The analytics platform will allow us to analyze the data and extract meaning and nuances from it. Here is a checklist to select an analytics platform.
The right analytics platform helps a business turn data into information it can use to make decisions. But choosing one requires more than comparing dashboards, query speeds, or feature lists. The platform must fit your business goals, existing data environment, security requirements, analytical workloads, and budget.
To choose an analytics platform, evaluate its business fit, data integration, analytical capabilities, performance, scalability, security, usability, implementation effort, and total cost of ownership.
Shortlist platforms against these criteria, then test them using representative data and real business questions before committing.
For example, a marketing team may need to combine campaign performance, customer behavior, sales, and customer relationship management (CRM) data to understand which activities contribute to revenue.
An enterprise data team may need a platform that handles complex queries across large datasets while maintaining access controls and consistent definitions.
Both organizations need analytics, but their requirements are different.
This guide explains what an analytics platform does, how it differs from transactional systems and business intelligence tools, which features matter, how to evaluate deployment options and costs, and how to test a platform before buying it.
How do I choose the right analytics platform for my business?
Choose an analytics platform by starting with the decisions you need to improve, then evaluating which platform can support those decisions reliably, securely, and within your budget.
A platform that offers the most features is not necessarily the best fit.
Use this six-step process to narrow your options.
Step 1: Define your business objectives
Identify the questions your teams need to answer. These might include:
a) Which marketing campaigns generate the most valuable customers?
b) Why are customer retention rates changing?
c) Which products, regions, or channels contribute most to revenue?
d) Where are operational costs increasing?
e) Which customers are most likely to purchase, renew, or leave?
Translate each question into a measurable use case. Specify the users, data required, expected analytical output, and business outcome.
Step 2: Assess your data environment
List the systems that hold the data you need, such as CRM platforms, marketing applications, ERP systems, transaction databases, cloud storage, and customer support tools.
Check whether the data is accessible, complete, consistent, and suitable for analysis.
An analytics platform can only produce dependable insights when the underlying data is sufficiently accurate, consistent, and relevant to the question being asked.
Also determine whether the platform must support batch processing, frequent updates, or near-real-time analysis.
Step 3: Identify essential capabilities
Separate essential requirements from optional features. Your priorities may include SQL querying, dashboards, data preparation, predictive analytics, machine learning, natural-language queries, or automated reporting.
Avoid paying for capabilities your team has no immediate plan to use unless there is a clear future requirement.
Step 4: Evaluate architecture and performance
Understand how each platform stores, processes, and retrieves data. Test it with realistic query patterns, data volumes, concurrent users, and refresh requirements.
Do not judge performance solely from a vendor demonstration. A demonstration may use a smaller dataset or a workload that does not represent your environment.
Step 5: Compare security, usability, and total cost
Review access controls, encryption, governance, auditability, integration requirements, training needs, support, and ongoing maintenance.
Compare the full cost of operating the platform, not just its advertised subscription or license fee.
Step 6: Run a proof of concept
Shortlist two or three suitable platforms and test them against the same business use case. Record query performance, result accuracy, integration effort, usability, security requirements, and estimated operating costs.
Choose the platform that best satisfies your documented requirements rather than the one with the longest feature list.
What is an Analytics Platform, and How is it Different from a Transactional Platform?
For the last 40 years or so, the entire technology industry has been focused on solving just one problem.
a) How to improve the ability to record and manage transactions?
This unrelenting focus has led to enormous improvements in transaction recording.
We have become so proficient that today we can record transactions in microseconds and Nanoseconds.
This has given rise to high-frequency trading on stock markets, massive-scale collaboration among billions of people on social media, and the recording of sensor data from machines.
It is well understood that the core requirement of a transaction management system is to be able to
3. Update any field of that record
These few functions (CRUDS) all operate on a single record.
Hence, we have developed database systems that are highly efficient in selecting a needle from the proverbial haystack.
