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Serverless Computing

Serverless computing, often referred to as "serverless," is a cloud computing model where developers can build and deploy applications without having to manage the underlying server infrastructure. In a traditional server-based architecture, developers need to provision, configure, and manage servers to run their applications, which can be complex and time-consuming.

In a serverless architecture, the cloud provider (such as Amazon Web Services with AWS Lambda, Microsoft Azure with Azure Functions, or Google Cloud with Google Cloud Functions) abstracts away the server management aspect. Developers can focus solely on writing code for the specific functions or tasks their application needs to perform without worrying about server provisioning, scaling, or maintenance.

Serverless services offer several significant benefits that can have a positive impact on application development, deployment, and management. Some of the key significances of serverless services include:

1. Simplified Infrastructure Management
2. Auto-Scaling
3. Rapid Development and Deployment
4. Event-Driven Architecture
5. Reduced Administrative Overhead
6. Global Scalability
7. Resource Optimization
8. Improved Fault Tolerance

Amazon Web Services (AWS) continuously introduces new capabilities and features to their serverless services for several reasons aimed at improving the developer experience, expanding use cases, and meeting evolving customer needs.

Latest buzz in AWS serverless services

1. General availability of AWS Database Migration Service Serverless

On June 2nd, 2023, AWS unveiled the widespread availability of AWS Database Migration Service (AWS DMS) Serverless. This release simplifies database migrations by automating the provisioning and scalability of migration resources. With AWS DMS Serverless, users gain the ability to seamlessly replicate data across a diverse range of widely used databases, analytics engines, and services—think PostgreSQL, MySQL, Oracle, Amazon Redshift, Amazon DynamoDB, Amazon Aurora, and more. By handling the often-cumbersome work of database migration, AWS DMS Serverless minimises the need for manual resource estimation, provisioning, monitoring, and scaling. This advancement translates to migration timeframes measured in hours, and cost savings realised through payment solely for consumed data migration resources.

2. Provisioned Concurrency for Amazon SageMaker Serverless Inference

As of May 10th, 2023, AWS has introduced the general availability of Provisioned Concurrency support for Amazon SageMaker Serverless Inference. This innovative feature ensures that models deployed on serverless endpoints offer consistent performance and impressive scalability. Through the integration of provisioned concurrency, users can infuse their serverless endpoints with a predetermined volume of concurrency, effectively maintaining the readiness and responsiveness of SageMaker endpoints. This offering particularly suits scenarios where traffic is predictable, yet throughput remains relatively low.

3. Amazon Aurora Serverless v2 is now available in 4 additional regions

The footprint of Amazon Aurora Serverless v2 has now expanded to include four additional regions. Aurora Serverless v2, an adaptive, automatic scaling configuration for Amazon Aurora, has the remarkable ability to instantaneously scale to accommodate even the most resource-intensive applications. By making precise capacity adjustments, Aurora Serverless v2 ensures that an application always receives the optimal database resources it demands. This dynamic resource allocation extends to encompass an impressive range of Amazon Aurora features, spanning read replicas, multi-AZ support, Performance Insights, and Global Database functionality. This powerful suite is ideally positioned to serve a diverse spectrum of applications. Enterprises dealing with an extensive array of applications or Software as a Service (SaaS) providers managing multi-tenant environments replete with numerous databases can harness the capabilities of Aurora Serverless v2 to deftly manage database capacity across their entire infrastructure.

4. AWS Lambda introduces response payload streaming

AWS Lambda, the bedrock of serverless computing, has ushered in an exceptional enhancement: response payload streaming. This capability enables AWS Lambda functions to gradually stream response payloads back to clients, even accommodating payloads that surpass the 6MB threshold. A monumental leap forward for web and mobile applications, this feature marks a departure from the conventional request-response model. Previously, applications built on Lambda necessitated the complete generation and buffering of responses before they could be sent to clients—an approach that often resulted in delayed first-byte transmission times. With response payload streaming, Lambda functions can transmit partial responses to clients as they are ready, substantially improving the all-important first-byte transmission time, a facet crucial for web and mobile applications alike. This innovation stands to elevate the performance of AWS Lambda-powered applications to new heights.

