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 true goldmine. And while it is, things are not that simple.
Just as ore isn’t gold yet, raw data has no value until you unlock the worthy insights hidden within. Unfortunately, with privacy issues and various data formats distributed across numerous locations, it can be difficult to extract value from the data. 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 and how you can capitalize fully on it.
Business Intelligence (BI) relates to the practices and technologies used by organizations to collect, process and present the extracted value from the 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 real information. When BI and analytics are used in a combo, businesses see the journey from the present moment of information towards data management, 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 clearly. How can financial institutions benefit from business intelligence software in practice? Though BI serves its specific mission for every particular financial services organization and the tooling differs from wealth management to investment to insurance to banking, in our opinion, there are some universal advantages of using business intelligence (BI) and analytics in finance.
Managing uncertainty related to all kinds of 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 real 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, by tracking customer behavior you can prevent fraudulent activities while tracing employee behavior can be effective in ensuring regulatory compliance and addressing potential insider threats. By complementing the existing data with the additional economic context information, the organization has a fuller picture for their credit portfolio analytics and can distinctly see any possible weak or strong points.
Money has never been an easy undertaking, even more so today when it’s critical for financial services providers to synchronize the organization in order to build resilient operations. It is not a one-man ship and it takes overall organizational efficiency. BI tools enable the system’s constant communication of transactional information between users, as well as unified data sharing and automated manual reporting, all 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.
Money management has become a commodity of the connected personalized world. With BFSI (banking, financial services and insurance) services generating huge 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 is having real numbers in front of you helping you to see where the business actually is, to identify value drivers alongside growth opportunities, and then monitor financial/non-financial KPIs against those. A well-put BI solution makes real-time data handling fast and to the point. It allows for the analysis of 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.
→ Explore Advanced BI Solution & Tableau-based Reporting for Effective Decision-Making
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 really 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 and patterns, etc., BI applications allow financial service providers to extract pragmatic insights of what your customers (as well as partners) truly expect from your services. Be it more flexible loan offers, simplified financial models, or transparent reports, ultimately customers seek to understand how their money works.
→ Explore How to Use Salesforce to Power Your Financial Services
Proper application of BI coupled with analytics tools in finance can become a visualized plan for a customer experience strategy. Armed with properly processed data, financial organizations can improve their targeted products and services, personalize marketing campaigns, and 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 ones are more profitable.
While traditional financial service’s offerings remain relevant, the sector is facing some critical hurdles with the big data expansion, rising competition, and elevated customer digital expectations across every area of wealth management. 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.
So, here comes another vital question. How do you make all the data you have on hand, work for your financial organization? Let’s explore this below.
In order 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 huge 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. It is arguable that a pure traditional BI toolset is capable of handling Big Data without employing data science and advanced analytics techniques.
While big data is an entity, business intelligence as a process extracts useful information from the data source, organizes it for analysis, creates reports and visualizes data from across the organization. Digital-savvy companies make good use of BI tools with its core concept in focus. The aim of BI is to prepare relevant datasets at the right time to be re-used rather than prepare all the data for the users.
Obviously, Business intelligence software is supporting a massive potential of financial organizations starting from market conditions, highlights, on up to financial KPIs, to expense management, and to customer behavior. However, since data amounts are growing, BI tactics will change and evolve, becoming more sophisticated. Thus, for future business success, it’s critical to stay abreast of the latest industry developments.
Though BI is a driver of organizational success, it is not a silver bullet. Unfortunately, BI can not play solo for data-rich business in the 2020s. It is a vital issue to get it right and it comes with its fair share of impediments, like skipping the part about the right digital strategy and BI implementation, ROI justification, etc. In order to extract the maximum value from big data by means of 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 considering a strategic partner for a more complex solution.
There are excellent modern tools to address these fundamental challenges though and you could utilize them with the utmost efficiency. Keeping in mind that innovation should bring simplicity & benefits, we will run through the most vivid applications of trends and tools for effective business intelligence.
Financial companies in today’s fast-paced business landscape don’t have the luxury of unlimited time to look at numerous columns and rows in a spreadsheet or study endless numeric reports. It’s not enough to just have a lot of data, it’s about communicating it to the right people at the right time. That is where data visualization comes into play.
Finance sector leaders turn to data visualized through already traditional charts and graphs or through modern dynamic dashboards, node diagrams, flash visualizations or multidimensional heatmap matrices, 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 actually shows, thus magnifying the value that the analytics and insights can potentially bring into business outcomes.
Taking into account that studies prove that 65% of people are visual learners, visualized information is also much better perceived. A visual representation of your numbers is like KPIs reporting, risk levels, transaction data trends, finance flows, tracking profits, and expenses by combining data across services that you already use which leads 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. They not only help you understand your business by presenting complex concepts in a vivid way, but also democratizes data across the organization and communicates the insights throughout the organization by determining why it is performing a certain way and what you can do to improve numbers.
