Archives May 2023

Data Analytics Helps Marketers Make the Most of Instagram Stories

Data Analytics Helps Marketers Make the Most of Instagram Stories

Big data technology has significantly changed the marketing profession over the last few years. One of the biggest changes brought on by big data has been in the field of social media marketing.

Most savvy marketers recognize the importance of using analytics technology to optimize their strategies to get a higher ROI. One example of this trend is by using analytics to measure the engagement of Instagram stories to get customers to interact more frequently.

Data Analytics is the Backbone of Instagram Marketing

Instagram CEO Kevin Systrom has stated that Instagram is in the process of becoming a big data company. Analytics Insight talked about some of the many ways that data analytics is becoming more important for the social media giant.

Instagram uses big data to identify and block offensive content, create personalized feeds for their users and optimize their advertising platform. This technology is understandably very beneficial for marketers as well. shrewd marketers know how to take advantage of the highly granular analytics data available to them, so they can create more targeted marketing campaigns.

One of the many ways that marketers can leverage analytics technology is to create more effective Instagram stories to improve their engagement. Keep reading to learn more.

Creating a data-driven Instagram story strategy to boost engagement

Katie Sehl of Hootsuite shared some very important tips for marketers that want to increase engagement and get better results with their Instagram stories. Katie points out that Instagram stories disappear after 24 hours. however, data is available for months afterwards. marketers can look at previous data to determine which Instagram stories got the best engagement, so they can improve their ROI for future campaigns.

The most important thing to do at the beginning is to know which metrics to focus on. They need to start by looking at the number of people that were reached with the campaign. they will also need to pay attention to the amount of time that those users interacted with their content. However, who is data points are not enough on their own. They will also need to look at analytics data to know more about the demographics of people interacting with their stories. this will give them better insights into which stories are best for maximizing engagement with their target audience.

Once they have used analytics technology to get a better idea of types of stories that their audience is most interested in, you can start creating and effective Instagram strategy. Your Instagram marketing strategy will have a greater chance of being successful, since you leveraged data analytics appropriately.

Are you wondering how to leverage Instagram stories to boost your visibility and engagement? Unlock the power of IG stories to boost engagement, spark conversations, and get more comments on your posts with these tips. 

In This Article

Advantages of Using Instagram Stories 

How Stories Encourage Comments

Tips for Getting More Comments Through Stories

What Not To Do on IG Stories

Wrapping Up

Do you often find getting more people to interact with your Instagram posts challenging? If yes, you’re not the only one! 

In today’s world of social media, given the competition and ever-changing algorithm, it has become more difficult than ever to get your content noticed and stand out.

However, there’s a simple solution that you might need to pay more attention to the use of. And that is Instagram Stories!

You can get more comments with IG stories by interactions or building excitement for your posts. This article will discuss tips for encouraging comments on your Instagram posts.

Advantages Of Using Instagram Stories

With the help of Instagram stories, you can: 

boost sales,

spread the word about your brand,

and even get people to interact with you.

You can share photos and videos via Instagram stories that disappear after 24 hours. Instagram introduced stories in 2016, and till then, it has become a fantastic marketing tool. 

Here are some reasons why Instagram stories can help you with your marketing goals.

Boost brand awareness: Stories are a fun and creative way to show your brand to a broader audience.

Boost your engagement with your audience: Keep your audience returning for more by providing exclusive content and BTS glimpses.

Stay top of mind: Regularly posting on Stories can help keep your brand at the forefront of your followers’ minds.

Reach New Customers: With hashtags and location tags, Stories can help you reach new audiences and attract potential customers.

Track Your Performance: Instagram’s analytics feature lets you track your Stories’ performance to refine your strategy and maximize your efforts.

How Stories Encourage Comments

Do you know that over 500 million daily active users interact via IG stories? 

Here are a few examples of how stories encourage comments:

IG stories are visually appealing and easy to consume. It means that users will watch them from beginning to end and feel more inclined to leave a comment.

Instagram stories have a range of interactive features that encourage users to engage. Some parts are polls, quizzes, stickers and GIFs, Q&A stickers, emoji sliders, and more.

Instagram stories only last for 24 hours. So, it creates a sense of urgency for users to engage with them before they disappear. It can motivate users to leave a comment before the story disappears.

Stories can be a great way to start conversations with your audience. By sharing personal stories or asking for opinions, users are more likely to participate in a discussion, thus helping you garner more comments and views on your post. 

Tips For Getting More Comments Through Stories

Use the Interactive Stickers

Use those funky stickers on your stories to make your account more appealing. These little stamps add fun and flair to your pictures and have a unique interactive twist.

Besides, you’ve also got polls, quizzes, emoji sliders, and questions designed to engage and intrigue your audience. And guess what? Responding is a piece of cake; just a simple tap or swipe does the trick.

Host A Contest

Contests are a great way to get your audience excited and involved.

Here’s what you can ask to do:

Ask your followers to comment on your story,

Sharing your story,

Tagging in your account,

Or like your latest posts.

Do keep track of your notifications, and make sure to choose a winner within 24 hours.

There are even apps that can help you pick a winner effortlessly. You can even record your screen using these apps. Then post the video as a story and give a shout-out to the lucky winner.

Buy Instagram Comments, Real

Consider buying automatic Instagram comments to increase your Instagram engagement and attract more followers. When people see others engaging with your posts, they also want to join the conversation.

Instagram has become a huge obsession, and standing out is vital. That’s where this comes in. With Skweezer, you can buy automatic Instagram comments from active, engaged users, making your posts more appealing.

Choose Skweezer for the best solution to buy Instagram comments organically and instantly to help garner the best traction for your posts. 

When you buy comments on Instagram, you’ll unlock many positive effects and benefits that can transform your Instagram experience.

Here’s what you can expect:

Boosted Engagement: Increase your profile visibility and explore page ranking.

Attract More Followers: Social proof catches attention, so more comments mean more followers.

Meaningful Connections: Comments show genuine interest, making you magnetic to others.

Time Efficiency: With a flurry of comments, others will engage, giving you more free time for content creation and personal activities.

If you want to take advantage of these benefits, buy custom Instagram comments and watch your Instagram dreams come true.

Keep Your Text Short and Sweet

Instagram is all about eye-catching visuals, so keeping your text short and sweet is smart. 

It is crucial for Stories. Why? Well, your audience only has 7 seconds to view your photo. Imagine if they spent that time struggling to read a long text—a total bummer.

Keep your text concise because it is easy for them to engage with your content. And you already know that more engagement means more comments. You don’t want excessive text being a distracting element to keep your users from the incredible visual elements, either.

So, simplicity is critical. To summarize, trim the text, let your visuals shine, and watch those comments roll in.

Tell a Story

By creating a compelling story through your slides, work towards captivating your followers and keeping them engaged. A great story has a clear beginning, middle, and end. Start with a hook that grabs your audience’s attention and leaves them craving more.

Now, here’s the exciting part.

You can boost your comments by weaving a story in IG Stories. People are likely to leave comments and join the conversation when intrigued and invested in your content.

Go Live

Instagram loves live broadcasts. It prioritizes them and puts them in front of your audience’s feeds. So, you’re guaranteed to get more eyeballs on your content.

You can engage with your audience instantly and directly when they like or comment on your live video. And you see it in real-time, allowing you to respond and connect with them immediately.

Here’s an extra perk; Your live stream doesn’t disappear after it’s done. It transforms into an Instagram Story for all those who missed it.

Create Your Hashtag

Using hashtags properly can significantly increase your brand’s exposure on social media. Creating your personalized hashtag is one of the best ways to get going. It will allow you to increase your visibility and gain more followers.

Ensure your hashtag is short, catchy, and easy to remember. It will encourage users to use and share the content with friends and followers.

Additionally, you should promote your hashtag on Instagram Stories. It will help you

get the word out,

get more people to use it,

and talk about it in the comments.

Who knows? Your hashtag could become the next social media craze.

What Not To Do On IG Stories

Here are some “not to do” tips for IG stories:

Avoid using stories as a platform to vent; keep it more like a conversation with a friend

Don’t rely on auto-captions, as they slow down the viewing experience; instead, make your stories skimmable and accessible

Always caption your stories to ensure inclusivity and accessibility

Keep your stories concise and to the point; people have a short attention span

Show your face, and don’t hide behind your work or product – people like human-to-human connection

Maintain visual consistency by using a limited number of fonts and colors that align with your brand

Instead of posting all your stories at once, spread them out throughout the day for better engagement

Aim for consistency in posting stories; find a schedule that works for you and sticks to it

Mix typed-out answers with talking responses in Q&A sessions to cater to different preferences

Wrapping Up

Instagram Stories help provide a valuable platform to engage with your audience and encourage comments on your posts. As discussed in the article, implementing a few strategic tactics increases the likelihood of receiving comments and fostering meaningful interactions. 

