End-to-End Data Analytics Solution Blog (1)

Where Can I Find an End-to-End Data Analytics Solution?

The data analytics landscape has exploded over the past decade with an ever-growing selection of products and services: literally thousands of tools exist to help business deploy and manage data lakes, ETL and ELT, machine learning, and business intelligence. With so many tools to piece together, how do business leaders find the best one or ones? How do you piece them together and use them to get business outcomes? The truth is that many tools are built for data scientists, data engineers and other users with technical expertise. With most tools, if you do not have a data science department, your company is at risk for buying technologies that your team does not have the expertise to use and maintain. This turns digital transformation into a cost center instead of sparking data driven revenue growth.

Data and AI Landscape

Image credit: Firstmark

Aunalytics' team of experts

Aunalytics’ side-by-side service model provides value that goes beyond most other tools and platforms on the market by providing a data platform with built-in data management and analytics, as well as access to human intelligence in data engineering, machine learning, and business analytics. While many companies offer one or two similar products, and many consulting firms can provide guidance in choosing and implementing tools, Aunalytics integrates all the tools and expertise in one end-to-end solution built for non-technical business users. The success of a digital transformation project should not be hitting implementation milestones. The success of a digital transformation project should be measured in business outcomes.

Real-Time Data Flow

Why it is Important to Have Real-time Data for Analytics

Analytics based upon stale data provides stale results. Fresh data powers up-to-date decision-making.

Real-time data ingestion, integration and cleansing to create a golden record of business information ready for analytics is critical to make better business decisions. This type of ingestion uses technology such as change data capture to bring across only new bits of data – changes to the existing data – as they are made in the business. Streaming allows for efficient processing (cleansing, matching, merging), rather than piling up changes all day and batching them overnight for processing. Streamed data provides changes in real-time so that business decisions are not made based upon yesterday’s data. Although some data sources and systems only support batch transfer, data ingestion technologies are ready for when the core systems modernize. Hopefully the days of stalled analytics waiting for data to arrive will soon be behind us.

Without real-time data management, the time gap causes lags in decision-making that can cost companies time, money, and energy. Real-time data management enables:

  • Rapid results
  • Faster scaling
  • Better decision-making
  • More efficient data delivery
  • Monetizing windows of opportunity
  • Timely actions in response to current insights
  • Improved and automated business processes
  • Proactive decision-making instead of reactive
  • Immediate responses as events unfold
  • More personalized customer experiences

How to Use AI for Smarter Financial Institution Service

Data has long been used in decision making in the financial services industry. Statistical scoring models based on consumer data like FICO® have been used for half a century to guide lending decisions in the financial services sector. But today’s analytics space has evolved to the point where many other factors not easily digested by credit scoring bureaus play a roll. Imagine a deeper understanding of lending risk factors not commonly reported in credit scores:

  • Number of changes of residence in the past five years
  • Householding status (single or cohabiting/married)

Imagine having the ability to look at a particular client and understand based on past data how that individual compares in terms of various factors driving that business relationship:

  • Which of our financial products is this customer most likely to choose next?
  • How likely is this customer to default or become past due on a mortgage?
  • What is churn likelihood for this customer?
  • What is the probable lifetime value of this customer relationship?

Aunalytics financial services experts understand the most pressing business questions specific for this industry.  Working with our financial services experts, Aunalytics’ Innovation Lab data scientists have developed proprietary machine learning, AI and deep learning algorithms based on a solid understanding of the data commonly collected by financial institutions. Our data engineers understand the types of data commonly created and used by the industry, common data sources and have created integrations to bring data from across a bank together into a single analytics-ready feed. The end result is data organized into industry specific relational data marts ready to answer questions posed by business users from financial services institutions.

SHAP graph for loan default risk model

A SHAP value chart for a remarkably accurate loan default risk model we developed. A benchmark with testing data provided by one client was able to predict 30% of that customer’s loan defaults with 99% accuracy, or predict 75% of all loan defaults with 75% accuracy (i.e. 0.99 precision at 0.3 recall or 0.75 precision at 0.75 recall).

