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Nevertheless, despite these challenges, SMBs should take heart in the fact that the global BI market is projected to grow to an impressive $43 billion by 2028. While the current adoption rate of BI among SMBs may be lower than that of larger companies, this simply means that there is plenty of room for growth and innovation in this space. By embracing BI and leveraging its power to make data-driven decisions, SMBs can level the playing field and even gain a competitive edge over their larger rivals. So, let's not lament the obstacles, but rather focus on the opportunities that BI presents for small businesses and startups to achieve success in the digital age.

The Solution: BI Tools

To overcome the challenges, SMBs can turn to technology and budget as more surmountable obstacles. While many businesses today utilize SaaS-driven technology to automate processes such as:

1. Creating Invoices
2. Sending Messages
3. Tracking Time
4. Running Marketing Campaigns

The data generated from these operations can be overwhelming and expensive to collect. This is where business intelligence (BI) tools come into play. SMBs that have undergone digital transformation are already generating data related to their business operations, and with the right BI features, they can derive valuable insights to meet their business objectives. With BI, small business owners can analyze consumer behaviour, estimate market trends, forecast sales, and improve customer experience - capabilities that were previously thought to be exclusive to enterprise companies. By leveraging BI, SMBs can level the playing field and gain a competitive edge.

Business Intelligence (BI) is a set of tools, techniques, and technologies that help businesses collect, analyze, and interpret data to make better decisions. BI is an essential tool for large enterprises, but its benefits are not limited to big organizations. Small and medium-sized businesses (SMBs) can also benefit from BI in many ways.

Top Benefits of BI for SMBs

The primary benefit of BI is improved decision-making. BI helps SMBs to make informed decisions by providing them with the necessary data and insights. By analyzing data from various sources, such as sales, customer feedback, and market trends, SMBs can make data-driven decisions that lead to better outcomes. For instance, if an SMB is looking to expand its business, BI can help it identify the best markets to target and the products or services that are likely to perform well in those markets.

BI can help SMBs to increase their efficiency by automating repetitive tasks and reducing manual errors. With BI, businesses can automate tasks like report generation and data analysis, which saves time and reduces the likelihood of errors. By freeing up time, SMBs can focus on more critical tasks like developing new products, improving customer service, and expanding their business.

BI can help SMBs to understand their customers better and improve their experience. By analyzing customer data, SMBs can identify customer needs and preferences, which enables them to offer more personalized products and services. For instance, if an SMB analyzes customer data and identifies that most of its customers prefer a particular type of product, it can focus on producing more of that product.

BI can give SMBs a competitive advantage by providing them with insights that their competitors don't have. By analyzing data from various sources, SMBs can identify new market opportunities, develop better products, and improve their services. With this information, SMBs can stay ahead of their competitors and respond quickly to changes in the market.

BI can help SMBs to improve their financial performance by identifying areas where they can cut costs and increase revenue. By analyzing financial data, SMBs can identify areas where they are overspending and find ways to reduce costs. Additionally, by analyzing sales data, SMBs can identify products that are not performing well and focus on developing or promoting products that are more profitable.

BI can help SMBs to manage their inventory more effectively by providing real-time information on inventory levels and demand. By analyzing sales data, SMBs can identify which products are selling well and ensure that they have enough inventory to meet customer demand. By having the right inventory levels, SMBs can avoid stockouts and lost sales.

BI can help SMBs to improve their operational efficiency by providing real-time data on key performance indicators (KPIs). By tracking KPIs like sales, inventory levels, and customer satisfaction, SMBs can identify areas where they need to improve and take action to address those areas. With this information, SMBs can make continuous improvements to their operations, which leads to better outcomes and increased profitability.

Summing Up

BI is a valuable tool for SMBs that want to improve their decision-making, increase efficiency, and gain a competitive advantage. By analyzing data from various sources, SMBs can make informed decisions that lead to better outcomes, improve their operational efficiency, and improve their financial performance. With the benefits of BI, SMBs can stay ahead of their competitors, improve their customer experience, and grow their business.
Business Intelligence services extended by Beinex deliver solutions to all your business questions. At-a-glance analysis facilitated by cutting-edge BI tools does wonder to every industry. Analysing enormous and complex data couldn’t be mind-boggling for you anymore with BI tools. With Beinex, you can interact with an agile and intuitive system to validate your data, navigate your vision, and execute it in a data-driven way to tap into potent entrepreneurial potential.