Data visualization is here for the financial services industry:
When building your BI strategy, data visualization is no longer a nice-to-have option but rather an additional instrument for keeping big data consumable. Data visualization tools are not cut the same and have to 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, Zoho Analytics, etc. or a free public tool like Google charts, Qlik Sense or D3, etc., they are supposed to serve their mission which is to help you view the facts behind your numbers and understand the connection between the operations and results. Among the whole plethora of data visualization tools, at Avenga, we have a profiled experience of working with many of those. You can take a closer look here.
For example, Trōv, one of Avenga’s clients: a world-leading insurance technology platform that is benefiting 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 with fast decision-making, all the while monitoring growth and sharing pertinent information and stats with the appropriate management groups and partners.
As a business user, chances are you are uncomfortable with the complexity of traditional BI tools. For the majority of people translating data into any kind of report or insight is a painful undertaking. At the same time, involving a data scientist who is able to do all the analytics can considerably inflate your operational expenses. And, 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 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 are ready to 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, QlikSense, etc. enable non-tech users to filter, sort and analyze data while finding key relationships and uncovering 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 Finance Online, cloud BI is becoming a dominant force in big data and analytics. Financial enterprises are turning to cloud-based BI tools like Tableau CRM, Power BI, Oracle Cloud EPM or Prophix, or integrated QlikView, etc., as modern BI 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 in a more agile way thus making business decisions with BI and data analytics that are 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 are able to install and maintain multiple security layers. Keeping in mind that cloud solutions are far from being a one-size-fits-all, cloud whales like Amazon Web Services or Microsoft Azure provide a level of data security that can hardly be matched by on-site hosting.
In the age of artificial everything, AI is perfectly capable of performing specific tasks much better than human beings, especially when raw unstructured data is involved. Digital transformation in the financial services industry includes many sections related to 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 behavior forecasts, optimize and develop investment strategies to task automation, and corporate performance management.
With its ability to process humongous amounts of information, artificial intelligence (AI) is not just a buzzword but a powerful means to unearth patterns that went previously unnoticed. So, why not take advantage of 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, 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 expedites customer service and liable processes.
In the connected personalized 21st century, data is the blood of any business. But here’s the rub: if it’s inaccurate, incomplete or outdated, the consequences can be disastrous. According to IBM stats, in the U.S. alone, businesses lose 3.1 trillion dollars per year because of poor data. That’s why data quality management (DQM) practices are essential for any business today in order to stay consistent and relevant. Trust in data being high quality directly impacts the reliability of BI and analytics that improve decision-making in organizations. Since BI is aimed at making your data comprehensible, it’s all about having the data you can trust, rather than just the data itself, to make important business decisions.
→ Explore what is Data quality at the source pattern
Data quality deserves a whole new article, as the topic is very multidimensional. But when applying BI, you might need some preliminary data quality control aspects within the data governance process:
There are some clearly defined data quality processes that start with the goals for data quality defined metrics, then continue with data quality profiling, standardization, data repairing, improvement implementation, and on to data quality status control.
→ Read more about Data Validation Testing
For instance, a 360-technology company providing services to the legal, financial services, and mortgage industry was struggling with making full use of the disparate volumes of data aggregated from various systems. Avenga implemented a critically needed business intelligence (BI) solution for effective reporting, thus making tracking of goal fulfillment visible and using that information to gauge overall organizational productivity.
In the business landscape, decision-making is not an isolated process. Even the best-in-class BI solutions are useless if not applied at every level of the organization. What the right BI tools can surely do is gain the necessary insights and then support the strategy shaped by the factors inside and outside the business organization.
With consolidated analytics tools and reporting features crafted by BI that are 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. 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 finance. Yet, the provided tips will help you make your big data analysis more efficient and insightful.
A skillfully tailored BI solution is a sort of an umbrella that supports financial service organizations with the right 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 key 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 consequently, client profitability. We translate raw data from multiple sources into value-producing decisions and implement relevant BI solutions that utilize big data opportunities to grow revenue, reduce risks and optimize costs across the business.
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, on to data visualization and optimization.
At the same time, with the financial industry shift towards the real-time analysis 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 flows and data warehouses, ensure data integration within storage platforms, and deploy scalable BI infrastructures used on-premises or in cloud environments.
On top of that, our business intelligence capabilities can provide embedded analytics that are tightly integrated into your existing systems (e.g., CRM, ERP, marketing automation, financial systems, etc.) which can help introduce new targeted information to support additional awareness, decisions-making, or analytic capabilities related to very specific tasks. With BI continuously evolving, Avenga applies and integrates analytical IT and AI (artificial intelligence) to cloud, web, and mobile-based business solutions for meaningful business results.
Where is the Financial Services Industry on its journey to digital? A whitepaper by Avenga explains digital transformation for financial business resilience. Demystify key challenges affecting the Financial Industry and find out how to evaluate your paths to digital.