Snowflake Summit 2025 at San Francisco Highlights: Latest AI-powered Innovations that Can Take You a Long Way

Tableau filters allow their users to perform simple filtering using available metrics, as well as perform advanced filtering using complex calculations. While developing dashboards, Tableau offers the flexibility to view different types of filters such as drop-down filters, wildcard filters, list filters and many more. To enhance dashboard interactivity, Tableau has provided “Filter Actions” which allow users to filter an entire dashboard as per the value selected by the user in a particular visualization.
To create a filter that suits the user's requirements, Tableau engages several filtering techniques which also enhance data processing efficiency Each filter is designed for a specific function, and the order in which they are run can significantly impact performance.

6 Types of Filters in Tableau

Tableau employs six different types of filters, which are listed in the sequence of execution:

1. Extract Filters
2. Data Source Filters
3. Context Filters
4. Dimension Filters
5. Measure Filters
6. Table Calculation Filters

1. Extract Filters

Extract filters are utilised during the initial stage of data filtering. When datasets are uploaded to Tableau, we can utilise the extract filter to minimize the number of Tableau queries for the data source. By applying filters to the extract, we can further decrease the size of the data.

2. Data Source Filters

Data Source filters sort out any crucial or sensitive information while putting data into Tableau. It is compatible with both the Live and Extract connections.
We can add the data source filter to any column by selecting the ADD option. After selecting the ADD option, the ADD Filter dialogue box will appear, displaying all the fields. We can then select the field to which we wish to apply the filter and it is possible to change or delete data source filters as needed.

3. Context Filters

A context filter is generally used when we require this filter to be applied before any other filter. It has the potential to enhance the performance of massive data sources. They are made by dragging the dimension to the filter section box and selecting the Add to context option. This will make the dimension pill change its colour to “Grey”, indicating it is a context filter. Similarly, by clicking "Remove from Context", the filter becomes a simple dimension filter. A common use case for this filter would be when a user would like to find the top N products in any category.

4. Dimension Filters

Dimension fields include discrete categorical data, and we can remove or add the values we want to investigate. The process is simple as outlined below:

Step 1: Drag the dimensions from the dimension list to the filter section box.
Step 2: From the Filter Dialog box, select the values that we want to analyze.

The Filter dialogue box contains four tabs:

1. General: To select the members present in the dimension to either include or exclude
2. Wildcard: To filter the result based on a particular pattern
3. Condition: To filter the result based on a particular condition
4. Top: To filter the Top N products belonging to a particular category.

5. Measure Filters

Measure filters are applied to the measure fields, which have quantitative data. It can be used as follows:

1. Drag the measured field from the Measure box to the Filter area
2. Filter dialogue box with several functions will appear.
3. Select the operation that needs to be performed and click Next.

In the subsequent dialog box, there are 4 types of filters:

1. Range: To select the range of values to include in the result.
2. At least: Select the minimum value of a measure to filter the data.
3. At most: To select the maximum value of a measure to filter the data.
4. Special: To select null or non-null values.

6. Table Calculation Filter

Table Calculation Filters, being the last type of filters to be executed in the sequence of execution, do not filter out the underlying data. It is mostly used to filter out contents in the current view. Table Calculations are functions that are utilised for building Calculated Fields like LOOKUP, WINDOW SUM, WINDOW AVG, and so on.

Summing Up

Tableau Filters are the most commonly used feature by many organizations. It allows the clients to filter out confidential data and allows them to provide controlled view access to different internal users so that only data relevant to the user is accessed. Filtering out data means data processing becomes more efficient, which in turn makes the report generation process faster which in turn helps the organization arrive at quick and insightful business decisions.

Use Cases Addendum

Let’s find out how Tableau filters work to solve various tasks :
Use Case 1:

A simple drop-down filter can be used to filter the view of our requirements as shown in the visualization above.
In this example, Dept A has been selected such that the Total Budget is shown only for Dept A. Once we select Dept B, the Total Budget shows the respective value for Dept B

Use Case 2:

In the visualizations above, we can see the customer demographics related to gender, age group and educational status. With the help of Tableau’s Filter Actions, one can click on a particular segment and filter the rest of the visualizations as per the requirement.
For example, if we require to find how many customers in the age group of 35-44 are males, all we have to do is to click on the doughnut chart for “Male”, and the visualizations will show only the data for males. Now, we can find that the number of male customers in the age group of 35-44 is 272.

Use Case 3:

The Filter Action feature in Tableau can be leveraged to develop several creative solutions for customers. One such solution is the “Reset Filter” as shown in the above visualization.
With a combination of filters and filter actions, one can develop a reset filter that, on clicking, would reset all the currently applied filters on the dashboard and bring it back to its original unfiltered view. This comes in handy if the dashboard consists of multiple visualizations and contains heavy data.

