Unlock the Power of Financial Data Analytics for Your Business

Any data reveals its full potential when analyzed. It’s like IKEA furniture – while pieces are sole-standing, they make no value, but once assembled, they serve people for decades.

As for financial data, it is of particular value to all companies, especially large corporations and banks. Analyzing this data contributes to smart recommendations for increasing company profitability or investments. However, it’s necessary to collect and organize financial data first using the right software described in this article.

Table of Contents

1. What Is Financial Data Analytics
2. Comparing Financial Data Analytics Software
3. Skyvia’s Role in Financial Data Analysis
4. Choosing the Right Data Analytics Tool
5. Conclusion
6. FAQ

What Is Financial Data Analytics

The primary purpose of financial data analytics is to help organizations gain insights from their financial data. It also promotes informed investment decisions, helps in financial planning and budget allocation, advances risk management, and assures regulatory compliance.

Day-to-day financial data analysis typically involves the following processes:

1. Data collection. It’s possible to perform high-quality analysis if enough data is available. Analysts gather data from internal company sources and external resources.
2. Research. Companies need to understand what’s going on outside their boundaries. It’s worth looking into macroeconomic trends and exploring industry changes.
3. Data organization. Data for financial analysis is often unorganized and unstructured. Analysts use data cleansing tools and programming languages like Python and C++ to organize data.
4. Forecasting. Financial professionals use historical data to predict what might happen in the future.

5. Building financial models. Typical financial models are the discounted cash flow model, leverage buyout model, mergers and acquisition model, etc. These models are intensively used in investment banking or corporate finance.
6. Analyzing financial results. This is the core activity defining such metrics as gross profit margin, net profit margin, cost-to-sales ratio, and others.
7. Providing recommendations. Everything a financial analyst has done until now serves as a base for recommendations for investors or executives. At this point, it’s possible to suggest reducing costs, tapping into new markets, increasing profits, etc.
8. Presenting results. Finance professionals use numbers and jargon that not everyone can understand. That’s why it’s crucial to use BI and visualization tools to present findings in an easy-to-understand manner by creating charts and graphs.

Benefits and Limitations of Financial Data Analysis

Comparing Financial Data Analytics Software

A tool kit needed for financial analysts depends on the industry and the company’s size. Here, we present software products dedicated particularly to gathering, processing, and elaborating on financial data that can be classified into different categories depending on the access type, data processing speed, and purpose for analysis.

On-Premise vs. Cloud-Based Solutions

Each business has its own data stack, which is based either in the cloud, on-premises or in a hybrid environment. Therefore, many modern data analysis tools are available in both online and offline versions to fit different businesses. Power BI, Tableau, Excel, and other solutions can be installed as desktop apps as well as accessed on the web. 

Note that online and offline versions slightly differ in functionality. Here are some major distinctions between them:

• Since desktop apps are older, they have a more ample set of features. For instance, the Excel desktop app has macros, Power Query, and Power Pivot, which aren’t available on the online version.
• Online apps usually have limited data management, cleansing, and modeling options.
• Desktop apps have limited sharing capabilities, which deprives teams of report sharing. Meanwhile, web-based tools are excellent for collaboration.

At the same time, there are plenty of BI tools available exclusively online. For instance, Looker Studio, Domo, and Qlik Sense can be accessed only from your browser and can’t be installed as desktop apps.

Real-Time Analytics vs. Batch Processing Solutions

BI tools mentioned above can collect data either using built-in connectors or with data integration tools. In any case, they rely on batch processing, where data is gathered and transferred over time intervals.

Real-time analytics implies instant processing and analysis of new data as soon as it becomes available. This is critical for transaction management, IoT monitoring, and other instances requiring instant insights.

Here are some tools suitable for real-time data collection, processing, and analysis:

• Apache Hadoop is an open-source framework designed to operate large amounts of data. It’s also very flexible since it supports both structured and unstructured data of any format.
• Apache Kafka is a distributed stream-processing platform used for data integration, real-time analytics, and mission-critical applications. Its primary objective is to provide a high-throughput and low-latency platform for handling real-time data needs.
• Amazon Kinesis is a service for collecting, processing, and analyzing real-time streaming data. It works well with user behavior monitoring and fraud identification. This platform can also capture gigabytes of data per second and perform predictive analytics operations.
• Altair RapidMiner is an open-source, cloud-based application for streaming data analytics. It also embeds machine learning models used for predictive analytics.

Financial data analytics usually relies on applications that support real-time streaming analytics. BI tools with batch data processing would be sufficient for generating ad-hoc reports.

