BI Implementation Archive - Bitwise https://www.bitwiseglobal.com/en-us/blog/tag/bi-implementation/ Technology Consulting and Data Management Services Tue, 11 Mar 2025 08:20:53 +0000 en-US hourly 1 https://cdn2.bitwiseglobal.com/bwglobalprod-cdn/2022/12/cropped-cropped-bitwise-favicon-32x32.png BI Implementation Archive - Bitwise https://www.bitwiseglobal.com/en-us/blog/tag/bi-implementation/ 32 32 Accelerating Time-to-Value with Looker: The Future of BI https://www.bitwiseglobal.com/en-us/blog/accelerating-time-to-value-with-looker-the-future-of-bi/ https://www.bitwiseglobal.com/en-us/blog/accelerating-time-to-value-with-looker-the-future-of-bi/#respond Mon, 10 Mar 2025 05:31:24 +0000 https://www.bitwiseglobal.com/en-us/?p=50104 Introduction The landscape of analytics has undergone a dramatic transformation, driven by the increasing complexity of data and the ever-growing demand for timely insights. Legacy BI tools, once the industry standard, are now struggling to keep pace with the evolving needs of modern businesses. The need to modernize these legacy reports has become imperative to ... Read more

The post Accelerating Time-to-Value with Looker: The Future of BI appeared first on Bitwise.

]]> Introduction

The landscape of analytics has undergone a dramatic transformation, driven by the increasing complexity of data and the ever-growing demand for timely insights. Legacy BI tools, once the industry standard, are now struggling to keep pace with the evolving needs of modern businesses. The need to modernize these legacy reports has become imperative to unlock the full potential of data-driven decision-making and Generative AI assisted analytics.

Let’s dive into what makes Looker a compelling BI tool and explore modernization challenges when migrating legacy BI from tools like Cognos and Tableau to Looker.

Why Looker for Modern BI

Looker, a powerful data platform offered by Google Cloud, has emerged as a leading choice to modernize BI, especially for companies already using Google BigQuery.

Key Features of Looker for Modern Analytics

Looker offers a unique blend of features that make it an ideal solution for businesses seeking to accelerate time-to-value from their data.

  • Intuitive User Interface: Looker’s user-friendly interface empowers business users to explore data independently, reducing reliance on technical teams.
  • Semantic Layer: This powerful abstraction layer provides a unified view of data across disparate sources, simplifying data exploration and analysis.
  • Advanced Analytics: Looker enables advanced analytics techniques like machine learning and predictive modeling, allowing organizations to uncover deeper insights.
  • Customization and Flexibility: Looker can be tailored to meet the specific needs of any organization, with customizable dashboards, reports, and alerts.
  • Collaboration and Sharing: Seamless collaboration and sharing of insights across teams foster a data-driven culture.
  • AI-Powered Analytics: Gemini in Looker provides AI assisted analytical workflows for more efficient self-service BI.

Why BigQuery Customers Modernize BI Reports in Looker

Specifically for Google BigQuery customers that are using other BI tools, there are many key drivers for modernizing those reports in Looker.

  • LookML provides a re-usable data model that is highly efficient within the BigQuery / GCP ecosystem.
  • Looker and BigQuery deliver faster query performance – up to 10x improved speeds.
  • Looker enables easy integration with BigQuery data sources.
  • Looker excels at complex data modeling, customization of reports, and real-time insights into data.
  • Looker works well with large volumes of data from multiple sources.

Now that we’ve explored why Looker is worth considering for your modern BI needs, let’s take a look at migration challenges.

BI Migration Challenges

Migrating from legacy BI tools to Looker can be a complex process and can present challenges, including issues related to data migration, report and dashboard reconstruction, user adoption, and integration with existing systems.

Common Challenges When Migrating to Looker

Looker offers unique features that require proven solutions and workarounds to achieve similar or better results when migrating from legacy BI tools.

  • Data Model Differences: Each BI platform has different data modelling approaches, leading to discrepancies in data representation and calculations.
  • Excel Migration: Looker does not have direct Excel import, so data needs to be extracted and converted.
  • User Training Needs: Users familiar with their existing BI tools may have difficulty adapting to Looker’s interface, navigation, and functionalities.
  • Data Blending in Looker: Data Blending, a widely used method for combining multiple sources, is not a feature in Looker and requires alternative methods for achieving similar outcomes.
  • Visualization Variances: Every BI tool offers its unique set of visualizations or formatting options, impacting the layout and design of dashboards.
  • Performance Optimization: Large data sets and complex queries can have an impact on performance without proper query optimization.