If you’re looking to accelerate the engagement process, you can consider exploring options to buy Instagram comments.

Although with tools like Skweezer, you can buy real Instagram comments, it’s important to remember that genuine and authentic engagement should always be the primary goal.

Strive to build an engaged community of followers who are genuinely interested in your content.

So, what are you waiting for? Start incorporating IG Stories into your social media strategy today and watch your engagement soar.

Marketers need to use big data to make the most of their Instagram stories

Big has significantly changed the marketing world. A growing number of Instagram marketers are finding clever ways to leverage data analytics 2 improve their strategies. one of the most important benefits is that they can use big data to create more engaging stories for their audiences.

Source : SmartData Collective Read More

Tackling Bias in AI Translation: A Data Perspective

Tackling Bias in AI Translation: A Data Perspective

The world of artificial intelligence (AI) is constantly changing, and we must be vigilant about the issue of bias in AI. AI translation systems, particularly machine translation (MT), are not immune to this, and we should always confront and overcome this challenge. Let us uncover its implications in AI translation and discover effective strategies to combat them.

Understanding Bias in AI Translation

Bias in AI translation refers to the distortion or favoritism present in the output results of machine translation systems. This bias can emerge due to multiple factors, such as the training data, algorithmic design, and human influence. Recognizing and comprehending the different forms of algorithm bias is crucial to develop effective strategies for bias mitigation.

Types of Algorithmic Bias

Algorithmic bias can manifest in several ways within AI translation systems. To help you better understand what machine learning biases are, we have listed some of the biases that machine translation companies encounter that affect the performance of their translation system.

Data Bias: Sources and Implications

Various sources, including historical texts, biased human translations, or imbalanced data representation, can originate limited training data. Making data bias significantly concerns and directly influences the performance and fairness of AI translation systems.

When you leave data bias unaddressed, it perpetuates discriminatory outcomes and undermines the credibility of AI translation. Always make it your top priority to identify and rectify these biases to ensure unbiased translations.

Pre-existing Bias in Training Data

Within training data, AI translation systems frequently reflect societal prejudice. They inadvertently reinforce prejudice, cultural bias, and gender bias in machine translation. Recognizing and acknowledging these pre-existing prejudices is the first step in minimizing their impact on translation outcomes.

Representation Bias: Challenges of Diverse Language Data

Representation bias occurs when the training data inadequately represents diverse language samples. This issue presents unique challenges because it underrepresents some languages or dialects, leading to less accurate translations for specific language groups.

Overcoming representation bias necessitates comprehensive data collection efforts that cover a wide range of languages and dialects, ensuring equal representation and inclusivity.

Labeling Bias: Impact on Model Performance

The presence of labeling bias in AI translation systems will significantly impact the model’s performance. When annotators train data with biased information, the model learns and replicates these biases, resulting in inaccurate translations and reinforcing discriminatory narratives.

Critically examining the labeling process and ensuring unbiased annotations will enhance the performance and fairness of AI translation models.

Assessing Bias in AI Translation Systems

To effectively tackle bias in AI translation, we listed methods for assessing and measuring bias in the output results. Robust evaluation metrics can offer insights into the presence and extent of prejudice, enabling us to identify areas that need improvement.

1. Measuring Bias in Output Results

Comprehensive and nuanced approaches are necessary to measure bias in AI translation output results. It involves analyzing translations for potential biases based on gender, race, culture, and other sensitive details. 

2. Evaluation Metrics for Bias Detection

Developing appropriate evaluation metrics for bias detection is essential in effectively addressing bias in AI translation systems. These metrics should go beyond surface-level analysis and consider the impact of translations on different language groups.

3. Identifying Disproportionate Impact on Specific Language Groups

Bias in AI translation can disproportionately impact specific language groups, perpetuating inequality and marginalization. Identifying such disparities and understanding the underlying causes to develop targeted mitigation strategies is crucial. 

Mitigating Bias in AI Translation

Addressing bias in AI translation requires a multifaceted approach. AI translation companies must implement various strategies, such as reducing bias through data preprocessing techniques, collecting unbiased data, and using annotation strategies, applying model regularization and fairness constraints.

Prioritizing explainability and interpretability for bias analysis while integrating ethical considerations into the development process is required to mitigate the AI translation bias.

Data preprocessing techniques significantly reduce bias in AI translation systems. These techniques involve carefully examining and cleaning the training data to remove or mitigate biases present in the text. By applying methods such as data augmentation, language-specific preprocessing, and balancing data representation, you can enhance the fairness and accuracy of AI translation.

AI translation models must collect and annotate data fairly. Impartial data collection tactics involve actively seeking diverse language samples and considering various cultural perspectives while assessing their viewpoints.

Implementing model regularization techniques and fairness constraints can help mitigate bias in AI translation systems. Model regularization will punish training biases, pushing the model to provide more equal translations. Fairness constraints ensure consistent translations across various language groups, minimizing disproportionate impacts and promoting fairness in AI translation.

Ensuring explainability and interpretability in AI translation systems is crucial for bias analysis. By providing transparent insights into the translation process and highlighting potential biases, users can understand the limitations and context of the translations. This transparency promotes accountability and trust in AI translation systems.

Ethical Considerations in AI Translation

Ethical considerations are paramount in addressing bias in AI translation. It is crucial to prioritize ethical decision-making throughout the development lifecycle. By incorporating principles such as fairness, inclusivity, and respect for user privacy, machine translation company builds AI translation systems that align with ethical standards and societal values.

Ensuring Accountability and Transparency

To effectively address bias, developers of AI translation systems must ensure accountability and transparency. Enabling external scrutiny requires developers’ accurate documentation of the training data, model architecture, and evaluation methodologies. Transparency builds trust and empowers users to have confidence in the fairness and reliability of AI translation systems.

User Consent and Privacy Concerns

Respecting user consent and privacy is crucial in AI translation. Users must have control over their data and be informed about how the translation process uses it. Implementing strong privacy measures and obtaining explicit consent ensures that user data is protected and used responsibly.

Interdisciplinary Approaches for Bias Mitigation

Addressing bias in AI translation requires interdisciplinary collaboration between language experts and AI developers. By fostering open dialogue and knowledge sharing, you will leverage the expertise of both communities to create more accurate and inclusive translation systems.

Bridging the Gap Between Language Experts and AI Developers

Building effective AI translation systems require bridging the gap between language experts and AI developers. Language experts can provide valuable insights into the nuances of language, cultural context, and potential biases. Collaborative efforts will yield more accurate translations that address the needs and preferences of diverse language users.

Continuous Learning and Improvement in Translation Systems

AI translation systems should continuously learn and improve to mitigate bias effectively. Continuous monitoring, assessment, and feedback are required to detect and address issues as they occur.


AI translation is a complex challenge that requires proactive measures. Bias can manifest in data, training data, representation, and labeling, impacting fairness. Strategies like data preprocessing, unbiased data collection, model regularization, and fairness constraints help mitigate bias. Explainability and interpretability promote transparency. Ethical considerations guide development. Collaboration between experts and developers is crucial. Continuous learning ensures ongoing improvement of AI translation systems.

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What to Know Before Recruiting an Analyst to Handle Company Data

What to Know Before Recruiting an Analyst to Handle Company Data

The rate of growth at which world economies are growing and developing thanks to new technologies in information data and analysis means that companies are needing to prepare accordingly. As a result of the benefits of business analytics, the demand for Data analysts is growing quickly.

The Bureau of Labor Statistics reports that the role of research and data analysts is projected to grow as much as 23% in the next 8 years. That is a staggering increase in comparison to most other industries. As the world and its technologies changes, so will the needs as dictated by the demands of businesses and their adoption of new technologies and techniques.

With these changes comes the challenge of understanding how to gather, manage, and make sense of the data collected in various markets. With the introduction and use of machine learning, AI tech is enabling greater efficiencies with respect to data and the insights embedded in the information.

