Analytics dashboard development
Developing analytical dashboards is essential for companies whose metrics are spread across CRM systems, advertising accounts, web analytics systems, spreadsheets, internal databases, and other sources. While data can be collected manually, this approach is time-consuming, increases the risk of errors, and complicates cross-departmental comparisons of results when reporting regularly.
Analytics dashboard development for business
We design dashboards tailored to specific business needs: we define the required KPIs, verify sources, configure data acquisition, calculate metrics, and assemble clear visualizations. As a result, the manager, marketer, sales team, or analyst receives a coordinated set of figures with filters, timeframes, and the required granularity. Analytical dashboard development can cover marketing, sales, finance, product analytics, and operational processes.
Creating a business dashboard begins with the question of what decisions the team should make based on the data. One company needs to see the return on investment from advertising channels, another needs to monitor sales by branch, and a third needs a comprehensive management dashboard with revenue, expenses, margins, and plan fulfillment.
Therefore, dashboard development involves working with data, calculation logic, and the structure of metrics. The dashboard should provide a clear answer to operational questions: where the result has changed, which channel is driving sales, at what stage conversion is declining, and how actual metrics differ from the plan.
What is an analytics dashboard?
An analytical dashboard is an interactive panel with key metrics, tables, graphs, filters, and other data visualization elements. Information is collected from connected sources and updated as frequently as permitted by the selected systems and project architecture.
The user can select a period, business line, channel, department, or other available dimension and immediately obtain the desired dataset. This format is convenient for regularly monitoring KPIs, analyzing trends, and identifying deviations. Creating an analytical dashboard is especially useful when working with multiple sources, when manually combining reports takes up a significant portion of the work time.
How is a dashboard different from a regular report?
A standard report typically captures the status of indicators over a specific period and is then shared with a manager or team. When the source data changes, it must be updated, recalculated, or regenerated. A dashboard works with a pre-configured data model and displays the current status after each source update.
Users can change the period, apply filters, drill down to more detailed data, and compare multiple segments without creating a new file. At the same time, the dashboard doesn't replace reporting: it reduces repetitive manual work and gives the team a unified view of consistent metrics.
What analytical dashboards do we develop?
BI dashboard development can encompass several analytical levels: from a compact dashboard for a single department to a system with multiple sources, roles, and related sections. Before starting a project, users, their tasks, metrics, and the required level of detail are defined.
In English-language projects, similar services may be referred to as analytics dashboard development or dashboard development services. Regardless of the name, the logic remains the same: first, business questions and data sources are defined, then metrics are calculated, and the visual component is designed.
BI dashboards
BI dashboard development is needed for tasks where data needs to be combined, calculated, and analyzed across multiple dimensions. A dashboard can include key metrics, trends, comparison tables, filters, and drill-down to the desired level.
The term "BI dashboard development services" is typically used for Business Intelligence projects in which visualization is linked to the data model and metric calculation rules. In complex architectures, sources, intermediate data processing, and user access logic are designed separately.
Business dashboards
Business dashboard development is aimed at owners, directors, and department heads. The main goal of such a dashboard is to organize regularly used metrics into a clear structure and reduce the time required to obtain management information.
In English, the term "business dashboard development" is used for such a project. The specific set of KPIs depends on the company: a single, universal dashboard is rarely suitable for businesses with different sales models, reporting systems, and internal processes.
Marketing dashboards
The marketing dashboard links data on traffic, advertising costs, requests, conversions, and, if integrated, sales. It's suitable for regularly monitoring advertising channels and comparing their results over similar periods.
Instead of transferring data from multiple accounts, marketers receive a consistent set of metrics and can more quickly identify changes in CPA, ROAS, lead volume, or revenue. The depth of this analysis is determined by the available data and attribution rules within the project.
Sales dashboards
The sales dashboard is designed around the CRM and the company's established sales funnel. It can include new inquiries, qualified leads, deals, conversions between stages, revenue, average order value, sales targets, and employee performance indicators.
A summary screen can be left for the executive, while managers can be provided with more detailed breakdowns. Access rights and information content are defined during the design process to ensure users see only the data they need to do their work.
Financial dashboards
A financial dashboard is built around agreed-upon financial indicators and periods. Before development, it's necessary to determine which data is considered primary and when revenues, expenses, payments, and other transactions are reflected.
Once the rules are agreed upon, you can build plan-vs-actual, trends, cost structure, margins, and other relevant metrics. Metric names should align with how company employees understand them; otherwise, visualization will create additional confusion.
Operational dashboards
Operational dashboards are used for metrics that need to be monitored regularly throughout the workflow. These may include order statuses, requests, department workloads, plan fulfillment, or other data available in the systems used.
