Snowflake Data Sharing

Data sharing in Snowflake equips you to share specific objects with another Snowflake account or a designated reader account. The beauty of this process lies in the fact that the data isn't duplicated or moved between accounts.
Now, why is this a game-changer for organisations? When constructing data pipelines and developing data products, it's a common practice to shuttle data between databases and diverse systems to blend different datasets.
Consider this scenario: You have transactional data within your online transactional processing (OLTP) database, and you wish to integrate it with external data for a machine learning model. Traditionally, organizations would export data into a data lake, import external data, and then employ tools like Apache Spark for analysis.
But what if, instead, you could simply deposit your data into Snowflake, and the external data source could seamlessly share its data with your organisation, eliminating the need to load it separately? This eradicates the challenge of keeping data copies synchronised, resulting in savings on storage, computing costs, and maintenance efforts.
Imagine your company possesses valuable information that can guide other companies in making informed decisions. For instance, let's say your company can provide precise estimates for product delivery times based on proprietary data, and you want to offer this information for sale to your customers.
Enter Snowflake data sharing—it empowers you to precisely do that.

Case Studies: Snowflake Data Sharing

Citing two instances where leading organisations use Snowflake to improve actionable data sharing, collaboration and reporting capabilities.

1. A Pioneering Technology Leader A well-known Swedish-Swiss multinational corporation successfully implemented a streamlined data strategy using the Snowflake Data Cloud, adopting an "extract once, use everywhere" approach that simplified data consolidation and enablement. By transitioning from nightly extracts, which caused significant system overhead, to a single, near real-time Change Data Capture (CDC) process, the company achieved efficient replication of information to Snowflake with minimal impact. The utilisation of Snowflake Secure Data Sharing facilitated secure and governed data collaboration across the four business areas.
2. A Leading fast-food Restaurant Chain Snowflake's data-sharing capabilities have revolutionised decision-making for a fast-food restaurant chain. They can effortlessly share crucial sales, inventory, and operational data with external entities, expanding from three to over 30 parties.
With a high-performance database platform hosting over 2 million transaction records, the restaurant chain has established a robust data management and analysis infrastructure through Snowflake, empowering its operational and marketing endeavours.
Moreover, by consolidating all data onto Snowflake, the organisation has achieved a remarkable 70% reduction in operational IT costs, demonstrating the platform's efficiency and cost-effectiveness.
Centralising and sharing data with Snowflake significantly eased the development of data products for various purposes, including marketing campaign analytics, quotation success metrics, production line tools, and supply chain dashboards. These data products are utilised by thousands of users globally, including internal stakeholders and external vendors, enhancing collaboration and efficiency across the organisation.

What are the best practices for Snowflake data sharing?

Optimize your Snowflake data sharing experience with these essential practices. Ensure data security by utilizing secure views to filter and mask sensitive information. Enhance clarity and understanding by employing descriptive names and comments for your shares. Monitor and fine-tune your sharing activities using Snowflake Information Schema or Account Usage views. Foster communication and collaboration with your consumers to create a seamless workflow.
Take command of your data sharing environment by setting quotas and limits with the ALTER SHARE command. Keep your consumers informed about any changes or updates to your shares, and actively seek feedback to refine your data-sharing strategy. Explore additional data sources through Snowflake Data Exchange or Data Marketplace to enrich your analytics.
These best practices safeguard sensitive data, ensure compliance with data privacy regulations, clarify the purpose of each share, and provide insights into usage and performance, ultimately enhancing your data analysis capabilities. Below are some best practices for data sharing with Snowflake:

1. Understand Snowflake Data Sharing
Familiarize yourself with Snowflake's data sharing features, such as Secure Data Sharing (SDS) and Sharehouse, to leverage the platform effectively.

2. Role-Based Access Control (RBAC)
Implement strong RBAC policies to control who can share data and who can access shared data. Define roles and permissions to ensure data security and compliance.

3. Secure Data Sharing
Use Secure Data Sharing (SDS) to securely share data with external parties without copying or moving the data. Implement encryption and access controls to protect sensitive information.

4. Sharehouse Best Practices
If using Sharehouse, follow best practices for creating and managing share objects. This includes defining share schemas, tables, and using the appropriate share options for your use case.

5. Data Masking and Redaction
Apply data masking or redaction policies to shared data to protect sensitive information. Ensure that shared data complies with privacy regulations and internal data governance policies.

