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Business intelligence in finance in 2023: a path to value
Explore the hidden talents of BI to understand how the phenomenon can help tap into your business better
Financial organizations are awash in terabytes of data pouring in from spreadsheets, invoices, journal entries, and, well, you name it. At first glance, such an abundance of data might seem like a goldmine. And while it is, things are more complex.
Raw data has value once you unlock the hidden insights. But unfortunately, extracting value from the data can be complicated with privacy issues and various data formats distributed across numerous locations. No wonder, as Inc.com states, up to 73% of companies’ data is left unused for analytics. So, how can you put your data to work?
Using the right tech will save your bottom line, and that’s where business intelligence software comes into play. In this article, you’ll discover the role of business intelligence in finance departments and how you can capitalize fully on it.
Big wins with business intelligence in finance
Business Intelligence (BI) relates to organizations’ practices and technologies to collect, process, and present the extracted value from existing information. Simply put, it’s all about converting data into an understanding of what it holds for better business decision-making with the help of visualizations, reporting, predictive analytics, and other features. Business Intelligence software forms the background for financial analytics and helps answer the Why questions based on factual information. When BI and analytics are used in a combo, businesses see the journey from the present moment of information toward data management tools, predictions, and future decisions.
It is specifically crucial for the finance industry as business intelligence tools and analytics are the instruments that help us to see reality. Though BI serves its specific mission for every financial services organization, and the tooling differs from wealth management to investment to insurance to banking, there are some universal advantages of using business intelligence solutions (BI) and analytics in finance.
BI dealing with risks mitigation
Managing uncertainty related to threats has always been a top priority for the financial services industry. Managing risks faced by the specialized nature of finance takes an integrated approach involving technologies embedded into everyday business operations. Business Intelligence tools are one of the numerous solutions highlighting accurate information that allows you to act on the data of a company’s reputation score, security issues, regulatory requirements changes and compliance, customer behaviors, etc.
For example, tracking customer behavior can prevent fraudulent activities, while tracing employee behavior can effectively ensure regulatory compliance and address potential insider threats. The organization has a fuller picture of its credit portfolio analytics by complementing the existing data with additional economic context information. Marketing and sales teams can distinctly see any possible weak or strong points.
BI for operations & performance management
Money has never been a manageable undertaking, even more so today when financial services providers must synchronize the organization to build resilient operations. It is not a one-person ship, and it takes overall organizational efficiency. BI tools enable the system’s constant communication of transactional information between users, unified data sharing, and automated manual reporting through clear dashboards with KPI (key performance indicators) and metrics correlations.
A well-recognized benefit of BI tools is performance management capabilities. The data you have in your system can identify every point of your business performance at every level in one single score, including operational procedures, teams’ productivity, customer management patterns, technology efficiency, etc. This allows for a clear organization performance health check and outlines the effectiveness of every operational procedure.
For example, by analyzing the performance of customer-facing employees (tellers, customer support staff, etc.), you can enhance the customer experience at the point of contact while staying lean and efficient.
BI to improve financial products and services
Money management has become a commodity of the connected, personalized world. With BFSI (banking, financial services, and insurance) services generating substantial data volumes, it is vital to make sense of the data quickly and in a targeted manner. The potential of BI technology is Data on Demand, which has actual numbers in front of you, helping you see where the business is, identify value drivers alongside growth opportunities, and then monitor financial/non-financial KPIs against those.
A well-put BI solution makes real-time financial data handling fast and to the point. In addition, it allows for analyzing the correlation between investments and profitability across multiple dimensions of the financial organization (products, customers, services, channels) to further strategize on valuation or growth optimization. As a result, financial organizations have solid proof for future go-to-market strategies and better overall financial services.
BI to understand customers & business partners
The era of customer loyalty to the financial services industry per se is over, and the customer experience is becoming a new benchmark for financial institutions. Your customers and partners have their expectations, but do you know what they are? BI for customer experience in financials is about stopping the guesswork in the first place.