To achieve this singular objective, we record the complete record as a row. Each row can have fields of number, date, or text types.
In the late 1990s and 2000s, we struggled to modify these database systems to accommodate audio and video content with limited success.
Therefore, every time we need to read a few fields in a record, we must bring the entire record into memory before we can operate on it.
So if the record has 100 fields and I mostly query the 10 most frequently used fields, I am moving the whole record even though I have no use for 90% of the fields in my current operation.
When I have to read millions of records from memory, this becomes a wasteful use of precious resources, such as server memory and CPU, at an enormous cost due to I/O from disk.
The result is a system that is sluggish and doesn’t respond before the train of thought has left the station.
Main analytics terms you should understand
| Term | Definition |
|---|
| Analytics platform | A technology environment for accessing, processing, analyzing, and presenting data to support decisions. |
| Transactional platform | A system designed to record and manage operational transactions, such as orders, payments, bookings, or account updates. |
| OLTP | Online transaction processing. It supports frequent operational transactions that require accurate and timely record updates. |
| OLAP | Online analytical processing. It supports analytical queries across data, often involving aggregation, comparisons, and multiple dimensions. |
| Business intelligence (BI) | Processes and tools used to organize, analyze, visualize, and communicate business information. |
| Data warehouse | A repository designed to consolidate and organize data for reporting and analysis. |
| Columnar storage | A storage approach that groups values by column rather than storing complete records row by row. It can improve efficiency for analytical queries that scan selected columns. |
| Data integration | The process of bringing data from different systems together for consistent use and analysis. |
| Predictive analytics | The use of statistical methods and models to estimate likely future outcomes from data. |
| Total cost of ownership (TCO) | The combined cost of acquiring, implementing, operating, supporting, and maintaining a platform over a defined period. |
| Proof of concept (PoC) | A limited, practical test used to establish whether a platform can meet defined technical and business requirements. |
According to IBM's overview of OLAP and OLTP, the central distinction is the workload each is designed to handle: transactional processing versus analytical processing. This distinction is important when deciding whether an existing operational database is sufficient for reporting or whether a dedicated analytical environment is needed.
What is the difference between an analytics platform and a BI tool?
A BI tool typically focuses on reporting, dashboards, data visualization, and business performance analysis, while an analytics platform may cover a broader range of data preparation, integration, processing, and advanced analytical capabilities.
The distinction varies by vendor, and many modern products combine both.
A BI tool may be all a team needs when the primary goal is to monitor metrics and explore performance.
| Capability | BI tool | Broader analytics platform |
|---|
| Dashboards and reports | Usually a core capability | Often included or integrated |
| Data visualization | Usually a core capability | May be included or supplied by another tool |
| Data integration and preparation | May be limited or connector-based | May provide broader integration and transformation capabilities |
| Complex analytical queries | Depends on the product and underlying data architecture | May include engines designed for analytical workloads |
| Predictive modeling and machine learning | May be limited or integrated | May include advanced analytics or integrate with specialist tools |
| Data storage and processing | Often depends on connected systems | May provide storage, processing, or integration with a warehouse or lake |
| Governance and access controls | Often supported, with scope varying by product | May include platform-level controls or integrate with enterprise governance tools |
A broader analytics platform may be appropriate when the organization also needs substantial data integration, complex processing, advanced modeling, or a shared analytical foundation.
These categories overlap. For example, a BI product can include data preparation and modeling, while a broader analytics platform may depend on a separate BI tool for dashboards.
AWS explains in its data warehouse overview that data warehouses consolidate information for analysis and can serve BI tools, SQL clients, and other analytics applications. This illustrates why a data warehouse, an analytics platform, and a BI tool should not automatically be treated as interchangeable terms.
What features should an analytics platform have?
The essential features of an analytics platform are reliable data integration, efficient query processing, appropriate analytical capabilities, understandable outputs, security, governance, and the ability to scale with business needs. The importance of each feature depends on your use cases.
