Data accuracy and speed are two key factors for effective BI reporting. However, they can sometimes conflict with each other, as ensuring data quality may require more time and resources, while delivering fast insights may compromise data integrity. How can you balance these two aspects and achieve optimal BI reporting performance? Here are some tips to help you.
Before you start building your BI reports, you need to have a clear understanding of what data you need, where it comes from, how it is structured, and how it is used. This will help you avoid unnecessary data collection, transformation, and validation steps that can slow down your reporting process. You can use data modeling techniques, such as conceptual, logical, and physical models, to define your data requirements and design your data architecture.
In my experience, the pursuit of data accuracy in BI reporting is an ongoing journey that demands a holistic approach. It's not just a technical endeavor; it's a cultural shift within an organization. One key aspect often overlooked is the human element. Ensuring that teams are trained and aware of the critical role they play in maintaining accurate data is paramount. The emphasis should not only be on the technical intricacies but also on instilling a sense of ownership and responsibility for the data they handle.
One way to ensure data accuracy without sacrificing speed in BI reporting is by implementing a robust data validation process at the source. This involves thorough testing and validation of data before it is loaded into the reporting system, ensuring that only accurate and reliable data is used. Additionally, maintaining a well-designed data architecture and data governance framework can help prevent data inaccuracies. Timely updates and regular data maintenance also contribute to maintaining accuracy. Lastly, utilizing advanced analytics algorithms and tools can help optimize data processing speed without compromising accuracy.
This is a foolish notion. We cannot have BI without data accuracy. You will only do wrong analysis faster with inaccurate data.
Data accuracy in BI reports is crucial for informed decision-making, yet maintaining this accuracy proves to be a formidable challenge. Data originate from various sources, and despite extensive automation, a substantial amount still requires manual entry at the initial stage of the process chain. You can enhance accuracy by not only training personnel on the critical role of data accuracy but also by emphasizing how vital their actions are for the entire process. When you engage collaborators and convey the significance of their contributions within the company, they naturally align with the company's purpose. This motivates them to act purposefully, rather than merely complying with data entry tasks that are often monotonous.
Certainly! Defining clear data requirements is crucial for any Business Intelligence (BI) project. Here's a step-by-step guide to help you define clear data requirements effectively: How to Define Clear Data Requirements: 1.Identify Stakeholders 2.Gather Business Requirements 3.Understand Data Sources 4.Perform Data Profiling 5.Define Data Entities 6.Create Conceptual Data Model 7.Develop Logical Data Model 8.Document Data Definitions 9.Establish Data Governance 10.Validate Data Requirements 11.Design Data Architecture 12.Create Data Dictionary By following these steps and utilizing data modeling techniques you can define clear data requirements and design a robust data architecture for your BI reports.
Data preparation and validation are essential steps to ensure data accuracy, but they can also be time-consuming and error-prone if done manually. You can use automation tools and techniques, such as data integration, data quality, and data governance, to streamline these processes and reduce human intervention. For example, you can use data integration tools to connect to different data sources, extract, transform, and load (ETL) data into a centralized data warehouse or lake, and apply data quality rules and checks to ensure data consistency and completeness.
Data preparation, validation, quality, stewardship is one of the hardest part in building proper data platform until the data is consumed by Business Intelligence, Automation of data prep and validation will help a lot to the process and it will contribute more to the accuracy of the output.
From my experience implementing automated data cleansing processes to handle common issues like missing values, duplicates, and inconsistent formatting. This ensures that your data is in good shape for analysis. A further step is to create a set of validation rules specific to your data. These rules should address data integrity, accuracy, and consistency. For example, you can set rules to validate that certain data falls within expected ranges.
First of all, BI data accuracy is gotten by not changing operational/transactional data. The only transformation operations must be aggregation and multidimentional arrangement. These transfomations leads to fasten BI visualizations.
Certainly! Automating data preparation and validation is a key component of efficient data management and analytics. Here's a step-by-step guide on how to achieve this: 1.Use data integration tools (e.g., ETL tools like Apache NiFi, Talend, or Microsoft SSIS) to connect to various data sources, such as databases, APIs, and files. 2. Automate the extraction, transformation, and loading (ETL) of data into a centralized data repository, such as a data warehouse or data lake. 3. Implement data governance policies and procedures to ensure data accuracy and compliance with regulations. 4. Automate data validation tests to ensure the accuracy of transformed data. 5.Regularly review and refine automated data preparation and validation processes.