Take the example of a recent model we developed at Aunalytics to predict loan default risk. Looking at a chart of the SHAP (Shapley Additive Explanations) values for this model, we can see a number of common-sense observations confirmed. For example, high interest loans (represented by a pink dot in the Account_InterestRate line) and low FICO scores (represented by a blue dot in the Lend_OriginalFICOScore) positively correlate with default risk. This model discovered some much less intuitive characteristics of high risk loans as well: For some loans, payment frequency (Lend_PaymentFrequency) was actually the single most important factor for predicting loan defaults. Moreover, a well-known but not always properly appreciated factor to default risk is illustrated visually: the type of loan being underwritten (Account_ProductType) is in many cases just as important as a customer’s credit score to default risk. Auto loan applicants with high FICO scores might be more of a default risk than customers with low credit scores shopping for a home mortgage.

In so many cases, machine learning techniques enable more accurate and understandable models of risk, propensity, and customer churn because they represent a more complex model understanding of the various factors that go into risk modeling. Our models deliver greater accuracy than simpler, statistical models because they understand the relationship between multiple indicators.

Through AI and machine learning enriched data points, clients can easily understand a particular customer or product by comparing it to other customers with similar data. Whether you want to know if a customer is likely to select a new product, their default risk, churn likelihood, or any other number of questions, our data scientists and business analysts are experienced and committed to answering these questions based on years of experience with financial services businesses.

Aunalytics Platform

What is the best analytics tool for business users?

What is the best data analytics tool for business users? As more business leaders face this question in recent years, most are finding just how hard it is to answer. The data analytics landscape has exploded over the past decade with an ever-growing landscape of products and services: literally thousands of tools exist to help business deploy and manage data lakes, ETL and ELT, machine learning, and business intelligence. With so many tools to piece together, how do business leaders find the best one or ones?

As with most things, the best tool set is simply the one that tailors itself best to the problems and questions that need to be solved. For some users, this could be how to integrate and clean data across siloed systems. Others may want to know how to publish analytical datasets of relevant data and metrics to analysts and marketing researchers. Others may have questions about how to derive value from large amounts of data with machine learning.

The Answers Platform

Aunalytics has built its data platform of tools to answer all of these questions. We believe in providing answers to questions with our integrated data analytics platform and leveraging our industry expertise to put these tools to work for you.

Unlike most tools on the market, Aunalytics provides a comprehensive, end-to-end data platform with all the tools your organization needs:

  • Data integration, cleaning, and migration in the loud with Aunsight™ Golden Record
  • Data transformation, processing, and delivery with Aunsight Data Platform
  • Machine Learning and Artificial Intelligence with Aunsight Data Lab
  • Delivery and exploration of the data lake with the Daybreak™ Analytical Database

Side-by-Side Service Model

More importantly, Aunalytics’ side-by-side service model provides value that goes beyond most other tools and platforms on the market by providing access to human intelligence in data engineering, machine learning, and business analytics. While many companies offer one or two similar products, and many consulting firms can provide guidance in choosing and implementing tools, Aunalytics integrates all the tools and expertise in one company as your trusted partner in digital transformation.

Aunalytics' team of experts

A Comprehensive Platform

While there are a large number of options to choose from, we at Aunalytics believe our distinctive approach and comprehensive platform tools provide the best solution for all but the largest companies who may wish to create custom analytics solutions in-house. Wherever you are on the data analytics journey, from just beginning to explore the possibilities in your data to global companies with established data science teams, Aunalytics can provide a path through the complicated landscape of tools and infrastructure to grow your company’s data analytics program.

Six Steps Toward a More Secure Technology Environment

In the past few months, it’s become obvious that despite the world and our communities being hurt by the ongoing pandemic, hackers and bad actors are continually on the hunt for a good score. Ransomware attacks have been hitting hard at key infostructures; particularly schools. Schools have already paid over a half million dollars in ransom to some of these attackers.

In the last seven days, there have been over 20 attacks on these targets, ranging from inaccessible systems to full payouts to the attackers.

