer into a repeat customer. One of the tools that can come in very handy for keeping an eye on online reviews is justLikeAPI.

Special deals: Offering a discount on a client’s next order is a good strategy for increasing customer retention. This can be included in the follow-up message or completed at the time of the transaction. Every voucher should be valid for a limited time after the original purchase in order to entice customers to return, at which point they will be given a new coupon. It will not attract every buyer, but those who can’t resist a great offer will return.

Support is crucial

Even the best companies will have some difficulties now and then. Users should be able to seek support in different formats, whether it’s through a site, chat support with client care agents, email, or telephone conversation. The level of service provided to a customer will determine whether or not that client returns. It will also most likely define whether or not you will create a brand ambassador who will promote a company to their peers and social media contacts. Businesses should invest in a mix of automated technical support tools and personnel capable of offering clients prompt, polite responses when they encounter problems because this is a very important part of the sales process.

Characteristics of the best sales automation tools

All of your quotes and transactions are together: Custom quotations can be generated, maintained, and shared straight from your sales automation platform. Enable customers to confirm a section with a single click, transfer money, automatically turn quotes into sales, and send email alerts to sales representatives when offers are taken or denied.

A simple landing page maker: Go with a user-friendly interface that allows you to develop landing pages without knowing how to code. You should optimize your landing pages for smartphones and make them load as fast as possible. If possible, look for a landing page builder that includes first-rate royalty-free photos.

Automate and customize interactions: Take advantage of the information you’ve gathered in your customer database to nurture leads and increase revenue. Send a series of tailored emails using autoresponders, or create nurturing series based on specific activities.

Funnel management: Automated follow-up notifications, modeling depending on potential customer demands and trends, and statistics by product, main source, and other variables can help you simplify your sales process.

Merging with third-party systems: Consider the solution that works with the toolkits you already use. Do you have an online shop? Sync with Shopify, BigCommerce, WooCommerce, etc. Would you like to make your financial reporting more efficient? Sync with Xero or QuickBooks.

Visual appearance: The best sales automation tools are only as good as the graphics and design that they utilize. You need to make sure that your templates or slides are easily customizable and that you have the right fonts and images before you release them to your audience. Sales automation tools can be a great way for businesses to improve their company culture, but only if the slides and templates aren’t too difficult to customize. For example, including fonts and graphics is a great way we can make our templates more appealing for clients.

When not to use sales automation

Even though sales automation brings a lot of benefits, it cannot replace humans. AI has not yet evolved to the extent shown in sci-fi films. We’ve only recently begun to witness talking robots.

There are specific activities where software is no match for a person, no matter how advanced machine algorithms are. While AI can assist in making the client experience more personalized, it cannot produce new sales techniques. We use tools to execute rather than create sales strategies.

A fully automated client journey is neither recommendable nor possible to achieve. While chatbots can assist salespeople, they can sometimes feel somewhat impersonal. They can deliver user experiences that are robotic and monotonous. Your customers will eventually prefer to speak with a human over a robot.

Also, have in mind that automation tools are usually sophisticated software. You might not be ready to use them right away after acquiring them – you will have to learn first. Always have a well-thought-out strategy to examine each UI screen of the tools under review. Luckily, there are free trial periods because of this.

Customer relationship management system

CRM system is the most powerful digital tool you can use. These tools use data to help companies built the best relationships with their customers. It’s where most of your user information will be stored, and it will also serve as the hub of your sales pipeline. Everything your workforce must have about buyers can be found in your CRM or CRM-integrated software.

Investing in a CRM and moving your data there should be one of the first steps. If you still work with paper-based data, think about what can be transferred to a new system. This will allow automatic customer data updates and corrections and categorize by demographics or lead qualification characteristics.

Close CRM allows you to keep track of all of your sales operations in one place. Emails, calls, voicemails, assignments, and notifications are automatically arranged, so you always know what’s going on and what to do next.