The update frequency depends on the source and the technical capabilities of the integration. The term "real-time" should only be used when the architecture truly supports such data transfer without significant latency.
Custom dashboards for company processes
Custom dashboard development is required when a ready-made template doesn't accommodate the company's structure, non-standard KPIs, or custom calculation logic. This type of project allows for multiple access levels, customized filters, additional metrics, and connections between different sources.
Customizing a business dashboard also simplifies future analytics development. As a new channel, department, or metric emerges, the system can be expanded if the initial architecture allows for this.
I would start developing an analytical dashboard by identifying the decisions the business makes based on the data. Then you need to document the sources and formulas for KPIs, agree on metric names, and verify the underlying data. If two departments calculate the same metric using different rules, a pretty chart won't fix the problem. Therefore, for each important KPI, I recommend identifying the primary source and manually reconciling several control periods after launch.
Turnkey BI dashboard development
Turnkey dashboard development covers the entire process, from defining the task to verifying the completed metrics. A project shouldn't begin with just a list of required charts, as the same visualization may rely on completely different calculation logic and different source data.
First, the team determines who will use the system, what decisions are made based on the metrics, and where the initial values are sourced. Next, the data model, page structure, filters, and calculation rules are designed. Only then is the visual component assembled and tested.
Analyze business objectives
The first stage involves mapping dashboard users, their work tasks, and the metrics they regularly monitor. Existing reporting is also analyzed: which spreadsheets are currently in use, what data needs to be transferred manually, and where discrepancies arise.
This analysis helps eliminate metrics that are routinely included in reports but not used in decision-making. At the same time, the required granularity is determined: a manager might be satisfied with a general figure, while a specialist needs a breakdown by channel, manager, or product.
Check the sources and quality of the data
Before setting up visualization, it's important to understand where each metric comes from and how trustworthy the source data is. CRM systems, web analytics systems, advertising accounts, spreadsheets, APIs, databases, and other sources involved in the project are checked.
If a single indicator is stored in multiple systems, the primary source is identified in advance. Gaps, duplicates, date format differences, and other issues that could impact the final values are also checked.
We define a single source for each indicator
For each KPI, the system from which the source value is taken is specified. If revenue is calculated from the CRM, this source must be used uniformly in all related sections, unless the project stipulates otherwise.
This approach simplifies data verification and reduces the likelihood of two departments receiving different figures under the same name. Exceptions and reasons for using a different source are clearly documented when necessary.
Forming a dictionary of metrics
The metrics dictionary contains the definition of each key metric and its calculation rules. It allows you to define what constitutes a lead, a sale, an active user, revenue, a conversion, and other relevant entities.
The document is especially useful when working across multiple departments. When a metric's definition changes, the team understands which reports, formulas, and dashboards need to be reviewed after the change.
Design the structure and KPIs
After reviewing the initial data, the structure of the future dashboard is determined. Key screens, KPIs, filters, periods, segments, and the level of detail available to different users are agreed upon.
At this stage, it's also decided which metrics should be displayed together. Related metrics are easier to analyze in a single context: for example, advertising costs, leads, sales, and revenue for the same channel over the same period.
Configure integrations
Sources are connected in accordance with the chosen architecture and technical capabilities of the systems. Data can come from CRM, ERP, web analytics, advertising accounts, databases, data warehouses, spreadsheets, or via APIs.
Not every source has the same update frequency and the same set of fields. Therefore, integration capabilities are tested before the dashboard structure is approved, and limitations are taken into account when designing metrics.
Create a data model and calculated indicators
Once the source data has been obtained, it must be structured in a manner suitable for analysis. Identifiers, periods, sources, categories, and other fields used in calculations or filters are matched.
Next, KPI formulas and derived metrics are configured. Consistent logic is especially important here: the same metric should be calculated identically in all sections where it is used.
Designing visualization
The visualization type is selected based on the task. A numerical card is sufficient for a single indicator, dynamics are more easily visualized on a chart, a large set of objects can be compared in a table, and the sequence of stages can be displayed using a funnel.
The number of elements on the screen is limited by common sense and the user's needs. If reading the dashboard requires a long search for the desired indicator among dozens of graphs, the structure should be reconsidered.
Testing data and formulas
The completed dashboard is tested against the source systems. Formulas, filters, periods, access rights, time zones, and the logic for combining data from multiple sources are tested.
For control periods, it's useful to manually reconcile key indicators. This check helps identify errors in calculations, filters, or source data before the dashboard becomes a permanent source of management reporting.