Data quality is very important in Data Analytics and the accuracy of BI reporting is often hinged on the integrity of data collected. Many times, errors creep in at the point of data collection when done manually. So say for example someone mistakenly enters text into a column meant for dates. To avoid/reduce such errors, automating the data collection process can be highly effective. Tools like Microsoft Power Apps can help streamline data entry, using validations to ensure that data types are correctly inputted. In more advanced settings, IoT devices can be used for automated data collection further enhancing accuracy and efficiency. These approaches not only help maintain data accuracy but also speed up the reporting process.
The way you store and access your data can have a significant impact on your BI reporting speed. You can use data optimization techniques, such as data partitioning, indexing, compression, and caching, to improve your data storage and access performance. For example, you can use data partitioning to divide your data into smaller and more manageable chunks based on certain criteria, such as date, region, or category, and index them to facilitate faster query execution. You can also use data compression to reduce the size of your data files and save storage space, and data caching to store frequently used data in memory and avoid repeated disk access.
Honestly, with all the tools we have now any company that has adequate resources can try different things and see what works best for them. At my job we have been using a Lakehouse approach (with different levels of data processing and transformation on our tables). Databricks is our main orchestrator and life couldn't be easier. These tools just allow us to have a more direct and quick impact on our operations and we can keep building on and improving what we already have. (Any company should have available resources to accommodate the data volume it's creating)
Optimizing data storage and access is crucial for ensuring fast and efficient BI reporting. Here's a guide on how to optimize data storage and access using techniques such as data partitioning, indexing, compression, and caching: How to Optimize Data Storage and Access: 1.Data Partitioning 2.Indexing 3.Data Compression 4.Data Caching 5.Columnar Storage 6.Materialized Views 7.Distributed and Parallel Processing 8.Query Optimization 9.Regular Maintenance 10.Data Archiving and Purging By employing these techniques, you can significantly enhance your data storage and access performance, leading to faster and more efficient BI reporting processes.
What I have seen be helpful in increasing accessibility, utilization, and visibility of how your data is stored is to create an internal data catalog of the data environments you own. The Data Mart structure organizes datasets by business subject which I have seen be effective in building data products.
ChatGPT ChatGPT Implement Data Partitioning: Divide large datasets into smaller, more manageable segments based on criteria like date, region, or category to expedite access and query execution. Utilize Indexing on frequently queried fields to reduce search time within databases, leading to quicker data retrieval. Apply Data Compression to decrease the size of your data files, enhancing storage efficiency and often improving I/O performance due to reduced disk reads. Leverage Data Caching: Store commonly accessed data in memory (cache) to avoid redundant disk access, which can speed up data retrieval Optimize Query Performance: Write efficient queries and use query optimization tools to minimize the data processing time.
This suggests that the only way to index and optimise data from source is to lift and shift that data elsewhere, adding steps to the customers route to data. I would suggest that the fastest route to data is direct, and that optimisation, translation and modelling can be done as an abstraction within a fabric, reducing the time to use.
The BI tools and platforms you use can also affect your data accuracy and speed. You need to choose the ones that suit your data needs, reporting goals, and user preferences. You can use criteria such as functionality, scalability, security, usability, and cost to evaluate and compare different BI tools and platforms. For example, you can use functionality to assess the features and capabilities of the BI tools and platforms, such as data visualization, dashboarding, analytics, and reporting. You can use scalability to measure the ability of the BI tools and platforms to handle large and complex data sets and support multiple users and concurrent requests. You can use security to check the compliance and protection of the BI tools and platforms with data privacy and security standards and regulations. You can use usability to gauge the ease of use and user satisfaction of the BI tools and platforms, such as user interface, navigation, customization, and collaboration. You can use cost to estimate the total cost of ownership and return on investment of the BI tools and platforms, including license fees, maintenance fees, hardware and software requirements, and training costs.
The most important thing is to actually use the BI tool before buying. So, I would really order the evaluation criteria as - 1) Usability - being the single most important decision factor. Typically all BI tools provide a limited trial version. Do install & build a few reports before making the decision. 2) Scalability - is difficult to test unless fed with real data. Typically, reference calls with other customers help here to understand the scale at which they've stress tested the BI tool. 3) The rest three (security, functionality, and cost) are relatively easy to conclude.
It is important to consider how these tools integrate into and enhance the existing environment and systems. If it doesn't align well with current solutions or demands extensive modifications to existing workflows; it can add little value and make data accuracy difficult. This approach can help the BI solution to meet immediate needs and support long-term goals and strategies, leading to more effective decision-making and better ROI.
