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How We Helped Clients Cut BI Licensing Costs by 96%

As a data analytics engineer at BALANCED+, one of the most common conversations I have with clients sounds something like this:

Were spending a fortune on our BI tool but no one seems to be using it.

This is not an isolated complaint its a pattern. Over the past two years, weve helped multiple organizations move away from Sisense, where they were paying close to $90,000 a year, and migrate to Power BI for just $3,000 a year a 96% drop in licensing costs.

But this wasnt just about saving money. It was about fixing fundamental issues in how these companies approached data and reporting.

The Real Problem: A Good Tool That Became the Wrong Fit

Sisense is a powerful BI platform. But as organizations grow and their data maturity evolves, several issues start to surface.

1. Cost vs. Usage Imbalance

Sisense licensing is structured in a way that quickly becomes expensive as teams expand. Many of our clients were paying for dozens (sometimes hundreds) of seats yet actual usage was limited to a handful of power users.

  • One client had 120 licensed users, but only 15 logged in more than once a month.
  • Another was paying for advanced modules they hadnt used in over a year.

2. Slow and Complex Development Cycle

While Sisense is capable, building and maintaining dashboards often required:

  • Specialized skillsets to handle complex data modeling
  • Additional support from IT teams for basic changes
  • Long development and deployment cycles

This led to bottlenecks, backlogs, and frustrated business teams waiting weeks for a new report.

3. Low Adoption from Non-Technical Users

Many end users found the Sisense interface unintuitive. Without strong internal champions or training, adoption dropped quickly. Dashboards were built but not used. Teams reverted to exporting CSVs or creating offline Excel workbooks. Data-driven decision making stalled.

4. Maintenance Burden

On top of the licensing cost, many companies underestimated the operational overhead:

  • Maintaining servers or dedicated cloud instances
  • Frequent version upgrades and patching
  • Dealing with performance issues at scale

When you put all this together, it became clear: the platform was delivering far less value than it was costing.

Why Power BI Was the Strategic Alternative

Power BI offered an immediate and strategic alternative:

  • Massive cost savings with a simple licensing model
  • Tight integration with Microsoft 365
  • Low learning curve for business users familiar with Excel
  • Modern, flexible visuals with fast rendering and easy sharing
  • Cloud-first architecture with minimal maintenance overhead

This made Power BI a natural fit for teams that wanted enterprise-grade reporting without the enterprise-grade bloat.

Our 5-Step Migration Framework

Over multiple projects, weve developed a proven 5-step migration framework designed to make the transition smooth, controlled, and impactful.

Step 1: Discovery & Audit

We start by identifying:

  • Which dashboards and KPIs actually matter to the business
  • Which data pipelines are still relevant vs. whats collecting dust
  • Who the real user base is and what level of interaction they need

This allows us to shrink the scope and avoid blindly lifting and shifting every asset.

Step 2: Data Model Rationalization

Most Sisense environments we inherit are bloated with unused tables, outdated joins, or overly complex schemas. We rebuild the data model cleanly for Power BI with streamlined data sources, simpler transformations, and faster refresh and query times.

Step 3: Dashboard Rebuild & Modernization

We dont just copy dashboards we redesign them for usability:

  • More intuitive layouts
  • Drill-through and interactivity where it matters
  • KPIs front and center without visual clutter
  • Consistent design language for easier onboarding

Step 4: Governance & Enablement

A successful migration isnt just technical its cultural.

  • We set up access policies that make sense for each team
  • Train analysts and business users to self-serve
  • Build documentation and templates to scale reporting

Step 5: Go-Live & Optimization

Finally, we run a staged rollout ensuring zero downtime:

  • Parallel run with old dashboards during the transition period
  • User feedback loops to tweak reports
  • Ongoing support for fine-tuning performance and usage monitoring

Real Results: Six-Figure Savings and Better Analytics

The results speak for themselves:

  • 96% average reduction in BI licensing cost (from ~$90,000/year to ~$3,000/year)
  • Faster dashboard load times and easier maintenance
  • Higher engagement from business teams in some cases, daily usage tripled
  • Leaner data stacks with fewer moving parts to maintain