Customer Order Frequency

This scenario involves understanding customer order frequency, specifically determining the count of customers who made varying numbers of orders. While calculating the number of orders per customer is straightforward, discerning how many customers placed one, two, or multiple orders requires breaking down the count of customers based on order frequency. Utilizing LOD (Level of Detail) Expressions becomes essential in transforming the count of orders into a dimension that segregates customers by their order count. This process aids in unraveling insights about customer behavior in relation to their order frequency within a sales database where multiple items are present per order.

Cohort Analysis

In the pursuit of understanding the impact of customer tenure on sales contributions, cohort analysis is employed to assess whether longer-tenured customers hold more significant sales influence. The presented view categorizes customers based on the year of their initial purchase, facilitating an annual comparison of sales contributions among these cohorts. To determine the first purchase date for each customer, the minimum order date per customer is crucial. However, as the displayed data isn’t structured by customer, employing an LOD (Level of Detail) Expression becomes necessary to establish and retain the minimum order date per individual customer for accurate cohort analysis.

Daily profit KPI

In evaluating daily profit as a key performance indicator (KPI), the focus shifts from observing profit trends over time to quantifying success based on total profit per business day. Understanding the count of profitable days per month or year becomes essential, particularly in investigating potential seasonal impacts. Utilizing LOD (Level of Detail) Expressions, this view demonstrates the seamless creation of bins for aggregated data, like profit per day, despite the underlying data being recorded at a transactional level. This approach allows for efficient analysis and visualization of daily profitability trends within the context of the broader business calendar.

Percent of Total

Determining each country's revenue contribution to global sales is crucial for assessing market performance. When visualized by coloring contributions as percentages, it's apparent that the US holds the highest share of global sales revenue. However, focusing on markets like the EU, which might have a relatively smaller absolute contribution, becomes challenging without LOD Expressions. Without this capability, filtering by market could lead to recalculating the percent of total, displaying each country's contribution relative to its market. Using a straightforward LOD Expression enables filtering by market while preserving the measurement of each country's global contribution, facilitating a more nuanced analysis of market performance within the broader global context.

New customer acquisition

Analyzing the daily trend of total customer acquisition across different markets serves as a crucial metric in assessing the effectiveness of regional marketing and sales efforts in generating new business. By tracking this trend, we gain insights into the performance of these organizations. A steeper line signifies a stronger acquisition trend, while a flattening line suggests a need for increased lead flow.

To accurately measure this trend, it's imperative to ensure that repeat customers aren't erroneously counted as new customers. This necessitates using an LOD (Level of Detail) Expression, allowing data evaluation at the customer level despite its visual representation being segmented by market and day. This meticulous approach ensures a precise assessment of new customer acquisition, enabling strategic actions to be taken based on the observed trends.

Comparative Sales Analysis

When aiming to determine the difference from a selected category rather than the average, the process becomes more intricate. Initially, isolating the sales figures of the chosen category is necessary. Subsequently, employing an EXCLUDE Expression becomes crucial to reiterate that value across all other categories. This technique enables a straightforward calculation of the difference between each category's sales and the rest, allowing for a comparative sales analysis that emphasizes the disparity between the selected category and others.

Average of top deals by sales rep

Determining the largest deal closed by each sales representative and subsequently computing the average of these top deals by country is a multi-layered analysis. LOD (Level of Detail) Expressions play a pivotal role in dissecting data down to the sales rep level, even when the visualization displays information at the country level.
The presented view showcases the average top deal size by sales rep, offering insights where countries colored blue exhibit higher average top deal sizes, while those colored orange indicate comparatively lower averages. This information serves as a guide for further drill-down analysis from the country level to the sales rep level, facilitating a deeper understanding of performance variations across both geographical and individual sales rep perspectives.