With so much of a demand for data analysis and those skilled and experienced enough to effectively make sense of it all that means certain caliber workers are going to be in short supply.

Here is a brief list of suggestions to inform the hiring for that role.

The Role of an Effective Analyst

Data analysts are responsible for the harvesting, management, analysis, and interpretation of big data gathered. They do this to help provide companies with valuable insights into how to make decisions by deciphering trends that emerge from internal and external forces in a company.

Analysts accomplish this through the use of a variety of tools, mostly computer systems aided by AI, to automate that harvesting and interpretation. Data analysts are in demand in nearly every industry nowadays, from sales, marketing, and even healthcare.

As a business, every customer interaction with that service creates markers and patterns that, when combined, tell a story into how that business, its customers, products, and systems work together and affect each other and the health of the organization.

While there are software systems that can do the job of an analyst in part, the insights that a keen and experienced mind can identify suggestions of what to do with the information rather than just seeing a set of data. Before moving into the hiring process though, it would be helpful to narrow down what type of data your business is managing.

Three Different Analysts

Data analysis as a whole is a very broad concept which can and should be broken down into three separate, more specific categories: Data Scientist, Data Engineer, and Data Analyst. Here are the differences, generally speaking.

Data Scientist

These employees are programmers and analysts combined. This type of analyst should have a broad skill set but one that has a grounding in mathematics, analytical skills and is able to combine those into helpful conclusions. Typically, this role will connect with other positions so teamwork and presentation skills would be great things to look for.

Data Engineer

These people specialize in programming. They use a myriad of IT tools to design and build the databases which store and support the analytical solutions while working in cooperation with management in departments that go beyond the IT roles.

Data Analysts

A combination of the last two roles in some ways but with an emphasis on the analysis, synthesis and presentation of the insights gathered from that data collection.

Skills Sets to Look For

When entering into the hiring process for a data analyst there are a few skills that are recommended to look for when narrowing down the pool of options.

Data modeling will result in how, in part, a business will set standards. Thus, an individual who understands how to present those findings accurately and clearly in actionable ways is important. The ability to artfully represent and explain how the data collected communicates a businesses effectiveness should be a standard skill.

A data analyst will need to be able to effectively utilize many different systems and software programs in order to gather and analyze that data into meaningful actions. Confidence in those softwares which are industry leading, and standards is key.

An intermediate understanding of Structured Query Language (SQL), a standard language among database systems like Oracle, Microsoft SQL, and My SQL, is a minimum requirement for data analysts.

While there are many other skill sets that would be helpful and useful in combination, that list should be more tailored to the company, the employees already in service, and the potential holes that need to be filled. Further research will help to formulate a deeper clarification of what and where to find the right person for the job.

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How Hospital Security Breaches Devastate Local Communities

How Hospital Security Breaches Devastate Local Communities

Healthcare systems are enticing targets for cybercriminals. Private health information can net a large profit on the dark web, making even just one patient’s personal records a potentially lucrative discovery. For cyber terrorists, the goal is even simpler: get in. Do damage. Get out. Their objective is only to create fear and distrust— something they can accomplish quite effectively by making people feel unsafe at their hospitals.

This is all to say that hospital cyber-security breaches can have a devastating impact on the people impacted.

Why Hospitals Are So Vulnerable

Hospital networks are beholden to very strict cybersecurity laws. The same HIPAA regulations that have been protecting patient privacy since the 90s are now applied to digital healthcare technology to ensure that patients enjoy the same level of privacy even in cyberspace. This involves elaborate rules and regulations for how healthcare professionals can use patient data, but it also applies to the software itself. Firewalls and encryption are in place to strengthen cyber security and protect patient records.

Criminals get in anyway.

There are a few factors that lend to their cause:

Hackers often operate beyond the law’s reach: Cybercrime is harder to regulate because attacks can be launched from anywhere in the world. If a group of Russian hackers attacks a rural hospital, there isn’t much that Iowa PD is going to be able to do about it.

They have a lot of access points: Putting patient records in the cloud gave patients an unprecedented level of control and autonomy over their health, but it also created millions of access points for potential hackers. They don’t necessarily need to break into the hospital’s network. If a patient with mobile healthcare technology on their phone uses the wrong WIFI hotspot or opens a questionable link, that could be all it takes.

Small mistakes have big ramifications: Most of the data breaches that you hear about on the news aren’t the result of some elaborate Oceans 11-type heist. Usually, it happens because someone opened a phishing email. Hackers need only the smallest opening to get in. Once they access a system, they can lurk there undetected for years.

All of these points of vulnerability give criminals a big advantage over hospitals.


Healthcare costs are so high for citizens that the idea that a hospital could itself go bankrupt seems absurd, or even obscene. And yet, it happens— most often in small towns and rural communities. In 2019, several dozen primarily rural hospitals closed their doors for good. Then, the pandemic hit. Rather than driving up business for hospitals as one might expect, it cost them hundreds of millions of dollars.

More closed.

Most hospitals operate on razor-thin margins. When a major event takes place— a pandemic, or a cyber security breach— it can have a devastating, sometimes permanent impact on the local community. Through strong leadership and constant vigilance, hospitals everywhere can stay safe from cyber attacks.

The average hospital data breach costs almost ten million dollars. For hospitals already operating within the margins of bankruptcy, that can be enough to do them in.

When hospitals close, it puts an enormous strain on the community they used to serve, and nearby hospitals that now have to absorb their medical needs.

Creates Fear

Establishing fear is sometimes the full motivation of a cyber-attack. In the Spring of 2019, a group of cyber terrorists called Wizard Spider hacked into Ireland’s digital healthcare network and locked the nation out of its own records. They demanded tens of millions of dollars— an outlandish sum that they most likely never had any intention of collecting.

What they wanted was to create fear, and that’s what they did. Ireland took the standard line and declined to negotiate with terrorists. Wizard Spider managed to keep them locked out for six weeks. During that time, hundreds of patients had their healthcare records published online.

If it can happen to Ireland, it can certainly happen to your local rural hospital. In fact, that’s part of the message. When strangers can reach out from anywhere in the world to make a highly coordinated cyber-attack, no hospital is safe.

That fear can lead to people deciding to stay away from organized healthcare altogether. Not only is this bad for them, but it also further harms the hospital itself. The legitimacy of that fear only worsens the situation. Breaches truly can happen anywhere, and they directly impact local citizens.

Cripples Productivity

Cyber-attacks also have a big impact on how hospitals are able to operate. We mentioned earlier that the Ireland breach resulted in six weeks of total system lockout. However, that is only the tip of the iceberg. It can take months to fully recover from the effects of a large-scale cyber-attack.

During that time the hospital won’t be completely destabilized but it also won’t be at its peak. Now, couple that with the plain fact that most hospitals are already in a tight spot because of staffing shortages, and a bigger problem begins to emerge.

Even in the best circumstances, hospitals have a difficult job. Throw in more obstacles and it can have a direct and negative impact on patient outcomes.

Keeping Hospitals Safe

Fortunately, it isn’t hard to keep hospitals safe. Regularly maintaining your cyber security networks does most of the legwork. Everything else is just a matter of staying alert. As mentioned earlier, the majority of breaches are the result of small mistakes.

Regular training and education efforts can go a long way toward keeping hospitals safe. While the work of keeping a hospital safe from cybercrime isn’t hard, it is a constant responsibility.

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How Igloo manages multiple insurance products across channels with Google Cloud

How Igloo manages multiple insurance products across channels with Google Cloud

Insurance management has come a long way in recent years, with new technologies and tools emerging to streamline processes and improve customer experiences. However, many insurance companies are still using legacy systems that are slow, inflexible, and difficult to integrate across different channels.

One of the biggest problems with legacy insurance management systems is their lack of agility. These systems are often built around specific channels or products, and are not designed to adapt to new technologies or changing customer needs. When companies want to introduce new products or channels, they need to go through a new development cycle, which results in a long time to launch. 

To help solve this issue with legacy systems, Igloo, a regional insurance technology company that provides digital solutions to players in the insurance value chain, developed its platform Turbo, which operates across multiple business lines including B2B2C, B2A (business to insurance sales intermediaries such as agents), and B2C. Through Turbo, Igloo is able to deliver the same products and services across multiple distribution channels, including e-commerce, offline retail stores, and Igloo’s own digital solution for insurance sales intermediaries, the Ignite mobile app. To achieve this level of consistency and flexibility, Turbo allows insurance experts without coding knowledge to self-manage the product launch process.

