When Does Your Company Need a Data Analyst?

By Okun Data · March 23, 2026 · 7 min read

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As a company grows, so does the volume of data it generates: more customers, more transactions, more systems, more departments. But data growth does not always come with the capacity to make sense of it. At some point along the way, the question arises: do we need someone dedicated specifically to data? This guide helps you identify that moment and understand what your options are.

The Signs That Your Company Needs Help With Data

It is not always obvious when the right moment has arrived. But there are concrete signals that indicate the problem is already affecting operations and decision-making:

• Reports take days to prepare. If getting last month's sales figures requires someone to spend two or three days consolidating Excel spreadsheets, filtering data, and building charts, there is an efficiency problem that can be solved with tools like Power BI and a properly structured data process.
• Nobody knows which number is "correct." In leadership or team meetings, discrepancies arise between the figures each department is working with. Sales says revenue was X, Finance has Y, and Operations is working with Z. This phenomenon — the absence of a single source of truth — is a clear signal that data is neither centralized nor governed.
• There are multiple versions of the same report. Every manager or team lead maintains their own version of the monthly report, with their own criteria and formulas. Cross-departmental comparisons are impossible, and every meeting begins with 20 minutes of reconciling numbers.
• Decisions are made on gut feeling, not data. When launching a product, opening a new location, or adjusting pricing is driven primarily by experience or intuition — without quantitative analysis — the company is operating blind. This is not a problem when the data simply does not exist. It is a problem when the data exists but is not being used.
• The team spends more time building spreadsheets than analyzing them. When analysts or department managers spend most of their time assembling and cleaning data rather than reading and interpreting results, there is a data infrastructure problem that directly impacts productivity and the quality of analysis.
• Data is scattered across systems that do not talk to each other. The CRM does not connect to the ERP, the billing system is standalone, and marketing metrics live in a separate tool. Nobody has a complete view of the business because there is no integration point. This is precisely what a Business Intelligence platform like Power BI solves — connecting all these sources and displaying cross-filtered, interactive data in one place.

Data Analyst vs Data Scientist vs Data Engineer

Before deciding which profile to bring in, it is important to understand what each one does. These are distinct roles with very different skills and focuses:

Data Analyst

A data analyst works with existing data to answer business questions: how much did we sell? which product is most profitable? which region saw a performance drop? They are proficient in advanced Excel, SQL, and especially Power BI, where they can build dashboards with cross-filters that allow exploring data from multiple dimensions simultaneously. This is the most in-demand profile for growing mid-sized businesses and the one with the highest immediate operational impact.

Data Scientist

A data scientist goes beyond descriptive analysis: they build predictive models and apply machine learning techniques to anticipate future behaviors. Which customers are most likely to churn? How much will we sell next quarter? They are proficient in Python, R, advanced statistics, and machine learning frameworks. This is a higher-cost, more specialized profile, and their value multiplies when the company already has clean, well-structured data to work with.

Data Engineer

A data engineer builds and maintains the infrastructure that makes analysis possible: data pipelines, ETL processes (extract, transform, load), databases, and cloud data warehouses. Without a data engineer, data does not arrive clean or on time to the analyst or data scientist. This is the most technically oriented of the three roles.

Which one do you need first? For most mid-sized companies, the first profile that makes sense to bring on is the data analyst. They can clean and consolidate existing sources, build useful Power BI dashboards, and generate visible value from the first month. A data scientist and data engineer make sense in a second stage, when data volume and analytical maturity justify it.

Hire In-House or Work With a Consulting Firm?

This is one of the most frequent decisions companies face when they reach this point. There is no universal answer, but there are clear criteria for each option:

When hiring in-house makes sense

Hiring a full-time data analyst makes sense when there is a consistent, sustained volume of analytical work, when the budget covers not only the salary but also onboarding time (which can take one to three months before the person generates real value), and when the company has a clear long-term data strategy in place.

When working with a consulting firm makes sense

A specialized consulting firm offers more flexibility, access to a team with multiple specializations (analysts, engineers, Power BI specialists, automation experts), faster time to value than a hiring process, and is ideal for specific projects or companies exploring their analytical capabilities for the first time. It also allows scaling the engagement up or down based on current needs — something that is very difficult to do with permanent employees.

At Okun Data, we are direct about this: we recommend what best fits each situation. If a company has the scale and volume to justify an in-house team, we say so. If the context makes working with a consulting firm more efficient, we say that too.

What a Data Analyst Can Do for Your Company

Beyond the concepts, here are the concrete results that a well-oriented data analyst can deliver:

• Centralize and clean data sources: Consolidate data from different systems — CRM, ERP, spreadsheets, marketing tools — into a single, clean, reliable data model. This eliminates discrepancies between reports and establishes a single source of truth.
• Build Power BI dashboards for each department: Sales dashboards with cross-filters by region, product, salesperson, and time period. Financial dashboards with margins, costs, and projections. Operations reports with efficiency KPIs. Each department with its own view, all fed by the same reliable database.
• Automate periodic reports: Eliminate the manual generation of weekly or monthly reports by combining Power BI with Power Automate so that reports update and distribute themselves. Read more in our article on how to reduce reporting time.
• Identify optimization opportunities: With data organized and visualized, it becomes far easier to spot inefficiencies: underperforming products, customers with high service cost and low margin, processes with bottlenecks. Analysis turns data into decisions.
• Prepare the company for machine learning: Data science requires clean, well-structured historical data. A data analyst builds that foundation, enabling the next stage of analytical maturity when the business is ready.

When Does Machine Learning Make Sense?

Machine learning is a powerful tool, but it is not the first step for most companies. It makes sense to consider it when these conditions are met:

• The company already has clean, well-structured historical data — generally one to three years of history depending on the type of analysis.
• Data volumes are large enough for statistical patterns to be meaningful.
• The goal is to predict future behaviors: demand, customer churn, payment default probability, stock-out risk.
• The company already has basic analytical maturity and its teams can interpret and act on predictions.

If your company is evaluating whether it is ready to take that step, we recommend reading our article on machine learning for business, where we explain in detail when and how to implement it.

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