Introducing Data Ops with MS Power BI — full version

Why all the fuzz?

Data helps us to understand the world around us. By collecting data, we are able to see patterns and trends that we would otherwise miss. This allows us to make better decisions, both in our personal lives and in our professional lives. It is also important because it allows us to communicate with others. When we share data, we are able to share our knowledge and understanding of the world. It helps us to learn new things and to solve problems.

Companies that use data to perform better in business are top performers. This is because data provides insights that can help businesses improve their operations, make better decisions, and ultimately achieve their goals. There are many ways to use data to improve business performance. For example, data can be used to:

• Improve customer service: Data can be used to identify customer service issues and then take steps to improve the quality of service. Data can be used to identify customer service issues and then take steps to improve the quality of service.
• Increase sales: Data can be used to identify opportunities for increased sales and then take steps to capitalize on those opportunities. Data can be used to identify opportunities for increased sales and then take steps to capitalize on those opportunities.
• Reduce costs: Data can be used to identify areas where costs can be reduced and then take steps to reduce those costs. Data can be used to identify areas where costs can be reduced and then take steps to reduce those costs.
• Improve marketing: Data can be used to improve marketing campaigns by targeting the right audience with the right message. Data can be used to improve marketing campaigns by targeting the right audience with the right message.
• Increase efficiency: Data can be used to identify areas of inefficiency and then take steps to improve efficiency. Data can be used to identify areas of inefficiency and then take steps to improve efficiency.

There are some strategies and processes that companies use to make data-driven decisions and the systematic use of data to perform better in business. Data Ops is one of them.

Why Data Ops?

Data ops is a term for the set of processes and tools used to manage data throughout its lifecycle.

The Data Ops professional orchestrates and automates the data analytics pipeline to make it more flexible while maintaining a high level of data quality.

The importance of data ops as a strategy is that it enables organizations to better manage their data as strategic assets and ensure that these assets are aligned with business goals. It helps organizations to improve their data management practices by automating tasks, improving communication and collaboration between teams, and establishing clear governance policies. By improving their data management practices, organizations can improve their decision-making processes and better utilize their data assets.

What is Data Lifecycle and why is it important to business?

The data lifecycle is the process that data goes through from its creation to its eventual destruction.

It encompasses all aspects of data management, from acquisition and ingestion to storage, processing, analysis, and finally disposition. A thorough understanding of the data lifecycle is necessary for effective data management and to ensure that data is properly handled at each stage.

1. Data is created: This is the first step in the data lifecycle. Data is created when it is first collected or generated.

2. Data is stored: Once data is created, it must be stored somewhere. Data is typically stored in a database or file system.

3. Data is processed: Once data is stored, it must be processed in order to be useful. Processing typically includes cleaning, transforming, and aggregating data.

4. Data is analyzed: After data has been processed, it can be analyzed to extract insights. Analysis typically includes statistical analysis and machine learning.

5. Data is visualized: Once data has been analyzed, it can be visualized to make the insights easier to understand. Visualization typically includes charts, graphs, and maps.

6. Data is destroyed: Finally, data is destroyed when it is no longer needed. This can happen when it reaches its expiration date or when it is explicitly deleted by a user.

Data Ops in action

A data pipeline is a set of automated processes that move data from one system to another. Data pipelines are used to streamline the flow of data between different data sources and systems, making it easier to manage and analyze data. Data pipelines can be used as part of a data ops strategy to automate the movement of data between different systems and to make it easier to monitor and analyze data. Data pipelines can also help to ensure that data is consistently formatted and accurate, and that data is available when and where it is needed.

In order to a data pipeline be effective and maintainable, some key aspects must be met:

1. Automation: Automating as much of the data pipeline as possible helps to reduce errors and improve efficiency.

2. Monitoring: Monitoring the data pipeline helps to identify issues early and prevent them from becoming bigger problems.

3. Collaboration: Collaborating with others involved in the data pipeline helps to ensure that everyone is on the same page and working towards the same goals.

4. Flexibility: Being flexible with the data pipeline helps to adapt to changing needs and requirements.

5. Scalability: Ensuring that the data pipeline is scalable helps to accommodate future growth.

6. Security: Securing the data pipeline helps to protect sensitive information and prevent unauthorized access.

7. Quality: Maintaining high quality standards for the data pipeline helps to ensure that the data is accurate and reliable.

8. Efficiency: Optimizing the data pipeline helps to improve performance and reduce costs.

9. Documentation: Documenting the data pipeline helps to improve understanding and communication.

10. Testing: Testing the data pipeline helps to identify issues and ensure quality.

The goal of data ops is to improve the quality and accuracy of data in this workflow, while also reducing the time it takes to process and analyze data. Data ops processes can help organizations achieve these goals by automating data management tasks, improving communication between data teams, and providing better tools for data analysis.