One example of this system in action is the way Igloo provides gadget insurance (covering electronics accidental damage, water damage, and extended warranty). The same product — with consistent benefits and levels of service excellence — can be distributed at scale via e-commerce platforms, sales agents from retail stores, or through direct channels. This not only ensures a consistent customer experience and, hence, customer satisfaction, it also allows Igloo and its insurer partners to reach a wider audience.

Turbo architecture
Analogy of Turbo architecture

A no-code platform for any user to easily and quickly launch new insurance products across channels

Another key issue associated with managing multiple channels and product launches is that it can be a complex and time-consuming process. Past methods of launching insurance products often require coding knowledge, limiting the involvement of non-technical staff. This can lead to delays, errors, and a lack of speed and flexibility when adapting to changing market demands.

Whether it’s launching a new product, or making changes or updates to existing insurance policies, Turbo’s no-code approach allows insurance experts to self-manage the product launch process. A user-friendly interface guides users through the process of setting up new products and launching them across multiple channels. This not only allows for faster and more efficient product launches, but also gives insurance experts more control and flexibility over the process.

In addition to providing more control and flexibility, Turbo reduces the risk of errors and inconsistencies. By centralizing the product launch process, Igloo can ensure that all channels receive the same information and that products are launched with the same level of quality and accuracy. This helps to build trust with customers and ensures that Igloo maintains its reputation as a leading insurance provider.

The diagram below illustrates how Turbo functions, following the insurance logic and process required for every new policy signup.

Turbo for insurance configuration

There are nine key benefits that Turbo provides to its users, namely: 

No-code – Anyone and everyone can use the platform, since no technical expertise is required

Re-utilization degree – Basic information is pre-filled so no reconfiguration is required, speeding up the process of filling in forms 

Streamlined collaboration – Anyone with access to the cloud-driven platform can use it

Insure logic and process variety – Easy set up with a step-by-step guide for every insurance journey

Presenting flexibility – Enable sales across channels

Purchase journey flexibility – Automate configuration of information for insurance purchasing flexibility to accommodate a variety of needs and use cases 

Low usage threshold – Simple interface and highly intuitive

Short learning curve – User friendly platform

Single truth domain definition – A centrally managed platform where all business logic is managed on the platform for consistency and reliability

no-code interface
Insurance journey flexibility

“By utilizing Google Cloud’s cloud-native solutions, our insurance product engine, Turbo, has effectively leveraged scalable, reliable, and cost-efficient technologies. This has led to the creation of a sturdy and high-performance platform that enables rapid digitization and seamless deployment of high-volume insurance products across various distribution channels.” – Quentin Jiang, Platform Product Director, Igloo

Helping insurance carriers, channels, and businesses make more informed decisions

In addition to providing a user-friendly interface for insurance experts to self-manage product launches, Igloo’s Turbo system also collects and analyzes valuable data insights after getting users’ consent, without Google Cloud having any visibility into the data. This data includes user views, clicks, conversions, and feedback, which can provide important insights into customer preferences. By automating the collection and analysis of this data using BigQuery, Igloo is able to make faster and more informed business decisions for insurers and insurance agents. For example, if a particular product is underperforming on a particular channel, Igloo can substitute this with a similar product while running analysis to identify issues and make improvements to the underperforming product. This helps to ensure that Igloo is always offering the best possible products and services to its customers, while also maximizing its own business performance. 

Overall, Igloo’s Turbo platform is a powerful tool that allows Igloo to leverage data-driven insights to make faster and more informed business decisions, thereby helping to reinforce its ongoing success as a leading insurtech.

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Subskribe brings full insights to its quote-to-revenue platform with embedded Looker

Subskribe brings full insights to its quote-to-revenue platform with embedded Looker

Subskribe helps SaaS companies keep up with modern demands by delivering a unified system for configure, price, quote (CPQ), billing, and revenue recognition. Now that it’s added comprehensive business intelligence (BI) and real-time data-exploration capabilities to its platform using Looker Embedded, Subskribe can also help SaaS providers improve decision making and drive growth with on-demand insights. By adopting Looker, Subskribe is also helping to drive its own growth. Not only has it accelerated customer onboarding by weeks and empowered business people to create customer-facing dashboards and reports, but its engineers can now quickly develop revenue-generating services such as embedded self-service BI tools for customers.

Subskribe’s quote-to-revenue platform

SaaS for SaaS providers

Virtually all software today is delivered as a service. But CPQ, billing, and revenue systems that support SaaS providers’ operations have traditionally been siloed, creating costly and time-consuming integration and reconciliation challenges as well as limited agility in pricing and selling. To address these challenges, Subskribe developed an adaptive quote-to-revenue system natively designed to support dynamic SaaS deals, packaging these systems in a single unified offering that delivers faster time-to-market, increased top-line growth, and operational savings to customers.

Improving the agility and value of our SaaS business platform

As Subskribe experienced rapid growth, it soon found that its manual SQL-based reporting processes were hindering its efficiency and innovation potential. Every new customer required weeks of engineering effort to develop custom reports for them, and engineers had to manually manage ongoing reporting changes. Subskribe needed a BI solution that made it easier to create custom reports with composable analytics, so its employees could easily create their own data experiences. And by adding embedded analytics to the Subskribe platform, including dashboards and self-service features, the company could make its platform more sticky by solving advanced BI challenges for their customers.

After evaluating the feasibility of authoring its own custom BI solution and evaluating various third-party tools, Subskribe chose to embed Looker into its platform. Not only does Looker best meet Subskribe’s product and compliance requirements, but it also provides the maturity and long-term reliability required for embedding it in the Subskribe platform.

Looker delivers advanced, enterprise BI capabilities for multi-tenancy, security, and embedded analytics — and it’s easy to use. Despite our system complexity, we got Looker up and running in about one month, with just two people. Durga Pandey, CEO, Subskribe

Delivering customer-specific insights from one multi-tenant platform

Subskribe connected Looker to its existing database without building any data pipelines, and integrated Looker with the company’s test and production environments, saving time for engineers. Now when product changes are made, they can be pushed to Looker using one consistent set of processes and pipelines. As a result, global product iteration is faster, collaborative, and controlled. Additionally, Subskribe implemented controls that ensure secure insights by using security technologies in Google Cloud and built-in features in Looker such as user attributes.

We designed our semantic model so that, in just a matter of hours, anyone can use Looker to build their own dashboards and reports with the data they’re authorized to see. Ugurcan Aktepe, Software Engineer, Subskribe

Keeping resources focused on what they do best

Looker facilitates composable analytics, so Subskribe’s customer-success and product-management teams quickly learned how to develop and update accurate and sophisticated reports without having to write any code. The company held a quick Looker training session and within a few days, Subskribe’s product managers built multiple dashboards that are now used as templates, and which Looker automatically populates for customers using their data. Additionally, product managers and analysts are now using fact and dimension tables to easily create other types of custom reports that provide aggregated insights into key figures such as the momentum of accounts receivable, monthly sales bookings, and canceled subscriptions.

With Looker, we now onboard customers weeks faster because it takes just a few hours to create their custom BI. We provide better customer experiences including real-time insights from dashboards. We respond faster to new BI requests. And we achieve all of this with fewer resources. Durga Pandey, CEO, Subskribe

Greater insights improve experiences, control, and outcomes 

Subskribe’s first use case — which took just six weeks to complete — vastly improved user experience. From dashboards, customers can now instantly see key metrics about their entire revenue-generating process such as quoting and billing, waterfall forecasts, and revenue recognition that includes annual and deferred insights. They can also drill down and explore the data behind their metrics to answer new questions.

Subskribe’s advanced analytics dashboard that leverages Looker Embedded

By offloading routine BI tasks for engineers with Looker, Subskribe has more bandwidth and opportunities to innovate. Teams are building an embedded analytics solution with Looker that will enable customers to create their own dashboards and reports. Expanded personalization options will drive product adoption and customer success by serving up trusted analytics that are tailored for user roles such as executives, finance staff, and client success teams. Subskribe also plans on using Looker to help customers streamline their business operations by providing alerts and helping to deliver data-informed recommendations such as when it’s time to close a deal and when it’s time to collect on payment due. 

Subskribe says the flexibility gained with Looker is game changing. Not only can it pivot faster to meet customers’ immediate needs but it’s also easier for Subskribe to continually evolve its platform to stay ahead of industry demands and achieve its long-term product vision.

