November 8, 2025
Content Team
If you’re reading this, chances are you would (or think that you would) like to think – we’ve been evidence-based for a long time, and so should we all, I think. It’s a statistician and quality pioneer’s famous words that “In God we trust, all others must bring data,” that W. Edwards Deming famously put it.
Research the following: – why does that line still resonate in 2025? Because there’s been a massive increase in the volume of data, an upsurge in the number of tooling, and high expectations for speed and accuracy. The global market for BI platforms and related services is well north of $30 billion a year today and expected to double by 2032-2034 roughly speaking, depending on the analyst you consult.
In this guide, we’ll help you make a system navigation of the landscape, and help pick the right partner. Not only will we lay out what “great” looks like in a BI engagement, we will also give you the 20 Background Intelligence partners across industries making the impact in the business world, and provide a pragmatic selection playbook you can use right from the start (even at a relatively early stage).
Along the road, get another reminder that’s become a now clooky saying of British data scientist Clive Humby: “Data is the new oil” — valuable, but only if you drill through it.
Before the list, a little bit of ground cover. The strongest partners:
You’ll notice these themes in the firms below.
Please note that strengths given are like best for scenarios – use them to identify the one that best fits your context, do not read this like a ranklist.
Why they matter Massive global scale Deep industry templates End-to-end delivery – from data strategy to implementation and managed run.
Best for: Enterprises that want to transform across commercial business, including the most complex multi-cloud estate with change management requirements
Why they matter: Strong strategy to execution muscle; the BCG GAMMA team is a combination of advanced analytics and AI with BI modernization.
Best for: Organizations that are trying to connect BI investments with value roadmaps and change in their operating model.
Why they matter: Modernization of data on clouds at a large scale, “European foot-print” and accelerators packaged for data migration and processed visual analytics.
Best for: Global organizations with the need to provide standardized delivery in a larger amount of regions.
Why they are important: Since this is a global delivery and global sector specialization, mature (healthcare, financial services) playbooks developed for self-service analytics and governance.
Best for: Enterprises having large application estates in which BI requirements will need to interact closely with operational systems.
Why they are important: Strong industry assets, risk/compliance know-how, and huge investments on AI assisted delivery.
Best for: Controlled industries as well as program of board level transformations in which control and auditability are of utmost importance.
Why they matter: Finance-forward analytics Data governance Performance management; Thoughtful KPI frameworks for CFO organizations.
Highest potential (value): Finance transformation, profitability analysis, and planning/reporting consolidation.
Why they are important Known for: Decision science, AI, and Trend Analysis crosses well with BI programs interested in going beyond descriptive reporting.
Best for: CPG, Retail, Healthcare and Insurance Use Cases that integrate of Forecasting and Explainable Insights
Why they matter: Cloud Data Engineering Depth, platform migrations and large scale managed services to reduce BI TCO.
Use Case: Enterprises needing to fully logout their diverse legacy reporting stacks into a modern day cloud data platform.
Why they matter: Hybrid Cloud Data lifecycle coverage (governance to MLOps) Strong Presidential hybrid cloud; pragmatic approach to include AI into BI flow.
Best for: Complex data estates and organizations for multi-cloud/bios/hybrid/As-a-Service/ToCloud adoption. Namely for enterprises mainly using Windows-based applications and servers providing multi-cloud support, Red Hatillantum provides businesses with the ability to standardise their use cases on Red Hat Antonioein OpenShift capabilities.
Why they are important: Cloud providing delivery, transformation (including transformation with accelerators) of data migration, modeling and visualization is of the highest order in major cloud providers.
Best for: Cost effective modernization programs Global delivery.
Why they matter: Controls, audit trails, trusted metrics Good fit when BI has to meet strict regulatory/ audit compliance.
Best for: Financial services, healthcare & public sector.
Why the changing value proposition matters: Value-based roadmaps, change management, and reusable yardsticks; Increasingly AI-enabled at scale delivery.
Best for: Executive-led programs of reinvening activities where there are tight linkages with P&L results.
Why are we interested in them: Global geographic reach, good integration heritage, templates for practical operational analytics
Ideal For: Manufacturing, Logistics, and Telecom with solution where it is a large transactional system.
Why they matter Finance/operations visibility, risk and performance management Strong with C-suite KPI design.