We cut our BI costs to a fraction and actually got a better reporting experience. The migration forced us to simplify and focus on what really mattered. Client CIO, Manufacturing Sector

Why This Matters

Migrating from Sisense to Power BI isnt just a technical exercise its a strategic reset. It allows organizations to:

  • Re-evaluate what they actually need
  • Eliminate inefficiencies built up over years
  • Empower teams with modern, accessible analytics
  • Free up budget for innovation rather than overhead

Lets Talk

If your BI platform feels like a cost center rather than a value driver, youre not alone. Many organizations reach this exact inflection point. At BALANCED+, weve helped multiple clients through this transition from audit to architecture to adoption unlocking six-figure annual savings while modernizing their analytics environment.

?? Book a free consultation to explore how much your organization could save by rethinking your BI strategy: https://balanced.plus/contact/

Difference between Data Lake and Data Warehouses

Unlock the full potential of your data by understanding the key differences between data lakes and data warehouses, their use cases, and when to implement each.

Introduction

Today more than ever, businesses are inundated with vast amounts of data from various sources. Effectively managing this data is crucial for gaining actionable insights and maintaining a competitive edge. Two primary solutions for data storage and analysis are data lakes and data warehouses. But what are they, how do they differ, and can data lakes replace data warehouses?

In this comprehensive guide, we’ll answer these questions and more to help you make informed decisions about your data management strategy.


Table of Contents

  1. What Is a Data Warehouse?
  2. What Is a Data Lake?
  3. Data Lake vs. Data Warehouse: Key Differences
  4. Can Data Lakes Replace Data Warehouses?
  5. Examples of Data Warehouses and Data Lakes
  6. Companies That Offer Data Lakes
  7. Why Use Data Lakes?
  8. Advantages and Disadvantages of Data Lakes
  9. What Are Data Lakes Used For?
  10. When to Use a Data Lake
  11. Conclusion

What Is a Data Warehouse?

A data warehouse is a centralized repository designed to store structured data from multiple sources. It supports business intelligence (BI) activities such as reporting, analysis, and data mining. Data warehouses use a predefined schema and are optimized for query performance and data integrity.

What Is a Data Warehouse Example?

An example of a data warehouse is Amazon Redshift, a cloud-based service that enables businesses to analyze large volumes of structured data. Companies use Redshift to consolidate data from CRM systems, sales platforms, and financial databases to generate insightful reports and dashboards.


What Is a Data Lake?

A data lake is a centralized storage repository that holds vast amounts of data in its raw, natural format, including structured, semi-structured, and unstructured data. Data lakes allow for high data ingestion speed and support a variety of data types, making them ideal for data scientists and engineers who require flexibility.

What Is a Data Lake Example?

An example of a data lake is Microsoft Azure Data Lake Storage, which allows organizations to store and analyze petabytes of data. Businesses use it to collect data from IoT devices, social media, and logs to perform advanced analytics and machine learning.


Data Lake vs. Data Warehouse: Key Differences

AspectData LakeData Warehouse
Data TypeStructured, semi-structured, unstructuredStructured
SchemaSchema-on-read (applied when data is read)Schema-on-write (defined before storage)
CostGenerally lower storage costsHigher costs due to complex architecture
UsersData scientists, engineers, analystsBusiness analysts, decision-makers
PurposeAdvanced analytics, machine learningReporting, BI, historical analysis
ProcessingELT (Extract, Load, Transform)ETL (Extract, Transform, Load)

Can Data Lakes Replace Data Warehouses?

While data lakes offer flexibility and scalability, they are not a complete replacement for data warehouses. Data warehouses are optimized for structured data and quick query performance, making them essential for BI and reporting tasks. Data lakes, on the other hand, are better suited for storing large volumes of diverse data types and supporting advanced analytics.

In practice, many organizations use both, leveraging the strengths of each to meet different business needs.


Examples of Data Warehouses and Data Lakes

Data Warehouses:

  • Google BigQuery: A serverless, highly scalable data warehouse offered by Google Cloud.
  • Snowflake: A cloud-based data warehousing platform that supports structured and semi-structured data.
  • Oracle Autonomous Data Warehouse: An automated database optimized for analytics and data warehousing workloads.