Businesses use BI for a multitude of purposes. Many people use it to assist with hiring, compliance, production, and marketing. When it comes to BI, it's impossible to find a department that doesn't benefit from more data to work with.

Faster and accurate reporting and data analysis, better data quality, improved employee satisfaction, lower expenses, increased revenue, and the ability to make insightful business decisions are just a few of the many benefits that businesses may gain by adopting BI into their business models. Many more benefits follow:

1.Rapid and precise reporting

Using templates or customised reports, employees can monitor KPIs using various data sources, including financial, operational, and sales. The pieces are created in real-time and use the most up-to-date data, allowing businesses to act quickly. Most reports include straightforward visualisations such as graphs, tables, and charts. Some BI software reports are dynamic, allowing users to experiment with various variables or quickly access data.

2.Data integration

Most businesses keep data in a variety of formats and across multiple solutions. Data processing and reporting become complicated and time-consuming as a result. Using a business intelligence solution, you can reduce data storage complications in various tools and spreadsheets.

BI tools connect all the data in your workplace in various forms with your existing software solution and use real-time data to create robust business decisions. Numbers do not deceive. A fully integrated BI solution can help you achieve total company success.

3.Making timely decisions

BI assists your company in growing. Businesses that use BI can quickly extract facts from massive amounts of disorganised data. With instant access to business data, you can analyse internal data and create better business decisions. BI teams ensure that the organisation receives real-time advanced business reports to better utilise the data.

Tasks like data collection, entry, analysis, control and use require substantial human time and effort. With the assistance of an automated BI tool, data can be collected, analysed, managed, and used more quickly and effectively. Reports can be generated soon because the data is already right behind the scenes.

4.Revenue growth

Revenue growth is an important goal for any business. Through comparisons across multiple dimensions and recognising sales weaknesses, data from BI tools can help companies to ask insightful questions about why things happened. Revenue is more likely to increase when enterprises listen to their customers, track their competitors, and enhance operations.

5.Recognising market trends

Discovering great possibilities and implementing the strategy with supporting data can provide organisations with a competitive advantage, long-term profitability, and a complete picture. Employees can combine external market data with internal data to identify new sales trends and business challenges by studying consumer data and market conditions.

6.Improving customer satisfaction

Business Intelligence tools can assist firms in better understanding customer behavioural patterns. Most organisations collect customer feedback in real-time, and this data can aid in client retention and acquisition. These techniques may also help identify buying patterns, allowing customer service representatives to anticipate demands and provide better assistance.

7.Improved operational efficiency

BI solutions consolidate disparate data sources, assisting with a company's overall organisation so that managers and employees can focus on delivering accurate and timely reports rather than hunting for information. Employees can focus on their short and long-term goals and examine the impact of their decisions when they have up to date and correct information.

8.Bigger profit margins

Most businesses are concerned about their profit margins. Fortunately, Business Intelligence technologies can identify inefficiencies and aid in margin expansion. Aggregated sales data assists firms in better understanding their clients and enables sales teams to establish more effective methods for allocating resources.

9.Reduce the risks

Business Intelligence solutions help you to reduce risks by inputting data into action. By tracking customer activity, you may quickly uncover fraudulent activities. You can also monitor employee behaviour to abide by industry regulations.

Using the data and knowledge about the current economic situation, you can examine credit portfolios and identify potential delinquency cases. Business Intelligence solutions offer a proactive approach to risk management in any financial industry.

10.Evaluate and improve inputs

Employees can improve the process of arriving at insights using fully integrated BI by implementing well-known accessible technologies. Individuals can successfully analyse and investigate information when data is received quickly. Personnel can engage with one another without barriers, and clever business plans can be developed.

11.Reduced training requirements

Business Intelligence can let employees access a variety of information. Implementing business tools that are widely available, common, and well-supported can considerably minimise an organisation's training costs.

12.Gain a competitive advantage

Personalisation is a hot topic in every industry, and the banking and finance sectors are no exception. As a result, having a competitive advantage is essential. You may quickly modify consumer experiences with business intelligence technology based on your data. Market trends can be used to strategise new investment opportunities, analytics can predict customer behaviour, and products can be tailored to each client's individual needs.

13.Employee authorisation

Suppose users are given direct access to simple data that can be comprehended and analysed quickly. In that case, performance may be considerably enhanced, and all company plans can be promoted if employees can process the data in various ways. Business Intelligence includes a variety of healthy, lively business score registering, investigation, and reporting equipment to assist quick and better decision making by practically every employee of the organisation.