As AWS continues to introduce new capabilities, it's evident that the serverless paradigm is here to stay. This shift isn't just a technological trend; it's a fundamental change in how we architect, deploy, and scale applications. With each enhancement, AWS reinforces its commitment to providing customers with the tools they need to succeed. With AWS's ongoing dedication to pushing boundaries, the future of cloud computing holds the promise of even more remarkable advancements.

Beinex+ AWS Offerings

AWS provides security services such as AWS Shield and AWS WAF to help protect against phishing attacks. Strengthen your defences by integrating robust security software from the AWS Marketplace and embracing Two-Factor Authentication (2FA). Safeguard against evolving threats like homograph phishing for a safer online experience.

Beinex is an AWS consulting partner, and we empower customers to host their BI solutions, provide security services and much more on the cloud. Our cloud migration experts bring in best-in-class stability and reliability by understanding your business strategy and working closely with you to deploy AWS infrastructure as a service.

Business intelligence (BI) software solutions are designed to analyse data that is input by users or fed from various data sources. The software then organises this data based on patterns or trends it identifies. Finally, the software presents these patterns and trends through visualisations, making the information easy to understand even for users without any statistical analysis experience.

Organisations can develop informed and current strategies by using the insights and trends revealed by these visualisations. With the advancements in technology and innovations, a wide range of BI applications are available for diverse types of data analysis.

Therefore, it is imperative for forward-thinking organisations to recognise the BI tools that market leaders offer and how these tools can impact their own operations positively. Here are four significant business intelligence applications that can enhance your organisation’s operations.

List of Four Business Intelligence Applications

1. Sales Intelligence
2. Visualisation
3. Reporting
4. Performance Management

Let’s take a deep dive into the four noteworthy Business Intelligence applications:

1. Sales Intelligence

One crucial application of BI is to improve customer engagement and sales performance. The sales department of any organisation should prioritise building solid relationships with customers. However, converting leads and convincing potential clients to purchase a product or service can be challenging. BI tools can make this process smoother and more predictable.

BI collects data on specific key performance indicators (KPIs) such as customer demographics, conversion rates, and sales metrics. It then presents this data in structured visualisations like graphs, pie charts, and scatterplots. This data lets users identify trends and insights into customer behaviour and business operations. Understanding the customer allows organisations to provide better service and improve sales performance.

Moreover, the reports and dashboards generated by BI are valuable in providing easy-to-interpret data to potential clients and supporting claims with solid evidence. Managers can use the insights from BI analysis to make data-driven decisions based on complex data and forecasting.

BI applications provide an excellent means of optimising an organisation’s sales operations. Sales and marketing teams can leverage BI to identify trends in client preferences, enabling the organisation to maximise sales within their ideal client base. This allows them to concentrate on targeting highly qualified leads, improving conversion rates and overall profit margins.

2. Visualisation

Furthermore, when used alongside customer relationship management (CRM) software, BI offers businesses a sophisticated method for understanding their customers and making informed sales decisions. By integrating CRM data with BI analysis, organisations can better understand their customers' needs and behaviours, enabling them to provide personalized products and services, strengthen relationships, and increase customer loyalty.

Another critical application of BI is data visualisation. Business intelligence software employs various data analytic tools designed to analyse and manage data related to an organisation’s operations. The resulting data is then presented in the form of visualizations, enabling the organization to monitor logistics, sales, productivity, and more. Some BI platforms offer custom reporting capabilities, allowing users to specify their own parameters, while others offer pre-designed reporting templates that include industry-standard metrics.

By presenting data in intuitive and easy-to-understand formats, BI systems enable inexperienced employees to draw insights from data. Rather than relying on trained data scientists to analyze data, employees can analyze and present their own data to shareholders, other departments, or teams.

3. Reporting

Reporting is a way of summarising data to keep track of business performance, while analysis is a way of exploring data to gain insights that can improve business practices. Business intelligence tools play a crucial role in reporting by collecting and analysing data and generating various types of reports related to staffing, expenses, sales, customer service, and other processes. While reporting and data analysis are related, they differ in purpose, delivery, tasks, and value.