Delving into Data Ops

Data ops is a critical part of any organization that relies on data for decision-making. By following these 10 important processes, organizations can ensure that their data is of the highest quality and integrity, and that it is being used effectively to drive business decisions.

There are many important processes in data ops, but 10 of the most important ones are:

1. Collecting data from various sources.

2. Cleaning transforming and preparing data for analysis.

3. Analyzing data to identify trends, patterns, and insights.

4. Sharing findings with stakeholders.

5. Monitoring data quality and integrity.

6. Securing data against unauthorized access.

7. Backing up data to prevent loss.

8. Archiving data for long-term storage.

9. Automating data processes.

10. Continuously improving data ops processes.

Microsoft Power BI can be used as part of a Data Ops strategy. It can help you monitor and visualize your data pipeline, as well as track the performance of your data operations. It can secure data among other key tasks in data ops. There are many features that make Power BI a good choice for dataops.

Let’s check how Microsoft Power BI we can perform these tasks:

1. Collecting data from various sources: In order to make data-driven decisions, organizations need to collect data from a variety of sources. This could include customer surveys, financial data, website analytics, social media data, and more. We can use Microsoft power bi to collect data from various sources like databases, spreadsheets, text files, etc.

2. Cleaning and preparing data for analysis: We can use Microsoft power bi to clean and prepare data for analysis. This includes removing invalid or duplicate data, and formatting the data in a way that makes it easy to analyze.

3. Analyzing data to identify trends, patterns, and insights: We can use Microsoft power bi to analyze data to identify trends, patterns, and insights. This step is critical for understanding what the data is telling us and making decisions based on that information.

4. Sharing findings with stakeholders: Once data is analyzed, the findings need to be shared with stakeholders. We can use Microsoft power bi to share findings with stakeholders. This could include presenting the findings in a report, creating visualizations, or presenting the data in a way that is easy to understand.

5. Monitoring data quality and integrity: We can use Microsoft power bi to monitor data quality and integrity on a ongoing basis to ensure that data is accurate and reliable. This includes checking for errors, verifying data sources, and ensuring that data is complete.

6. Securing data against unauthorized access: We can use Microsoft power bi to secure data against unauthorized access, including viewing and modifying information. This includes encrypting data, setting up user permissions, and using data security tools.

7. Backing up data to prevent loss: We can use Microsoft power bi to back up data to prevent loss in the event of a system failure or data corruption. This includes creating backups of data, storing backups in a secure location, and testing backups to ensure they can be restored.

8. Archiving data for long-term storage: We can use Microsoft power bi to archive data for long-term storage to keep it accessible for future use. This includes storing data in an accessible format, tagging data for easy retrieval, and setting up a retention policy.

9. Automating data processes: We can use Microsoft power bi to automate data processes to improve efficiency and accuracy. This includes setting up automated data collection, data cleansing, and data analysis.

10. Continuously improving data ops processes: We can use Microsoft power bi to continuously improve data ops processes to ensure that they are effective and efficient. This includes reviewing data ops processes on a regular basis, making changes as needed, and monitoring the results of changes.

Data ops tasks using Microsoft Power BI

There are a few things to consider when dealing with data ops tasks in Microsoft Power BI. First, it is important to have a clear understanding of the data that is being collected and how it is being used. This will help to ensure that the data is accurate and up to date. Secondly, it is important to have a plan for how the data will be managed and monitored. This will help to keep the data organized and ensure that it is being used effectively. Finally, it is important to have a system in place for dealing with data changes and updates. This will help to keep the data current and ensure that it is being used correctly.

Data ops tasks in Microsoft Power BI can be divided into 5 main categories: data collection, data management, data analysis, data visualization and data security. Each of these categories has its own set of tasks that need to be completed in order to effectively manage data.

Data Collection

The first step in data ops is data collection. This involves collecting data from various sources and then storing it in a central location. Data can be collected manually or automatically. Manual data collection involves manually entering data into a database or spreadsheet. Automatic data collection involves using software to collect data from various sources. Microsoft Power BI can perform data collection using the UI, Power Query M language, R language or Python. It can also make SQL queries to relational databases. This is the best performance option.

Data Management

The next step in data ops is data management. This involves managing data so that it is accurate and up to date. Data management includes tasks such as data cleansing, data quality assurance, and data security. Data cleansing involves removing inaccurate or outdated data from a database. Data quality assurance involves ensuring that data is accurate and up to date. Data security involves protecting data from unauthorized access.

Checking if data is up to date with Microsoft power bi is a manual process. To check if data is up to date, open the power bi service, select the dataset, and select ‘check refresh history’. If the data is up to date, it will say ‘Data is up to date’. You can set a automate data refresh schedule by selecting ‘Edit Settings’ under the ‘Manage’ tab.