Final Thoughts

To cite from Information Week, it is predicted that a third of large-scale organisations will adopt decision intelligence by 2023 (Source: Information Week). Business intelligence software has several advantages. It's a burgeoning sector with numerous demonstrated benefits when correctly handled. Users can obtain specific insight into your company's past, present, and future to make informed business decisions. BI software collects, organises, compiles, and visualises critical KPIs. It reduces waste and guesswork while also improving inventory management and sales intelligence. This potent combination of business intelligence software capabilities and benefits provides customers with a core competency that can make a huge difference.

When you gather consumer insights, you will learn about how your prospects interact with your marketing platforms, sales team, support personnel, and product. Your team is equipped with this crucial information, so you can make informed judgements about the efforts they should be making.
The environment of analytics, business intelligence (BI), and data science is changing at an accelerated rate due to increased consumerization of analytics technology and the demand for communities. The introduction of tools like UPI has effectively integrated small and medium-sized businesses into the financial system. The ability to prove creditworthiness through payments placed straight into current accounts has increased for firms.
The goal of marketing has always been to influence consumers. The core purpose of marketing is to alter behaviour, whether it be to encourage the purchase of a new product or merely to increase brand recognition in a crowded marketplace. Therefore, it makes sense for your marketing measurement plan to be centred on the behaviours that bring in money for your company. You can improve the return on investment of your marketing spend by better understanding your market by researching important customer behaviours. The understanding that analysis needs to be more focused on marketing indicators rather than the conventional web metrics like site visits, time on site, bounce rate, etc. that we've grown so accustomed to has also matured along with digital analytics.

The key advanced analytics techniques that help to understand consumer behaviour are enlisted below:

1. Identifying Revenue-oriented Metrics and ROI
2. Understanding the Importance of Multi-Channel Attribution
3. User-centric Monitoring

Identifying Revenue-oriented Metrics and ROI

Understanding how user behaviour on your site translates into money for your company is a crucial point. In other words, are visitors who do particular actions on your website more likely to buy something? The majority of the time, the response is a stunning yes! You can monetize all on-site behaviours by assigning a monetary value to them, even if the behaviour does not immediately result in a sale. The first step is to identify the key customer engagement points and track customers who have taken the desired actions. From there, a simple calculation may be created to calculate the income they produce. Based on this you can calculate your ROI.

Understanding the Importance of Multi-Channel Attribution

Multi-channel attribution has an important part to play here. It is the process of identifying marketing interactions in a customer journey that finally leads to conversions.
It goes without saying that as consumers are exposed to more online and physical marketing channels across more devices, the complexity of tracking keeps growing. But it's critical to keep up with the most recent trends and statistics. The advantages are many. It helps to achieve a more precise understanding of the ways media platforms and devices affect behaviour and financial outcomes. A comprehensive view of how various channels interact and function within your media mix and at various phases of the funnel can also be achieved. Finally, the accumulation of information for scenario modelling and budgeting to enhance ROI and optimise the media mix. Although high-end enterprise clients still have access to the most sophisticated attribution analysis tools, recent acquisitions and mergers indicate that mid-market and small firms will likely use these techniques more frequently in the near future.

User-centric Monitoring

User's "session” starts when a person joins the website and ends when they leave; this has been the primary unit of measurement for traditional web metrics. The growth of technology and the popularity of mobile devices, have, however, given marketers, in general, a new "demand." We now want to be able to track users as they interact with our sites through different channels and analyse their behaviour as they switch between different devices. Modern analytics software is driven by the need to continuously follow individual user behaviour across sessions and devices in addition to gathering data from all these different channels and devices. Understanding these various behavioural patterns is crucial for both developers and marketers to customise messaging and user experiences across a variety of channels and devices.

How to Gather Customer Insights Using Advanced Analytics

Marketing professionals all over the world are utilising analytics, which allows them to gain insights and create customised marketing messages. But, how? In what ways do data analytics and big data assist marketers in creating tailored ads based on consumer behaviour? How can you take advantage of the opportunity to make use of current data and improve consumer understanding?
Any firm that wants to excel at client interaction must have real-time analytics. While businesses of all colours have been substantially investing in technologies to better understand their customers, most of them miss out on the opportunity because of outdated IT systems and deeply ingrained structures and processes. It is no longer enough to just collect client data in your CRM software without figuring out how to interpret it. A successful firm must have a sizable, devoted customer base. However, how can you build such a customer base? You must be intimately familiar with your target market to build and keep consumer loyalty. You will need consumer behaviour analytics for this to better understand them and increase sales.
The necessity to concentrate on consumers' requirements is one thing all organisations have in common. To meet the expectations of the consumer, a thorough understanding of their needs and desires is necessary. Long-term success depends on giving your team the tools they need to gather data on client behaviour. To optimise customer journeys, it is essential to gain insight into the motives and actions of customers. Start utilising customer insights for the expansion of your organisation with Advanced Analytics.