Simply put, reporting takes raw data and transforms it into easily understandable information, while analysis takes data and extracts valuable insights to enhance business practices. Although both processes can incorporate visualisations, their approaches are distinct. Reporting reveals what's happening, whereas analysis explains why it's happening. Traditionally, data visualisations were static, requiring the creation of a new one for every variable change. However, contemporary BI software provides interactive dashboards that can update in real-time, resulting in enhanced usability and flexibility in data analysis.

4. Performance Management

BI tools can help with performance management by allowing organisations to set and track performance goals using data-driven insights. This can include goals related to project completion, delivery time, or sales targets, among others. For example, a BI system can analyze past sales data and recommend a realistic sales goal for the future based on previous performance. This helps organisations stay on track with their goals and make data-driven decisions to improve performance.

With BI applications, organisations can closely track their progress towards pre-defined or customisable goals within specific timeframes. The data-driven plans could include meeting project completion deadlines, target delivery times, or sales targets. For instance, if an organisation wants to achieve a specific sales target, the BI system can analyse previous data and suggest a reasonable goal based on past performance.

By monitoring goal progress in real-time, businesses can stay informed of any remaining gaps and take timely action to bridge them. Users can also set alerts to notify them when they are nearing their target or when the time limit is approaching, and they haven't achieved their goal. This helps managers and employees stay on track and focused on achieving their goals.

Moreover, users can also assess the overall productivity of an organisation by monitoring the fulfilment of goals and tracking progress data. Since the information is readily accessible, there is no time wasted in tracking down urgently needed data, thus saving businesses time and money.

Three Steps to Choose Right Business Intelligence Tools

To choose the right Business Intelligence software for your organisation, it's crucial to identify the features and capabilities that your organisation requires. Follow the three steps below to find out which Business Intelligence tool suits you the best:

1. Selection
2. Compare Applications
3. Shortlist and Trials

Now, let's explore in detail the three steps to choose the right Business Intelligence tool:

1. Selection

It's recommended to select only the modules you will use rather than opting for a solution with a long list of features you don't need. Overbuying can increase the cost and lower the chances of a successful implementation, so it's better to start small and upgrade as your company expands.

2. Compare Applications

You should compare various options based on your specific requirements to choose the right BI software for your organisation. Each vendor may have different strengths and specialities within the BI field, so it's essential to prioritise your needs and preferences. Instead of a one-size-fits-all approach, it's better to focus on the most critical features and evaluate solutions based on how well they meet those requirements. It's also important to remember that the most expensive solution is not always the best one, and sometimes paying a higher price can result in better quality and long-term benefits.

3. Shortlist and Trials

Once you have a shortlist of vendors, it's time to narrow it down further by considering factors such as pricing, demos, and trials. Many vendors offer free trials or demos so that potential users can get a feel for the system's user interface. Make sure to choose a system that most users can use and keep your budget flexible. Consider the type of user support each vendor offers, determine whether you need any integrations with other business software, and confidently make your final decision.

Summing Up

Business Intelligence applications can benefit organisations, from improved decision-making to enhanced performance management. By gathering and analysing data, businesses can gain valuable insights into their operations and customers and use this information to drive growth and success. When selecting a BI tool, it's essential to identify your specific requirements and carefully compare different vendors based on their features, pricing, and support.

Business Intelligence services extended by Beinex deliver solutions to all your business questions. At-a-glance analysis facilitated by cutting-edge BI tools does wonders for every industry. With BI tools, analysing enormous and complex data couldn’t be mind-boggling for you anymore. With Beinex, you can interact with an agile and intuitive system to validate your data, navigate your vision, and execute it data-driven to tap into the potent entrepreneurial potential.

What is Competitive Intelligence (CI)?

Competitive Intelligence (CI) is a systematic process for gathering, analyzing, and applying information about the competitive landscape in which a business operates. It equips organizations with valuable insights, helping them make strategic decisions, mitigate risks, and gain a competitive edge.

Competitive Intelligence services play a vital role in helping businesses gain a strategic advantage through the smart utilization of data and information. By gathering, analyzing, and applying information about the competitive landscape, CI equips organizations with valuable insights, enabling them to make data-driven decisions, mitigate risks, and ultimately outperform their competitors.