6. Query Performance Optimization
Optimize query performance for shared data by using clustering keys, partitioning, and indexing. This helps enhance the efficiency of queries on large datasets.

7. Versioning and Change Tracking
Implement versioning and change tracking mechanisms to keep track of updates and changes in shared data. This ensures data lineage and helps with auditing and troubleshooting.

8. Documentation and Metadata
Maintain comprehensive documentation and metadata for shared datasets. Include information about the source, purpose, and any transformations applied. This helps users understand the shared data context.

9. Governance and Monitoring
Establish governance practices for data sharing, including regular reviews of shared data objects and access logs. Monitor data-sharing activities to identify any anomalies or potential security issues.

10. Educate Users
Provide training and documentation for users involved in data-sharing activities. Ensure they understand the best practices, security protocols, and the impact of data sharing on performance.

11. Regular Audits and Reviews
Conduct regular audits and reviews of shared data objects, permissions, and access controls. This helps maintain data integrity, security, and compliance with organizational policies.

12. Cost Monitoring

Keep an eye on the costs associated with data sharing, especially if you're sharing data externally. Understand the pricing model and optimize data sharing configurations to manage costs effectively.

By adhering to these best practices, you can:
1. Shield Sensitive Data: Employ secure views to fortify sensitive information.
2. Navigate Data Privacy Regulations: Ensure compliance with data privacy regulations by controlling access and usage.
3. Illuminate the Purpose of Each Share: Maintain transparency regarding the intended purpose and content of each shared dataset.
4. Efficiently Monitor Usage and Performance: Keep a finger on the pulse of usage patterns and optimize performance for streamlined data sharing.
5. Elevate Your Data Analysis Journey: Enrich your analytics by exploring diverse data sources and unlocking fresh perspectives.

What’s Next

1. Enhanced Data Collaboration Tools:

Snowflake is expected to introduce advanced features for more streamlined and efficient data collaboration, addressing the evolving needs of industries.

2. Innovations for Privacy-Preserving Analytics:

Snowflake aims to develop and implement solutions that uphold data privacy while enabling powerful analytics, aligning with the growing concerns in this area.

3. Adaptation to Changing Industry Dynamics:

Snowflake is likely to evolve its platform to meet the challenges posed by changes in industry dynamics, ensuring it remains a robust solution for data management and collaboration.

4. Integration of Cutting-Edge Technologies:

Snowflake may integrate emerging technologies, enhancing its capabilities to support new use cases and data-intensive analytic methods.

5. Continued Emphasis on Secure Data Collaboration:

Future updates from Snowflake may include even more robust security measures and innovations in data clean rooms, reflecting a commitment to secure data collaboration in the face of a dynamic data landscape.

Best Way to Share Data for Your Business

For secure collaboration, old ways of copying data are no longer the best. If you're working with trusted partners and it's privacy-compliant, Snowflake Secure Data Sharing is a quick and secure option. But, if you're dealing with sensitive or regulated data, especially when the risk is high, consider using a data clean room for an extra layer of security and compliance.

Beinex + Snowflake Offerings

Beinex’s partnership with Snowflake enables us to offer you advanced features like automated tuning and elastic compute, along with analytics modernisation services, to help your organisation realise exponential Return on Investment.

5 Steps in Alteryx Predictive Analytics Process 

The five major stages of the predictive analytics process cycle include selecting a target variable, examining the data, collecting the data, creating the model, and scoring the model.

A detailed description of the steps involved in the predictive analytics process in Alteryx:

• Step 1: Select a Target Variable
• Step 2: Analyse Your Data
• Step 3: Run Calculations/ Collect New Data
• Step 4: Model Building
• Step 5: Score the Model

Step 1: Select a Target Variable

Select the target variable which is the column that should be predicted. It could be a binary or non-binary categorisation or a numerical value and it can be continuous or time-based. Each of these target variables helps in finding business solutions. But just because time is a variable in the problem does not mean that a time-based model will be the best way to solve it. Simultaneously if a field has a numeric value, it does not mean that a binary model cannot be utilised in finding insights.

Step 2: Analyse Your Data

The largest contributor to excellent predictive models is the sample size. Anything less than 5000 records is counted as under-sampled and using it is not considered the best practice.
Alteryx and Tableau Prep are both excellent tools for understanding data by creating histograms, scatterplots, and correlation matrices. Before step 3, in the data transformation procedure, it is better to know what types of variables are in the data. There are various sorts of predictor variables and several types of target variables, and each must be structured differently.