Beinex Offerings

Advanced Analytics services from Beinex explain the why and how of change in your enterprise – the top line, bottom line behaviours and everything in between, from your organisational data. Enhance efficiency and expand your market share and presence. Make the most out of Advanced Analytics by partnering with the right people. Beinex!

While sharing the content, permissions can be given to each user that restricts them from editing, applying different types of filters, and sharing it further. Users can create apps and add specific visuals to create live dashboards that can be accessed through smartphones and tablets. Moreover, one can collaborate with other designers and administrators to work together and create highly customized reports for a particular field.

Challenge

• The IT infrastructure of Atlantis where the Tableau 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 Tableau 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, Atlantis wanted to look for another viable solution.
• Though the hardware setup at Atlantis 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 Atlantis to select a cost-effective solution that can host Tableau 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

• Atlantis decided to migrate Tableau Server, database servers, and archival data to AWS.
• The Tableau 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

• Atlantis uses AWS services to provision infrastructure and deploy the Tableau Server platform to other departments within it. In addition, the Tableau Server resources that are no longer required to be run all the time are made to auto shutdown thus saving cost. Atlantis reported a 28% cost reduction after implementation of AWS for the Tableau Server platform.
• The implementation of Tableau Server on AWS made Atlantis 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, Atlantis 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 Tableau Server platform, Atlantis has been able to innovate and experiment to a degree previously impossible. For example, Atlantis 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 Tableau Server application on top of it would have taken months.

9 Key Areas to Focus on During Cloud Migration

1. Data Compression

Efficient data compression conserves bandwidth and speeds up data transfers. Best Practice: Use reliable compression formats like gzip to prepare data for upload. How Snowflake Helps: Snowflake supports ingesting compressed files and automatically compresses uncompressed files during uploads, saving time and resources.

2. Initial Data Uploads

Large-scale data transfers require secure and efficient methodologies. Best Practice: Use tamper-proof, one-time transfer solutions for large datasets. How Snowflake Helps: Snowflake integrates with AWS Snowball, Azure Databox, and Google Transfer Appliance, making massive data migrations seamless and secure.

3. Ongoing Data Uploads

Continuous data ingestion is essential for keeping cloud databases updated. Best Practice: Build pipelines to automate the ingestion of newly generated data. How Snowflake Helps: Tools like Snowpipe, COPY commands, and Snowpipe Streaming support real-time and batch data uploads, ensuring uninterrupted data flow.

4. Data Set Prioritization

Prioritizing critical data minimizes redundancy and ensures efficient migration. Best Practice: Start with "master data sets" and avoid unnecessary duplication. How Snowflake Helps: With user-friendly options like its web interface, Snowflake simplifies the process of structured data migration.

5. Data Lifecycle Management

Managing data retention optimizes storage costs and aligns with compliance requirements. Best Practice: Implement policies to archive or delete obsolete data. How Snowflake Helps: Snowflake’s cost optimization tools and upcoming policy-based features streamline lifecycle management.

6. Data Security and Encryption

Protecting data during and after migration is paramount. Best Practice: Use encryption and private connectivity for secure transfers. How Snowflake Helps: End-to-end encryption, robust key management, and features like Private Link ensure data security throughout its lifecycle.

7. Data Validation

Validating data quality builds trust and ensures accurate analytics. Best Practice: Monitor metrics like null values, duplicates, and data freshness. How Snowflake Helps: Snowflake’s built-in functions detect anomalies and maintain data integrity.

8. Disaster Recovery

Cloud platforms simplify disaster recovery with built-in capabilities. Best Practice: Leverage cloud-native DR features to enhance data resilience. How Snowflake Helps: Snowflake’s Snowgrid technology and features like replication, failover, and time travel ensure business continuity.

9. Managing Multiple Environments

Cloud scalability simplifies managing development, testing, and production environments. Best Practice: Automate environment deployment and resource allocation. How Snowflake Helps: Zero-copy cloning, CI/CD tool integration, and instant resource access streamline environment management.

How Snowflake Transforms Migration Challenges

Snowflake’s cutting-edge tools address common migration challenges, offering: Efficiency: Automated compression, seamless data uploads, and scalable pipelines. Security: Robust encryption and compliance-friendly features. Flexibility: Support for diverse data ingestion methods and environment setups.