Launch the dashboard and hand over the documentation
After verification, the client receives a finished dashboard with a consistent structure, connected sources, filters, and calculated metrics. It's advisable to store a description of the metrics and their calculation rules along with the system to ensure the analytics remain understandable even after changes within the team.
If a new source, section, or metric needs to be added later, the changes are made taking into account the existing data model. This process helps the dashboard evolve without creating multiple incompatible versions of the same metric.
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Dashboard development timelines
The time frame depends on the number and quality of sources, the complexity of calculations, API availability, and the volume of approvals. A panel with a single, pre-built source typically requires significantly less work than a system that integrates advertising, a website, CRM, financial data, and a proprietary database.
Before estimating the timeframe, access rights and the structure of the source systems are verified. If some required fields are missing or the data requires additional preparation, this is taken into account before the main development begins.
For dashboard development, it makes sense to first define the minimum set of metrics needed for the first production release. The remaining sections can be added sequentially if the project architecture is designed for expansion.
Cost of developing an analytical dashboard
The cost is calculated after analyzing the task, as two seemingly similar dashboards can differ significantly in the scope of work. The price is affected by the number of sources, data status, number of screens and KPIs, formula complexity, the need for intermediate storage, and update frequency requirements.
User roles, custom integrations, volume of data preparation, and visualization complexity are also taken into account. Therefore, dashboard development services are best evaluated after a brief technical review, rather than solely based on the number of charts in the future dashboard.
What is included in the price?
The project may include task analysis, source data validation, structure design, integration setup, data model creation, metric calculation, visualization, and testing. A precise list of tasks is determined before work begins.
If a project requires additional storage, non-standard processing, or complex API integration, these tasks are factored in separately during the estimate. This approach provides a clear project scope and reduces the risk of unplanned tasks emerging after development begins.
Why should you trust Seo-Gen with your dashboard development?
Seo-Gen views dashboard development as a task at the intersection of analytics, integrations, and business logic. First, we analyze the sources and definitions of metrics, then we design the structure, and only then does the visual component come together.
This order is especially important for projects where marketing, sales, and other departments use common metrics. Business dashboards are built around consistent data to ensure that the same KPIs don't change value across sections.
We also consider the project's future development. If the company requires a new source, additional screen, or a different level of detail, the changes should be integrated into the existing model, rather than creating parallel reporting with separate rules.
Answers to your questions
What is an analytics dashboard?
The analytical dashboard displays selected business metrics in a single interface and retrieves data from pre-connected sources. It can include numeric KPIs, tables, graphs, funnels, filters, and other visualization elements.
The user selects the desired period or segment and receives the corresponding data sample without preparing a separate report. The dashboard's composition is determined by the company's objectives, so there is no universal set of metrics for all projects.
How much does it cost to develop an analytics dashboard?
The price depends on the number of sources, integration complexity, source data quality, number of KPIs, screens, and user roles. The need to create a data warehouse, calculate custom metrics, or process data before uploading also impacts the estimate.
Therefore, the cost is determined after analyzing the task and available systems. This calculation is more accurate than a fixed price for a single dashboard, as the amount of technical work can vary significantly with the same number of visual elements.
How long does it take to create a business dashboard?
The timeline is determined after reviewing the sources and dashboard requirements. If the data is already prepared and accessible via a standard connection, the project is completed more quickly than if it requires linking multiple systems and pre-processing the data structure.
The timeframe is also affected by KPI approval, formula complexity, the number of screens, and the review process. It's best to plan the creation of a business dashboard in stages, starting with the metrics the team needs for regular work.
What data can be connected to a BI dashboard?
Sources may include CRM, ERP, website, web analytics, advertising systems, databases, data warehouses, spreadsheets, and services with an accessible API. Specific connectivity is verified for each system separately.
After connecting, you need to define fields, periods, and rules for linking between sources. If data from multiple systems is used to calculate a single metric, additional checks are made to ensure the mapping is correct and there are no duplicates.
How does a custom dashboard differ from a ready-made template?
The template uses a predefined structure, metrics, and set of sources. It's suitable when the company's requirements are close to the original scenario and no complex custom logic is required.
A custom development takes into account the internal statuses, KPIs, roles, filters, and calculation rules of a specific business. This option is chosen when the standard dashboard doesn't reflect real processes or requires too many workarounds.
Is it possible to combine marketing and sales in one dashboard?
Yes, if the available data allows us to link advertising sources, requests, and deals. For example, costs can be obtained from advertising systems, user behavior from web analytics, and sales information from a CRM.
To achieve accurate results, it's necessary to define data matching rules and an attribution model. Without these, the same lead or sale may be counted multiple times or attributed to the wrong advertising source.
How often is the data in the dashboard updated?