To learn more about the benefits of Competitive Intelligence, click here

Why Competitive Intelligence (CI) Matters in Business

In today's fast-paced business world, staying ahead is crucial, and Competitive Intelligence services can be the game-changer your business needs. Here's why Competitive Intelligence matters:

Informed Decision-Making

In the modern business landscape, decisions need to be grounded in data, not guesswork. CI services equip decision-makers with valuable insights that help them make well-informed choices. By analyzing competitor behavior, market trends, and consumer preferences, businesses can develop strategies that are not based on hunches but on hard evidence.

For example, consider a scenario where a tech startup is contemplating the launch of a new product. CI can provide insights into the competitive landscape, revealing what similar products are in development, their features, and their projected market reception. Armed with this data, the startup can make crucial decisions about product design, pricing, and marketing with a higher degree of certainty.

Anticipating Market Changes

The business world is in a constant state of flux. Markets evolve, consumer preferences shift, and disruptive technologies emerge. In this environment, businesses that can anticipate and adapt to change gain a significant advantage. CI allows organizations to do just that.
By monitoring industry trends, tracking competitor movements, and analyzing consumer behavior, businesses can identify early indicators of change. This insight provides a head start in adjusting strategies, launching new products, and capitalizing on emerging opportunities. It's the difference between reacting to change and proactively shaping the market.
For instance, an established retail chain with a robust CI process may spot the growing consumer interest in sustainable and eco-friendly products. Armed with this knowledge, the chain can pivot its product selection and marketing to cater to this demand, staying ahead of competitors who are slower to adapt.

Outsmarting the Rivals

Competition in business is not just a matter of survival; it's a race to outperform rivals. In this race, CI is the secret weapon that enables organizations to outmaneuver their competitors.
By gaining insights into competitors' strategies, strengths, and weaknesses, businesses can develop tactics to gain an edge. For example, a restaurant chain can analyze a competitor's menu changes, pricing strategies, and customer reviews to refine its own offerings and marketing approach. This allows for not only maintaining market share but also expanding it.
Moreover, CI is not just about reacting to competitors; it's also about predicting their moves. By understanding the future direction of the market and the strategies competitors are likely to employ, businesses can position themselves strategically. This foresight is a cornerstone of successful long-term planning.

Examples of Competitive Intelligence (CI) in Action

Competitive Intelligence services are used in various business aspects:

Market Research: Imagine a tech company that monitors competitors' product launches, pricing strategies, and customer reviews to refine its own offerings.

Competitor Analysis:A restaurant chain may analyze its competitors' menu changes, customer reviews, and marketing strategies to refine its own offerings.

Supplier and Vendor Insights:By employing CI, companies can assess their suppliers' performance, ensuring a streamlined supply chain.

Types of Competitive Intelligence

CI comes in various flavors, each with its own focus and strategic horizon:

Tactical CI:This short-term focus involves addressing immediate competitive threats and challenges. For example, a retailer may use tactical CI to respond to a competitor's sudden price drop.

Strategic CI:With a long-term view, strategic CI aims to identify trends, market shifts, and opportunities. An example is a global tech corporation analyzing market trends to position itself as a leader in a specific technology sector.

Product CI: This type focuses on tracking competitors' product development and launches, ensuring companies are aware of what's in the pipeline and can adjust their product roadmaps accordingly.

Pricing CI:It involves tracking competitors' pricing strategies and understanding how they position themselves in the market. Companies can then make informed pricing decisions.

The Role of Competitive Intelligence (CI) in Marketing

Incorporating CI into your marketing strategy is a game-changer. It allows you to align your marketing efforts with the competitive landscape and customer preferences, optimizing your strategies for success. Here's how to do it:

Content Creation

Leverage CI to create content that addresses the pain points of your target audience better than your competitors. Research the keywords they're targeting and tailor your content to rank higher in search results.

Adapt and Personalize

Analyze your competitors' marketing campaigns to identify what's working and adapt your strategies accordingly. Personalize your messaging to resonate with your specific audience.

Keyword Analysis

Studying your competitors' keywords is a goldmine for optimizing your website and content. Use tools to identify the keywords your competitors are ranking for and incorporate them into your SEO strategy.