Data cleasing in Microsoft Power BI is a process of identifying and correcting inaccurate data. This process can be performed manually or through the use of automated tools. The first step in data cleansing is to identify the source of the inaccurate data. This can be done by reviewing data entry logs, auditing data entry processes, or by using data validation tools. Once the source of the inaccurate data is identified, the next step is to correct the data. This can be done manually or through the use of automated tools. Finally, the data cleansing process is complete when the data is verified to be accurate. In Microsoft Power BI you can use Power Query M language to perform these tasks in the advanced editor or you can use the UI in the query editor.

Data security is such an important task in Data Ops that we dedicated a full topic to it later on.

Data Analysis

The third step in data ops is data analysis. This involves analyzing data so that it can be used to improve business processes. Data analysis includes tasks such as data mining, data modeling, and data warehousing. Data mining involves extracting data from databases and then analyzing it. Data modeling involves creating models that can be used to predict future trends. Data warehousing involves storing data in a central location so that it can be accessed by business users.

Data Mining in Microsoft Power BI is a process of extracting useful information from large data sets. It involves analyzing data to find patterns and trends. Data mining can be used to predict future trends and behavior. It can also be used to make decisions about marketing, product development, and other business processes. Microsoft Power BI is a powerful tool for data mining. It has a wide range of features that make it ideal for this purpose. It can handle large data sets quickly and efficiently. It can also find patterns and trends in data. Microsoft Power BI is a good choice for data mining because it is easy to use. It has a user-friendly interface. You can perform Data Mining tasks in Power BI using the Q&A feature. This feature allows you to ask questions in natural language about your data and get answers from the data connected to Power BI. You can also use the insights feature to get insights into your data. This feature provides you with information about your data that you can use to make decisions. It shows you trends and anomalies in fields and metrics you chose. You can also use R language and Python to make your own data mining process. Just remember that not all Python libraries are available in Microsoft Power BI Service. You can check the supported libraries in

https://docs.microsoft.com/en-us/power-bi/connect-data/service-python-packages-support/

Data Visualization

The fourth step in data ops is data visualization. This involves creating visual representations of data so that it can be easily understood. Data visualization involves creating charts, graphs, and other visual representations of data.

Microsoft Power BI has native and custom visualizations. Native visualizations are those that are built into Power BI, while custom visualizations are those that are built by third-party developers. You can choose native visualizations from the Visualizations pane in Power BI Desktop, or you can download custom visualizations from the AppSource store. Remember that when you are embedding a report, each Visualization is loaded as a full library in the webpage, so it is important to use only the visualizations that you need in your report. Some of the most popular Power BI visualizations include:

- Bar charts

- Column charts

- Line charts

- Pie charts

- Scatter plots

- Maps

You can create your own custom visualizations by using the Power BI Developer Tools. Learn more in https://powerbi.microsoft.com/en-us/developers/custom-visualization/

Data Security

The fifth step in data ops is data security. This involves protecting data from unauthorized access. Data security includes tasks such as data encryption, data access control, and data recovery. Data encryption involves encrypting data so that it cannot be read by unauthorized users. Data access control involves restricting access to data so that only authorized users can access it. Data recovery involves restoring data that has been lost or corrupted.

Microsoft Power BI can secure data using some features like:

• Row-level security (RLS), which restricts data access to specific rows in a dataset based on user identity. You can Manage roles to create RLS in the Modeling Ribbon. Learn more in https://radacad.com/dynamic-row-level-security-with-organizational-hierarchy-power-bi
• Colunm-level security, which restricts data access to specific columns in a dataset based on user identity. This can be achieved by using RLS and modeling related reference tables to unloaded Fact tables with different set of columns. To learn more how to create this effect in the data model in Power BI check the post https://community.powerbi.com/t5/Community-Blog/Column-Level-Security/ba-p/1309392
• Data Masking, which obscures sensitive data in a dataset. You can perform this in Microsoft Power BI by using some data transformations in the Query Editor.
• Data Encryption at Rest, which encrypts data stored in Power BI. Power BI encrypts data at-rest and in process. By default, Power BI uses Microsoft-managed keys to encrypt your data. In Power BI Premium you can also use your own keys for data at-rest that is imported into a dataset. Learn more at https://docs.microsoft.com/en-us/power-bi/enterprise/service-encryption-byok

Conclusion

So as you could see, DataOps breaks down the barriers between data and operations, it makes data more easily accessible to users by designing the data pipeline to be more responsive, efficient and robust.

While we could also see that Power BI can be used to perform much more tasks in a data-driven company than to just visualize and share data.