The frequency depends on the source, API, chosen architecture, and project requirements. Some systems can transmit data quite frequently, while others update it with a delay or impose limits on the number of requests.
Therefore, the frequency is fixed after each connection is tested. The term "real-time" should only be used for sources that technically support data transmission at the required speed.
Is it possible to refine the dashboard after launch?
Yes, if the architecture allows for expansion. New metrics, sources, pages, filters, or user roles can be added to the dashboard after agreeing on how the changes will impact the existing model.
When refining, the compatibility of new data with current metrics is first checked. This helps maintain consistent calculation rules and avoid creating separate versions of already used metrics.
An analytical dashboard is useful when a company needs to regularly work with metrics from multiple sources and view them according to uniform rules. The quality of such a system depends on the source data, consistent metrics, proper integrations, and calculation verification before launch.
To begin developing analytical dashboards, provide Seo-Gen with a list of tasks, users, and systems where the data currently resides. We will determine the necessary structure, integrations, and scope of work to launch a dashboard tailored to your business needs.
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More on: Analytics dashboard development
What tasks does a business dashboard solve?
Developing business dashboards makes sense where data is regularly used for management. The set of metrics depends on the user's role and the task itself. A manager needs overall results, a marketer cares about expenses and acquisition costs, a sales team focuses on deals and funnel conversion, and a finance professional needs to monitor revenue, expenses, and margins.
For a single company, several linked dashboards can be developed with a common data model. Each department then receives its own level of detail, and key metrics are calculated using the same rules. This approach reduces disputes over different versions of the same figure and simplifies regular monitoring of results.
Marketing and advertising
The marketing dashboard can collect costs, impressions, clicks, inquiries, leads, conversions, CPA, CPL, CAC, ROAS, ROMI, and other metrics used by the company. Data can be broken down by advertising systems, campaigns, regions, devices, time periods, and other available parameters.
The dashboard helps compare advertising channels based on results, not just clicks. With sales data, marketing expenses can be compared with revenue and deals. This is especially useful when advertising metrics are stored in ad accounts, requests are recorded on the website, and final sales are stored in a CRM.
Sales
Sales departments typically analyze the number of new inquiries, deals, revenue, average order value, plan fulfillment, manager performance, and customer progression through the funnel. The specific metrics depend on the CRM structure and the rules by which the company records results.
A business dashboard can show conversion rates between stages, deal duration, sales distribution among employees, and dynamics by period. Managers get an overall picture and, if necessary, drill down to a specific department, manager, product, or other available dimension.
Finance
Financial analytics can include revenue, expenses, profit, margins, targets, and trends across business lines. If data resides in multiple systems, the sources and calculation rules for each indicator must be agreed upon before visualization.
Particular care must be taken with metrics that are calculated differently by different departments. For example, revenue may be recorded based on payment, shipment, or deal closure. This definition must be agreed upon before charting, otherwise, identically labeled metrics may show different values.
Management and operations
A management dashboard typically contains a limited set of key KPIs that the manager regularly reviews. These may include sales, revenue, expenses, plan fulfillment, conversions, departmental metrics, and performance relative to previous periods.
From the general dashboard, you can drill down to more detailed analytics if the project architecture allows for drill-down. The manager sees the change in the metric, and the responsible employee receives a breakdown that helps identify the specific channel, department, or process that influenced the result.
E-commerce and product analytics
For online stores and product-based projects, you can analyze sales, categories, products, average order value, revenue, margins, inventory, advertising costs, and landing page performance. The dashboard's composition is determined by the availability of data in accounting, advertising, and analytics systems.
The product dashboard is especially useful for large product ranges, where overall sales trends obscure differences between categories. Filters help you view products, brands, categories, periods, and other parameters already present in the source system separately.
What data sources can the dashboard be integrated with?
The composition of integrations depends on the systems already used by the company. Sources may include the website, CRM, ERP, advertising accounts, web analytics systems, databases, data warehouses, spreadsheets, and APIs. Before development, the availability of the required fields and the technical limitations of each connection are verified.
Marketing projects often use data from GA4 and advertising systems, while sales projects rely primarily on CRM, and more complex business analytics may require SQL databases or BigQuery. The specific stack is selected after analyzing the task. Power BI, Tableau, Looker Studio, Grafana, and other BI systems are considered only if they fit the project's architecture and requirements.
| Source | What data can be used? |
|---|---|
| Web analytics | traffic, sources, events, conversions, user behavior |
| CRM | leads, deals, funnel stages, managers, sales, revenue |
| Advertising systems | costs, impressions, clicks, campaigns, inquiries, and conversions |
| ERP and internal systems | products, transactions, finances, stock levels and other business data |
| Databases and data warehouses | prepared tables, historical data, calculated indicators |
| API and spreadsheets | additional data from systems for which information transfer is available |
After connecting the sources, the data must be compared. Integration alone does not guarantee accurate results: link keys, periods, filtering rules, and formulas for calculating the final indicators must be defined.
