Analytics using Microsoft POWER BI for Banking Sector (Data-Driven Analytics in a Nutshell)

Analytics is the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making within an organization.

Business analytics is the process of discovering, interpreting, and communicating significant patterns in data and using tools to empower your entire organization to ask any question of any data in any environment on any device. Business analytics adds even more opportunities to drive desired outcomes, such as optimization, cost savings, and customer engagement. Those who are successful with analytics ignore their instincts and chose their results based on what the data reveals. In a perfect scenario, business leaders set up a methodology without bias so that insights and discoveries can be obtained without adding preconceived notions or experiences in the equation.

It has been said that “a picture is worth a thousand words.” And today, in the era of big data, when businesses are inundated with information from varied data types and from on-premises and cloud-based sources, that old saying has never been more relevant.

Sifting through information to understand what matters and what doesn’t is becoming more difficult. Visuals make analysis much easier and faster and offer the ability to see briefly what matters. What’s more, most people respond far better to visuals than text—90 percent of the information sent to the brain is visual, and the brain processes visuals at 60,000 times the speed of text. Those points make a strong case for the use of data visualization for analyzing and conveying information.

THE VALUE OF ANALYTICS:

A New Way to Work

The nature of business is changing, and with that change comes a new way to compete. Keeping up with the demands of today’s tech-savvy workforce means having a method for creating value and running quickly. Deliver speed and simplicity to your users while maintaining the highest standards for data quality and security. A centralized analytics platform where IT plays a pivotal role should be a fundamental part of your business analytics strategy. The combination of both business-led and IT-led initiatives is the sweet spot for innovation.

Uncover New Opportunities

Advancements in analytics technology are creating new opportunities for you to capitalize on your data. Modern analytics are predictive, self-learning, and adaptive to help you uncover hidden data patterns. They are intuitive as well, incorporating stunning visualizations that enable you to understand millions of rows and columns of data in an instant. Modern business analytics are mobile and easy to work with. And they connect you to the right data at the right time, with little or no training required.

Visualize Your Data

You want to see the data signals before your competitors do. Analytics provides the ability to see a high-definition image of your business landscape. By mashing up personal, corporate, and big data, you can quickly understand the value of the data, share your data story with colleagues, and do it all in a matter of minutes.

ANALYTICS TREND

Amid the constantly evolving analytics market, the fundamental shift from IT leading the charge to pursue business analytics initiatives, to one where the business and IT share in this decision is now the new normal. There is no doubt that analytics has become strategic for most organizations today, and as such, has introduced a new wave of both new consumers and new expectations.

What has changed is the way that decisions must be made in real-time and shared with a wide audience. The workforce is changing, and that change brings a new way to work. Gone are the days where training manuals are commonplace in the office—today’s workforce expects to get up and running quickly with an intuitive interface. But it doesn’t end there. While speed and simplicity are key, business leaders still have high expectations around data quality and security. A centralized analytics platform where IT plays a pivotal role is still a fundamental part of any analytics strategy. The combination of both business-led and IT-led initiatives is the sweet spot for innovation.

We believe that putting analytics in the cloud is much more than just a deployment choice—it breaks down the barriers between people, places, data, and systems to fundamentally shift the way people and processes interact with information, technology, and each other.

TYPES OF ANALYTICS:

Augmented analytics:

1.   Intelligent search

2.   Smart Data Discovery

3.   Smart Data Preparation

4.   Natural Language

5.   Auto-suggest

Self-Service Analytics:

1.   Visualizations and Dashboards

2.   Global transformation policies

3.   Experience continuity

4.   Smart collaboration

5.   Knowledge fabric

6.   Integrated data science

WHAT IS POWER BI?

Power BI is a business analytics service by Microsoft. It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their own reports and dashboards.

Microsoft Business Intelligence or Power BI is a suite of business analytics tools to analyze data and share insights. Monitor your business and get answers quickly with rich dashboards available on every device. The Data Analytics field is growing exponentially!