Everything that requires attention is in your Inbox. Whenever you fulfill a task or return a missed call, that activity will disappear from the list. Close syncs everything you do to Inbox, ensuring you stay on top of your workflow.

Your CRM should speed up your sales process, not slow it down. Close was built with the salespeople in mind – it works the way you do.

Most of the manual processes that employees would have to handle – like altering contact details, arranging meetings, and possible closing deals – are eliminated by sales automation. It maximizes your productivity and performance while also saving your time. As a result, you are free to do business without worrying about administrative responsibilities.

Big Data Helps Automate Sales Processes

There are many great benefits of using big data to improve your marketing strategy. One of the biggest advantages is that it can help automate your sales processes. The tips listed above will go a long ways towards streamlining these functions.

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How AI Can Supercharge Your Ransomware Defense In 2022?

How AI Can Supercharge Your Ransomware Defense In 2022?

Human-operated ransomware attacks have threat actors using certain methods to get into your devices. They depend on hands-on-keyboard activities to get into your network.

AI can protect you in the event of these and other attacks. Since the decisions are data-driven, you have a lower likelihood of falling victim to attacks. The decisions are based on extensive experimentation and research to improve effectiveness without altering customer experience.

With AI, the risk score for a device doesn’t depend on individual indicators. Instead, it is influenced by a variety of features and patterns. They alert you when there is an imminent attack.

Even if attackers use an unknown or benign file, the AI system will ensure that the process or file isn’t launched. Here are a few ways that AI will supercharge your ransomware defense in 2021. 

1. Predicting If a Device Is at Risk

Ransomware removal is great but preventing attacks is even better. If your device comes under attack, there are a few indicators to look out for. While they wouldn’t mean much in isolation, they are very meaningful when put together over time.

AI-driven protection assesses your device when a new signal is detected. Therefore, the risk score is always being adjusted accordingly. Signals to look out for include malware encounters, behavior vents, and threats. 

 If a device has wrongly been scored as ‘not at risk’ when it is really at risk, attackers could conduct activities that detection technologies will have a hard time catching. On the other hand, if a device is determined to be at risk when it really isn’t, customer experience will suffer.

AI technology finds the perfect balance. You get to find out if a device is at-risk without compromising on customer experience. 

2. Identifying and Blocking Out the Abuse of Legitimate Files and Processes

Human-operated ransomware attacks have a hands-on-keyboard phase. During this phase, attackers take advantage of legitimate files and processes.

Network enumeration, for example, is naturally a benign behavior. However, observing it on a compromised device could prove that attackers have been performing reconnaissance activities. 

 Adaptive protection is designed to block network enumeration behavior. It cuts off the chain of attack, preventing more attacks. 

3. Personalized and Contextual Protection

The blocking mechanism on the cloud is very responsive to real-time risk score computation. This means that the system can make informed decisions. They result in stateful or contextual blocking in your device. 

 The protection customization that comes with AI ensures that every device has a unique protection level. For example, process A may be permitted on one device and blocked on another. It all depends on the risk score. 

 The personalization feature is especially helpful for customers. They are unlikely to get false negatives or positives. Unlike ML models trained on datasets, every device gets the level of protection it needs. 

4. Stopping Ransomware Payloads

Some attacks aren’t detected or blocked until they get past the mid-stages. With AI-driven adaptive protection, you can still get a lot of value on the final ransomware payload.

If your device has already been compromised, AI-driven protection systems will automatically use aggressive modes to block out ransomware payloads. They will prevent the encryption of essential data and files. It would be impossible for attackers to demand ransom. 

 Are you trying to improve your ransomware defense in 2022? Consider using AI to supercharge your efforts. It works by predicting if your device is at risk, stopping ransomware payloads, and offering personalized protection. Preventing these attacks is a lot easier on your business than dealing with actual attacks. Successful ransomware attacks can cost you your time and data.


Ransomware has become a very serious problem in recent years. The good news is that advances in artificial intelligence have helped companies protect themselves. You should not overlook the importance of using AI as a first line of defense.

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