Defining Clear Objectives for Migration:

1. Primary Drivers of Migration for an Organization: Common drivers include: • Reducing operational costs. • Enhancing scalability to manage growing datasets and user demands. • Enabling advanced analytics capabilities to improve decision-making. • Improving system performance for faster query execution. • Lowering maintenance overhead by moving to a cloud-native platform. 2. Critical Migration Assessment: Before committing to migration, organizations should evaluate whether the benefits outweigh the risks and costs. This involves: • Assessing alignment with strategic goals. • Identifying potential technical challenges or limitations. • Determining readiness for organizational change and adoption.

Assessment Before Migration:

1. Evaluate Current Architecture, Data Volume, and Workload: Conduct a thorough audit of the existing data infrastructure, analyzing data volume, complexity, and system performance to determine migration readiness. 2. Identify All Data Sources, Pipeline/ETL Processes: Create a comprehensive inventory of data sources and existing ETL pipelines to understand the flow and transformations applied to data. 3. Map Dependencies Between Datasets and BI/Analytics Applications: Identify interdependencies between datasets, applications, and users to ensure no analytics or reporting post-migration disruption.

Utilize Snowflake's Features:

1. Data Sharing: Enable seamless live data sharing between internal teams and external partners without duplication. 2. Scalability: Automatically scale resources up or down based on workload demand, ensuring cost-effective performance. 3. Time Travel: Leverage historical data snapshots for recovery, audits, or analytics within a specified retention period. 4. Built-in Security: Utilize Snowflake’s enterprise-grade security features like end-to-end encryption and multi-factor authentication. 5. Streamlit and Cortex Functions: These features can be harnessed to build custom applications and natively perform advanced machine-learning operations on the Snowflake platform.

Data Governance:

1. Establish Policies, Access Control, and Data Classification: Snowflake’s role-based access control (RBAC) and discretionary access control (DAC) features can be used to implement governance policies and classify data. 2. Data Masking: Apply dynamic data masking to protect sensitive information and ensure compliance with regulations like GDPR and HIPAA.

Optimize Data Pipeline:

1. Replace Batch Processing with Real-Time or Micro-Batch Processing: Adopt real-time data processing to improve analytics and decision-making. 2. Utilize Change Data Capture (CDC): Leverage Snowflake features like dynamic tables and streams to minimize transformation overhead and support incremental updates. 3. Orchestrate Workflow with Tasks: Use Snowflake tasks and dependent tasks for workflow automation. For larger deployments, consider tools like Apache Airflow or dbt.

Upskill the Team:

1. Train the Team on Governance Policies: Provide data governance, security practices, and compliance training to ensure a seamless transition. 2. Managing and Monitoring the Cloud Environment: Equip teams with skills to effectively manage and monitor the Snowflake environment for performance and cost optimization.

MONITORING POST-MIGRATION

After migrating to Snowflake, monitoring and optimizing your system is crucial to maximize performance and cost-efficiency. • Query Performance Monitoring: Continuously track the performance of your queries using Snowflake's Query Profile and Query History features. Identify long-running or resource-intensive queries and optimize them to improve system efficiency and user experience. • Fine-Tune Workloads and Frequency to Ensure Cost-Efficiency: Review your scheduled workloads and the frequency of data pipelines. Adjust execution timings and resource allocation to balance performance and cost. Snowflake's dynamic scaling can help allocate resources based on workload demands, ensuring you're not over-provisioning. • Review Unused Data to Optimize Storage Costs: Regularly audit your storage to identify and remove unused or redundant data. Implement data retention policies that archive infrequently accessed data to lower-cost storage options or delete obsolete data. Utilize Snowflake's Time Travel and Fail-safe features wisely to manage historical data without incurring excessive costs.

ENGAGE WITH PARTNERS

Collaborating with experienced partners can streamline the migration process and ensure your organization is fully equipped to harness the power of Snowflake. Engage with certified Snowflake partners who specialize in cloud data migrations. These experts can help design an optimal architecture, manage data transfer securely, and ensure a seamless transition with minimal disruption to business operations. By focusing on monitoring and leveraging expert partnerships, organizations can not only transition smoothly but also unlock the full potential of Snowflake’s cloud data platform.

Summing Up

Migrating enterprise data to the cloud is a significant step toward modernizing business operations. Organizations can ensure a smooth, cost-effective, and secure transition by focusing on the critical areas outlined above and leveraging Snowflake’s innovative features. Connect with us for a free demo, and see how Snowflake can transform your business: https://www.beinex.com/snowflake/