1. Data integration and preparation
The platform should connect to the systems your organization uses and support the data formats and refresh schedules required by your workflows.
Look for capabilities to combine datasets, standardize fields, handle missing or inconsistent values, and document data transformations. Check whether connectors are native, require third-party tools, or involve custom engineering.
2. Query performance and analytical processing
Analytics often requires examining many records, selecting relevant fields, filtering datasets, joining information, and calculating aggregates.
A platform should handle the queries and concurrency levels expected in your environment. Ask vendors to demonstrate performance using representative data rather than relying only on general benchmark claims.
According to AWS's columnar database overview, columnar storage can improve analytical query and storage efficiency because data is stored by column. However, storage format alone does not determine overall performance; query design, data organization, compute resources, and workload also matter.
3. Reporting and visualization
Reports and dashboards should make results understandable to their intended users. Evaluate whether teams can filter data, compare periods, drill into results, and use consistent metric definitions.
A visually appealing dashboard is not enough if users cannot verify where the numbers came from or reconcile them with trusted business records.
Depending on your objectives, you may need forecasting, customer segmentation, anomaly detection, recommendation models, or predictive analytics.
Advanced analytics is valuable when it addresses a defined business problem; the presence of AI or machine learning features alone is not evidence that a platform will deliver better business outcomes.
Ask whether the platform supports the required methods directly, integrates with specialist tools, and provides ways to validate model outputs.
5. Security and data governance
Check role-based access controls, encryption, audit logs, data retention settings, data lineage, and support for your organization's regulatory obligations.
Determine who can access sensitive data, who can modify transformations, and how changes to metrics or datasets are tracked.
6. Scalability and reliability
Evaluate whether the platform can accommodate expected growth in data volume, user numbers, and query complexity. Ask how it handles workload spikes, failures, recovery, and service availability.
Scalability should be evaluated against a realistic growth scenario, not just a vendor's maximum advertised capacity.
7. Usability and integration
Consider the needs of analysts, engineers, business users, and administrators. The platform should fit existing workflows and connect to the tools teams already rely on.
A technically capable product may still be a poor choice if it requires more specialist skills or maintenance than the organization can reasonably provide.
How much does an analytics platform cost?
The total cost of an analytics platform depends on its pricing model, data and compute requirements, user numbers, integrations, implementation effort, and ongoing operating needs. There is no single price that applies to all analytics platforms.
| Cost category | What to include |
|---|
| Licensing or subscription | Per-user, capacity-based, usage-based, or other recurring charges |
| Infrastructure | Compute, storage, networking, and related services |
| Data integration | Connectors, data pipelines, transformation, and custom development |
| Implementation | Architecture, configuration, migration, and testing |
| People and training | Administration, engineering, onboarding, and user enablement |
| Operations and support | Monitoring, maintenance, vendor support, and troubleshooting |
| Governance and security | Required controls, auditing, compliance work, and additional tools |
| Growth and overages | Increased data volumes, compute usage, users, and additional workloads |
Before comparing vendor quotes, identify the full cost categories.
Some platforms charge mainly by user or license; others vary costs with compute capacity, storage, queries, or consumption. A low initial price can become expensive if usage grows or significant engineering work is needed.
How to compare platform costs
1. Define the evaluation period. Compare costs over the same period, such as three years, where reliable estimates are available.
2. Estimate expected usage. Include users, data volume, refresh frequency, query workloads, and peak demand.
3. Request a detailed quote. Ask vendors to identify included features, usage limits, additional charges, and support terms.
4. Estimate implementation and staffing. Include integration, migration, training, maintenance, and administration.
5. Model growth scenarios. Estimate how costs change as the number of users, amount of data, and complexity of analysis increase.
6. Compare cost against measurable value. Consider potential improvements in reporting efficiency, decision speed, forecast quality, or other outcomes relevant to the use case.
