Once a workflow is built, users want to share, secure, schedule, and monitor the assets that have been built. Alteryx Server allows users to publish their workflows to a central location where the workflows can be stored, shared, and controlled in a safe and “enterprise-grade” solution.

Challenge

• The IT infrastructure of MyWhoosh where the Alteryx Server platform was hosted was in an on-premise datacenter which was designed to be scalable and robust with multi node physical clusters including the server, storage and network components. However, most of the physical hardware was quite old and not equipped with the latest generation of physical servers.
• Frequent hardware crashes and portal downtime kept troubling the availability of the Alteryx Server application. Assigning a touch hand support person to power on the hardware that was down seemed quite impossible due the restrictions during covid period. Hence, MyWhoosh wanted to look for another viable solution.
• Though the hardware setup at MyWhoosh was well equipped to meet the occasional spikes in the traffic, it was observed that over a course of 6-month time, most of the IT infra was underutilized than predicted. It was realized that spending huge amount of money on an old hardware plus software maintenance, license costs, internet bandwidth, datacenter cooling and maintenance, touch support personnel and electricity costs – were keeping the business operations challenging.
• There was an attempt by MyWhoosh to select a cost-effective solution that can host Alteryx Server application servers, web servers and archival data. This way IT infra can be re-provisioned to host sensitive data on-premise and the rest on the cloud, thereby reducing the overall physical hardware costs spent on a yearly basis.

Why AWS

• MyWhoosh decided to migrate Alteryx Server, database servers and archival data to AWS.
• The Alteryx Server’s AWS architecture includes Amazon Elastic Compute Cloud (Amazon EC2), that provides complete control of its computing resources, updates to tables in Amazon Relational Database Service (Amazon RDS) and AWS Elastic Load Balancer was used to distribute the traffic to the underlying EC2 instances based on the load.

Benefits

• MyWhoosh uses AWS services to provision infrastructure and deploy the Alteryx Server platform to other departments within it. In addition, the Alteryx Server resources that are no longer required to be run all the time are made to auto shutdown thus saving cost. MyWhoosh reported a 30% cost reduction after implementation of AWS for the Alteryx Server platform.
• The implementation of Alteryx Server on AWS made MyWhoosh confident in the security of its data, and its accreditation team is enthusiastic about the monitoring and auditing capabilities provided by AWS tools. With the implementation of IAM roles, MyWhoosh IT team was able to isolate systems and tightly control user accesses. These capabilities were harder to achieve within the existing infra but were available out of the box with AWS
• By adopting AWS to host the Alteryx Server platform, MyWhoosh has been able to innovate and experiment to a degree previously impossible. For example, MyWhoosh compared the performance and cost-effectiveness of three different cloud solutions. Without moving to the AWS, the costs associated with running an outdated on-premise hardware would have creeped up and the alternative way of upgrading the existing on-prem infrastructure to the latest hardware models and then hosting the Alteryx Server application on top of it would have taken months.

What is Social Media Listening?

Social media listening is the practice of constantly scanning social media channels for comments and feedback regarding your brand, product, and services and using that data to take appropriate action. Searching for specific terms and subjects related to a business or sector and competitor remarks is a common aspect of social listening.

Two processes were engaged in social media listening: monitoring and action. Both processes analyse online interactions and conversations and act on the results. Social media listening becomes effective only when a concrete action follows information collection.

Including social media listening in your overall social media strategy has many advantages. Major benefits are:

1. Increased Customer Engagement

One of the main advantages of social media listening is the ability to monitor social media users’ conversations about your business or brand, whether they specifically mention you in the chat. This allows you to directly answer queries or comments or write posts dealing with recurring issues or questions. When businesses try to communicate with their customers, it fosters trust, boosts engagement, and strengthens brand loyalty.

Follow-up action is a vital component of social media listening. One fantastic start approach is leveraging the information you gather to develop a social media plan that responds to frequent comments or directly engages your audience.

2. Opportunities to Observe and Monitor Trends

You can gain a broad understanding of people’s opinions about your company and its sector through social media listening, enabling you to identify emerging trends or hot topics. This is very helpful for social media purposes and your research and design department and public relations team to explore potential partnerships or new avenues for your brand, depending on your product or service.

3. Conflict Resolution

For business, crises might happen at any time. In that case, customer reactions will be crucial to resolving the issue effectively. Social media listening can assist you in keeping track of client mood and guiding your decision-making if your business experiences a customer fallout or crisis. Assessing the actual responses of your customers can help you to move forward. Proper social media listening and subsequent action based on that can directly impact the development of your business.