All our customers have different requirements and processes, and they ask us to tailor their insights this way or that. With Looker, we have the agility to quickly build what they want. Tim Bradley, Director of Engineering, Subskribe

To create your own custom applications with unified metrics, learn more about Looker Embedded. To learn more about Subskribe, visit

Source : Data Analytics Read More

Get more insights out of your Google Search data with BigQuery

Get more insights out of your Google Search data with BigQuery

Many digital marketers and analysts use BigQuery to bring marketing data sources together, like Google Analytics and Google Ads, to uncover insights about their marketing campaigns and websites. We’re excited to dive deeper into a new type of connection that adds Google Search data into this mix. 

Earlier this year, Search Console announced bulk data exports, a new capability that allows users to export more Google Search data via BigQuery. This functionality allows you to analyze your search traffic in more detail, using BigQuery to run complex queries and create custom reports. 

To create an export, you’ll need to perform tasks on both Cloud Console and Search Console. You can follow the step-by-step guide in the Search Console help center or in the tutorial video embedded here.

Intro to Search performance data

The Performance data exported to BigQuery has three metrics that show how your search traffic changes over time:

Clicks: Count of user clicks from Google Search results to your property.

Impressions: Count of times users saw your property on Google search results.

Position: The average position in search results for the URL, query, or for the website in general.

Each of those metrics can be analyzed for different dimensions. You can check how each of the queries, pages, countries, devices, or search appearances driving traffic to your website is performing. 

If you’d like to learn more about the data schema, check out the table guidelines and reference in the Search Console help center.  

Querying the data in BigQuery

If you need a little help to start querying the data, check the query guidelines and sample queries published in the help center, they can be handy to get up and running. Here’s one example, where we pull the USA mobile web queries in the last two weeks.

code_block[StructValue([(u’code’, u”SELECTrn query,rn device,rn sum(impressions) AS impressions,rn sum(clicks) AS clicks,rn sum(clicks) / sum(impressions) AS ctr,rn ((sum(sum_top_position) / sum(impressions)) + 1.0) AS avg_positionrnFROM searchconsole.searchdata_site_impressionrnWHERE search_type = ‘WEB’rn AND country = ‘usa’rn AND device = ‘MOBILE’rn AND data_date between DATE_SUB(CURRENT_DATE(), INTERVAL 14 day) and CURRENT_DATE()rnGROUP BY 1,2rnORDER BY clicksrnLIMIT 1000″), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e4770d77f50>)])]

Benefits of Bulk data exports

There are several benefits of exporting Search Console data to BigQuery:

Analyze Google Search traffic in more detail. If you have a large website, this solution will provide more queries and pages than the other data exporting solutions. 

Run complex queries and create custom reports. While the Search Console interface allows you to perform simple analyses, it’s optimized for speed and for the average user. Using BigQuery will open many possibilities in data processing and visualization.

Store data as long as you want. Search Console stores up to sixteen months of data; using BigQuery you can store as much data as it makes sense to your organization. Please note that by default data is kept forever in your BigQuery dataset, if you’d like to limit your storage costs you can update the default partition expiration times

Create and execute machine learning models. Machine learning on large datasets requires extensive programming and knowledge of frameworks; using BigQuery ML, you can increase development capabilities and speed with simple SQL.

Apply pre-existing data security rules. If you use BigQuery data security and governance features, you can expand them to include your search data on BigQuery. This means you don’t need separate rules for separate products.

We hope that this solution will help you store, analyze, and visualize your Search data in a more effective and scalable way. If you want to try out the Search Console export in BigQuery, you’ll need a billing account to do so. You can sign up for afree trial and add your billing account to get started analyzing Search Console data.

Source : Data Analytics Read More

Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices

Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices

We live in a digital age, where data is the new currency. Every day, a massive amount of information is generated, processed, and stored, and it is critical for everyone who offers their services online to prioritize privacy and ensure responsible data practices. No matter whether you provide educational software development services or sell smartphones in a Shopify store, you’re likely dealing with lots of customer data—and if you’re not responsible with that, you will ruin customer trust and the reputation of your company.

Don’t want to mess things up? Then adhere to data ethics.

Data ethics involves the ethical handling of data, safeguarding privacy, and respecting the rights of individuals. In this article, we will explore its importance and discuss how organizations can uphold privacy and ensure that they work with data the right way.

Keep reading to learn more!

The core idea behind data ethics

At the core of data ethics is the concept of safeguarding privacy. Individuals have the right to control their personal information and decide how it is used and shared. It is essential for organizations to handle data in a way that respects individuals’ privacy rights. This includes obtaining informed consent from individuals before collecting their data, being transparent about how the collected materials will be used, and providing mechanisms for individuals to opt out or have their data deleted if desired. Respecting privacy builds trust between organizations and individuals and ensures that data is used in a fair and ethical manner.

Responsible data practices go beyond privacy and extend to the overall handling, processing, and sharing of information. Organizations must ensure that data is collected and used for legitimate purposes and that appropriate security measures are in place to protect it from unauthorized access or breaches. Data should only be collected and retained as long as necessary and should be disposed of securely once it is no longer needed. Responsible data practices also involve ensuring the accuracy and integrity of the data, as well as providing individuals with access to their data and the ability to correct any inaccuracies.

Data ethics: key principles

Transparency is a key principle of data ethics. Organizations should be transparent about their data practices, including how data is collected, stored, and used. They should provide clear and easily understandable privacy policies and terms of service to individuals, outlining the purpose of data collection, the types of info collected, and how it will be used and shared. Organizations should also be transparent about any third parties with whom data is shared and ensure that these parties adhere to similar data ethics principles. By being transparent, organizations empower individuals to make informed decisions about their data and foster a culture of trust and accountability.

Data ethics also involves addressing biases and discrimination in data collection and analysis. As data becomes increasingly used for decision-making processes, it is important to be aware of potential biases and ensure that data-driven decisions are fair and unbiased. Organizations should regularly evaluate their collection practices to identify and mitigate any biases that may arise, and they should strive to provide equal opportunities and outcomes for all individuals, regardless of their personal characteristics.

To uphold data ethics, organizations should establish clear guidelines and policies that promote responsible data practices. This includes training employees on data ethics principles, implementing data protection and security measures, and regularly auditing and reviewing data practices to ensure compliance. Organizations should also stay updated with evolving privacy regulations and industry best practices to ensure that their practices align with legal and ethical standards.

How data ethics benefits organizations

Organizations can derive numerous benefits from implementing ethical data practices:

Enhanced reputation and trust. Upholding ethical data practices helps organizations build a positive reputation and establish trust with their customers, clients, employees, and stakeholders. When individuals see that an organization values their privacy, respects their rights, and handles data responsibly, they are more likely to trust the organization and engage with its products or services.

Improved customer relationships. Data ethics fosters stronger relationships with customers. By being transparent about data collection and usage, obtaining informed consent, and respecting individuals’ preferences, organizations can enhance customer satisfaction and loyalty. Customers feel more confident in sharing their information and engaging with organizations that prioritize their privacy and data protection.

Competitive advantage. Ethical data practices provide a competitive edge in the market. As data breaches and unethical data practices become more prevalent, consumers are increasingly concerned about privacy and data security. Organizations that proactively address these concerns and demonstrate ethical data practices differentiate themselves from their competitors. Such organizations are more likely to attract customers who prioritize privacy and security.

Compliance with regulations. Implementing ethical data practices ensures compliance with data protection regulations. Governments and regulatory bodies around the world have introduced strict data privacy laws, such as the GDPR, CCPA, and others. Adhering to these regulations not only avoids legal consequences and penalties but also demonstrates an organization’s commitment to operating within legal and ethical boundaries.

Mitigation of risks and reputational damage. Unethical data practices result in significant risks and reputational damage. Data breaches, misuse of personal information, and unethical data handling can result in severe financial and legal consequences, as well as loss of customer trust. By implementing ethical data practices, organizations reduce the likelihood of data breaches, protect against reputational damage, and mitigate potential risks associated with non-compliance and unethical behavior.

Wrapping Up

In conclusion, data ethics plays a critical role in safeguarding privacy and ensuring responsible data practices. Organizations must prioritize privacy, respect individuals’ rights, and handle information in a transparent and ethical manner. Responsible data practices involve collecting and using data for legitimate purposes, implementing security measures, being transparent about data practices, addressing biases, and promoting equal opportunities. By upholding data ethics, organizations build trust, protect individuals’ privacy, and contribute to a data-driven society that benefits everyone.

