Why is Data Analytics the Top Priority for Finance

Why is Data Analytics the Top Priority for Finance

In the age of information business performance is only as good as the ability to process data. This begins with harvesting big data or large, complex collections of raw information about business activities, customers and stakeholders. The large data lakes must then be combed through in order to obtain meaningful insights that can help businesses make more meaningful decisions.  Data Analytics 

Six out of ten leaders within the finance sector have identified data science and analytics to be a top priority for the upcoming year. To understand the importance we begin by understanding what data analytics is; 

Data Analytics? 

Data analytics are the scientific processes of taking raw data and turning it into meaningful information that can be used to optimize business performance. The broad term includes a number of different data analytics techniques used to uncover industry trends and useful metrics. This information would easily be concealed and forgotten within the sheer volume of data and information transmitted across business processes.  

There are four main types of data analytics; 

  1. Descriptive – Helps businesses understand events or activities conducted over a period of time allowing for comparison and contrasting. 
  2. Diagnostic – Helps businesses understand causation of an activity. This includes why sudden boosts in sales occur or mapping the reason undesirable results were produced. 
  3. Predictive – Helps businesses plan for the future by understanding what is likely to happen short term and long term.  
  4. Prescriptive – Helps businesses craft a blueprint to move business forward while considering industry conditions and standards. This helps create a practical method to move forward. 

All four types of data analytics are imperative to building a strong business strategy that minimizes inefficiencies, unnecessary costs, controls quality and leads to better products, services and well-rounded customer satisfaction. 

Data analytics is also helpful to understand  new market trends. As the transition into different methods or tools occurs, the information is relayed to the business allowing them to adjust and adapt to stay market competitive. 

Data Analytics and the Financial Sector 

There are three areas of focus around data as per today’s financial institutions and service providers; 

  • Data helps the finance sector understand their customer and the requirements that must be fulfilled. 
  • Data is critical to maintaining security protocols and compliance measures. 
  • Data is imperative to improving overall business efficiency and effectiveness around demand fulfilment.  

Data analytics in the financial sector help process large amounts of structured and unstructured information to produce valuable insights to guide the decision-making process. Processing data in a field as structured as the Finance sector requires precision, the data analytics process minimizes human error allowing for more accurate data models and pinpoint decision making. 

Data analytics also provides financial teams detailed information around KPIs. This allows them to identify areas of weakness and strength and build plans of attack accordingly. In turn, this helps the business refine their internal course of action creating optimized output. 

Another critical point of application for data analytics in finance is to equip teams with parameter detection and metrics for possibilities of fraud or security breaches. This information is used by upper management to make key decisions around prevention and management. Data analysis can be a handy tool to detect chinks in business operation and introduce proactive and problem solving measures before situations spiral out of control. 

Data analytics offers the finance sector the ability to filter through copious amounts of business critical information with a trained eye. The results data analytics provides are accurate and are the key to understanding why competitors are operating as they are and how key players within the finance sector are able to secure market share.  

Ensuring data quality is strongly dependent on the method used to ascertain the meaningful information. A growing number of  data analytics companies keeps competition healthy and, as a result, minimizes the risk around undesirable or inaccurate results. 

 Data analytics services can also be applied to financial organizations of any size. It is strongly advised to introduce a system for data filtering and management within the overall business strategy. This allows the organization to build optimization plans quickly and assist employees with understanding the importance of data analytics within the industry. 

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