4. Monitoring the Competition

You can follow the discussions involving your competitor by using keywords. This can assist you in locating any weaknesses in their business strategies or products that you can later exploit. Similarly, you can use this information to strengthen your brand in areas where you believe they are stronger than you.

5. Leveraging Influencers to Promote Marketing

With social listening, you can locate influencers who are already talking about your company and influencing your customers. Similarly, if influencers disparage your company, you can connect with them and see if there are any chances to change their opinions. You can also monitor their concerns and work to improve on suggestions. It's crucial to take these creators' comments seriously because they have sizable fan bases that can impact the thoughts of millions of consumers.

6. Tracking Events

Social listening will be beneficial to better understand how participants feel before, during, and after an event. Utilise social listening to learn about your participants' experiences and identify areas where the event may be improved or continued. This is particularly useful because it enables you to deal with potential roadblocks as they arise or keep doing what's working.

List of Social Media Listening Tools

You can use various social listening tools to communicate information, raise brand awareness, provide service and support, and more. Let’s discuss a few such tools:

1. Hootsuite

Hootsuite allows you to create social media streams that keep an eye on conversations, hashtags, mentions, and keywords. Instead of signing in and out of numerous social platforms, you may monitor conversations and comments directly from your social media dashboard. By keeping an eye on the competition, networking with influencers on social media, and identifying prospective brand champions, Hootsuite lets you stay on top of developments in your sector.

2. Adview

Adview is used exclusively for social listening on Facebook and Instagram ads. It can monitor up to three Facebook Ad Accounts across unlimited pages. Using Adview, you can respond to comments on all of your Facebook and Instagram advertising from one location. Furthermore, you receive data on the ads receiving the most comments so that you may adjust your campaigns accordingly.

3. Talkwalker

Talkwalker provides powerful social listening software features that monitor blogs, forums, videos, news websites, reviews, and social networks in one dashboard. Advanced filters enable you to segregate your data so that you may concentrate on the messages and audience that are most important to you. You may also set up alerts to inform you of any mentions or keyword usage increases. With Talkwalker, it's simple to keep an eye on conversations involving your brand and analyse their mood, engagement, reach, and comments.

4. Synthesio

Synthesio monitors talks on incredibly niche subjects in carefully selected communities. You may segment your social listening data using various criteria, including language, geography, demographics, sentiment, gender, and more. A valid social reputation score is also included in the reports to see how you compare to your rivals.

5. Mentionlytics

Mentionlytics tracks mentions, keywords, and sentiment across several languages on blogs, news websites, and social media platforms. Additionally, it makes it easy to locate influencers across social media platforms and other internet sources. You can quickly monitor terms in several languages, keep track of your top influencers, and even identify the sentiment behind each mention.

6. Brand 24

Brand 24 uses automated sentimental analysis to separate positive, negative, and neutral mentions by monitoring online reviews, mentions on social media, blogs, message boards, news websites etc. It helps to find out about your online mentions, boost client happiness, and increase lead generation for your company.

Final Words

It is high time for companies to get out of their bubbles and accept the masses' opinions, remarks, compliments, complaints, and inquiries on social media. In all sense, social media listening has excellent benefits and tools that can guide you in building your market presence by paying heed to social media.

Beinex Digital Offerings

Our bespoke digital solutions with a comprehensive suite of independent products are focused on addressing specific business gaps, use cases, and needs. No matter where your digital maturity currently stands, we can provide you with a Quantum Leap by deploying the unique business-consulting-technology model. Reach out to us: https://beinex.com/beinex-digital/

Uncovering Data Sharing

Traditional data-sharing methods involve copying files via FTP/cloud, employing ETL pipelines, or utilising APIs. While these methods facilitate data exchange, they come with inherent limitations. Security vulnerabilities, data inaccuracies, and high maintenance costs are just a few of the challenges organisations face. Snowflake's Secure Data Sharing offers a standard shift by enabling seamless, governed access to data within the Snowflake platform.

Secure Data Sharing

Secure data sharing is ideal for granting raw data access across business units or to trusted partners for analysis. It fosters informed decision-making and facilitates data monetisation by providing secure, revocable access to valuable datasets.

Snowflake's Role in Secure Data Sharing

Snowflake's Cloud Data Platform empowers organisations to collaborate securely, driving cost savings and uncovering new insights. With Snowflake Secure Data Sharing, businesses can:

Introducing Data Clean Rooms

In the era of heightened data privacy concerns, data clean rooms emerge as a game-changer. Unlike traditional clean rooms, modern data clean rooms go beyond physical boundaries. They operate as a framework, eliminating the need to relocate data. In essence, a data clean room allows providers to dictate query rules without granting access to underlying data.