Actual vs. Target

Within this visualization, we present the variance between actual and target profits per state for a chain of coffee houses. The top view distinctly showcases states surpassing or falling short of set targets. Yet, this aggregated view might overlook subtleties: some states exceed targets due to every product sold meeting or exceeding goals, while others rely on a single product surpassing its target to compensate for others missing theirs. Employing an LOD Expression enables the identification of the percentage of products sold within a state that surpass their set targets, offering a more nuanced assessment.

Value on the Last Day of a Period

Data reflecting specific day statuses—like inventory, employee headcounts, or daily stock values—require distinct handling compared to aggregatable metrics like sales or profit. Displaying the value on the last calendar day of a month holds significance in such cases. Moreover, transitioning from a monthly to a weekly view should dynamically update to showcase the last day of the week. For instance, in the stock data example below, assessing multiple ticker values at a daily level compares the average daily close value against the close value on the final day of the period. Employing a straightforward LOD Expression enables diving into daily granularity even within a visual display at a higher level of aggregation.

The following 6 examples illustrate how level of detail expressions can be applied to more advanced scenarios:

Evaluating the return purchases among customers holds significance, especially considering the costliness of acquiring new customers. Understanding the patterns of customers making repeat purchases within varying quarters—whether it's the first, second, third, or beyond—is essential. Additionally, assessing the count of customers who have never made a repeat purchase contributes valuable insights. This analysis, segmented by quarterly cohorts, sheds light on customer behavior over time.
Leveraging a FIXED Expression becomes instrumental in identifying each customer's first and second purchase dates, enabling the derivation of the time span in quarters for a repeat purchase. This nuanced approach offers a comprehensive understanding of customer return behavior within distinct quarterly cohorts.

Percent Difference from Average Across a Range

While Example 6 highlights comparing against a single selected item, what if the aim is to assess comparisons across a spectrum of values? Consider a scenario where one desires to evaluate the daily close value of a stock against the average daily close value before a significant industry-impacting event occurs.
In such instances, examining the percent difference becomes essential. By comparing the daily stock close values against the pre-event average, insights into the magnitude and impact of the event on stock performance can be gleaned. This analysis offers a broader perspective, aiding in understanding the deviation from the average within the context of industry-wide fluctuations.

Relative period filtering

When analyzing performance through year-to-date (YTD) and month-to-date (MTD) comparisons relative to the previous year, filtering relative to today is straightforward. However, when data undergoes weekly refreshes, discrepancies can arise. For instance, if the last refresh was on March 1 but the current day is March 7, a month-to-date comparison might inadvertently compare March 1 through March 7 of the previous year against March 1 of the current year, potentially causing unwarranted concern.
Employing a simple LOD (Level of Detail) Expression resolves this issue by identifying the maximum date within the dataset. This approach ensures accurate time-based comparisons, preventing misleading contrasts between different periods and providing a more precise evaluation of performance trends.

User login frequency

Understanding user login frequency is pivotal for assessing user engagement on websites or applications. This analysis aims to segment users based on their login frequency—whether it's monthly, bi-monthly, quarterly, and so on—and derive insights regarding the average login rate and its distribution around this average.
The dataset's granularity involves a log-in date per user ID, implying a row for each day a user accesses the platform. Slicing the number of customers by their login rate entails a more intricate analysis, necessitating the slicing of one measure by another measure. As showcased in Example 1, leveraging LOD (Level of Detail) Expressions streamlines this analysis, enabling an easy breakdown of user cohorts based on their login frequency and facilitating a comprehensive understanding of user behavior.

Proportional Brushing

In the realm of analysis, the pivotal question often revolves around comparison—specifically, "Compared to what?" Proportional brushing introduces a valuable technique for filtering where the aim is not merely to narrow down to the selection but to compare the selection against the total context.
This approach allows for a more comprehensive analysis by providing insights into how the selected subset relates to the entirety of the dataset. Proportional brushing aids in understanding the significance and impact of the chosen subset within the broader context, offering a richer perspective for informed decision-making.