Source : SmartData Collective Read More

8 Crucial Tips to Help SMEs Guard Against Data Breaches

8 Crucial Tips to Help SMEs Guard Against Data Breaches

With the ever-increasing number of cyber-attacks, small businesses must take steps to prevent data breaches. Data security is essential for any business, regardless of size. Small businesses are particularly vulnerable to data breaches as they often lack the resources and expertise to protect their data from malicious actors.

Fortunately, there are a number of measures that small businesses can take to protect their sensitive information from unauthorized access. These include implementing strong password policies, encrypting data, and regularly updating software and hardware. Additionally, it’s important for small businesses to have a comprehensive cybersecurity plan in place in order to identify potential threats and respond quickly if a breach does occur. Fortunately, new technology and training processes can help fight data breaches.

If you think that a data breach won’t be a big deal for your business, then you have been misinformed. Recent statistics indicate that 43% of cyberattacks target small businesses, and 60% of the attacked enterprises go out of business in six months. Additionally, cybercrime costs SMEs over $2.2 million yearly. Cybercriminals target these businesses primarily due to their resource inadequacy and often lack of progressive cyber security measures. These data breaches may lead to massive financial losses, damaged reputation, and business closure.

The most significant cyber security threats SMEs face are ransomware, phishing, malware, weak passwords, business email compromise, and insider threats. Good cyber security practices help safeguard customer data and business reputation, improve productivity, prevent site crashes, maintain credibility and trust, promote remote working, ensure regulation compliance, reduce financial losses, protect your bottom line, and more. This article discusses eight crucial cybersecurity tips for small and medium-sized businesses that want to avoid being the victims of data breaches.

1.     Leverage cyber security solutions

Cyber security solutions include technological services and tools that help safeguard businesses against cyberattacks, which may lead to application downtime, damaged reputation, compliance fines, sensitive data theft, and other severe effects. The modern security landscape has a wide array of constantly-changing threats, making these solutions vital to cyber security. Cyber security solutions can be categorized into endpoint security, app security, internet of things security, network security, and cloud security.

The best cyber security solutions for small and medium-sized businesses should offer protection as your organization grows. Since you must address a broad spectrum of security needs, a vendor who holistically approaches cyber security will offer you the perfect solutions. The right vendor should also provide constant technical support coverage and have an excellent reputation for maintaining high customer satisfaction.

2.     Develop a cybersecurity plan

A cybersecurity plan, a written document, comprises details regarding your business’s security procedures, policies, and countermeasure-remediation plan. It aims at ensuring operation integrity and your company’s critical asset security. A cybersecurity strategy is essential for protecting employees, corporate, and customers’ confidential data. It also empowers your IT department to communicate effectively concerning cybersecurity operations and structure.

With a cybersecurity plan, businesses understand risks well and can enable proactive protection while ensuring prompt responses to cyberattacks. It also helps ensure that necessary compliance requirements are met and prevents insider threats. To create a successful cybersecurity plan, start by conducting a cybersecurity risk evaluation, setting your security goals, assessing your technology, choosing a security framework, reviewing security policies, developing a risk management plan, implementing your security strategy, and examining the security plan.

3.     Organize employee cyber security awareness training

Security awareness training involves educating your staff to understand, recognize, and avert cyber threats. It aims at mitigating or preventing harm to your business and its stakeholders while minimizing human cyber risk. Cyber security awareness training teaches employees how to spot cyber threats, helping SMEs protect sensitive customer data. It also enables your staff to identify possible cyber risks, including ransomware, phishing scams, social engineering attacks, and malware.

When employees are trained to identify suspicious links, attachments, or emails, small and medium-sized businesses have the possibility of a team member falling prey to a cyberattack. Data breaches are expensive and time-consuming for SMEs to recover from. With security awareness training, businesses can prevent data breaches and associated costs. The training also boosts employee productivity by reducing the time spent handling security incidents. It also helps your business maintain its reputation and growth, gaining customer trust.

4.     Provide a strong firewall protection

A firewall is a software or hardware network security system for protecting your network against unauthorized access. It acts as your business’s first line of defense against cybercriminals and other unauthorized users by monitoring all traffic out and into your network.

A firewall helps your business block inappropriate sites and phishing emails. It also reduces the possibility of your website getting hacked, verifies remote connections, monitors bandwidth use, and provides a virtual VPN (virtual private network). When choosing a business firewall, ensure it has unified security management, identity and application-based inspection, threat prevention, hybrid cloud support, and scalable solutions.

5.     Update your software regularly

Software updates are essential for small and medium-sized businesses. They help fix bugs and malware that can bring security issues. Since hackers constantly look for sophisticated means to attack, updating your software provides better security. Depending on user needs and current trends, software updates may also come with new features.

Keeping your software up to date can help your business benefit from these features. It also reduces downtime, preventing profitability and production loss. As software updates are rolled out, older versions might lose support. Keeping your software updated protects your business from possible cyber threats.

6.     Invest in data encryption

Data encryption involves converting plaintext information into coded ciphertext to keep hackers from using or reading stolen data. This reduces the risk of damages associated with breaches. Encrypting your company data shows customers you value data security, protecting their trust in your business. It also makes it difficult for cyber attackers to intercept your information. With data encryption, you can nullify brute-force methods.

Through data encryption, SMEs can protect vital data from unauthorized access, safeguard personal information, and secure file transfers between devices. It also keeps business messages private, secures your information, stands against hackers, and protects against identity theft. Encrypting your emails prevents phishing attacks, safeguards against virus and malware attacks, and ensures emails are forwarded securely.

7.     Consider regular data backup

Backups are an excellent way to protect data from unauthorized access and accidental loss. They safeguard against virus attacks, human errors, natural disasters, hardware failures, and more. They can also help save money and time when data loss occurs. Data backup aims at depositing your information to a secure and separate location from your devices where you can retrieve it whenever necessary. Backing up your data regularly protects your business from the effects of data loss.

8.     Develop a solid password policy

A password policy consists of a set of regulations meant to improve cyber security by motivating users to develop and use stronger passwords. Passwords are a critical cyber security element because they largely determine the chances of hackers breaking into your system. With an effective password policy, SMEs can prevent data breaches, build a cybersecurity culture, and establish trust. A good password policy should include password strength, expiry, history, and change.

Businesses Must Guard Against Data Breaches At All Costs

Data breaches are becoming more common with each passing year. Sadly, SMEs are more vulnerable to data breaches due to limited resources and poor security infrastructure. However, implementing these cyber security tips for small and medium-sized businesses in 2023 can help safeguard your company.

Source : SmartData Collective Read More

Streaming graph data with Confluent Cloud and Neo4j on Google Cloud

Streaming graph data with Confluent Cloud and Neo4j on Google Cloud

There are many ways to classify data. Data can be characterized as batch and streaming. Similarly data can be characterized as tabular or connected. In this blog post, we’re going to explore an architecture focused on a particular kind of data — connected data which is streaming.

Neo4j is the leading graph database. It stores data as nodes and relationships between those nodes. This allows users to uncover insights from connections in their connected data. Neo4j offers Neo4j Aura, a managed service for Neo4j.

Apache Kafka is the de facto tool today for creating streaming data pipelines. Confluent offers Confluent Cloud, a managed service for Apache Kafka. In addition, Confluent provides the tools needed to bring together real-time data streams to connect the whole business. Its data streaming platform turns events into outcomes, enables intelligent, real-time apps, and empowers teams and systems to act on data instantly.

Both these products are available on Google Cloud, through Google Cloud Marketplace. Used together, Neo4j Aura and Confluent Cloud provide a streaming architecture that can extract value from connected data. Some examples include:

Retail: Confluent Cloud can stream real-time buying data to Neo4j Aura. With this connected data in Aura, graph algorithms can be leveraged to understand buying patterns. This allows for real time product recommendations, customer churn prediction. In supply chain management, use cases include finding alternate suppliers and demand forecasting.

Healthcare and Life Sciences: Streaming data into Neo4j Aura allows for real-time case prioritization and triaging of patients based on medical events and patterns. This architecture can capture patient journey data including medical events for individuals. This allows for cohort based analysis across events related to medical conditions patients experience, medical procedures they undergo and medication they take. This cohort journey can then be used to predict future outcomes or apply corrective actions.

