Data Analytics Cost For Small, Medium & Enterprise Businesses

Data analytics costs for a business can be between $1,000 USD and $100,000 USD per year depending on many factors. The total cost is made of the payments to data analysts and licensing costs for data analytics tools.

This article breaks down everything you need to know about data analytics costs: pricing models, service types, and so on, so you can have the highest ROI.

By the end of it, you will hopefully be able to understand a ballpark estimate of the costs that you need to budget for.

How Data Analytics Costs Are Determined

The costs associated with data analytics projects can vary significantly depending on four primary factors: project scope, technology used, technical expertise, and specific requirements.

The following diagram and explanations illustrate how each of these factors impacts overall project costs.

The cost of data analytics can be high or low depending on a couple of factors, some of which include:

Number Of Working Hours

The project scope determines how many hours are necessary to complete the data analytics project. Naturally, the more hours are needed to complete it, the higher the cost is going to be. The main project scope factors affecting the data analytics cost are:

1. The number of data sources that you want to analyse. Blending the data between different data sources takes a lot of time. The more data sources you have, the more time it takes to blend the data and quality check the calculation accuracy.
2. Automated vs manual data refresh. If you want to extract the data automatically, data analysts will need to spend time to write the code to do so. It takes at least 15 working hours from per data source to write the code for automated data extraction.
3. The specific KPIs you want to calculate. Complex calculations take more time to code.

To give you an example, creating a 1-2 page KPI dashboard with the data from Excel would cost you around $1,000 USD.

However, if you want to create a fully automated KPI dashboard from Xero, Clickup, Google Analytics and Google Ads, a project like this would cost $3,500-7,000 USD.

Being detailed with your project scope often helps to more accurately estimate the project cost and ensure that they do not increase unexpectedly. If you want to be really detailed, you can prepare an implementation plan and disuss it with your data analysts.

Technology Used

Sometimes you can spend more money on technology but save money on payments to the data analysts. The technology that goes into a data analytics project is:

1. Data connectors – instead of manually extracting your data from cloud-based data sources like Facebook Ads or Jira, you can buy ready-made data connectors. This is often much cheaper than coding integrations yourself and then maintaining them over time. With data connectors you usually pay per data source that you need to extract the data from.
2. Reporting tools – this is the data analytics software that you would be using e.g. Power BI, Tableau, etc. These tools charge you a monthly subscription based on the number of users. Don’t rush choosing the cheapest one because it might lack the functionality that you need. As a result, your data analysts would have to spend more working hours to create the analysis.

For example, if you wanted to analyse your Clickup data in Power BI, you would need a Clickup Power BI integration which is $1000 per year and a license to Power BI which is $14 per user per month.

Technical Expertise

Data analytics consultants that create KPI dashboards usually charge $75-150 USD per hour. This level of expertise is sufficient for most small and medium businesses.

If you want to work with a data scientist to create predictive analytics or analyse image and video footage, be prepared to pay more. We find that this level of technical expertise is usually only needed for enterprise clients.

Data Analytics Services Pricing Models

When it comes to data analytics, you can either engage data analytics consultants or hire a data analyst in-house.

Consultants usually work on an hourly or a fixed price basis whereas in-house data analysts would come at a recurring salary cost. It is important that the pricing model that you choose suits the project that you are working on.

Fixed-Price

Fixed pricing offers a predictable cost structure. Businesses pay a set fee for a defined set of services, ideal for companies with well-scoped projects and limited budgets. This model simplifies planning, provides financial certainty, and minimises billing surprises.

In our experience fixed-price projects start from $1,000 USD and most of them can be done under $10,000. The most expensive enterprise data analytics project that we have done was $100,000 but this was for 1 year of working every week.

Fixed pricing can be restrictive. If the scope of analysis expands or unforeseen data complexities arise, the service provider may be limited in how much support they can offer without renegotiation. A predetermined, set price for a defined scope of work or service package, regardless of usage or complexity.