When to Opt for Data Clean Rooms

While secure data sharing suffices for many collaborations, data clean rooms shine in scenarios involving sensitive or regulated data. Regulations like CCPA and GDPR mandate stringent privacy adherence, making data clean rooms indispensable for privacy-preserving collaboration. For industries like media and advertising, data clean rooms enable personalised insights without compromising consumer privacy.

Snowflake Global Data Clean Room

Snowflake Global Data Clean Room revolutionises multi-party collaboration by safeguarding data privacy. Leveraging Snowflake's robust collaboration and governance features, it facilitates secure analysis across clouds and regions, mitigating the risk of data exposure or re-identification.

Choosing the Right Approach

In navigating data collaboration, enterprises must prioritise security and compliance. While Snowflake Secure Data Sharing suits low-risk collaborations, data clean rooms are indispensable for handling sensitive or regulated data. By embracing these innovative solutions, businesses can foster a culture of secure collaboration, driving insights and innovation while safeguarding data privacy.

Many businesses make a concerted effort to create work cultures that increase job happiness, encourage people to find meaning and satisfaction in their work, reward and recognize employees for their actions, and foster both personal and professional growth.

While many tactics are adopted for the company’s benefit and to considerably increase retention, leaders cannot rely only on them. They must face the uncomfortable reality that they will eventually lose significant talent if they do not keep an open mind and a realistic outlook on the future. Influential leaders act daily to safeguard themselves, their teams, and their companies from the risk of attrition because wishful retention thinking is not a viable business strategy.

Is it possible to foresee attrition so that it only impacts the business a little less, given that it is impossible to stop employees from leaving? Well, with the help of technology, it is possible. The churn model, among others, can help in this situation. Are you wondering how? Through an employee churn prediction model, we can make it happen. After understanding which employees are on the verge of leaving using the churn model, it is possible to reach out to them and understand their grievances.

Employee Churn Prediction Model

It's a predictive model that calculates the likelihood (or vulnerability) of each employee leaving. It tells us how likely we will lose employees or a specific employee in the future at any given time. It classifies employees into two groups (classes): those who quit and those who don't. It will typically tell us the probability of the employee belonging to which of the groups in addition to placing them in one of the two groups. Thus, a churn model can be used to estimate the chances of resignation.

Explaining the Model

Modern churn models frequently draw their foundation from machine learning, more specifically from binary classification methods. There are several of these algorithms; therefore, it's important to test which one works best in each circumstance. Here we have made use of four machine learning models:

Random Forest

Supervised machine learning algorithms like random forest are frequently employed in classification and regression issues. On various samples, it constructs decision trees and uses their average for classification and majority vote for regression.

The Random Forest Algorithm's ability to handle data sets with both continuous variables, as in regression, and categorical variables, as in classification, is one of its most crucial qualities. In terms of classification issues, it delivers superior outcomes.

KNN

One of the simplest machine learning algorithms, based on the supervised learning method, is K-Nearest Neighbour. The K-NN algorithm assumes that the new case and the existing cases are comparable, and it places the latest instance in the category that is most like the existing categories.

A new data point is classified using the K-NN algorithm based on the similarities after storing all the existing data. This means new data can be quickly and accurately sorted into a suitable category using the K-NN method. Although the K-NN approach is most frequently employed for classification problems, it can also be used for regression.

Decision Tree

The supervised learning algorithms family includes the decision tree algorithm. The decision tree technique, in contrast to other supervised learning methods, can handle classification and regression issues.

By learning straightforward decision rules derived from previous data, a Decision Tree is used to build a training model that may be used to predict the class or value of the target variable (training data).

Support Vector Machine

One of the most well-liked supervised learning algorithms, Support Vector Machine, or SVM, is used to solve Classification and Regression problems. However, it is employed mainly in Machine Learning Classification issues.

The SVM algorithm's objective is to establish the best line or decision boundary that can divide n-dimensional space into classes, allowing us to quickly classify new data points in the future. A hyperplane is a name given to this optimal decision boundary.

SVM selects the extreme vectors and points that aid in creating the hyperplane. Support vectors representing these extreme instances form the basis for the SVM method.

A Step-By-Step View of the Process

Step 1: Loading data to databricks

In the initial stage, CSV data collected are loaded to the churn model. Any type of data sets can be employed here depending on the situation.

Step 2: Transformation: converting to the requisite format

Step 3: Feature selection

Step 4: Splitting the data

Step 5: Standardisation

Step 6: Model selection

During model selection, datasets are provided to the machine algorithms like Random Forest, KNN, Decision tree and Support vector machine. Each algorithm produces its own sets of accuracy values; from that, the most accurate predictions are selected. Using the same procedure, we can categorize the employees into groups, for example, those who are planning to resign and those who are not.

Step 7: Result generation