Financial Services: Streaming transaction data with Confluent Cloud into Neo4j Aura allows for real time fraud detection. Previously unknown, benign-looking fraud-ring activities can be tracked in real-time and detected. This reduces the risk of financial losses and improves customer experience.

This post will take you through setting up a fully managed Kafka cluster running in Confluent Cloud and creating a streaming data pipeline that can ingest data into Neo4j Aura.

In this example we generate a message manually in Confluent Cloud. For production implementations, messages are typically generated by upstream systems. On Google Cloud this includes myriad Google services that Confluent Cloud can connect to such as Cloud Functions, BigTable and Cloud Run.


So let’s start building this architecture. We’ll need to set up a few things:

Google Cloud Account: You can create one for free if you don’t have one. You also get $300 credits once you sign-up.

Confluent Cloud: The easiest way to start with Confluent Cloud is to deploy through Google Cloud Marketplace. The relevant listing is here.

Neo4j Aura: To get started with Neo4j Aura, just deploy it via Google Cloud Marketplace here.

A VM: We need a terminal to execute confluent CLI commands and run docker. You can create a VM using Google Compute Engine (GCE).

Creating a Kafka topic

To start we’re going to need to create a Kafka cluster in Confluent Cloud. Then we’ll create a Kafka topic in that cluster. The steps below can be done via the Confluent Cloud UI. However, let’s do it via command line so that it is easier to automate the whole process. 

First, open a bash terminal on your GCE VM. Then, let’s install the Confluent CLI tool.
curl -sL –http1.1 | sh -s — latest

Login to your Confluent account
confluent login –save

We have to create an environment and cluster to use. To create an environment:
confluent environment create test

To list down the environments available, run:
confluent environment list

This command will return a table of environment IDs and names. You will find the newly created `test` environment in the result. Let’s try to use its environment ID to create all the resources in the `test` environment. In my case, `env-3r2362` is the ID for the `test` environment.
confluent environment use env-3r2362

Using this environment, let’s create a kafka cluster on the GCP `us-central1` region.
confluent kafka cluster create test –cloud gcp –region us-central1

You can choose some other region from the list of supported regions:
confluent kafka region list –cloud gcp

You can obtain the cluster ID by executing:
confluent kafka cluster list

Now, let’s use the environment and cluster created above.
confluent environment use test
confluent kafka cluster use lkc-2r1rz1

An API key/secret pair is required to create a topic on your cluster. You also need it to produce/consume messages in a topic. If you don’t have one, you can create it using:
confluent api-key create –resource lkc-2r1rz1

Now, let’s create a topic to produce and consume in this cluster using:
confluent kafka topic create my-users

With these steps, our Kafka cluster is ready to produce and consume messages.

Creating a Connector instance

The Neo4j Connector for Apache Kafka can be run self-managed on a container inside Google Kubernetes Engine. Let’s create a `docker-compose.yml` and run a Kafka connect instance locally.

In the docker-compose file, we are trying to create and orchestrate a Kafka Connect container. We use the `confluentinc/cp-kafka-connect-base` as the base image. The connector will be running and exposed on port 8083.

code_block[StructValue([(u’code’, u”version: ‘3’rnservices:rn kconnect-neo4j-confluent:rn image: confluentinc/cp-kafka-connect-base:7.3.1rn container_name: kconnect-neo4j-confluentrn ports:rn – 8083:8083″), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ea7588de750>)])]

Upon container start, we are going to install a Neo4j Sink Connector package via confluent-hub. Once the package is installed, we should be good to create a Sink instance running within the container.

First, let’s set the environment variables that the base image expects. 

In the following snippet, replace your Kafka URL and Port, which can be gotten from Confluent Cloud. 
`<KAFKA_INSTANCE_URL>` with your Kafka URL 
`<KAFKA_PORT>` with your Kafka Port. 

We are creating topics specific to this connector for writing configuration, offset and status data. Since we are going to write JSON data, let’s use JsonConverter for `CONNECT_KEY_CONVERTER` and `CONNECT_VALUE_CONVERTER`.

Our Kafka cluster inside confluent is protected and has to be accessed via a Key and Secret.

Kafka API and Secret created during setup has to be used to replace `<KAFKA_API_KEY>` and `<KAFKA_API_SECRET>` inside CONNECT_SASL_JAAS_CONFIG and CONNECT_CONSUMER_SASL_JAAS_CONFIG variables.

code_block[StructValue([(u’code’, u’environment:rn CONNECT_BOOTSTRAP_SERVERS: <KAFKA_INSTANCE_URL>:<KAFKA_PORT>rn CONNECT_REST_ADVERTISED_HOST_NAME: ‘kconnect-neo4j-confluent’rn CONNECT_REST_PORT: 8083rn CONNECT_GROUP_ID: kconnect-neo4j-confluentrn CONNECT_CONFIG_STORAGE_TOPIC: _config-kconnect-neo4j-confluentrn CONNECT_OFFSET_STORAGE_TOPIC: _offsets-kconnect-neo4j-confluentrn CONNECT_STATUS_STORAGE_TOPIC: _status-kconnect-neo4j-confluentrn CONNECT_KEY_CONVERTER: org.apache.kafka.connect.json.JsonConverterrn CONNECT_VALUE_CONVERTER: org.apache.kafka.connect.json.JsonConverterrn CONNECT_PLUGIN_PATH: ‘/usr/share/java,/usr/share/confluent-hub-components/’rn CONNECT_REQUEST_TIMEOUT_MS: “20000”rn CONNECT_RETRY_BACKOFF_MS: “500”rn CONNECT_SSL_ENDPOINT_IDENTIFICATION_ALGORITHM: “https”rn CONNECT_SASL_MECHANISM: “PLAIN”rn CONNECT_SECURITY_PROTOCOL: “SASL_SSL”rn CONNECT_SASL_JAAS_CONFIG: ‘ required username=”<KAFKA_API_KEY>” password=”<KAFKA_API_SECRET>”;’rn CONNECT_CONSUMER_SECURITY_PROTOCOL: “SASL_SSL”rn CONNECT_CONSUMER_SSL_ENDPOINT_IDENTIFICATION_ALGORITHM: “https”rn CONNECT_CONSUMER_SASL_MECHANISM: “PLAIN”rn CONNECT_CONSUMER_SASL_JAAS_CONFIG: ‘ required username=”<KAFKA_API_KEY>” password=”<KAFKA_API_SECRET>”;’rn CONNECT_CONSUMER_REQUEST_TIMEOUT_MS: “20000”rn CONNECT_CONSUMER_RETRY_BACKOFF_MS: “500”‘), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ea7588f32d0>)])]

With all the Connector variables set, let’s focus on installing and configuring the Neo4j Sink connector. We have to install the binary via Confluent-hub
confluent-hub install –no-prompt neo4j/kafka-connect-neo4j:5.0.2

Sometimes, the above command might fail if there is any bandwidth or connection issue. Let’s keep trying until the command succeeds.

code_block[StructValue([(u’code’, u’while [ $? -eq 1 ]rn dorn echo “Failed to download the connector, will sleep and retry again”rn sleep 10rn confluent-hub install –no-prompt neo4j/kafka-connect-neo4j:5.0.2rn done’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ea7588f31d0>)])]

Once the package is installed, we have to use the RESTful API that the connector provides to install and configure a Neo4j Sink instance. Before that let’s wait until the connector worker is running:

code_block[StructValue([(u’code’, u’echo “Start Self-managed Connect Worker…”rn/etc/confluent/docker/run &rnwhile : ; dorncurl_status=$$(curl -s -o /dev/null -w %{http_code} http://localhost:8083/connectors)rnecho -e $$(date) ” Listener State : ” $$curl_status ” (waiting for 200)”rnif [ $$curl_status -eq 200 ] ; thenrnbreakrnfirnsleep 5rndone’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ea7588f3290>)])]

After the worker is up, we can use the REST API to create a new Neo4j Sink Connector instance that listens to our topic and writes the JSON data in Neo4j. 

In the config below, we are listening to a topic named `test` “topics”: “my-users” and ingest the data via this cypher command: “MERGE (p:Person{name:, surname: event.surname})” defined in the “neo4j.topic.cypher.test” property. Here, we are using a simple command to create or update a new Person node defined in the test topic. 