While there are no guarantees that your organization will not be hit, there are many things every organization can do to become less of a target and strengthen your own boundaries. Though some of these recommendations require an operations cost, many are free and only take a little time. ALL tactics are extremely valuable towards ensuring your organization is harder to hit.

Please contact Aunalytics if you’re interested in discussing the following steps toward a more secure technology environment.

  1. Only do work-related activities on work computers
  2. Check for password strength
  3. Review your spam and phishing filters (Microsoft Security)
  4. Train your users so they can maintain vigilance (in-house education, Knowbe4)
  5. Apply Multi-Factor Authentication (Microsoft, Okta)
  6. Put CXOs, VPs and organizational decision makers on an anti-spoof list (Microsoft Advanced Threat Protection)

Daybreak Analytic Database

Daybreak: A Foundation for Advanced Analytics, Machine Learning, and AI

Financial institutions have no shortage of data, and most know that advanced analytics, machine learning, and artificial intelligence (AI) are key technologies that must be utilized in order to stay relevant in the increasingly competitive banking landscape. Analytics is a key component of any digital transformation initiative, with the end goal of providing a superior customer experience. This digital transformation, however, is more than simply digitizing legacy systems and accommodating online/mobile banking. In order to effectively achieve digital transformation, you must be in a position to capitalize one of your greatest competitive assets—your data.

However, getting to successful data analytics and insights comes with its own unique challenges and requirements. An initial challenge concerns building the appropriate technical foundation. Actionable BI and advanced analytics require a modern specialized data infrastructure capable of storing and processing a large magnitude of transactional data in fractions of a second. Furthermore, many financial institutions struggle not only with technical execution, but also lack personnel skillsets required to manage an end-to-end analytics pipeline—from infrastructure to automated insights delivery.

In this article, we examine some of the most impactful applications of advanced analytics, machine learning, and AI for banks and credit unions, and explain how Daybreak for Financial Services solves many of these challenges by providing the ideal foundation for all of your immediate and future analytics initiatives.

Machine Learning and Artificial Intelligence in the Financial Industry

Data analysis provides a wide range of applications that can ultimately increase revenue, decrease expenses, increase efficiency, and improve the customer experience. Here are just a few examples of how data can be utilized within the financial services industry:

  1. Inform decision-making through business intelligence and self-service analytics:

While banks and credit unions collect a wide variety of data, traditionally, it has not always been easy to access or query this data, which makes uncovering the desired answers and insights difficult and time-consuming. With the proper analytics foundation, employees across the organization can begin to answer questions that directly influence both day-to-day and long-term decision-making.

For example, a data-informed employee could make a determination on where to open a new branch based on where most transactions are taking place currently, or filter customers by home address. They could also determine how to staff a branch appropriately by looking at the times of day that typically have the most customer activity, and trends related to that activity type.

  1. Improve collection and recovery rates on loans:

By implementing pattern recognition, risk and collection departments can identify and efficiently target the most at-risk loans. Loan departments could also proactively reach out to holders of at-risk loans to discuss refinancing options that would improve the borrower’s ability to pay and decrease the risk of default.

  1. Improve efficiency and effectiveness of marketing campaigns:

Banks and credit unions can create data-driven marketing program to offer personalized services, next-best products and improve customer onboarding, by knowing which customers to reach out to at the right time. Data-driven marketing allows financial institutions to be more efficient with their marketing dollars and track campaign outcomes better.

  1. Increased fraud detection abilities

Unfortunately, fraud has become quite common in the financial services industry, and banks and credit unions are investing in new technologies to fight it. Artificial intelligence can be used to detect triggers that indicate fraud in transactional data. This gives institutions the ability to proactively alert customers of suspected fraudulent activities on their accounts to prevent further loss.

These applications of machine learning and AI simply scratch the surface of what outcomes can be achieved by utilizing data, but they are not always easy to implement. Before a financial institution can embark on any advanced analytics project, they must first establish the appropriate foundational analytics infrastructure.