Data Lakes:

  • Amazon S3: Object storage service by AWS, often used as a data lake due to its scalability and durability.
  • Google Cloud Storage: A unified object storage for developers and enterprises, suitable for building data lakes.
  • Apache Hadoop: An open-source framework that allows for distributed storage and processing of large data sets.

Companies That Offer Data Lakes

Several companies provide data lake solutions:

  • Amazon Web Services (AWS): Offers AWS Lake Formation and Amazon S3.
  • Microsoft Azure: Provides Azure Data Lake Storage and Azure Synapse Analytics.
  • Google Cloud Platform: Features Google Cloud Storage and BigLake.
  • IBM: Offers IBM Cloud Object Storage and IBM Data Lake.
  • Cloudera: Provides Cloudera Data Platform for enterprise data management.

Why Use Data Lakes?

Data lakes offer numerous benefits:

  • Flexibility: Store all data types without schema limitations.
  • Scalability: Handle massive data volumes with ease.
  • Cost-Effectiveness: Utilize low-cost storage solutions.
  • Advanced Analytics: Support machine learning, AI, and real-time analytics.
  • Data Democratization: Make data accessible to various stakeholders.

Advantages and Disadvantages of Data Lakes

Advantages:

  1. Data Consolidation: Centralize data from multiple sources.
  2. Enhanced Analytics: Enable complex analyses and data modeling.
  3. Future-Proofing: Accommodate new data types and analytics tools.
  4. Faster Ingestion: Quickly store data without transformation delays.

Disadvantages:

  1. Data Governance Challenges: Risk of a “data swamp” without proper management.
  2. Security Risks: Potential vulnerabilities if not secured properly.
  3. Complexity: Requires skilled personnel to manage and extract value.
  4. Performance Issues: Slower query performance compared to data warehouses.

What Are Data Lakes Used For?

Data lakes are utilized for:

  • Machine Learning and AI: Training algorithms with large, diverse datasets.
  • Real-Time Analytics: Monitoring live data streams for immediate insights.
  • Data Exploration: Allowing data scientists to discover patterns and correlations.
  • Archival and Compliance: Storing data for regulatory requirements and audits.

When to Use a Data Lake

Consider implementing a data lake when:

  • You need to store diverse data types from multiple sources.
  • Your organization requires advanced analytics capabilities.
  • You anticipate rapid data growth and need scalable storage.
  • Flexibility is crucial, and you want to avoid upfront schema design.

Conclusion

Both data lakes and data warehouses play pivotal roles in modern data management strategies. Data lakes offer the flexibility and scalability needed for advanced analytics and handling unstructured data, while data warehouses provide optimized environments for structured data and BI tasks.

Choosing the right solution depends on your organization’s specific needs, resources, and goals. Often, a hybrid approach that leverages the strengths of both can deliver the most value.


Frequently Asked Questions (FAQs)

Q1: Can data lakes and data warehouses coexist?

Yes, many organizations use both to meet different data management and analysis needs.

Q2: Is a data lake cheaper than a data warehouse?

Generally, data lakes are more cost-effective in terms of storage, but overall costs depend on management and processing requirements.

Q3: What skills are needed to manage a data lake?

Data engineers and data scientists with expertise in big data technologies, data governance, and security are essential for managing a data lake.

7 Reasons Business Intelligence Is Vital To Business Success

Todays customer-centric, digital-first world, many business owners and managers are bombarded with information overload and are urgently seeking ways to derive greater control, understanding, and intelligence from their organizations data.

One of the best solutions to this growing problem is to adopt a Business Intelligence (BI) strategy, but in reality many companies have been slow to do so due to a lack of knowledge of exactly what it involves, where to start, and how long it will take to see any benefit. The truth is: Business Intelligence should be an integral part of your operation.

Technology analyst Gartner describes Business Intelligence as including the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

So in practical terms, how would Business Intelligence deliver benefits to your company? 

This article outlines seven ways in which BI can deliver benefits, including smart decision-making, smashing sales and marketing goals, deepening customer knowledge, delivering an outstanding customer experience, powering up productivity, tightening up data accuracy and compliance, and accelerating return-on-investment.