Ad Campaign Insights

CI can reveal your competitors' successful ad campaigns. Use this information to improve your ad targeting and copy to drive better results.
CI is more than a buzzword; it's a strategic imperative for businesses. By incorporating CI into your strategies, you can gain a competitive edge, make data-driven decisions, and secure your position in the market. Whether through market research, competitor analysis, or innovative marketing strategies, CI can be the key to success in today's fast-paced business world.

How Beinex Can Help You

Robust Competitive Intelligence is essential in strategising for your next move, enabling you to anticipate your competitors’ direction and future performance. Our data harvesting team validates the entire data manually before the CI application consumes it. Our clients can easily integrate our CI database with their actual transactional data.
Efficient, Competitive Intelligence analysis can give a significant boost to your strategy. Beinex provides comprehensive competitive intelligence solutions that help businesses gain a competitive edge in their industry. Supercharge your competitive strategy with Beinex!

To start, access the Superstore Dataset, available for download on Kaggle. Use Tableau Public to connect to this dataset and explore the rows and columns to understand the documented data related to the Superstore. This exploration sets the stage for the subsequent cohort retention analysis. Move over to the Sheet 1 tab; this will serve as your dedicated workspace where you'll craft your visualizations. This area will be your canvas to build and design the subsequent analyses and visual representations.

To perform cohort analysis effectively, specific data points are necessary:

Unique Identifier: Utilize the Customer ID as the unique identifier for each customer.

First Purchase Date: This marks the date when a customer made their initial purchase, a pivotal point for creating cohort groups.

Revenue Data: Information regarding the financial aspect of each transaction.

Creating Calculated Fields

One crucial calculated field needed is the "Customers’ First Purchase Date (quarter)." Since the dataset doesn't contain the first purchase date field, a calculated field is essential. This field is derived from computations based on existing data in the dataset, enabling the identification of when each customer made their initial purchase. Calculated fields are instrumental in manipulating and analyzing data by performing calculations based on existing dataset information.

To create quarter and year cohorts based on the customers' first purchase date:

Defining Cohorts: In this dataset spanning four years (2014–2017), cohorts will be based on the quarter and year when customers made their initial purchase. This approach ensures a manageable cohort table for analysis.

Calculation for Quarter Identification: The calculation to establish the quarter in which a customer made their first purchase involves determining the quarter and year from the purchase date.
This calculation utilizes the 'DATETRUNC' function to extract the quarter from the 'First Purchase Date' field, aligning customers based on the quarter they made their initial purchase.

Flexibility in Cohort Creation: While quarters and years are chosen for cohort segmentation in this scenario, other time parameters like days, weeks, or months could also be used for cohort creation, depending on the dataset and analytical objectives. However, for this particular dataset, quarters and years were deemed more practical for effective analysis.

Assembling the cohort retention table involves using the calculated fields previously created to form a comprehensive table that illustrates cohort-based metrics, particularly the number of unique customers per first quarter and the retention rate.

Here's how to assemble the cohort table:

Customers per First Quarter: Utilize the calculated field that determines the number of unique customers who made their initial purchase in each quarter:

Retention Rate Calculation: Calculate the retention rate using the formula:

This formula divides the count of unique customers by the total number of customers from the first quarter, providing the retention rate for each subsequent period.

Cohort Table: Combine these calculated fields into a table format, displaying the quarters in rows and showcasing the number of unique customers per first quarter and their corresponding retention rates in columns. Each row represents a cohort (based on the quarter of their first purchase), and the columns display subsequent periods with retention rates.

This table allows for a clear visualization of how customer retention varies across different cohorts over subsequent periods, providing insights into the effectiveness of retaining customers acquired in specific quarters.

This step-by-step guide is incredibly detailed for constructing the cohort table in Tableau. It breaks down the process systematically, ensuring proper visualization of the cohort analysis.

Interpreting the retention rates from this table involves examining both the rows and columns:

Rows (First and Second Columns): Here, you'll find different year and quarter groups representing cohorts, along with the count of customers who made their first purchase in each respective period.

Columns (Third Column): As you move across the table, you'll encounter the percentages indicating how many customers continued making purchases at the Superstore across subsequent quarters after their initial purchase.