Create a data-driven culture throughout your organization. Easily share and collaborate on interactive data visualizations using Power BI Pro for self-service analytics.

POWER BI can be used by any department of the Banking sector. Data is the primary key and this tool is one of the most advanced tools for Visualizations and Analytics for various businesses.

ADVANCED ANALYTICS FEATURES OF POWER BI:

Quick Insights: This feature in Power BI is developed in conjunction with Microsoft Research and on a developing set of advanced analytical algorithms. This provides the user with a new and intuitive way to search insights from the business data. A user can discover interesting insights from a different subset of data set while applying advanced algorithms. With just one click, Quick Insights let the user find better visibility to data insights within a given span of time.

Ask a Question: This feature gives the user liberty to add a ‘question’ button within the report. This enables the user to carry out random analysis whilst developing a report or while reading it. This feature gives the freedom to ask a question in plain English (natural language).

Integration with R: Using R connector, a user can run R scripts in Power BI. Then, the resulting data sets can be imported into a Power BI data model.

Intelligent App Suggestions: The sophisticated model of this feature helps the users to list down their app based on popularity, relevance, content, and review of other users.

Integration of Azure Machine Learning: With the integration of Machine Learning in Power BI, users can now visualize the results of Machine Learning algorithms by just dragging, dropping, and connecting data modules.

Data Shaping with R: The integration of R in Power Query editor enables the users to accomplish data cleansing and then, with just a few clicks, data sharing, and advanced analytics of the data set can be performed.

Segmentation & Cohort analysis: It is one of the simplest yet powerful ways to explore the relationship between data sets. It breaks or combines different data sets into one meaningful cluster. It then compares those clusters to identify meaningful relationship between the data sets. The feature also helps in developing a hypothesis of the available business data or understands the requirement for any further analysis. Clustering, Grouping and Binning are Power BI tools that take this process ahead.

Data Analysis Expression: DAX or Data Analysis Expression helps in achieving one or more values out of a data set by calculating multiple data with the current data. It is basically a set of functions that calculates with formulas/expressions. It works like Microsoft Excel minus the complexity with numbers and rows. DAX reports are easy to understand and build.

Integration with Microsoft Azure Stream Analytics: Power BI integration with Azure Machine Learning and Azure Stream Analytics allows users to get access to real-time data. Stream Analytics gives shape and combines different data sets. This powerful combination enables predictive intelligence allowing business users to take proactive action.

Data Visualization in Power BI: Power BI gives users better visibility of their data to find business insights in real-time. It gives you vast options of pre-built visualizations, adds customization to the existing ones or chooses from the expanding list of in-built visualization in the community gallery.

WHY VISUAL ANALYTICS ARE IMPORTANT

Good data visualization is essential for analyzing data and making decisions based on that data. It allows people to quickly and easily see and understand patterns and relationships and spot emerging trends that might go unnoticed with just a table or spreadsheet of raw numbers. And in most cases, no specialized training is required to interpret what’s presented in the graphics, enabling universal understanding.

A well-designed graphic can not only provide information but also heighten the impact of that information with a strong presentation, attracting attention, and holding interest as no table or spreadsheet can.

PUT POWER BI TO WORK FOR YOUR BUSINESS

Companies in the financial services industry are using Power BI today, not only to track the latest balance sheet and profit and loss data but to gain insights into delivering the best service to their customers.

Take Metro Bank, the first new major bank in the UK in over a century. It’s using Power BI to gather rich and detailed operations data to obtain a full view of customer behavior, all within a single tool. Staff can combine account activity data with data from customer satisfaction surveys, wait times, and more to understand and improve the customer experience. The reporting data is refreshed each night so staff can see an up-to-date view of the previous day, weeks, months or year.