Daybreak is a Foundational Element for Analytics

There are many applications for analytics within the financial services industry, but the ability to utilize machine learning, AI, or even basic business intelligence is limited by data availability and infrastructure. One of biggest challenges to the achievement of advanced analytics initiatives is collecting and aggregating data across multiple disparate sources, including core data. In order to make truly proactive decisions based on data, these sources need to be updated regularly, which is a challenge unto itself.

Additionally, this data needs to be aggregated on an infrastructure built for analytics. For example, a banking core system is built to record large amounts of transactions and is designed to be a system of record. But it is not the optimal type of database structure for analytics.

To solve these challenges, Aunalytics has developed Daybreak, an industry-intelligent data mart built specifically for banks and credit unions to access and take action on the right data at the right time. Daybreak includes all the infrastructure components necessary for analytics, providing financial institutions with an up-to-date, aggregated view of their data that is ready for analysis. It offers users easy-to-use, intuitive analysis tools for people of all experience levels—industry-specific pre-built queries, the Data Explorer guided query tool, or the more advanced SQL Builder. Daybreak also provides easy access to up-to-date, accurate data for more advanced analytics through other modeling and data science tools.

Once this infrastructure is in place, providing the latest, analytics-ready data, the organization’s focus can shift to implementing a variety of analytics solutions, such as advanced KPIs, predictive analytics, targeted marketing, and fraud detection.

Daybreak Uses AI to Enhance Data for Analysis

In addition to providing the foundational infrastructure for analytics, Daybreak also utilizes AI to ensure the data itself is both accurate and ready for analysis. Banks and credit unions collect large amounts of data, both structured and unstructured. Unfortunately, unstructured data is difficult to integrate and analyze. Daybreak uses industry intelligence and AI to convert this unstructured data into a structured tabular format, familiar to analysts. To ensure accuracy, Daybreak utilizes AI to perform quality checks to detect anomalies as data is added or updated.

This industry intelligence also allows Daybreak to create Smart Features from existing data points. Smart Features are completely new data points that are engineered to answer actionable questions relevant to the financial services industry.

Banks and credit unions are fortunate to have a vast amount of data at their disposal, but for many institutions, that data is not always easily accessible for impactful decision-making. That is why it is necessary to build out a strong data foundation in order to take advantage of both basic business intelligence and more advanced machine learning and AI initiatives. Daybreak by Aunalytics provides the ideal, industry intelligent foundation for financial institutions to jump start their journeys toward digital transformation, with the tools they need in order to utilize data to grow their organizations.

Customer Intelligence

Aunalytics’ Client Success Team Drives Measurable Business Value

Transitioning to a more data-driven organization can be a long, complicated journey. A complete digital transformation is more than simply adopting new technologies (though that is an important component.) It requires change at all levels of the business in order to pivot to a more data-enabled, customer-focused mindset. Aunalytics Client Success is committed to helping organizations digitally transform by guiding and assisting them along every step of the journey, and ultimately, allowing them to thrive.

Below, the Client Success (CS) team has answered some of the most common questions about what they do and how they help organizations achieve measurable business outcomes.

What is Client Success?

Aunalytics CS partners with clients to become their trusted advisor, by building a customized CS Partnership Plan utilizing the client’s unique business needs as the core goals. The CS Partnership Plan creates an exceptional client experience by consistently applying a combination of our team and technology to deliver measurable value and business outcomes for our clients.

What are the main goals of the Aunalytics Client Success team?

The Client Success team has four main goals:

  1. Designing targeted client experiences (by industry, product, and digital transformation stage)
  2. Recommending targeted next steps by simplifying and synthesizing complex information
  3. Delivering proactive and strategic support from onboarding to solution launch, ongoing support, and consulting
  4. Collecting and responding to client feedback on ways our service delivery can evolve

What are the various roles within the CS team?

There are two main roles within the CS team that interact with clients on a regular basis. The first is the Client Success Manager (CSM). The CSM manages day-to-day client tactical needs, providing updates and direction throughout the onboarding process. As the liaison between clients and the Aunalytics team, the CSM synthesizes complex information into clear actions, mitigates any roadblocks that may occur, and clearly communicates project milestones. The CSM works closely with the clients throughout their partnership with Aunalytics, from onboarding, adoption, support, and engagement.