By using the data, you already have, like customer profiling, behaviors, sentiments, patterns, etc., BI applications allow financial service providers to extract pragmatic insights into what your customers genuinely expect from your services. Whether it is more flexible loan offers, simplified financial models, or transparent reports, customers ultimately seek to understand how their money works.
Proper application of BI coupled with analytics tools in finance can become a visualized plan for a customer experience strategy. Armed with adequately processed data, financial organizations can improve their targeted products and services, personalize marketing campaigns, stay atop the competition, and as a result, drive profitability. They can also track individual revenue streams to see which products and services fail to respond to the customer’s sentiment and which are more profitable.
While traditional financial service offerings remain relevant, the sector faces some critical hurdles with significant data expansion, rising competition, and elevated customer digital expectations across every wealth management area. Some key benefits of BI applications in finance become viable incentives for future digital experiences in financial management, starting from using data for enhanced internal operations to transparency, as well as connectivity and personalized service offerings generating revenue and brand recognition at the end of the day.
How to make big data an instrument for finance
To avoid further speculations regarding the very term Big Data, I’d like to state that Big Data here is referred to as the identification of vast amounts of diverse data streaming as a result of everyday business operations, contrary to the pure technology term Big Data with highlighted 3 V (sometimes 5V) characteristics. A traditional BI toolset can handle Big Data without employing data science and advanced analytics techniques.
While big data is an entity, business intelligence as a process extracts valuable information from the data source, organizes it for analysis, creates reports and visualizes data from across the organization. Considering the staggering revenue growth, the phenomenon brings us to understand the importance of big data analytics in finance.
Digital-savvy companies use BI tools with their core concept in focus. BI aims to prepare relevant datasets at the right time to be re-used rather than prepare all the data for the users.
Business intelligence software supports the massive potential of financial organizations starting from market conditions, and highlights, up to financial KPIs, expense management, and customer behavior. However, since data amounts are growing, BI tactics will change and evolve, becoming more sophisticated. Thus, staying abreast of the latest industry developments is critical for future business success.
Though BI drives organizational success, it is not a silver bullet. Unfortunately, BI can not play solo for data-rich businesses in the 2023. Therefore, it is a vital issue to get it right. Unfortunately, it comes with its fair share of impediments, like skipping the part about the right digital strategy and BI implementation, ROI justification, etc.
To extract the maximum value from big data utilizing BI for your financial organization, it’s vital to handle your data correctly (aggregation, processing, cleansing) by: measuring the right indicators, consuming, storing, and visualizing; using the right self-service BI tools; considering cloud solutions; taking advantage of AI; focus on data quality; and evaluating a strategic partner for a more complex solution.
There are excellent modern tools to address these fundamental challenges, though, and you could utilize them efficiently. Keeping in mind that innovation should bring simplicity & benefits to profitable customers, we will run through the most vivid applications of business trends and tools for practical business intelligence.
Visualize your business strategy with BI
Financial companies in today’s fast-paced business landscape need more time to look at numerous columns and rows in a spreadsheet or study endless numeric reports. However, more is required to have much data. It’s about communicating it to the right people at the right time. That is where data visualization comes into play.
Finance sector business leaders often turn to data visualized through traditional charts and graphs or modern dynamic dashboards, node diagrams, flash visualizations or multidimensional heatmap matrices, and even interactive augmented reality walls. Well-tailored data visualization in the finance industry combines a design’s aesthetic and the statistics of what the data shows, thus magnifying the value that analytics and insights can potentially bring to business outcomes.
Considering that studies prove that 65% of people are visual learners, visualized information is also much better perceived. A visual representation of your numbers includes KPIs, financial reporting, risk levels, transaction data, market trends, finance flows, and tracking profits and expenses by combining data across services you already use, leading to fact-based decisions.
A robust data visualization solution explores financial sector data faster and more fully while enabling you to spot previously hidden patterns and trends, reveal connections, see new opportunities, and detect threats. In addition, they help you understand your business by vividly presenting complex concepts, democratizing data across the organization, and communicating insights by determining why it is performing a certain way and what you can do to improve numbers.