RETAIL BANKING SOLUTIONS USING ANALYTICS

With many customers carrying out a multitude of different transactions daily, banks need help understanding business performance, analyzing their pipeline and growth, and keeping an eye on profitability. Success requires continuous tracking of the business as a whole and the ability to view detailed performance information of individual products and lines of business.

Benefits:

1.   Analyzing customer profitability from a comprehensive perspective, assessing the profitability of the industry, customer segments, loan types, and geography.

2.   Visibility into the performance of different revenue channels and the close monitoring of the revenue generation of different customer divisions and territories.

3.   Leveraging historical data to provide insights to the year-over-year or month-over-month growth of different bank divisions; allowing strategic decisions to be made about which divisions require support and which are showing healthy growth.

4.   Monitoring pipeline health by setting strategic KPI targets for the sales pipeline and tracking the current pipeline against these targets.

5.   The ability to drill from summary level dashboards, that capture the holistic view of the organization, into the opportunity level details; thus, supporting the capability to do key investigations on the data and enabling sound decision-making.

6.   A live connection is with CRM online data, allowing these dashboards to continually update automatically. This ensures that the information displayed on these dashboards always provides the most current view of the business and how it responds to the strategic decisions of the company’s leadership.

Key features:

·        Totally scalable & secure.

·        Presents state-of-the-art interface controls.

·        Entirely cloud based.

·        Allows to search & drilldown.

·        Customizes metrics & KPIs.

·        Drives expertise & insights.

·        Points out where to focus on.

·        Accessible collaboration anywhere.

·        Real-time information.

BANKING CLIENTS AND RISK ANALYSIS

Total review of all banking credit risks, for management reporting and relationship banking dashboards. Drill into clients by relationship, location, risk weighting, and more. Identify credit risk at the regional level and/or drill into data at the individual client level.

Review all current bank deposits and loans across all key areas of the bank including retail, commercial, and institutional banking. Analyze the fee structure and optimize future marketing spend.

Report Breakdown

Client Summary - Where are your key clients located? Which regions or suburbs have exposure to the most loans or deposits? Drill into key client information. Switch between key areas of banking relationships.

Client Details - Drill into key details at the client level. Quickly gain an understanding of where your greatest risks and opportunities are to grow margins.

Retail/Commercial/Institutional - Breakdown of key relationship areas within a bank. Review loan and deposit books in these key areas, and quickly understand if pricing is appropriate and/or if marketing decisions should be changed to take advantage of current market opportunities.

NEXT GENERATION ANALYTICS

As businesses shifted from just gaining data visibility and requiring more insight, the tools and their capabilities have evolved as well.

The first analytics toolsets were based on the semantic models forged from business intelligence software. These helped with establishing strong governance, data analysis, and alignment across functions. One drawback was that reports were not always timely. Business decision-makers were sometimes unsure the results were aligned with their original query. From a technical standpoint, these models are primarily used on-premises, making them cost-inefficient. The data is also often trapped in silos.

Next, the evolution of self-service tools advanced analytics to a broader audience. These accelerated the use of analytics since they did not require special skills. These desktop business analytics tools have gained popularity over the past few years, particularly in the cloud. Business users are excited about exploring a wide variety of data assets. While the ease of use is appealing, the blending of data and creating a "single version of the truth" becomes increasingly complex. Desktop analytics are not always scalable to larger groups. They are also susceptible to inconsistent definitions.

Most recently, analytics tools are enabling a broader transformation of business insight with the help of tools that automatically upgrade and automate data discovery, data cleansing, and data publishing. Business users can collaborate with any device with context, harness the information in real-time, and drive outcomes.

Today, humans are still doing most of the work, but automation is gaining support. Data from existing sources can be combined easily. The consumer works by executing queries, then gains insight by interacting with visual representations of the data and builds models to predict future trends or outcomes. These are all managed and controlled by people at a very granular level. The inclusion of data gathering, data discovery, and machine learning provides the end-user with more options in a faster time frame than ever before.