What metrics should be included in an analytics dashboard?
The set of KPIs is determined by the specific user's tasks. A generic list quickly turns the dashboard into an overloaded screen, so each metric should have a clear purpose and be used for regular analysis.
For marketing, these might include CPA, CPL, CAC, ROAS, ROMI, traffic, leads, and conversions. For sales, deals, average order value, revenue, and conversion rate are more often relevant. Finance might include revenue, expenses, profit, and margins, while product analytics might include user activity, events, and retention.
| Direction | Examples of indicators |
|---|---|
| Marketing | Costs, CPA, CPL, CAC, ROAS, ROMI, leads, conversions |
| Sales | deals, revenue, average order value, plan-actual, funnel conversion |
| Finance | revenue, expenses, profit, margins |
| E-commerce | products, categories, sales, stock levels, average order value |
| Product | Active users, events, retention, funnel progress |
If a metric can't be clearly defined or verified against the original source, it shouldn't be added just to fill a dashboard. First, agree on the formula and business rationale, then incorporate the metric into regular reporting.
How do we ensure data reliability?
The quality of an analytical dashboard depends on the quality of the source data and the rules for processing it. A filter error, an incorrect time zone, or different definitions of the same indicator can change the final figure even with a correct visualization.
Therefore, creating an analytical dashboard involves verifying sources, formulas, and control periods. For key metrics, the calculation logic is recorded, and the adjusted values are compared with the original system.
Basic checks include:
- a single source for each agreed indicator and a clear rule for data selection;
- a dictionary of metrics with definitions of terms used in different departments of the company;
- correct operation of periods, filters, segmentation and the selected time zone;
- checking attribution and links between sources if multiple systems are involved in the same metric;
- manual reconciliation of several control periods before using the panel in regular reporting.
After verification, the results are recorded so that, when changes are made later, it is clear where the logic has shifted. This process is especially necessary for dashboards used by management to evaluate sales, marketing, or finance.
Benefits of Custom Dashboard Development
Ready-made templates are suitable when sources and metrics fully align with the intended scenario. With a non-standard business structure, it's necessary to consider specific CRM statuses, sales rules, financial metrics, employee roles, and other internal details.
Custom analytical dashboard development allows you to connect analytics with your company's real-world processes. Turnkey dashboard development also includes data validation, KPI calculation, integration, and testing, so the result is more than just a ready-made set of charts.
Practical benefits of this approach:
- key indicators are collected in one interface and calculated according to agreed rules;
- regular reports do not require constant manual transfer of identical data between files;
- employees receive different levels of detail according to their tasks and rights;
- filters help you compare periods, channels, divisions, products and other available segments;
- the architecture can be designed taking into account new sources and metrics that will appear after launch.
For international projects, this service is also known as custom dashboard development. The choice between a ready-made template and custom development depends on the complexity of the data, the number of sources, and the business logic requirements.
What does the client receive after development?
Upon project completion, the client receives a working dashboard with agreed-upon KPIs, connected sources, and configured calculation rules. Users can work with available filters, periods, and dimensions without having to manually compile identical reports on a regular basis.
Depending on the agreed scope of work, the project may also include a description of sources, a metrics dictionary, calculation documentation, and rules for future system modifications. This package simplifies support and helps new employees quickly understand the analytics logic.
The project outcome may include:
- a ready-made structure of analytical dashboards for agreed user roles;
- integration of the planned CRM, advertising, analytics, and internal sources;
- automatic updating of data at technically feasible intervals;
- configured KPIs, formulas, filters, segments and levels of detail;
- verified control values and description of the logic of key performance indicators.
Before handing over the dashboard, key metrics are verified against the source systems. Afterwards, the dashboard can be used for regular work and further developed as new challenges arise.
Order the development of an analytical dashboard
To evaluate a project, you need to define the future users' tasks and list the systems where the source data currently resides. This will help you understand what integrations will be required, what KPIs are available, and how much preparation is needed before visualization.
Analytics dashboard development begins with an analysis of these inputs. If a turnkey dashboard is required, the project can include a process from source verification and data model design to testing the completed dashboard and handing over the approved documentation.
For English-language projects, the service may be referred to as analytics dashboard development, business dashboard development, or BI dashboard development services. On this page, the primary focus remains on developing BI dashboards and creating analytical dashboards for specific business needs.