Features:

• Predictable Costs
  
• Typically tied to subscription tiers or service bundles
  
• Often based on features and number of working hours required
  
• Suitable for projects with clear, stable requirements
  

Advantages:

• Budget certainty and easier forecasting
  
• Simplified vendor-client agreements
  
• No surprise expenses from increased usage or feature demand
  

Disadvantages:

• It may be more expensive if actual use is much lower than anticipated
  
• Less flexible for rapidly changing needs or scaling up/down quickly
  

Hourly Pricing

If the scope of your project is not fully defined, an hourly data analytics pricing model would work better. Working on an hourly basis is better if you are looking to iterate multiple times until you reach the final deliverable. If your project requires quick delivery, it often easier to work on an hourly basis instead of negotiating separately on every new requirements.

The standard hourly rate for data analytics services is $75-120 USD per hour. If this is above your budget, you can look for beginner freelancers or offshore services if this is above your budget. This involves more risk and can compromise the speed of delivery but is still an option to consider.

The downside? Variable pricing can lead to cost unpredictability. Without careful monitoring, expenses can quickly escalate, especially in organisations with growing or poorly managed data ecosystems.

Advantages:

• Flexibility to scale up or down with actual needs
  
• Lower upfront cost for smaller businesses or projects with variable workloads
  
• Direct correlation between cost and value received
  

Disadvantages:

• Costs can be unpredictable, especially during periods of rapid growth
  
• Difficult to budget for long-term projects

Monthly Retainers

Data analytics projects are rarely performed under montly retainers but it is possible in some cases

1. Maintenance – when a data analytics project is completed, sometimes a predictable amount of work should go every month into maintenance. The maintenance retainers are usually only a few hundreds USD per month.
2. Part time engagement – sometimes businesses only require around 10 working hours per week of data analytics services. When this is the case, it make sense to hire an agency for a monthly retainer instead of hiring an employee. A monthly retainer of 10 working hours of data analytics services per week would cost around $1,000 USD per week.
3. Short-term projects – montly retainers also make sense when projects take up to 6 months of full-time work to complete. Once the project is complete, the number of working hours usually goes down to 5-10 per week. A full-time monthly retainer for data analytics services would cost around $10,000-15,000 per month.

Advantages:

• The same flexibility as hourly projects
  

• Predictable monthly cost for a predictable output
  

• Part-time retainers are often cheaper than hiring a full time data analyst
  

Disadvantages:

• Full time retainers are usually more expensive than hiring an in-house data analyst
  

In-House Data Analyst

Hiring an in-house data analyst makes sense if you want to develop new analysis every month. If you simply want to maintain the data analytics reports that you have built, it is cheaper to go for a monthly maintenance retainer.

You would need to pay a salary of about $40,000 USD per year for junior data analyst. A more senior data analyst could cost around $100,000-150,000 USD per year depending on the state you are in.

The most cost-efficient approach is to maximise the junior data analyst working hours. For example you can employ a junior data analyst to build data analytics reports from Excel data and involve outsourced senior consultants for more technical tasks like building a data warehouse.

Advantages:

• Cost per hour is cheaper than outsourced consultants
  

• An in-house data analyst has more time to learn about your company and the business processes
  

• In-house data analysts have more availability than freelancers or agencies
  

Disadvantages:

• You have to pay tax, pension and benefit for employing someone full-time
  
• You need some data analytics skills yourself to manage the data analysts effectively
  

Data Analytics Cost-Benefit Analysis

A cost-benefit analysis (CBA) in data analytics is a structured approach for weighing the projected or actual costs of analytics initiatives against their expected benefits to determine if a project is financially and strategically justified.

A comprehensive cost-benefit analysis requires capturing all relevant costs, often categorised as:

Technology & Infrastructure:

• Software: Analytics platforms (BI tools, data visualisation, advanced analytics/Algorithms), data warehouses/lakes, ETL tools, cloud storage/compute costs (ongoing).
• Hardware: Servers, storage upgrades (if on-premise), specialised hardware for AI/ML.
• Integration: Costs to connect disparate systems and data sources.
• Maintenance & Upgrades: Ongoing fees, version updates, infrastructure patching.