1. Categorical Data: String fields with no order are categorical data. It contains data in the form of text.
2. Ordinal Data: String fields with an order are ordinal data. It can be substituted into numeric order in a predictive workflow.
3. Numeric Data: It represents information with a measurable value.
4. Cyclical Information: Data which gets repeated as such in a cyclic process is cyclic information.

Step 3: Run Calculations/ Collect New Data

Obtaining the greatest data or inferring fields from present data, such as adding seasonality, can be a powerful predictor variable. Always be inventive in the choice of variables. It is crucial to note that if there is to infer a piece of data, it is sometimes unwise to include both that data and the original data column in the same model because the predictive model would automatically give higher weight to this column. It is also critical to recognise that while it is beneficial to include factors with correlation, variables that drown out all other variables must occasionally be removed.

Step 4: Model Building

a. Make Use of the Decision Tree
Using a Decision Tree, it is possible to rapidly discover which of the factors are the most crucial for predicting the target variable. This model will not be utilised in the final forecast since it will over-fit, but it will show whether some of the variables are overly connected to the target variable.

b. Experiment with Different Models
Data Science is complicated, and it is difficult to know which model will yield the best results, therefore a variety of models, such as Random Forest, Boosted Models, and Neural Networks can be employed for better results.

Step 5: Score the Model

Alteryx offers a scoring tool that may be used to score models. During this step, data should be withheld for the model to test and score. Even though different models can provide different scores, through testing and reconfiguring, accurate predictions can be made.

What makes Alteryx an exceptional tool for predictive analytics?

These remarkable capabilities make Alteryx an excellent tool to carry out predictive analytics tasks easily:

• • No or Low coding required
• • Predictive analytics by drag and drop
• • Predictive tool kit for specifically performing predictive analytics
• • Integration to R and Python
• • Variety of built-in and custom ML models are available
• • Model customizations are possible
• • Automation and/or scheduling of predictive analytics workflows

Predictive Analytics Tools

Predictive analytics solutions use the power of data to help businesses in identifying trends in customer behaviour, making predictions, and developing optimised marketing plans.

The tools that aid in predictive analytics are enlisted below:

1. Data Investigation Tools
2. Predictive Tools
3. Tools for the Modern Statistical Learning Method
4. Tools for Predictive Model Comparison and Hypothesis Testing
5. Tool for Predicting Values for All General Predictive Modeling Tools

1. Data Investigation Tools

Data investigation tools contain tools that help to get a better understanding of data. To better understand the data used in a predictive analytics project including both visualization tools and tools that provide tables of descriptive statistics.

The list of data investigation tools is given below:

1. Field Summary Tool
2. Heat Plot Tool
3. Histogram Tool
4. Plot of Means Tool
5. Scatterplot Tool
6. Violin Plot Tool

2. Predictive Tools

This category contains general predictive modelling tools for classification and regression models, and also tools for predictive modelling related to model comparison and hypothesis testing.

Predictive tools are enlisted below:

1. Count Regression Tool
2. Gamma Regression Tool
3. Linear Regression Tool
4. Logistic Regression Tool
5. Naïve Bayes Classifier Tool
6. Neutral Network Tool
7. Stepwise Tool
8. Support Vector Machine Tool

3. Tools for the Modern Statistical Learning Method

1. Boosted Model Tool
2. Decision Tree Tool
3. Forest Model Tool
4. Spline Tool

4. Tools for Predictive Model Comparison and Hypothesis Testing

1. Cross-Validation Tool
2. Lift Chart Tool
3. Model Coefficients Tool
4. Model Comparison Tool
5. Nested Test Tool
6. Test of Means Tool
7. Variance Inflation Factors Tool

5. Tool for Predicting Values for All General Predictive Modeling Tools

1. Score Tool

6. Time Series Tools

1. ARIMA tool
2. ETS tool
3. TS Compare Tool
4. TS Covariate Forecast Tool
5. TS Filler Tool
6. TS Forecast Tool
7. TS Forecast Factory Tool
8. TS Model Factory Tool
9. TS Plot Tool