Examining the correlation between customer tenure, measured by the year of acquisition, and loyalty, gauged through annual purchase frequency, provides valuable insights into customer behavior.
While Example 1 illustrates customers purchasing a specific number of times, marketers often seek insights beyond exact counts—particularly identifying customers who purchased at least a certain number of times. Moreover, understanding the loyalty trends within different acquisition cohorts is crucial. Simply assessing absolute customer numbers across cohorts might not reveal nuanced insights. Therefore, a more insightful approach involves evaluating the percentage of total customers within each cohort based on their purchase frequency thresholds.
In essence, this analysis combines variations of the number of orders LOD Expression, cohort Expression, and percent-of-total Expression to determine what percentage of customers within each cohort made at least one, two, three, or more purchases in a year. This approach offers a comprehensive understanding of loyalty trends across different customer acquisition periods.

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.

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What is Tableau Desktop:

Tableau Desktop is a desktop-based version of the software that is installed locally on your computer. It provides a user-friendly interface that allows users to create interactive visualizations, reports, and dashboards by dragging and dropping data onto the canvas. Tableau Desktop supports a wide range of data sources, including spreadsheets, databases, and cloud-based services. It also provides advanced features such as calculated fields, table calculations, and data blending. Users can save their work locally or publish it to Tableau Server or Tableau Online.

For the purpose of learning and exploring dashboards developed by various users online, Tableau Public can also be utilized. This free-to-use feature allows users to create interactive visualizations and publish them on the web. It is designed to help users of all skill levels, from beginners to advanced analysts, to explore, create, and share data visualizations online.

Key features of Tableau Desktop

• • Powerful and flexible: Offers a full range of features for data analysis and visualization, allowing for deep customization.
• • Fast performance: Enables quick data exploration and visualization creation due to local processing power.
• • Offline functionality: Work on your visualizations even without an internet connection.
• • Customization options: Provides control over server configuration and some customization options.

What is Tableau Cloud (formerly Tableau Online):

Tableau Online is a cloud-based version of the software that allows users to access and share their Tableau visualizations, reports, and dashboards from anywhere with an internet connection. It provides the benefit of being able to collaborate with other users in real time. Users can connect to various data sources and perform advanced data analysis using features like calculated fields and table calculations. Tableau Cloud also provides a range of sharing options, including embedding dashboards in websites and sharing them via email.

Tableau Pulse is available on Tableau Cloud, empowering every employee with intelligent, personalized, and contextual insights delivered in the flow of work. Tableau Pulse leverages generative AI to present insights in both natural language and visual formats, simplifying the process of identifying critical metrics, gaining actionable insights, posing questions, and connecting data to real-world business scenarios. Integrated with the AI capabilities of the Einstein Trust Layer, a secure AI architecture, Pulse ensures that teams can harness the power of generative AI while safeguarding their customer data.

Key features of Tableau Cloud

• • Cloud-based convenience: Accessible from anywhere with an internet connection, facilitating remote work and collaboration.
• • Scalability and ease of use: Highly scalable to fit various team sizes and offers a user-friendly interface for sharing insights.
• • Reduced IT burden: Tableau handles maintenance and upgrades, freeing up your IT resources.
• • Always up-to-date: Ensures access to the latest features and functionality without manual updates.

Tableau Desktop, Tableau Server, and Tableau Cloud: A Comparison

Let’s dig deeper into the capabilities of Tableau Desktop, Server, and Cloud, exploring how each solution can empower organizations to make use of the full potential of their data and drive actionable insights:

1. Tableau Desktop

What does Tableau Desktop Offer You:

• • Fast performance.
• • Offers a wide range of features for data analysis and visualization, including data blending, calculations, and advanced chart types.
• • Enables complete customization of dashboards and views to meet specific needs.
• • Provides a familiar desktop application experience for many users.

Benefits of Tableau Desktop:

• • Leverages local processing power for quick data manipulation and visualization creation.
• • Ideal for working with large datasets or complex calculations.
• • Enables smooth interaction with dashboards and visualizations.

Tableau Desktop Use Cases:

• • Individual data analysts and creators exploring complex datasets.
• • Building custom dashboards and reports for specific departments or projects.
• • Creating prototypes and mockups for data visualizations.