People & Expertise:

• Salaries & Benefits: Data engineers, data scientists, BI analysts, data architects, database administrators.
• Recruitment & Training: Costs to hire scarce talent and upskill existing staff.
• Consulting & External Services: Hiring specialists for specific projects or expertise gaps.
• Management & Governance: Time spent by managers overseeing the analytics function, establishing data governance frameworks.

Data Acquisition & Management:

• Purchasing External Data: Market research, third-party datasets, APIs.
• Data Cleansing & Preparation: Significant time and resources spent making data usable (often 50-80% of project time).
• Data Governance Implementation: Tools and processes for data quality, security, lineage, and compliance (e.g., GDPR, CCPA).

Operational & Process Change:

• Project Management: Dedicated PM time for implementation and rollout.
• Change Management & Training: Costs associated with user adoption, training end-users, shifting workflows, and overcoming resistance.
• Opportunity Cost: Time and resources diverted from other projects or operational tasks.

Key Steps in Cost-Benefit Analysis

1. Define Objectives and Framework

• Clearly state the goals for the analytics initiative (e.g., increase operational efficiency, optimise marketing spend).
• Set measurable key performance indicators (KPIs) to track outcomes.

2. Identify and Quantify Costs

• Direct Costs:
  • Analytics software licenses and subscriptions
  • Infrastructure upgrades (cloud or on-premises)
  • Data acquisition and cleaning
• Indirect Costs:
  • Staff training and hiring of data professionals
  • Change management and adoption effort
  • Maintenance and support
• Intangible/Opportunity Costs:
  • Productivity disruption during rollout
  • Potential loss of alternative investments

3. Identify and Quantify Benefits

• Direct Benefits:
  • Increased revenue from targeted campaigns
  • Reduced costs through process efficiencies
• Indirect Benefits:
  • Higher customer retention and satisfaction
  • Improved forecasting and inventory control
• Intangible/Competitive Benefits:
  • Stronger market positioning
  • Enhanced data-driven decision culture

A positive ROI validates the investment in data analytics.

Comparative Table: Example Cost and Benefit Categories

Category	Example Costs	Example Benefits
Software/Tools	Licensing, integration	Automation, faster analysis
Infrastructure	Cloud storage, upgrade fees	Scalability, lower hardware spend
Personnel	Data scientist salaries, training	New insights, process improvements
Operations	Maintenance, support	Reduced operational delays
Strategic/Intangible	Productivity disruption, opportunity	Market differentiation, culture shift

Data Analytics Cost Based on Business Size

The cost of data analytics implementation can vary significantly depending on the size and complexity of the business. From startups to large enterprises, the scope of data usage, infrastructure needs, and talent requirements all influence the final investment. Understanding these differences is crucial for selecting the right analytics approach and avoiding under- or over-investment.

1. Small Businesses and Startups

Typical Cost Range: $1,000 – $20,000 (initial setup); $100 – $1,000/month (ongoing)

Small businesses typically have limited data volumes and fewer decision-making layers. As a result, small business analytics needs are often modest, focused on descriptive and diagnostic insights rather than advanced predictive models.

Cost Drivers:

• Cloud-based analytics tools (e.g., Google Looker Studio, Power BI, Tableau) charge a monthly fee based on the number of users.
• Pre-built dashboards can be purchased for a monthly/annual subscription.
• Outsourcing to freelancers or small analytics firms is a common strategy to avoid full-time hiring costs.

Considerations:

• Startups should prioritise ROI-driven use cases such as marketing analytics, sales funnel optimisation, or customer segmentation.
• Costs can escalate if custom solutions or in-house data teams are pursued prematurely.

2. Medium-Sized Businesses

Typical Cost Range: $10,000– $100,000 (initial setup); $1,000 – $5,000/month (ongoing)

Mid-sized organisations often need more robust analytics to support departmental operations, regulatory reporting, and forecasting. Their data complexity increases, often requiring integration from multiple systems (CRM, ERP, marketing platforms, etc.).