1. Smart Decision Making

As a business manager or owner, its vital to have a firm grip on what your organizations data is telling you. As we all know, information does not necessarily equal intelligence! This is especially the case if that information is siloed in disparate parts of your business. The overriding goal of a BI initiative is to convert your companys information into structured, analyzable insight in other words, real business intelligence that can inform strategic decision-making across the company. Based on our customers own experiences, its clear that having up-to-date, data-driven intelligence at your fingertips not only leads to better business decisions but will ultimately contribute to superior financial performance. 

The technical backbone to intelligent decision-making is a single, centralized repository that draws together data on all of your business activities and customer interactions. Customer Relationship Management (CRM) solutions typically play an invaluable role here. A well-implemented CRM acts as the bridge between teams and will enable you to run reports that deliver a range of key business metrics on productivity, staff performance, product preferences, sales cycles, customer behavior, core customers, revenues and market trends all ready for analysis by the management team. Its then possible to quickly pinpoint performance hotspots and to flag up where processes could be emulated in other parts of the business, or where adjustments need to be made. Any resulting decisions will be based on hard facts rather than guesswork or assumptions. 

2. Smash Sales and Marketing Goals

Your Business Intelligence program will deliver in-depth analysis to kickstart sales, boost the performance of your marketing function, and crucially shake up the way that both teams work together. 

Lets take sales first. Providing your salespeople with tools that can measure their activity and identify trends in customer behavior is fundamental if they are to time their approaches more strategically and exploit all up-selling or cross-selling opportunities. 

This is exactly what one of our customers in the financial services sector, EMCORE, has achieved since implementing Maximizers CRM solution. The company has created dashboards of bespoke indicators including sales leads by stage, to give a visual representation of their cold, warm and hot prospects. One of their Relationship Managers, Carolina Newton, provides a powerful testimonial to the way that this gave an immediate shot in the arm to sales: Its a tremendous tool that has brought insight to the whole process. The biggest difference is the detailed insight into our sales pipeline which has improved forecasting, work scheduling and process analysis. 

In her blog post, Carolina explains how theyve reaped the benefits of business intelligence to spot trends and react quicker to potential sales opportunities. 

When it comes to marketing, your team will benefit greatly from BI through improved visibility of sales information, which can be used to finely tune and target their marketing campaigns. They can use technical features to track and measure every campaign and use information garnered to ensure future marketing initiatives are as profitable and effective as possible. 

Interestingly, our customer EMCORE mentioned specifically that they have achieved significant collaborative gains by linking up sales and marketing with the same intelligent platform a very topical issue given the fast-evolving dynamic between the two functions. 

3. Deepen Your Customer Knowledge

One of the main reasons behind the rising demand for tools that deliver Business Intelligence is that its never been more important to understand how your customers are interacting with you and how best to reach them or, more accurately, to encourage them to reach you. Without this knowledge, you are likely to find yourself falling behind your competitors. 

The reality facing business owners today is that people are less and less receptive to being sold to. The typical journey from initial interest to point of purchase has changed radically over the last five years. The emphasis nowadays is on engagement rather than promotion; drawing prospective customers to you rather than relying on outdated, outbound techniques based on the hard sell. 

Solutions such as CRM are a critical tool to provide the intelligence necessary to adapt your business to the New Customer Journey. Weve talked about drawing together data from various departments; but its important to stress the importance of deploying your chosen platform as widely as possible across sales, marketing, customer service, operations, product development and finance. This is the springboard to creating the single version of the truth holistic customer profiles based on their every interaction with you along their journey, regardless of the communication channel. This is true commercial intelligence, delivering detailed insight on buyer behavior and trends and allowing you to hone your sales, marketing and business growth strategies accordingly. 

4. Deliver an Outstanding Customer Experience

According to Gartner, Customer Experience (CX) has become the new marketing battlefront with more than two thirds of marketers stating that their companies compete mostly on the basis of CX. 