For instance, if 160 customers made their first purchase in 2014 Q2, you'd observe that 24.4% returned to make purchases in 2014 Q3, and 36.3% made purchases in 2014 Q4. This trend continues across the subsequent periods.
In conclusion, creating calculated fields is an essential part of conducting cohort analysis in Tableau. It involves utilizing functions effectively. If you're new to Tableau functions, exploring articles or resources on Tableau functions could fill any knowledge gaps and provide a deeper understanding of their usage in data analysis and visualization. The provided Tableau article on functions might be particularly helpful for a more comprehensive understanding.

The adoption of digitalised solutions in the healthcare sector has been sparked by the pandemic and a sharp change in people's expectations and lifestyles. Additionally, consumers are in a better position since they enjoy high levels of digital connectivity thanks to reasonably priced internet access, various mobile device technologies, and a sizable app development ecosystem.

What is Gamification

Gamification, frequently used across various business use cases like loyalty management, has gained popularity in the healthcare sector over the past few years. It is the process of introducing game components to solutions, such as activity design, and providing incentives within already-existing processes to create engaging experiences and increase process adoption.

The main goals of gamification in healthcare are to tailor each patient's care and participation and to develop better patient-centred services. Since it incorporates action-based challenges and rapid rewards, gamification has increased patient motivation and engagement. This makes it easier for patients to keep track of their progress.

By using gamification tactics to organise their activities, gamified actions to fulfil the targets, and the chance to share their progress with others, patients can be guided, monitored, and kept interested.

Tracking symptoms and overall health: Instead of consulting doctors, most internet users conduct online searches for information on health-related topics or related topics. The patient's journey begins at this digital entrance. Healthcare providers have the chance to establish themselves as industry leaders by offering digital platforms for consumers to interact, measure wellness, and monitor their health metrics. Some widely used apps use gamification techniques to deliver physical treatment through a virtual learning environment. Motion-guided technology is used to monitor and guide patients. Healthcare professionals can connect to this platform, which enables patients to communicate virtually with licensed physical therapists.

Appointment scheduling: Giving patients a seamless appointment-making experience is one of the critical elements in patient engagement. Patients can use digital health platforms or applications to automatically book appointments with qualified practitioners based on their locations and symptoms. The gamification tactics that engage patients before entering medical facilities can reduce their worry about health outcomes.

Treatment and ongoing care: Data from activity and behaviour monitoring is integrated with health systems that can be used to enhance patient profiles. By better understanding the symptoms, doctors can correctly identify severe disorders. Remote patient activity and critical health metrics can be monitored with data integration between clinical health systems and health apps on patients' mobile devices. Some gamification applications are made to help users create customised health plans, track, and analyse behavioural patterns, and encourage adherence to plans by delivering reminders and tracking user behaviour.

Health insurance: The amount of engagement between patients and insurance companies is minimal. Medical bill payments, reimbursement, and claims are often the focus of this conversation. Nevertheless, by using gamification techniques, health insurance providers can expand and improve their services. Using virtual money and discounts to provide quotations is one example of how such tactics have been used. Price reductions for customers may be offered.

Healthcare providers who lead the charge in the digitalisation of healthcare will be those who use data linkages and gamification techniques to build and improve the patient journey. Applying gamification strategies across healthcare processes for proactive patient engagement and data analytics to monitor, predict, and design true personalisation across the illness or wellness lifecycle are critical components to achieving a fully digitalised healthcare ecosystem. Compliance, data security, and privacy continue to be critical issues. Healthcare providers must comply with patient privacy laws when implementing system integrations and gamified applications.

Gamification opportunities for the entire patient journey

A technology component at each stage of the patient journey can be leveraged to enhance the user experience. Gamified health platforms are projected to progressively use relationships with health providers, health data products, mobile apps, wearable devices, and insurance companies for data integration. Many data aggregators gather information from numerous other health monitoring applications on patients' mobile phones and wearable technology like smartwatches.

Digital services have been enthusiastically accepted all over the UAE. It has been incorporated into UAE healthcare to benefit patients and enhance outcomes in areas including chronic illness management, diagnostics, and preventative care. The UAE's most popular digital health solutions are online pharmacies, fitness applications, teleconsultations, online fitness classes, and diet-management apps.

You can build gamification capabilities and digitally transform your healthcare ecosystem by implementing data integration and analytics strategies, which Beinex Digital can assist you with.