Total cost of ownership is a more useful comparison than the license price alone because it accounts for the broader cost of implementing and operating the platform.
Do not assume that cloud platforms are always cheaper or that on-premises systems are always more expensive. The result depends on utilization, existing infrastructure, staffing, licensing, and operational requirements.
Should I choose a cloud-based or on-premises analytics platform?
Choose a cloud-based or on-premises analytics platform based on your security and compliance requirements, workload patterns, existing infrastructure, scaling needs, operational skills, and total cost of ownership. Neither deployment model is automatically the right choice for every organization.
When cloud may be a better fit
| Consideration | Cloud-based platform | On-premises platform |
|---|
| Infrastructure management | The provider manages some or most of the underlying infrastructure, depending on the service model | The organization is generally responsible for its own infrastructure |
| Scaling | Capacity can often be adjusted through the provider, subject to service limits and configuration | Scaling may require purchasing, installing, or reallocating hardware |
| Initial infrastructure investment | May reduce the need to purchase dedicated hardware upfront | May require capital investment in hardware and facilities |
| Cost management | Requires monitoring usage, capacity, and consumption charges | Requires accounting for hardware, facilities, staff, licensing, and refresh cycles |
| Control | Depends on the service model and provider controls | Offers direct control over the organization's infrastructure |
| Maintenance | Shared with the cloud provider, with responsibilities depending on the service | More infrastructure maintenance is usually handled internally |
| Data location and compliance | Must be evaluated against provider regions, contractual terms, and compliance needs | May offer more direct control over where infrastructure and data reside |
Cloud-based analytics may suit organizations that want to provision capacity flexibly, avoid managing all underlying infrastructure, or integrate with other cloud services.
However, cloud adoption still requires careful planning around access, data movement, usage costs, service dependencies, and governance.
When on-premises may be a better fit
On-premises deployment may suit organizations with existing infrastructure investments, specific control requirements, or workloads that need to remain within managed internal environments.
The organization must also account for hardware capacity, upgrades, resilience, security, and the staff needed to maintain the environment.
Consider a hybrid architecture
Some businesses use a combination of cloud and on-premises systems. For example, operational data may remain in an existing internal environment while selected data is transferred to a cloud-based analytical service.
A hybrid model can meet varied requirements, but it can also increase integration and governance complexity. Evaluate the whole architecture rather than treating deployment choice as an isolated decision.
How do I test an analytics platform before buying it?
Test an analytics platform using a proof of concept built around representative data, realistic workloads, and measurable acceptance criteria. A structured test helps establish whether the platform can meet your needs before you commit to a long-term contract or major implementation.
Step 1: Select a real business use case
Choose a problem with a clear outcome, such as combining marketing and sales data to measure campaign performance or analyzing customer behavior to identify retention opportunities.
Define what a successful result looks like before testing begins.
Step 2: Prepare representative data
Use data that reflects the volume, structure, quality, and complexity of your expected workload. Where appropriate, use masked or synthetic data to protect sensitive information.
Include realistic edge cases such as missing values, inconsistent identifiers, and records from different systems.
Step 3: Test data integration
Connect the required sources and measure the effort needed to ingest, transform, and reconcile the data.
Record any custom development, connector limitations, or manual steps that would be required in production.
Step 4: Measure performance and accuracy
Run representative queries, aggregations, reports, and transformations. Check response times under expected workloads and verify that results match trusted calculations or source records.
A successful proof of concept must demonstrate both acceptable performance and correct results; speed alone is not sufficient.
Step 5: Evaluate security and governance
Test access permissions, user roles, auditability, and relevant data protection controls. Confirm that users can access the information they need without receiving unauthorized access to other data.
Ask intended users to complete common tasks. Determine whether they can find the right data, understand reports, explore results, and repeat their work without excessive support.
Include the data engineers and administrators who will maintain the environment, not just dashboard users.