Tableau Migration to Looker Success Story

With extensive BI Modernization experience, Bitwise understands the differences between legacy tools and modern tools like Looker. We’ve been through the migration process and have developed solutions to address the unique challenges of migrating to Looker with proven success in keeping BI migrations on track and within budget.

For instance, a leading American ticketing company faced the challenge of maintaining and scaling its existing Tableau reports. As their data volume and complexity grew, they sought a modern, scalable solution to streamline their BI operations and unlock deeper insights. Bitwise solution helped the firm in migrating Tableau Report Migration to Looker while reducing the development and maintenance costs.

Recommendations for BI Modernization

To overcome the challenges mentioned above, a structured approach to legacy BI modernization is essential.

Proven BI Modernization Approach

Bitwise recommends the following approach to ensuring a seamless transition from legacy BI to Looker.

  • Assessment: Evaluate the current BI landscape, identify pain points, and define modernization goals.
  • Planning: Develop a detailed migration plan, including data migration strategies, report and dashboard mapping, and user training.
  • Implementation: Migrate data, build reports and dashboards, and configure Looker to meet specific business needs.
  • Testing and Validation: Thoroughly test the migrated environment to ensure data accuracy and report functionality.
  • Deployment and User Training: Deploy Looker to end-users and provide comprehensive training to maximize adoption.
  • Ongoing Support and Optimization: Continuously monitor and optimize the Looker environment to ensure optimal performance and user satisfaction.

Access our whitepaper on modernizing BI and transforming data into actionable insights to know more about a strategic approach to modernizing your legacy BI environments to become truly data driven.

Achieving BI Modernization Success with Looker

Using the above approach, Bitwise helped a global financial services company that was struggling with a legacy WebFocus-based reporting and visualization application. The company was plagued by high maintenance costs, performance issues, and poor user experience.

To address these challenges, they partnered with Bitwise to modernize legacy reporting on the cloud with Looker. By leveraging Looker’s powerful analytics capabilities, the company significantly improved report performance, reduced costs, and enhanced user experience. The intuitive interface and self-service capabilities empowered users to access real-time data and generate insights more efficiently, driving data-driven decision-making accelerating business growth.

Getting Started

While there are many challenges to modernizing BI in Looker, the benefits can outweigh the challenges and set your organization up for a modern, data-driven future. In addition to using a proven migration approach, Bitwise, a trusted Google Cloud Partner and leading provider of data solutions, offers a range of accelerators and value-adds to expedite the BI modernization process.

Our proven BI modernization methodology will guide you through the entire process, from assessment to deployment. Contact us today to start your BI Modernization journey and accelerate time-to-value with Looker.

The post Accelerating Time-to-Value with Looker: The Future of BI appeared first on Bitwise.

]]> https://www.bitwiseglobal.com/en-us/blog/accelerating-time-to-value-with-looker-the-future-of-bi/feed/ 0 5 Keys To Nailing a BI Implementation https://www.bitwiseglobal.com/en-us/blog/5-keys-to-nailing-a-bi-implementation/ https://www.bitwiseglobal.com/en-us/blog/5-keys-to-nailing-a-bi-implementation/#respond Mon, 24 Aug 2015 13:35:00 +0000 https://www.bitwiseglobal.com/en-us/5-keys-to-nailing-a-bi-implementation/ 1. BI Strategy Organizations need to have a vision before they set themselves on a BI journey. Laying down a BI strategy with answers to below questions would help bringing clarity to this vision: What are the expectations from the BI implementation initiative? How are these expectations going to be achieved? Who will be the stakeholders? ... Read more

The post 5 Keys To Nailing a BI Implementation appeared first on Bitwise.

]]>

1. BI Strategy

Organizations need to have a vision before they set themselves on a BI journey. Laying down a BI strategy with answers to below questions would help bringing clarity to this vision:

  • What are the expectations from the BI implementation initiative?
  • How are these expectations going to be achieved?
  • Who will be the stakeholders?

Along with these answers, BI strategy needs to identify metrics and KPI’s that align with the corporate strategy and objectives (e.g. Increase Customer base, Increase Customer Satisfaction, 3600 Customer view, etc.). It should also contain a comprehensive approach describing the current and future behaviour of processes, technology, people and other components due to this implementation. The BI strategy should be treated as a living artefact and needs to be constantly tuned and adjusted to reflect the needs of the business.