Best for: ERP custom adjacent Reporting feature and Enterprise Planning/BI Harmonization
Why they matter Nimble delivery Modern cloud chops (AWS/Azure/GCP) Design-first culture for business adoption
Best use case: Quick time to market, user experience and adoption side of the BI.
Why they are important Scalability, depth in domain and cost effective global scale execution; Proven in long run managed analytics models.
Best suited for: Large footprints that have a strong interest and need to standardize BI across a very large geography and business units.
Why they matter Long Pedigree in enterprise analytics and performance at massive scale Strong query tuning and workload management.
Best for: Very high concurrency and complicated mixed workloads
Why they are important: Boutique look with enterprise chops; great data engineering + analytics science inlaid on BI.
Best for: Revenue and supply-chain analytics where getting the insight faster is key.
Why they are important: Deep specialization in Microsoft Azure data and analytics; bolstered further through the BlueGranite acquisition.
Best for: Microsoft-centric Organizations who are modernizing Power BI, Fabric/Azure Synapse, and Azure Data stacks
Why they matter Global delivery Platform accelerators Cost-effective modernization at scale
Best for: Enterprises that are looking for BI standardization with high operational SLAs.
Notice that it is not dashboards but decisions that you need to anchor them on. Make a list of the top 10 choices you are looking to make faster and/or better next quarter. Try to trace each back to the metrics and data that you will need. That is the context – not literally a list of charts.
Demand a “value tree.” Request a model showing data work as it relates to actual results – revenue, margin, working capital, customer satisfaction, etc. If a proposal is unable to say this, that is an initial warning sign.
Decide on your operating model from the outset; Will you centralize BI in a data COE? Or federate analysts into business units? That reality rather than the other way around should be the foundation for your partner’s design of governance and tooling.
Insist on lineage/ governance since day one Broken trust kills adoption. Also strive for are business partners who use data quality, lineage and semantic models as a first-class citizen.
Plan the human side. Real time budget for enablement and internal communities of practice A great business intelligence consultant is aware that success is adoption and not merely delivery.
Make it measurable. Set up success metrics (e.g. decision cycle time, forecast accuracy, inventory turns). Go over them at each steering meeting.
Conduct a 12 week pilot before taking to scale. Get together with your team 1-2 high value use case co-deliver. Bake on a plan “build the muscle” your people are able to run and extend the platform.
From Power BI and Fabric to Tableau, Looker and Qlik (not to mention dbt, Snowflake, Databricks and the big three clouds), the stack is full. Instead of running because of every glittery thing:
Select a single main visualization layer to provide standardisation with respect to UX of Note and governance.
Choose one semantic modeling tool (i.e. Power BI semantic model, Looker’s semantic layer, or dbt metrics) and transfer the knowledge to teams.
Make data products discoverable through catalog and lineage tools.
Conduct some light business intelligence software comparison, especially when it’s time for a vendor refresh cycle, so it makes sense to optimize for fit as well as functionality, but not for checking a box of features.
And remember: tooling is a force-multiplier and not a replacement for process, governance and decision design.
The boundary between BI and AI is becoming blurred fast. Jungsten is the leader in implementation of AI assistants for analysts and business users that automate data prep and data documentation and insight generation. The upshot: BI is increasingly conversational and progressively proactive (surfacing anomalies, offering suggestions on actions to take, even simulating the outcomes), if your data groundwork is tamed.
Use the following to rate any partner in your shortlist:
Great BI is not learning to construct more beautiful charts; it’s the exercise of being able to deliver the right measure at the right place to the right person every time. That’s why Business intelligence consulting is, at its best, all about partnership: strategy plus engineering plus design plus change management. Choose a business intelligence consulting firm which can meet you where you are and has the potential to elevate your data practices and build capabilities that your teams will own long after go live.
If you would like a third set of objective eyes that can help you compare options in your short-list, or a no-nonsense road-map of what’s applicable to your particular situation, then TAV Tech Solutions will get things moving for you with the ability to scale what works, and to identify what doesn’t. We work closely with your people so that you get results that stick.
At TAV Tech Solutions, our content team turns complex technology into clear, actionable insights. With expertise in cloud, AI, software development, and digital transformation, we create content that helps leaders and professionals understand trends, explore real-world applications, and make informed decisions with confidence.
Content Team | TAV Tech Solutions
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