Data visualization in finance
Data visualization is here for the financial services industry:
- To turn complex information patterns into easily digestible representations to better understand what is happening within the company
- To connect multidimensional data sets into a single view across the organization for further interpretations throughout the company.
- To provide vivid operating dynamics and permit senior executives to analyze financial and non-financial KPIs vs. business performance
- To present a data-based dynamic view of marketing trends, product interest, and customer sentiment to highlight future strategic opportunities
- To unveil unspotted patterns in the information with all-inclusive reports, allowing you to focus on the weak areas
When building your BI strategy, data visualization is no longer a nice-to-have option but an additional instrument for keeping big data consumable. Data visualization tools are different and must be scrupulously examined before applying one to your relevant business needs. Be it a top-notch popular tool like Microsoft Power BI, Tableau, Looker, Salesforce Einstein Analytics, Sisense, or Zoho Analytics. It can also be a free public tool like Google Charts, Qlik Sense, or D3. They are supposed to serve their mission to help you view the facts behind your numbers and understand the connection between the operations and results.
For example, Trōv is a world-leading insurance technology company data and platform that benefits from an all-inclusive BI Solution & Tableau-based Reporting. It allows the organization to operate on all its data, across focused markets and departments, with customized reports enabled by fast decision-making while monitoring growth and profitability management, sharing pertinent information and stats with the appropriate management groups and partners.
Make data accessible to everyone with self-service BI tools
As a financial business intelligence user, you are likely uncomfortable with the complexity of traditional BI tools. Translating data into any report or insight is painful for most people. At the same time, involving a data scientist who can do all the analytics can considerably inflate your operational expenses. Financial businesses in the 21st century can not spare one extra minute that might hinder improving time to value or exploiting innovation as quickly as possible.
No wonder self-service BI tools are gaining in popularity these days. They combine all of the critical data handling attributes on the back end with the intuitive usability of the front end to enable regular non-typical users to filter, select, analyze, report, and visualize information by themselves. The resulting dashboards and charts can be easily shared across the company to view, understand and participate in the data facts. Moreover, modern self-service BI tools offer embedded predictive analytics for refined analysis and predictions.
Services like Tableau, Microsoft Power BI, SAP BI, Atlassian Jira, SAS BI, Yellowfin BI, Sisense, Zoho Analytics, and QlikSense enable non-tech users to filter, sort, and analyze data while finding key relationships and uncover insights without having to wait for iterative OLAP (online analytical processing) cube development by IT. Thus, banking on self-service tools will accelerate your processes, reduce costs and enable you to react timely.
According to IntechOpen, cloud BI is becoming dominant in big data and analytics. This is particularly important in the context of the massive growth of cloud computing in finance, which is expected to experience a compound annual growth rate (CAGR) of about 20% from 2022 to 2030
Financial enterprises are turning to cloud-based BI tools like Tableau CRM, Power BI, Oracle Cloud EPM, or Prophix, integrated QlikView. Modern BI systems can create even more outcomes with cloud deployments.
Cloud-based BI solutions enable financial service companies to scrutinize operational health for unity at any time and improve the communication inside and outside of the company more agilely, thus making business decisions with BI and data analytics hosted on virtual networks.
The beauty of cloud BI solutions for financial management is that cloud BI allows for having one unified point of truth. In addition to the benefits of traditional cloud dynamism and accessibility, ease of use, intuitive GUI, scalability and elasticity, and fast deployments, all topped with self-service options.
These enable you to invest your time in fresh innovation, valuable insights, and other critical business aspects. Besides, adopting a cloud service can also resolve cyber-security concerns, as premium cloud service providers can install and maintain multiple security layers. Keeping in mind that cloud solutions are far from one-size-fits-all, cloud whales like Amazon Web Services or Microsoft Azure provide data security that can hardly be matched by on-site hosting.
