Best Data Analysis Tools for CFOs

CFOs are responsible for more than ever before. From overseeing their teams to providing essential decisions, there are a lot of moving pieces to consider on the daily job. With big data and technology, there exists more inputs and outputs to be scrutinised. To remain compliant and ahead of the curve, data analysis tools can help to make your life as a CFO easier. The analysis provided by these tools can help you to lower costs, increase efficiency, reduce compliance risk and monitor the market. 

By using automation and machine learning, these tools will transform your business for the better. As a CFO, you have to be concerned about the timeliness of information, accuracy of data, as well as security measures. Software systems and data analysis tools are there to tackle all of these concerns. 

But, how do you choose the tool that is right for your needs?

We will outline what to look for, as well as stack up some competitors to weigh their pros and cons. Every tool has its respective team available for support and questions. This guide is meant to serve as an introduction to some of the top-ranking tools in the marketplace. 

Best Data Analytics Process - What to Look For?

Data analysis tools rely on the outlined steps below to provide accurate information. As such, it’s essential to collect complete data that help to answer questions that work to achieve business goals. 

You will want to make sure that your chosen data analysis tool performs the below, or can connect with another tool to create a robust solution:

• Data collection: This is the first step in which you establish and collect the relevant information that will help to answer questions, test hypotheses, and evaluate results. 
  

• Data quality: There are different ways to assess the quality of data, but overall data quality refers to how well the data fit to serve its intended purpose. 
  

• Build model: To use data, you must set up a model, or collection of notations to describe data relationships and constraints. 
  

• Train model: To train the data and machine learning model, you define the target or correct answer. Then, the algorithm works to achieve your intended results based on patterns. 
  

• Run model: As the last step before analysis, you let the model run. 

The Best Data Analysis Tools 

Here’s a look at some of the best analysis tools, broken down by their benefits and downsides. 
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1. SolveXia 

SolveXia is human analytical automation software that centralises data and provides real-time analytics and data dashboards. It is made for everyone on the team to be able to use. It can store processes and automate models, and everyone in the organisation can use it for all data systems, including legacy systems. This helps to provide error-free data, maximise compliance, remove low-value manual tasks and avoid any bottlenecks within your business. 

Pros:

• Centrally and securely stores data 
• Connects with existing tool stack and can pull data from any format 
• Produces dashboards and reports automatically
• Uses AI to run models 
• Bank-grade cloud-based security for your information
• Provides audit trails with the full history of process runs
• Contains live and up to date documentation 
• 24/7 best-in-market support 
• Easy-to-use, setup and collaborative 
  

Cons: 

• Doesn’t offer RPA used for basic automation needs as SolveXia is a more advanced tool.
  

2. Zoho Analytics

Zoho Analytics is a data analytics tool that allows users to visualise data. It’s a self-service software and Business Intelligence platform. 

Pros: 

• Customizable visualisation - many different styles of charts, graphs, etc. 
• Ready-made connectors to utilise your existing tool stack and data 
• Uses artificial intelligence to help answer questions 
• Library of pre-built reports 
  

Cons: 

• Lack of collaboration within the app 
  

3. Sisense

Sisense is an analytics software designed to meet the needs of any business user. It has customisable dashboards and easy-to-use. 

Pros:

• Uses NLG (natural language detection) to detect patterns and trends
• Machine learning catches any errors in data 
• Multiple data visualisation widgets to choose from
  

Cons:

• Multiple models compete on a single server 
• Not highly flexible 
  

4. IBM Cognos Analytics

IBM Cognos Analytics is an AI business intelligence tool made to prepare, explore and share your data. 

Pros:

• AI-enabled and use of machine learning for pattern recognition
• Schedule reports and automatically send to chosen users 
• Data visualisation tools 
• Verify and combine data sources 
  

Cons: 

• Steep learning curve 
  

5. Sisense

Sisense provides data warehousing, mining, and storage solutions for businesses. It is a web-based system compatible with Windows and Mac. 

Pros: 

• Easy to use 
• Multiple visual dashboards to choose from 
• Use of SQL, R and Python
• Query revision and data history storage 
• Compatible with other software and tools 
  

Cons: 

• Must be built by the user using SQL
• Steep price point 
  

6. R-Programming

R is a programming language that is used for statistical analysis. Software automation tools use R, but you can also choose to set up analysis yourself using this coding language. 

Pros:

• Open-source 
• Effective at handling data and storing information
• Integrated tools for analysis
  

Cons: 

• Need technical know-how to deploy
  

7. RapidMiner

RapidMiner provides a visual UI to create models and explain data. 

Pros:

• Provides suggestions and automatically manages data loads based on previous activity
• Fast testing and deployment 
• Can be a managed service of self- serviced
• Runs on the cloud so accessible everywhere 
• Pay as you go pricing available 
  

Cons:

• Limited library of existing models 
• Cannot use coding to alter, must use programming interface 
  

8. KNIME

Knime gathers, merges, models and manages data. It is beneficial for operational activities and management. 

Pros:

• Can help predict operational anomalies before they happen (by measuring downtimes) 
• Optimises inventory levels
• Can detect customer satisfaction levels 
• Open-source solution 
  

Cons:

• No proper visualisation 
• Requires a level of tech savviness for best use 
  

9. Qlik

Qlik is an AI solution for data analytics and strategy. 

Pros:

• Easy to build visualisation models and dashboards
• Supports many data sources
• Drag and drop interface
  

Cons:

• Lacks full governance capabilities 
• Challenging to write your functions
• Connectors can be expensive 

The Importance of Data Analytics 

As a CFO, you probably spend a lot of time worrying about the performance of your and their responsibilities. It feels like there isn’t enough time to answer all the questions from the business and provide the best data for proper decision-making. You want data to be connected and useful, but that may seem to require too many resources. 

However, with the use of data automation software and data analysis tools, you can have all the answers at your fingertips all the time.

Data analytics provide a wide array of benefits for any business. They can help to:

• Improve operational efficiency 
• Implement business strategies
• Increase customer satisfaction 
• Decrease compliance risk 
• Assess the current market and opportunities 
  

These tools are designed to help businesses function optimally. For the most part, they are set up so that anyone can learn how to use them. While some are more user-friendly than others, data analysis tools will make your job as CFO easier to do. 

The visual dashboards and real-time analytics provide a 24/7 look at your organisation’s current standing. 

Conclusion 

The large amount of data that your business has amassed should not go unused, nor should it cause any overwhelm. Instead, it needs to be utilised for pulling essential insights for better decision-making with the usage of data analysis tools. 

Data automation and analytics tools like SolveXia work with algorithms to take raw data to a usable level. Finding the tool that is right for your business means outlining your current needs and ensuring the tool can scale with your growth. 

Data analytics is not a one-size-fits-all approach. You should talk to your team about their needs. You should also define your business goals to make sure that the tool you choose can help to answer your questions.