When the sliver of thought -- that we have seen all that Tableau can do -- crosses our mind, just pay witness to a meteor shower of new features lighting the horizon up! Tableau is officially upgraded to 2021.3. Tableau 2021.2 was a remarkable update in itself: it included ‘ask and explain data’ for viewers, allowing connected desktops, Collections, and many more valuable options and features. So, what does the latest upgrade feature? In one go, the following list encapsulates the whole gamut of supplements:

1. Improvements to Data Prep Experience
   1. Linked tasks
   2. Generate rows
2. Improvements to Tableau Catalog
   1. Data quality warnings in subscription emails
   2. Inherited descriptions in web authoring
3. Slack Integration
4. Additional Features
   1. Customise the set of workbooks on the homepage
   2. Rename published data sources directly in Tableau Online or Server

Improvements to Data Prep Experience Tableau Prep, an indispensable backstage prior to staging your real and original data show, has found a high-power addition. With Linked Tasks in Tableau Prep Conductor, one can now bring enhanced harmony to his data preparation. Creators with Data Management or DM can now schedule flows to run one after the other on Tableau Online or Server. This feature has a wide range of applications; for instance, if the sales data for a designated time is yet to show up on the system and fails at source for some reason and there are multiple flows connected to that data source, stale data issues will unnecessarily pop up in all the workflows. With linked tasks, this issue can easily be prevented from manifesting. Linked Tasks on, it’s now simple to automate the orchestration of multiple flow jobs, ensuring they happen in sequence after each task completes successfully. Essentially, if your data didn’t update and there are two workflows linked to the same datasets at different time intervals, they both run, consuming time and resources; the scenario can be avoided, and the other linked task can be stopped from running; thanks to the new feature. A multitude of benefits come power-packed as the feature unfurls:

• Authors of a flow can get alerted automatically provided any of the jobs fail and can set up an appropriate warning on the data for consumers well in advance.
• Any flow can be scheduled by customers, or they can extract refresh to run when new data arrives, saving them time and resources.

Please see Image 1 to have a better understanding.

(Image 1: Linked Tasks on Tableau Prep)

Besides, Tableau Prep Conductor can generate a set of rows that are otherwise missing based on dates, date times, or integers. This is of huge importance as it allows users to fill gaps in data quite easily to ultimately ensure that processes downstream have all the requisite datasets to work on and create highly accurate and precise visualisations. Please see Image 2 for a quick understanding of the feature:

(Image 2: Generate rows on Tableau Prep)

Improvements to Tableau Catalog Next in the line comes improvements to Tableau Catalog. Two features need special mentioning:

1. Data quality warnings in subscription emails, and
2. Inherited descriptions in web authoring

Data quality warnings are of paramount importance. Accurate warnings, when they are around, lends confidence to the customers that the right, trusted data is being utilised to arrive at sound business decisions. Formerly, this was restricted to Tableau Online or Server, but now it is delivered straight into your inbox bundled with the subscriptions to the dashboards you care for. The power of data visualization with visibility into the quality of the data feed add top-notch credibility and renders confidence to the customer who spends a good portion of the day paying attention to emails. In the 2021.3 version, Data Quality Warnings are available in Web Authoring. This will soon be available in Tableau Desktop as well. Slack Integration Yes, Tableau notifications can now be directly accessed on Slack! Admins and Users will control what notification types can be dispatched on Slack. Notifications include:

• Shared content
• Data-driven alerts
• @mention

This is just the commencement of the Tableau + Slack integration. Other Stellar Features Up until the upgrade, the same three sample workbooks have occupied the Desktop Homepage of Tableau. They may give a good starting point for new users to get acquainted with Tableau. But the datasets were always generic and did not get updated. Now with the customizable set of workbooks on the home page, this has undergone a radical change. (See image no.3)

(Image no.3)

Add to this the ability to rename published data sources directly in Tableau Online or Server on the data source page; the upgrade is a real treat to data rockstars. (See image no. 4) Practitioners point out that The REST API can also be used when changing a large number of workbooks to minimise efforts.

(Image no.4)

No need to generate a newly published data source to change the name. No need to manually change all workbooks on the Desktop to use that newly published data source, which was highly frustrating! So, welcome to Tableau 2021.3. Let us uncomplicate and perpetually so! Co-Author : Rakesh Neelakandan

Introducing Enterprise Project Management and Enterprise Performance Management (STRACT) solutions:

Enterprise Project Management

Project management is akin to running a small city. An innumerable number of projects, resources, and deadlines are to be taken care of, along with budget management, proper risk prediction and communication. It is stressful to handle so many things, and finally, it results in never-ending email chains, missed deadlines and dissatisfied clients. Despite this, managers fail to find solutions for all these nerve-wracking issues.