2. Alignment to Business

“Without business in business intelligence, BI is dead”- Gartner

As per Gartner, fewer than 30% of business intelligence projects meet the objectives of the business. Thus, unless there is clarity, and buy-in on the BI initiative program throughout the org chart, organizations will not be able to generate the expected results out of this initiative. The key to any successful implementation is to follow a collaborative approach amongst IT and Business, enabling the merger between technology and business goals.

3. Architectural Blueprint

Once the business goals are defined and the method to measure ROI is clear, it is critical to lay down the architectural blueprint that will best support the generation of expected results.
Correct, Clean, Complete and Compliant Business data are key success factors of any BI implementation program. Thus, the architecture should evolve around collection, centralization, cleaning and converting this data into reliable, integrated, secured, available and usable business information. To achieve this, a holistic approach towards architecture foundation needs to be taken considering various parameters covering aspects such as:

  • Data Management – Centralization/Decentralization, Data Modelling, Metadata Management, Data Quality, Data Lineage, Data Availability, etc.
  • Hardware – Data sizing, Performance, Scalability, Cost, etc.
  • Software Tools for Data Integration, Scheduling, Reporting & Analytics – Data sizing, Performance, Analytical Capabilities, Licensing Cost, etc.

This kind of architectural blueprint will help organizations envision the progress of its BI initiative from its establishment to its maturity over a period of time.

4. One Step at a Time

Achieving quick results is no simple feat. Move step-by-step. The architectural road map must systematically be supported by having the right business analysts, technical architects and suitable BI tools along with well-established governance structure, policies, management processes & practices with assigned ownership and accountability. Taking one step at a time also provides the opportunity to learn from mistakes and bring in improvements. For every part of BI architecture, be it data integration, data quality, metadata management, reporting or analytics, it is recommended to create prototypes aligning to smaller business objectives. Then once implemented; extending it further. This kind of BI framework introduces agility and scalability throughout the BI implementation program.

5. BI Value Assessment & Amendment

Measuring the ROI at every milestone defined in the strategy will help keeping the BI initiative lean, focused on cost efficiencies and identifying improvements in terms of technology upgrades, migrations or adoptions and benefits. With the assessment factor built-in, the BI strategy supports identifying roadblocks and taking corrective measures. At the same time it provides opportunity to make amendments to the strategy itself to align or re-align to the changing business needs maximizing ROI.

Conclusion

BI Implementations can be costly and unpredictable sans experience of implementation. It is the experience which brings in a pragmatic roadmap that caters to business and technology keeping the 5 keys always in focus for a solution to work as well on day 100 as it did on day 1. Bitwise has worked on very large to medium scale BI deployments building some of its most complex solutions which it brings in as proven Reference Designs that help projects fall into a quadrant of success.

Do start a conversation with us if you are looking at any initiative in BI within your organization, we would be more than glad to bring in our experience.

The post 5 Keys To Nailing a BI Implementation appeared first on Bitwise.

]]> https://www.bitwiseglobal.com/en-us/blog/5-keys-to-nailing-a-bi-implementation/feed/ 0 How To Conduct Effective Testing Of Business Intelligence Applications https://www.bitwiseglobal.com/en-us/blog/how-to-conduct-effective-testing-of-business-intelligence-applications/ https://www.bitwiseglobal.com/en-us/blog/how-to-conduct-effective-testing-of-business-intelligence-applications/#respond Mon, 24 Aug 2015 13:04:00 +0000 https://www.bitwiseglobal.com/en-us/how-to-conduct-effective-testing-of-business-intelligence-applications/ BI Testing Strategy The goal of testing BI applications is to achieve credible data. And data credibility can be attained by making the testing cycle effective. A comprehensive test strategy is the stepping stone of an effective test cycle.   The strategy should cover test planning for each stage, every time the data moves and state ... Read more

The post How To Conduct Effective Testing Of Business Intelligence Applications appeared first on Bitwise.

]]>

BI Testing Strategy

The goal of testing BI applications is to achieve credible data. And data credibility can be attained by making the testing cycle effective.

A comprehensive test strategy is the stepping stone of an effective test cycle.   The strategy should cover test planning for each stage, every time the data moves and state the responsibilities of each stakeholder e.g. business analysts, infrastructure team, QA team, DBA’s, Developers and Business Users.  To ensure testing readiness from all aspects the key areas the strategy should focus on are:

  • Scope of testing: Describe testing techniques and types to be used.
  • Test environment set up.
  • Test Data Availability: It is recommended to have production like data covering all/critical business scenarios.
  • Data quality and performance acceptance criteria.