Prescriptive vs. Predictive vs. Descriptive Analytics Tools

We have already touched upon predictive analytics above. Now, let’s explore different types of analytics and tools for them.

Skyvia’s Role in Financial Data Analysis

You already know that analysts have to collect and structure data as a part of their daily workflows. However, they often face challenges since data is dispersed across different sources and is of different formats. Therefore, it’s necessary to consolidate data in a centralized location and make it ready for analysis. This process could be automated by using dedicated data integration tools like Skyvia.
Skyvia is a universal cloud data platform designed for a wide set of data-related tasks. It contains five principal products: Data Integration, Query, Connect, Automation, and Backup.

The Data Integration product, in particular, has everything needed to gather and transform data, making it analysis-ready. Data Integration comprises the following tools:

1. Import is the ETL-based tool for migrating data between cloud applications, databases, and data warehouses. It allows users to apply transformations on data, like lookup and expression, and perform mapping to match data structures.
2. Export extracts data from cloud apps into CSV files that can be saved on a computer or cloud storage.
3. Replication is the ELT-based tool for copying data from cloud apps into data warehouses and databases. It uses the CDC approach to capture data differences when making incremental updates on schedule.
4. Synchronization performs the bi-directional data sync between two sources. It also uses the CDC to identify which data was changed in one source and make the appropriate changes in another.
5. Data Flow allows users to build more complex data pipelines, including several sources, and apply multistage data transformations. This tool also supports error processing logic.
6. Control Flow creates logic for task execution, performs preparatory and post-integration activities, and configures automatic error processing logic.

Take a look at how a Skyvia user has successfully set up the Salesforce to QuickBooks integration, which streamlined their financial reporting.

Overall, Skyvia could be a good choice to assist you with financial data analysis because it:

• Has an extremely understandable user interface, allowing even non-tech professionals to set up complex integration scenarios.
• Connects to more than 200+ sources, including databases, data warehouses, apps, storage services, etc.
• Grants web-based access via a web browser.
• Covers a wider range of scenarios for collecting and processing financial data.
• Offers powerful data transformation and organization capabilities.
• Provides different pricing plans suitable for various companies.

Choosing the Right Data Analytics Tool

To select the proper tool for analyzing financial data in your organization, it makes sense to consider a number of factors.

1. Business goals and data needs. Companies that aim to get instant insights about financial performance should implement real-time analytics tools. If occasional and non-urgent reports are fine, then batch-processing tools would work fine. 
2. Scalability and flexibility. Explore whether a tool of interest has limits on the data volumes. See how these numbers correlate with your expected data load, and decide whether this tool is flexible and scalable enough for you.
3. Ease of use. Pay attention to the user interface and the overall complexity of the analytics service. If it’s too complex, be prepared to spend some time mastering your skills.
4. Integration with other finance data sources. See how the analytics tool connects to other sources where your financial data is stored. Consider using data integration tools that support both the analytics service of your interest and apps containing financial data.
5. Cost and budget. Decide whether a specific analytics tool aligns with your budget. Pricing details are provided on each tool’s official website.
6. Advanced analytics and machine learning. If you need these functions, check whether the chosen solution has them.
7. Security and compliance. Make sure that an analytics tool corresponds to modern security standards and protocols, which is particularly crucial when working with financial data.
8. Community and support. Check whether the preferred solution provides customer support and community forums. This might be helpful at the implementation stage and further stages of the product use.

Conclusion

Financial data is a diamond in the rough that needs to be well-refined to become a high-cost gem on the shop front. To convert it from the raw state to valuable information, the process of collection, cleansing, structuring, and analysis takes place. So, basically, you will need data preparation and analysis tools.

The choice of software for data analysis depends on your business needs and resources. You may select from online or offline solutions, batch processing or real-time services, and so on. Despite the analytics tools chosen, it’s worth using data integration tools like Skyvia to aggregate your financial data and prepare it for analysis.

FAQ for Financial Data Analytics

What is the approximate cost of the toolkit for financial data analysis?

If you decide to select Skyvia for gathering, processing, and consolidating financial sources, it might cost you $0 up to a certain amount of data. Other spending depends on the chosen BI and analytics tools. Overall, you can operate financial data at zero cost, but it the spending will certainly grow as you need more data to be processed and additional features to use.

What is the difference between financial data analytics and data analytics?

Data analysis is an umbrella term used across various sectors of the economy. Financial data analysis is focused on invoices, payments, budgeting, and other operations related to corporate finance. It helps businesses make informed decisions and develop weighted investment strategies.

What are the data sources for financial analysis?

There are many different sources of financial data, though there are several most important ones. Those are cash flow statements, income statements, and a balance sheet.