Cost Drivers:

• Data warehousing solutions like Snowflake or Amazon Redshift are used to manage larger volumes.
• Investment in ETL (Extract, Transform, Load) tools to streamline data flow.
• Hiring of small in-house data teams (analysts, engineers, and data-savvy managers).
• Licensing costs for advanced BI platforms or industry-specific analytics tools.

Considerations:

• Medium-sized businesses benefit from modular analytics strategies—scaling systems incrementally rather than overhauling them all at once.
• Data governance and compliance start becoming important and may add to the cost.

3. Large Enterprises

Typical Cost Range: $20,000 – $300,000 + (initial setup); $5,000 – $25,000/month (ongoing)

Enterprises operate with massive datasets across departments, locations, and digital channels. Their analytics functions are typically advanced, involving predictive modelling, machine learning, AI, and real-time reporting.

Cost Drivers:

• Custom-built data platforms with proprietary integrations and dedicated servers or hybrid-cloud environments.
• Large teams of specialists—data scientists, engineers, ML experts, and analysts—across business units.
• Strong investments in data security, compliance frameworks, and data governance platforms.
• High-end analytics platforms (e.g., SAS, IBM Cognos, SAP Analytics Cloud) with enterprise-level licensing.

Considerations:

• The total cost includes not just tools and talent but also change management, training, and organisational restructuring.
• These investments often lead to transformative outcomes, such as automated decision-making, real-time analytics, and competitive data-driven strategies.

Summary Table: Cost by Business Size

Business Size	Initial Setup Cost	Ongoing Monthly Cost	Key Focus Areas
Small Businesses	$1,000 – $20,000	$100– $1,000	Basic data analytics dashboards
Medium Businesses	$10,000 – $100,000	$1,000 – $5,000	Departmental reporting, forecasting
Large Enterprises	$20,000 – $300,000	$5,000 – $25,000	AI, ML, real-time analytics, data strategy

How to Choose a Data Analytics Service for Your Business

• Assess your organisation’s data maturity

Before spending on analytics, ask yourself how ready your team and systems are. Do you already collect data? And how clean is your data?  Are your teams used to working with reports? If you’re just starting, going straight into complex analytics solutions might be a waste. The best way to start is to start small, with tools that scale with your data and needs and with a consultancy that can guide you every step of the way.

• Match the service scope with the budget

As you’ve seen in previous sections, different services have their respective costs. You must be clear and honest about your needs so you don’t end up blowing your budget on services that will only eat into your analytics ROI. Don’t go for a full-suite enterprise solution if all you need is a monthly report.

• Off-the-shelf tools vs custom consulting

You might use a low-cost tool like Power BI or Looker Studio if you have simple and general needs. But if you’re dealing with industry-specific metrics, messy data, or want help making sense of everything, a data analytics consulting service will save you time and errors. Off-the-shelf tools are cheaper upfront, but custom consulting pays off when the stakes are high.

Case Studies

Vidi Corp data analytics consultancy has provided data cleaning, visualisation, and many other data analytics services to hundreds of clients.

Let’s see some examples of their dashboard and visualisation services:

HR Dashboard

This workforce overview dashboard helps HR teams and senior management understand employee satisfaction, attrition, and team performance on one page.

You can quickly know the roles or departments with low satisfaction or work-life balance and dig into tenure trends. Insights like these help HR teams improve retention and employee experience, which translates to high business throughput.

You can read more HR analytics dashboard case studies from Vidi Corp.

Sales & Finance Dashboard

This sales revenue dashboard gives financial directors of Tikkurila a full view of how the sales team is performing by region, payment terms, and individual salesperson.

The revenue is broken down by month and compared to the target and the previous year.

It helps them pinpoint high-performing reps and underperforming regions and adjust sales strategy in real time based on data-backed facts, not guts.

Read the full sales and finance dashboard case study

Conclusion

Depending on your goals, tools, and team, data analytics may cost you something or nothing. With the right partner and service fit, it will surely pay for itself 100%. Make Vidi Corp consultancy your data analytics partner today and see for yourself.

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