2. Tableau Server

What Does Tableau Server Offer You:

• • Secure on-premises deployment.
• • Collaboration and sharing.
• • Scalability for on-premises.
• • Customization options.

Benefits of Tableau Server:

• • Data remains on your own infrastructure, ideal for organizations with strict data security and compliance requirements.
• • Enables real-time collaboration on visualizations with colleagues.
• • Can be scaled to accommodate growing teams and increasing data volumes.
• • Offers some control over server configuration and customization of user roles and permissions.

Tableau Server Use Cases:

• • Financial institutions and healthcare organizations with sensitive data.
• • Sales teams collaborating on sales performance dashboards.
• • Large enterprises with a high volume of data users.
• • IT administrators managing user access and security settings.

3. Tableau Cloud (formerly Tableau Online)

What Does Tableau Cloud Offer You:

• • Cloud-based convenience.
• • Scalability and ease of use.
• • Reduced IT burden.
• • Always up-to-date.

Benefits of Tableau Cloud:

• • Accessible from any device with an internet connection, facilitating remote work and collaboration.
• • Highly scalable to fit various team sizes, from small teams to large enterprises.
• • Tableau handles server maintenance, upgrades, and security patches.
• • Provides access to the latest Tableau features and functionalities like Tableau Pulse and AI capabilities for next-gen experiences

Tableau Cloud Use Cases:

• • Remote teams working on data analysis projects together.
• • Small and medium-sized businesses looking for a quick and easy BI solution.
• • IT departments with limited resources to manage on-premises BI infrastructure.
• • Organizations that need to stay current with the latest data visualization trends.

Tableau Pricing

Tableau provides flexible pricing options tailored to different organizational needs. Pricing is based on factors such as headcounts, licensing type, and specialized roles. Users can opt for individual licenses suited to their specific requirements.

For per-user licenses, Tableau offers three distinct options, allowing users to select the most suitable plan for their needs. Additionally, Tableau provides flexibility in licensing models, including role-based, usage-based, or core-based options for embedded analytics.

Tableau offers three pricing editions ranging from $15 to $70, catering to various budgetary constraints. Users can explore the features and capabilities of each edition through a free trial before deciding. Evaluate the different pricing editions to determine which aligns best with your budget and requirements.

Tableau Creator

Includes: Tableau Desktop, Tableau Prep Builder, Tableau Pulse, and one Creator license on Tableau Cloud Pricing: $70.00 (1 User Per Month)

Tableau Viewer

Includes: Tableau Pulse and one Viewer license of Tableau Cloud Pricing: $15.00 (1 User Per Month) Learn More: https://www.tableau.com/pricing/teams-orgs

Book a Tableau Free Trial

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Disclaimer: The pricing information provided in this blog for Tableau products and services is subject to change at any time without notice. The pricing may vary based on factors such as promotions, changes in pricing policies, or updates from Tableau. We recommend visiting the official Tableau website or contacting Tableau sales representatives for the most current pricing information. This blog is not responsible for any discrepancies or changes in pricing.

• Virtual Connections
• Centralized Row-Level Security
• Edit Published Data Source
• Parameters in Tableau Prep

• Inputs /Outputs File path/File name
• Published data source name
• Calculated fields
• Filters

1. Data Management
   1. Virtual Connections
   2. Centralized Row-Level Security
2. Tableau Catalog
   1. Inherited column descriptions
   2. Create flows from Catalog pages
3. Tableau Prep Builder
   1. Tableau Prep Parameters
   2. Create Tiles
   3. Flow output Subscriptions
4. Tableau Online
   1. Connected Apps
   2. Tableau Bridge Multi-Pool
5. Analytics
   1. Metric Updates
      1. Show Metric status with colors
      2. Customize comparison period and data window
      3. Embed metrics
   2. Replay Animations
   3. Web authoring improvements
      1. Edit published data sources on Server and Online
      2. Copy/Paste Dashboard Zones
   The journey towards unveiling the full potential of data analytics can be experienced in a better way with this new adoption of Tableau. Guaranteed to make your work polished and effortless, Tableau 2021.4 opens up its myriad capabilities to help you derive meaning out of data. Let all the tech geeks armor up to savour the essence of this exciting update.