Projects are completed frequently under enormous time constraints that result in poor quality output. Every client has strategic goals, and poor project management ruins their objectives. Project management is critical in this case because projects that veer off course or fail to adapt to business needs may be costly and inappropriate.

Well, what is the proper solution?

Enterprise Project Management solution from Beinex Digital assists businesses in accurately measuring & controlling scope and consistently delivering projects aligned with business goals to ensure project success. A standalone tool for niche buyers with project portfolio management, resource management, risk management, demand management, and project analytics, this application can work wonders.

Beinex’s enterprise app is an ideal tool that act as a force multiplier in enhancing project management efficiency; the goal of the solution is to connect the company's goals with recurring projects to ensure that resources are directed to the right spot at the right time. It also entails managing the interdependent elements between projects and ensuring they are completed on time and within budget.

Use Cases

• Risk Analysis: Identification and mitigation of project risks
• Structured Estimating: Calculate the project's costs, resources, and duration
• Project Evaluations: Keep track of the current project portfolio's progress.
• Project Management Coaching: Entails providing project managers with training.
• Escalated Issue Management: Draw attention to critical issues.
• Time Management: Create a time management system
• Information System Management: Create a centralised management platform for information systems

Enterprise Performance Management (STRACT)

In today's workforce, inefficient performance and management are roadblocks leading to heavy financial losses. Traditional data collection, validation, budgeting, forecasting, and reporting approaches are time-consuming and inaccurate. The regularity of mundane tasks drains employees' energy and keeps them away from value-added assignments.

So, what is the remedy?

Enterprise Performance Management (EPM) is essentially a set of management processes, often aided by technology, which supports the improvement of strategic decisions made by people in organisations daily. The quality of those decisions distinguishes successful businesses from the rest. As a result, performance management is a broad term for a set of management approaches that enable organisations to define and execute their strategy and measure and monitor performance to inform strategic decision-making and learning.

STRACT by Beinex is an indisputably efficient tool that helps deliver optimised business performance. It allows businesses to forecast, track, assess, and identify areas for improvement across all operations. Companies can thoroughly and comprehensively analyse all relevant financial and operational metrics across various levels of the business by consolidating data and performance metrics into one centralised database and then measuring this against their strategic goals.

STRACT is a comprehensive solution with features that allow you to create more accurate and flexible budgets, which will enhance your current business and resource planning and forecasting. It has a Performance Cockpit that has a comprehensive performance management interface with modules covering the creation, editing and maintenance of performance.

Use cases

Consolidation of Financial Statements: Equity roll-ups, minority interest calculations, intercompany transactions, and currency translation adjustments are consolidated.

Financial Close: Improving business processes and efficiency within the Finance Department to streamline the monthly, quarterly, and year-end close.

Financial Reporting: It is essential whether you are a public company reporting to the SEC or a private company reporting to your stakeholders.

Financial Planning and Analysis: It includes trend analysis, variance analysis, line-item detail, and commentary.

Operational Planning: Capacity and Demand Planning, Sales and Marketing Planning, and Capital Expenditures (CAPEX) Planning are examples of operational planning.

Strategic Planning: It can model long-term capital, working ratios, and acquisitions and divestitures.

Real-Time Decision Making: With better data, subject matter experts can make better decisions faster.

These are the features that facilitate both EPM and STRACT:

Three-way Multi-factor Authentification: Fully secure platform with User Authentication, SMS OTP and Captcha Confirmation and dedicated user access management and control page.

Activity Tracking and Notification: Keeps track of progress status through designated stages of the project lifecycle and maintains transaction logs for Audit purposes and automated alerts and progress notification.

Interactive Dashboards and Reports: Simplified and interactive dashboard and reporting module to display project status dynamically with drill-down options. Automated reports allow real-time progress tracking.

Collaboration-based User Experience: Ability to collaborate and communicate on a Project line level with features to Add/ Edit tasks, upload attachments and communicate with other users through comments.

End-to-end Project Management: Comprehensive project management interface with modules covering the creation, editing and maintenance of Projects.