Many companies around the world are already benefiting from small business data analytics. According to Score, 51% of small business owners believe that big data is a must, but only 45% of them perform data analysis. Deloitte revealed 63% of small businesses using analytics tools report higher profitability within 6 months
You don’t need a Fortune 500 budget to unlock the potential of data analytics. The licensing cost of data analytics software is starting from $10 per month and there are some free options too. Data analytics services and software are not cost-prohibitive for small businesses and it can offer significant benefits.
Considering today’s economy, where annual growth hovers below 3%, the gap between industry leaders and laggards is widening. Small and midsize businesses are no exception, many are now seizing market share by turning raw data into actionable insights. From customer behavior patterns to operational efficiencies, big data isn’t just a buzzword; it’s a competitive lifeline.
The message is clear: in a slow-growth era, analytics isn’t optional. It’s the aggressive edge separating stagnation from success.
Data analytics is all about answering business questions from data. Some questions need to be answered once while others deserve attention regularly. Answering these questions enables businesses to make better decisions and drive business development strategies.
The business questions are usually answered in the form of analytics reports which are presenting data as a series of graphs. Sometimes these graphs have written commentary that goes along with them. Visualising the data helps to answer business questions in a clearly and enables the users to explore the data themselves.
There are 4 types of data analytics for small businesses:
This is probably the biggest worry of small businesses when they plan for data analytics projects. Understandably, SMEs are budget-conscious and need to make sure that they choose the most cost-competitive options for their data analytics.
A quick answer is that for small scale analytics project you would need a budget of USD $1,000+ for development and USD $20+ per month for licensing.
It is important to understand what the cost of data analytics initiatives in made of:
Software cost for data analytics is the first factor to consider. It is made of 3 factors:
Depending on your project you might not have any data warehouse or connector licensing costs but it is still good to know that these can potentially come up later.
Development cost for data analytics is the second big factor which is mostly related to paying to your data analysts. Very often development costs are much higher than licensing so if you can pay for a more expensive software that saves time to your developers, usually this is a good idea.
Small and medium businesses usually have three staffing options for data analytics project: hiring an employee, hiring a freelancer or partnering with an agency. Each of the options has its advantages and disadvantages while the decision is usually based on the business resources available.
You usually need a budget of USD $3-5k to hire an in-house data analyst. Hiring an in-house data analyst is ideal for businesses that have ongoing and evolving data analysis needs. If you need to produce different data analysis every week, this option is usually best. However, you need to have some data analytics skills yourself to ensure that you can hire a technically proficient developer. The costs of employment can also be pretty high including salary, benefits, pension, taxes, etc.
Businesses with one-off requirements may consider hiring a freelancer. Freelancers in the UK charge around £400-600 per day and in the US they charge $100+ per hour. This is a cost effective solution that allows you to experiment with data analytics in your company. On the other hand, your project might need experience in data engineering, data analytics and data visualisation. In this case a freelanced might be good at one domain and beginner level at others.
Finally, a budget of USD $1,000-5,000 is enough for most data analytics projects with a professional data analytics agency. Working with a data analytics consultancy gets you access to a team of experts with a wide range of skills. Professional consultancies usually have several team members with specialised experience and are better suited for end-to-end data analytics projects.
Choosing the right analytics tool can make or break your data strategy. For small businesses, affordability, ease of use, and integration with existing systems are critical. Here’s a deep dive into two standout platforms, Power BI and Looker Studio, and how they empower small teams to turn data into decisions.
Why It’s Great for Small Business:
Power BI is a powerhouse for businesses already using Microsoft ecosystems (e.g., Excel, Azure, Teams). Its drag-and-drop interface and AI-driven insights make it ideal for users who need advanced analytics without coding expertise.
Key Features:
Why It’s Great for Small Business:
Looker Studio is 100% free and integrates natively with Google services (Analytics, Ads, Sheets). It’s perfect for businesses prioritizing ease of use, collaboration, and real-time reporting.
Being a free tool, Looker Studio has more limitations for customising your data visualisations as compared to Power BI. Google monetises Looker Studio through encouraging users to buy Looker Studio connectors to other data sources and paying database fees for Google Big Query.
Key Features:
Looker Studio is widely used in marketing analytics so we would recommend considering it if you want to create marketing analysis.
Data analytics for small businesses is all about efficiency and finding the easiest way to achieve the analysis that you need. The good news is that most small businesses have similar data analytics needs so the analysis you need has likely been created before. We have a few actionable tips that follow from this principle below.
Let’s imagine you work in an SME and you want to analyse data from QuickBooks Online. In this case, find a company or a freelancer with a proven record of creating this analysis. They will likely have some files that you can re-use to create the analysis that you need more efficiently. They would also know the data model very well helping them produce the analysis that you need faster.
When you are selecting a data analyst for your SME, ask them questions like:
There are many pre-made templates in Power BI, Tableau and Looker Studio for all kinds of data sources. Those are pre-created dashboards that already have all the formulas that you need to make the numbers match. As a result you can use these templates to get started quickly and customise them later.
Starting from a pre-made template saves you time having to develop the analysis from scratch and allows you to achieve more for the same budget.
If you want to automatically extract data from certain data sources you have 2 options:
Coding an integration from scratch takes a lot of working hours which you need to pay for. As a result, this is often too expensive for most SMEs. It is therefore often more affordable to pay for a data connector that helps you automate data extraction.
As a rule of thumb, buying ready-made solution would be more cost efficient than developing them from scratch.
Based on our experience in data analytics consulting for small businesses, the following data sources are most common to analyse.
SMEs have most of their data on Excel since many are trying to avoid additional software costs. The data on these spreadsheets can be related to pretty much anything.
We often see client billing data being stored on Excel in SMBs since many record weekly sales on spreadsheets before sending invoices to clients.
Excel might also contain data grouping for items into categories. Those mapping tables would be brought into data analytics reports like Vlookups.
Financial data analytics is a treasure trove of insights into profitability, cash flow, and spending patterns.
Accounting analytics is focused around:
The data analysis from marketing platform is usually focused around finding the most efficient customer acquisition channel.
Marketing analytics is usually focused around:
Your online store holds clues about customer behavior, product popularity, and sales bottlenecks.
E-commerce analytics is focused around
These platforms track team’s productivity, project timelines, and resource allocation—critical for scaling efficiently.
Project Management Analysis is focused around:
KPIs are performance indicators that show how effectively a company achieves its long term objectives. For instance, KPIs can include sales growth, customer retention rate, employee turnover etc. The first step to small business data analytics is identify KPIs, the data to calculate those KPIs and also the most effective way to visualise them.
There are 3 possible approaches:
Once the data is available, the next step is to develop a data analytics report that will provide real-time insights, allowing businesses to monitor its performance. A data analyst would use different charts to ensure that a report is developed in a clear and visually appealing way.
The data analysts usually schedule a call with the business managers to discuss results and all the limitations of the analytics report. Sometimes additional analysis is required after such meetings. All parts of the completed analysis are critically evaluated.
The data analysts in small businesses then maintain, ensuring the dashboards are updated as new data sources are added, business needs or data format change. It is vital that the report will answer business questions over time, not just once.
If you need a reliable partner for your small business data analytics needs, reach out to us today! We have successfully delivered 1000+ projects for 600+ international clients so we have a significant amount of experience to bring into your projects.
Because of our experience we also have many files which we can re-use to deliver the project more efficiently. This enables us to spend less working hours on your data analysis projects delivering them cheaper and faster than competitors.
Finally, we have our own software for data extraction which helps us extract the data for our clients quickly and efficiently.