Here's a step-by-step guide on how to choose the right BI tools and platforms: 1.Assess Your Needs 2.Evaluate the features and capabilities of BI tools. 3.Determine whether the BI tools can handle the volume and complexity of your data. 4.Verify that the BI tools comply with data privacy and security standards and regulations relevant to your industry. 5.Evaluate customization options to tailor the tool to your specific needs. 6.Check if the BI tools integrate seamlessly with your existing data sources, databases, and other software systems. 7.Determine whether the BI tools provide mobile accessibility, allowing users to access reports and dashboards on various devices. 8.Calculate the overall cost of using the BI tools
Mostly IT person selects a BI system for Business, but in my view there should be involvement of a Super user form Business to guide and become a bridge between IT & various Business Units, to ensure successful implementation and maintenance of the reporting structure within BI platform.
PowerBI remains one of the best BI Tools in the market ?Free to download ?New features & functionality added monthly to either PowerBI Desktop, PowerBI service ?Relatively easy learning curve, and free learning resources such as Microsoft Learn ?Go from imported data to visualization in minutes - this is also a caveat as you must learn Data modelling, Data transformation/ cleaning and Data visualization best practises ?Different licensing options available
Your BI reports are not static, but dynamic and evolving. You need to monitor and update them regularly to ensure data accuracy and speed. You can use metrics and feedback to monitor and evaluate the performance and effectiveness of your BI reports, such as accuracy, timeliness, relevance, and usability. You can use data sources and business needs to update and improve your BI reports, such as adding new data sources, changing data models, modifying data queries, and enhancing data visualizations and interactivity.
By following these tips, you can ensure data accuracy without sacrificing speed in BI reporting. You can also deliver more reliable, relevant, and actionable insights to your stakeholders and support better decision making and business outcomes.
We’ve fallen victim to one paradigm that is outdated and no longer relevant with modern times. This is the existing process of going through so many steps to build a report without knowing how long, frequent or useful it will be. Most will be used once as they serve their purpose. So why do we insist on applying the same process to all reports when not all of them have equal value ? We shouldn’t. Reports should be treated as data products and the time to consume should be a critical factor. If I need a report to make a dry decision in the next 30 minutes but I may not use that report again, then why should that have the same process as my monthly accounting report? It’s time to adapt.
Here is a simple and practical way to make sure your reports are reviewed periodically. Simple add an expiration date to your report and make it prominent when the report is either run or sent to users. This will force users to review the report on a periodic basis to make sure it is still used and still accurate. Without forcing reports to expire the will continue to be on auto-pilot and users may be relying on inaccurate informataion.
In my experience more than 60% of reports will become redundant within months, either a new question arises or the reports purpose shifts. If we focus on the data products which feed reporting, or any other medium, and simply ask questions of that data, we ensure the freshness and relevance of the product, how we make it look pretty is incidental?
Here's a guide on how to monitor and update your BI reports effectively: 1.Define Key Performance Indicators (KPIs) 2.Regularly Monitor Data Sources 3.Collect User Feedback 4.Utilize Analytics and Usage Metrics 5.Perform A/B Testing 6.Stay Updated with Business Needs 7.Implement Version Control 8.Regularly Update Data Models and Queries 9.Document Changes and Best Practices 10.Provide Training and Support 11.Schedule Regular Review Meetings By consistently monitoring user feedback, report performance, and business needs, and by staying proactive in making updates, you can ensure that your BI reports remain accurate, relevant, and valuable to your stakeholders. Regular updates will enable you to deliver actionable insights.
The trick to keep your BI platform up-to-date and modern with current trends, make Business Unit as the owner of BI reporting Unit. they will make sure that reports are timely and regularly updated and doesn't have not obsolete or irrelevant data for reporting purpose.
Establish a Center of Excellence, which will govern and advise on numerous BI issues including: ?Data Governance/ Ethics / Privacy ?Data security ?Best practices to be adopted ?Data visualization guidelines ?Training of BI tool/ reports ?Drive innovation- keep up to date with latest industry developments
When dealing with large datasets, consider using data sampling techniques to provide faster insights, while periodically validating against the full dataset for accuracy.
Algo que me ha resultado muy útil es tener dimensiones muy claras y reutilizar los pasos de limpieza cuando los datos tienen la misma estructura.
I have worked at large and small companies and the thing I have noticed over and over that kills BI usefulness is not having clear definitions for business terminology or KPIs, especially if you are working across departments or external customers. You can have the cleanest data displaying visualizations in the perfect BI tool, but if you aren't speaking the same language, the insights are meaningless.
As any democratic country, the system would be safe and healthy if there is proper governance structure in place and strong firewalls to defend itself from threats of outside world.