You might have to replace the <NEO4J_URL>, <NEO4J_PORT>, <NEO4J_USER>, <NEO4J_PASSWORD> placeholders with appropriate values.

code_block[StructValue([(u’code’, u’curl -i -X PUT -H “Accept:application/json” \rn -H “Content-Type:application/json” \rn http://localhost:8083/connectors/neo4j-sink/config \rn -d ‘{rn “topics”: “my-users”,rn “connector.class”: “streams.kafka.connect.sink.Neo4jSinkConnector”,rn “key.converter”: “”,rn “value.converter”: “org.apache.kafka.connect.json.JsonConverter”,rn “value.converter.schemas.enable”: “false”,rn “errors.retry.timeout”: “-1”,rn “”: “1000”,rn “errors.tolerance”: “all”,rn “errors.log.enable”: “true”,rn “errors.log.include.messages”: “true”,rn “neo4j.server.uri”: “neo4j+s://<NEO4J_URL>:<NEO4J_PORT>”,rn “neo4j.authentication.basic.username”: “<NEO4J_USER>”,rn “neo4j.authentication.basic.password”: “<NEO4J_PASSWORD>”,rn “”: “MERGE (p:Person{name:, surname: event.surname})”rn }”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ea7588f3950>)])]

Finally, let’s wait until this connector worker is up.

code_block[StructValue([(u’code’, u’while : ; dorn curl_status=$$(curl -s -o /dev/null -w %{http_code} http://localhost:8083/connectors/neo4j-sink/status)rn echo -e $$(date) ” Neo4j Sink Connector State : ” $$curl_status ” (waiting for 200)”rn if [ $$curl_status -eq 200 ] ; thenrn breakrn firn sleep 5rn done’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ea7588f38d0>)])]

This is the complete docker-compose.yml. Ensure that you replace all the placeholders mentioned above:
docker-compose up

code_block[StructValue([(u’code’, u’—rnversion: ‘3’rnservices:rn kconnect-neo4j-confluent:rn image: confluentinc/cp-kafka-connect-base:7.3.1rn container_name: kconnect-neo4j-confluentrn ports:rn – 8083:8083rn environment:rn CONNECT_BOOTSTRAP_SERVERS: <KAFKA_INSTANCE_URL>:<KAFKA_PORT>rn CONNECT_REST_ADVERTISED_HOST_NAME: ‘kconnect-neo4j-confluent’rn CONNECT_REST_PORT: 8083rn CONNECT_GROUP_ID: kconnect-neo4j-confluentrn CONNECT_CONFIG_STORAGE_TOPIC: _config-kconnect-neo4j-confluentrn CONNECT_OFFSET_STORAGE_TOPIC: _offsets-kconnect-neo4j-confluentrn CONNECT_STATUS_STORAGE_TOPIC: _status-kconnect-neo4j-confluentrn CONNECT_KEY_CONVERTER: org.apache.kafka.connect.json.JsonConverterrn CONNECT_VALUE_CONVERTER: org.apache.kafka.connect.json.JsonConverterrn CONNECT_PLUGIN_PATH: ‘/usr/share/java,/usr/share/confluent-hub-components/’rn CONNECT_REQUEST_TIMEOUT_MS: “20000”rn CONNECT_RETRY_BACKOFF_MS: “500”rn CONNECT_SSL_ENDPOINT_IDENTIFICATION_ALGORITHM: “https”rn CONNECT_SASL_MECHANISM: “PLAIN”rn CONNECT_SECURITY_PROTOCOL: “SASL_SSL”rn CONNECT_SASL_JAAS_CONFIG: ‘ required username=”<KAFKA_API_KEY>” password=”<KAFKA_API_SECRET>”;’rn CONNECT_CONSUMER_SECURITY_PROTOCOL: “SASL_SSL”rn CONNECT_CONSUMER_SSL_ENDPOINT_IDENTIFICATION_ALGORITHM: “https”rn CONNECT_CONSUMER_SASL_MECHANISM: “PLAIN”rn CONNECT_CONSUMER_SASL_JAAS_CONFIG: ‘ required username=”<KAFKA_API_KEY>” password=”<KAFKA_API_SECRET>”;’rn CONNECT_CONSUMER_REQUEST_TIMEOUT_MS: “20000”rn CONNECT_CONSUMER_RETRY_BACKOFF_MS: “500”rn command:rn – bashrn – -crn – |rn echo “Install Neo4j Sink Connector”rn confluent-hub install –no-prompt neo4j/kafka-connect-neo4j:5.0.2rn rn while [ $? -eq 1 ]rn dorn echo “Failed to download the connector, will sleep and retry again”rn sleep 10rn confluent-hub install –no-prompt neo4j/kafka-connect-neo4j:5.0.2rn donernrnrn echo “Start Self-managed Connect Worker…”rn /etc/confluent/docker/run &rn while : ; dorn curl_status=$$(curl -s -o /dev/null -w %{http_code} http://localhost:8083/connectors)rn echo -e $$(date) ” Listener State : ” $$curl_status ” (waiting for 200)”rn if [ $$curl_status -eq 200 ] ; thenrn breakrn firn sleep 5rn donernrnrn echo -e “\n–\n+> Create Neo4j Sink Connector”rn curl -i -X PUT -H “Accept:application/json” \rn -H “Content-Type:application/json” \rn http://localhost:8083/connectors/neo4j-sink/config \rn -d ‘{rn “topics”: “my-users”,rn “connector.class”: “streams.kafka.connect.sink.Neo4jSinkConnector”,rn “key.converter”: “”,rn “value.converter”: “org.apache.kafka.connect.json.JsonConverter”,rn “value.converter.schemas.enable”: “false”,rn “errors.retry.timeout”: “-1”,rn “”: “1000”,rn “errors.tolerance”: “all”,rn “errors.log.enable”: “true”,rn “errors.log.include.messages”: “true”,rn “neo4j.server.uri”: “neo4j+s://<NEO4J_URL>:<NEO4J_PORT>”,rn “neo4j.authentication.basic.username”: “<NEO4J_USER>”,rn “neo4j.authentication.basic.password”: “<NEO4J_PASSWORD>”,rn “”: “MERGE (p:Person{name:, surname: event.surname})”rn }’rnrnrn echo “Checking the Status of Neo4j Sink Connector…”rn while : ; dorn curl_status=$$(curl -s -o /dev/null -w %{http_code} http://localhost:8083/connectors/neo4j-sink/status)rn echo -e $$(date) ” Neo4j Sink Connector State : ” $$curl_status ” (waiting for 200)”rn if [ $$curl_status -eq 200 ] ; thenrn breakrn firn sleep 5rn donern #rn #rn sleep infinity’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ea7588f3610>)])]

Sending a message

Let’s write some messages via Confluent UI to test whether they get persisted on Neo4j. Go to your Confluent Cloud UI, click on your environment

You will now see the clusters within the environment. Click the cluster you created previously.

From the sidebar on the left, click on the `Topics` section and the `my-users` topic we created previously.

From the messages tab, you can start producing messages to this topic by clicking on the `Produce a new message to this topic` button.

Click the `Produce` button once you are done.

Alternatively, you can also write messages to our `my-users` topic via the command line.

Confluent CLI provides a command to write and consume messages from topics. Before using this command ensure that you are using an api-key.
confluent api-key use <API_KEY> –resource lkc-2r1rz1

confluent kafka topic produce my-users –parse-key –delimiter “:”

Using the last command, we can add messages containing key and value separated by a delimiter “:” in the topic.
“event”:{“name”: “John”, “surname”: “Doe”}

Go to your Neo4j Browser and check for the new Person node created with name ‘John’ and surname ‘Doe’.


In this blog post, we walked through setting up Confluent Cloud and Neo4j Aura on Google Cloud. We then used the Neo4j Connector for Apache Kafka to bridge between them. With that environment created, we tested sending a message through Confluent Cloud and capturing it in the Neo4j database. You can try this yourself with a Google Cloud account and the marketplace listings for Neo4j Aura and Confluent Cloud.

Confluent is a great data streaming platform to capture high volume of data in motion. Neo4j is a native graph platform that can sift through the connected data to deliver highly contextual insights in a low latency manner. In a highly connected world, real-time insights can add huge value to businesses. Customers across verticals are using Confluent Cloud and Neo4j to solve problems the moment they happen. Graph Data Science algorithms are leveraged to understand the seemingly random network, derive hidden insights, predict and prescribe the next course of action.

To know more about Neo4j and its use cases, reach out to

Source : Data Analytics Read More