It is therefore imperative that your efforts to improve customer knowledge bear fruit in terms of driving up CX, overall customer satisfaction and retention. Linking to our last point, having immediate access to the single customer view is the enabler here. It is perfectly achievable for everyone who needs it to have an up-to-date, at-a-glance view of each customer that takes in all real-time information, including ongoing service cases, previous purchases and where they are in the sales cycle. This provides the basis for enhanced customer service response, as well as allowing your business to spot when a prospect or customer might be in buying mode, and to manage any danger points where a customer service failure risks undermining CX. 

Another of our customers, Laura Mould, Managing Director at SSG Recruitment Ltd. puts it like this: Having access to all our information in one place, anywhere anytime has helped improve data accuracy and management. Up-to-date interaction tracking allows the business to substantially improve our customer engagement, support and experience.

With your newfound customer insight in place, you can also conduct segmentation work to identify the typical profile of your most profitable customers, helping you to check that resources are being applied appropriately and that you are attracting the right type of prospects to meet your business growth targets. 

5. Power Up Productivity

Business Intelligence has the potential to release inefficiency bottlenecks, refine existing business processes, automate routine tasks and bring new levels of organization and prioritization to everybodys work. The efficiency and productivity gains can be considerable, including more responsive customer service, better use of salespeoples time, and closer measurement of product development cycles and marketing campaigns. The efficiency element is also evident at a more senior level thanks to automated reporting and dashboards. 

Centralization of data, in itself, and making that data accessible on any device through the Cloud, cuts everybodys administration time. One of our customers whose employees often work remotely told us that their Cloud CRM solution has halved the number of calls back to the office significantly boosting productivity, not to mention data integrity. 

6. Tighten Up Data Accuracy and Compliance

A recent article on Forbes.com identified some of the most complex elements of implementing Business Intelligence as discovering precisely where your data resides, deciding what is important, and who should have access to it. The article states: Without addressing those issues, the business is at risk through poor decision making based on inaccurate data and from increasingly strong data compliance regulations. 

So not only does holding data in separate siloes make it almost impossible to achieve the 360-degree view of your customers, it jeopardizes the very practical matter of data accuracy and consistency, which will have a negative impact on all areas of your business. It is indeed vital to address the underlying integrity of your data in any BI project but we also see improved data governance as a strong motivating factor behind such investments. 

For instance, centralizing data helps to improve transparency and expose inaccuracies and gaps that will lead to wasted marketing spend, not to mention potential brand damage caused by sending insensitive or poorly targeted communications. 

Whats more, data protection regulations around the globe are gradually tightening the rules around the capture, storage and usage of personal data. The new laws include requirements to keep data accurate and up-to-date, to demonstrate grounds for processing data and formulate a clear privacy policy for improved transparency. If your company has poorly managed records, you are highly unlikely to be in a position to meet these requirements and risk falling foul of the regulator as well as your customers. 

7. Accelerate Return-on-Investment

The culmination of achieving all of the points above should be a vast improvement in your return-on-investment across the company from managing day-to-day efficiency, sales deal conversion metrics and Customer Experience, right through to analyzing, modelling and crafting future growth strategies. Without the right insight and disciplines, its easy to fall back on old ways of doing things, on hypotheses and preconceptions especially about customer behavior and preferences and that could set your company on entirely the wrong course. 

In particular, smart technologies are being embraced to track, inform, guide, manage and measure Customer Experience embedding the notion firmly into the company culture that every team and every individual bears responsibility for putting the customer at the heart of the business. 

As we have implied already, the focus is turning away from department-specific solutions and towards enterprise-wide deployments that help companies keep all tactical and strategic business activity tightly aligned with current objectives. This is why BI can be succinctly described as providing managers with a clearer idea of how well their companies are running, and if they are meeting goals. 

To Finish it Off

Adopting a Business Intelligence strategy can bring a wide range of benefits to businesses of all sizes. By centralizing and analyzing data, businesses can make more informed decisions, improve sales and marketing efforts, deepen their understanding of customers, provide outstanding customer experiences, increase productivity, ensure data accuracy and compliance, and ultimately achieve a better return-on-investment.

With the growing importance of data and the increasing availability of BI tools, now is the time for businesses to prioritize a BI strategy and stay competitive in today’s fast-paced digital landscape.