The below diagram depicts the data entry points and lists a few sample checks at each stage. – Data Collection, Data Integration, Data Storage and Data Presentation.

data-entry-points

Data Acquisition

The primary aim of data completeness is to ensure that all of the data is extracted that needs to be loaded in the target.  During the data acquisition phase it is important to understand the various data sources, the time boundaries of the data selected and any other special cases that need to be considered.  The key areas this phase should focus on are:

  • Validating the data required and the availability of the data sources from which this data needs to be extracted.
  • Data profiling:  Embedding data profiling activity helps in understanding the data, especially identifying different data values, boundary value conditions or any data issues at early stages.  Identifying data problems early on will considerably reduce the cost of fixing it later in the development cycle.

Data Integration

Testing within the data integration phase is the crux as data transformation takes place at this stage.  Business requirements get translated into transformation logic.  Once the data is transformed, thorough testing needs to be executed to ensure underlying data complies with the expected transformation logic.  Key areas this phase should focus on are:

  • Validating the Data Model: This involves validating the data structure with business specifications.  This can be done by comparing columns and their data types with business specifications and reporting column requirements ensuring data coverage at source.
  • Reviewing the Data Dictionary: Verifying metadata which includes constraints like Nulls, Default Values, Primary Keys, Check Constraints, Referential Integrity, Surrogate keys, Cardinality (1:1, m: n), etc.
  • Validating the Source to Target Mapping:  Ensuring traceability throughout will help build the quality aspects like consistency, accuracy and reliability.

Data Storage

The data storage phase refers to loading of data within the data warehouse/data mart or OLAP cubes.  The data loads can be one time, incrementally or in real-time. Key areas this phase should focus on are:

  • Validating data loads based on time intervals.
  • Performance and Scalability: Testing of initial and subsequent loads with performance and scalability aspect ensures that the system is within acceptable performance limits and can sustain further data growth.
  • Parallel Execution and Precedence: Verifying appropriate parallel execution and precedence during ETL process is important as it may impact directly on performance and scalability of the system.
  • Validating the Archival and Purge Policy: Ensures data history based on business requirements.
  • Verifying error logging, exception handling and recovery from failure points.

Data Presentation

This is the final step of the testing cycle and has the privilege of having a graphical interface to test the data.  Key areas this phase should focus on are:

  • Validating the Report Model.
  • Report layout validation as per mockups and data validation as per business requirements.
  • End to End Testing:  Although individual components of the data warehouse may be behaving as expected, there is no guarantee that the entire system will behave the same.  Thus execution and validation of end-to-end runs are recommended.  Along with data reconciliation discrepancies, issues might surface such as resource contention or deadlocks. The end-to-end runs will further help in ensuring the data quality and performance acceptance criteria are met.
dw-bi-testing-best-practices1

While above considerations are given, one important aspect that still remains to be addressed is the issue of ‘Time’.  BitWise has created a platform based on DW/BI Testing Best Practices that automates and improves the overall effectiveness of DW/BI Testing. If you’re interested in learning more about this platform, please contact us.

With the features and benefits of this platform, the intention is to address most of the DW/BI testing challenges:

  • End-to-end traceability achieved right through source extraction to reports.
  • 100% requirement and test data coverage through test cases.
  • Test case automation of standard checks achieving considerable time savings.
  • Up to 50% time savings through automated regression testing.
  • Defect or bug tracking involving all the stakeholders.
  • Improved testing cycle time through reusability.
  • Process improvements with analytical reporting showcasing test data, test cases & defect trends.

Conclusion:

Testing BI applications is different than testing traditional enterprise applications.  To achieve truly credible data, each stage of the lifecycle must be tested effectively – Data Collection, Data Integration, Data Storage and Data Presentation. If you’re not comfortable with your internal capabilities to test your BI applications, turn to the BitWise DW/BI Testing platform and lean on BitWise’s expertise and experience gained through testing business intelligence applications for clients over the past decade.

The post How To Conduct Effective Testing Of Business Intelligence Applications appeared first on Bitwise.

]]> https://www.bitwiseglobal.com/en-us/blog/how-to-conduct-effective-testing-of-business-intelligence-applications/feed/ 0