Discover Artificial Intelligence (AI) as equipment for your core BI
In the age of artificial everything, AI can perform specific tasks much better than human beings, especially when raw unstructured data is involved. Digital transformation in the financial services industry includes many aspects of AI, from statistical methods to simulation of natural choices. But there is always a heavy dose of machine learning (ML), as an AI branch is designed to digest data and automate the learning applied to specific financial tasks.
To name a few: fraud detection, cyber risks for corporate finance to robo-advisors for personalized wealth management, personalized on-demand insurance quotations, credit scoring, financial analytics and market valuation with consumer forecasts, optimizing and developing investment strategies to task automation, and corporate financial performance and cash flow management.
With its ability to process amounts of information, artificial intelligence (AI) is not just a buzzword but a powerful means to unearth previously unnoticed patterns. So, why not use AI’s ability for your financial database?
For example, Salesforce Einstein (an integrated set of AI for Salesforce CRM) can help you understand your customers better with a set of BI tools, dashboard templates that generate visual analytics, and data connections. This AI-driven tool can generate insights on customer behavior for financial advisors and wealth managers with on-the-spot access to critical data that expedite customer service and liable processes.
Ensure high-quality of data
In the connected, personalized 21st century, data is the blood of any business. But here’s the rub: if it needs to be more accurate, complete, or updated, the consequences can be disastrous. According to IBM stats, businesses in the U.S. alone lose 3.1 trillion dollars annually because of insufficient data. That’s why data quality management (DQM) practices are essential to staying consistent and relevant for any business today. Furthermore, trust in high-quality data directly impacts the reliability of BI and analytics that improve decision-making in organizations. Since BI aims to make your data comprehensible, it’s all about having the data you can trust to make essential business decisions rather than just the data itself.
Data quality deserves a whole new article, as the topic is multi-dimensional. But when applying BI, you might need some preliminary data quality control aspects within the data governance process:
- Data quality assessment is the initial stage to spot possible issues and help you determine a data metrics strategy.
- Identify what kind of data, out of all the volumes, you need to use before cleansing and analysis. That said, modern BI solutions support the decisions on which data sets fill under the category.
- Apply pre-defined business rules to block poor or invalid data from entering your BI repository.
Some clearly defined data quality processes start with the operational and strategic goals for data quality defined metrics, then continue with data quality profiling, separate data source analysis, standardization, data repairing, improvement implementation, and on to data quality status control.
What we see working: a combo of different tools and approaches
In the business landscape, decision-making is not an isolated process. Even the best-in-class BI solutions are only helpful if applied at some levels of the organization. The right BI tools can gain the necessary insights and support the strategy shaped by the factors inside and outside the business organization.
With consolidated analytics tools and reporting features crafted by BI combined with data solutions, financial services analysts and data scientists can use data and related resources for business insights in real-time. This allows for a vivid visual representation of data, updating relevant data sets, applying prescriptive and predictive analytics, and reporting historical and current information. In addition, integrated third-party analytic BI services or custom BI solutions allow you to address even more specific business needs and process critical data.
As you can see, there is no one-size-fits-all BI solution for financial analysts or all finance teams. Yet, the tips will help you make your extensive data analysis more efficient and insightful.
The bottom line
A skillfully tailored BI solution is an umbrella that supports financial service organizations with the correct answers to their questions about confusing and uninspiring data.
Using our technological expertise, our clients in the financial sector accelerate business operations automate reporting, monitor relevant vital metrics, and focus on specific customer data. Additionally, finance professionals make smarter fact-based decisions through predictive analytics, impacting efficient risk management and improving automation and client profitability.
As practice proves, to capitalize fully on big data in finance, you should combine several tools, solutions, and services, from BI analysis and assessment to data processing and BI solution deployment to data visualization and optimization.
At the same time, with the financial industry shift towards the real-time analysis revenue management of “bigger, faster and wider” data flows, financial companies are seeking a single well-orchestrated system. Our business intelligence software engineers create new solutions, build ETL, combine internal data flows and data warehouses, ensure data integration within storage platforms, and deploy scalable BI infrastructures on-premises or in cloud environments.
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