Step 7: Estimate operational cost
Use the test workload to estimate compute, storage, licensing, integration, support, and staffing costs. Ask vendors to explain how the estimate changes with higher usage.
Step 8: Score each shortlisted platform
| Evaluation criterion | Suggested weight |
|---|
| Business and use-case fit | 20% |
| Data integration and quality | 15% |
| Performance and scalability | 15% |
| Security and governance | 15% |
| Usability and adoption | 10% |
| Total cost of ownership | 15% |
| Implementation and support | 10% |
Use the same evaluation criteria for every vendor. An example scorecard is below.
These weights are an illustrative starting point, not an industry benchmark. Adjust them to reflect your organization's priorities and treat mandatory security or compliance requirements as pass/fail criteria where appropriate.
Score each platform against documented evidence from the proof of concept, vendor responses, and cost estimates. Record gaps and assumptions before making a decision.
An expert perspective: Choose for decisions, not feature lists
The right analytics platform is not necessarily the one with the most advanced technology. It is the one that helps the organization answer important business questions with trusted data, at the right level of performance and cost. Start with the decisions you need to improve, validate the data behind those decisions, and test whether the platform can support the work in practice.
For a marketing analytics use case, that means checking whether campaign, customer, conversion, and revenue data can be connected and interpreted consistently. For an enterprise analytics use case, it may mean evaluating workload performance, data governance, integration complexity, and operational reliability.
A platform should be assessed against the work it needs to perform and the outcomes it is expected to support.
Common mistakes to avoid when selecting an analytics platform
Even a feature-rich platform can disappoint if the selection process overlooks operational requirements.
Avoid these common mistakes:
1. Choosing based on features alone. Prioritize capabilities that support defined business needs.
2. Ignoring data readiness. Inconsistent definitions, missing values, and fragmented sources can undermine analytical results.
3. Testing only vendor demonstrations. Validate the product using representative data and your own use cases.
4. Underestimating implementation effort. Account for integration, migration, training, and ongoing maintenance.
5. Comparing license prices instead of total costs. Include infrastructure, staffing, support, and expected growth.
6. Overlooking governance and security. Establish access controls, ownership, and data-handling requirements before rollout.
7. Buying more capability than you need. Distinguish immediate requirements from speculative future needs.
8. Skipping adoption planning. A platform delivers limited value if intended users cannot or do not use it effectively.
The selection process should give equal attention to technical suitability, business value, and the organization's ability to operate the platform over time.
d) How much does an analytics platform cost?
The cost depends on licensing, users, data volume, compute usage, integrations, implementation, staffing, support, and maintenance. Compare the total cost of ownership over a consistent period rather than relying only on the initial license or subscription price.
e) Should I choose a cloud-based or on-premises analytics platform?
Choose cloud when its flexibility, service model, and integration options fit your needs. Consider on-premises deployment when direct infrastructure control or existing investments are important. Evaluate security, compliance, scaling, staffing, and total cost before deciding; hybrid architectures may also be appropriate.
f) How do I test an analytics platform before buying it?
Run a proof of concept using representative data and a defined business use case. Test data integration, result accuracy, query performance, security, usability, and operating costs. Use the same criteria for each shortlisted vendor and document the results before making a purchase decision.
Selecting an analytics platform is a business and technology decision. The best option is the one that fits your objectives, works with your data environment, supports the analytical workloads you need, and can be operated securely within your budget.
Start with the business questions, assess your data readiness, compare the capabilities that matter, and evaluate total cost of ownership. Then use a proof of concept to test performance, accuracy, integration, usability, and governance.
A platform should be selected on demonstrated fit with business requirements, not on feature count or marketing claims alone.
If your organization is evaluating analytics platforms for marketing, customer, or enterprise data, Express Analytics can help assess the requirements, data environment, and analytical use cases that should guide the selection process.
Ready to evaluate your analytics requirements? Talk to our team about your data and analytics goals.