The Client Success Advisor (CSA) works on high-level strategy with each client, translating Aunalytics’ technology solutions into measurable business outcomes. They partner with the clients’ key stakeholders to understand their strategic objectives and create a custom technology roadmap that identifies the specific steps necessary to reach their digital transformation goals. These goals are integrated into the client’s specific CS Partnership Plan to ensure we are aligned on objectives and key results, with clear owners, timelines, and expected outcomes.

How often can a client expect to hear from a CS team member throughout their engagement with Aunalytics?

The CS team is introduced to clients at the initial kickoff meeting and CSMs initiate weekly touch points to ensure onboarding milestones are being met and to communicate action items, responsible parties, and next steps. During these calls the CS team (CS Manager, CS Advisor, Data Engineer, & Business Analyst) will review the project tracker—highlighting recent accomplishments, key priorities, and next steps. Each item is documented, assigned an owner, a timeline, and clear expectations around completion criteria.

What is the Aunalytics “Side-by-Side Support” model and how does the CS team help facilitate this?

Our side-by-side service delivery model provides a dedicated account team, comprised of technology (Data Engineers (DE), Data Scientists (DS), and Business Analysts) and data experts (Product Managers, Data Ingestion Engineers, and Cloud Infrastructure Team), to help transform the way our clients work. The account team collaborates across the company, in service of the client, to ensure that everyone on the team is driving towards the client’s desired outcomes. The CSA captures this information in the CS Partnership Plan to ensure alignment, key priorities, and ownership of time-bound tasks.

The CS team partners with Aunalytics’ Product, Ingestion, and Cloud teams to share client questions, recommendations, and future enhancement ideas. The Partnership Plan is a custom document that evolves with the client’s ever-changing needs. The CSA reviews the Partnership Plan with the client every quarter to capture new goals, document accomplishments, and create feasible timelines for implementation. The goal of the CSA is to create a relationship with the client, in which they view the CSA as a key member of their internal team (e.g. the same side of the table vs. a vendor).

A successful partnership with Aunalytics’ Client Success team is when concrete business outcomes and value are realized by the client, through the use of Aunalytics’ solutions (products + service).

What are some examples of business outcomes that CS has helped Daybreak for Financial Services clients achieve?

In addition to guidance throughout the initial implementation of Daybreak, CS has assisted banks and credit unions with the execution of a number of actionable business cases, such as:

  • Assisting Financial Institutions with implementation of self-service analytics programs;
  • Improving collection and recovery rates on loans;
  • Implementing pattern recognition to make sure that risk and collection departments are efficiently targeting the most at-risk loans;
  • Creating data driven marketing programs to offer personalized services, next-best products, and onboarding. Data-driven marketing allows financial institutions to be more efficient with their marketing dollars and track campaign outcomes better;
  • Integration with 3rdparty software systems.

The Aunalytics Client Success team is instrumental in helping clients realize measurable business value. Together with Aunalytics’ strong technology stack, this side-by-side delivery model ensures that all clients are equipped with the resources they need to affect positive change within the organization and achieve their digital transformation goals.

Daybreak Analytic Database

Daybreak’s Built-in Industry Intelligence Leads to Faster, More Actionable Insights for Banks and Credit Unions

When choosing a piece of technology for your business, it is important to consider technical specs, features, and performance metrics. But that isn’t all that matters. Even though a product or solution may fit all of your technical requirements, it might not be a great fit for your bank or credit union. As an all-in-one data management and analytics platform, Daybreak is a uniquely strong contender on technical abilities alone, but it also offers features specifically engineered to answer the most prevalent and actionable questions that banks and credit unions currently ask, or should be asking themselves, every day. This is made possible through its built-in industry intelligence.

Industry Intelligence Increases Speed to Insights

Daybreak was developed specifically to help mid-market banks and credit unions compete, leveraging the same big data and analytics technologies and capabilities as the largest, leading institutions in the industry. We know that financial services organizations have a wealth of data—but not all of it is actionable, nor even accessible in its current state. We quickly break down data silos and integrate all the relevant data points from multiple systems; including internal and external, structured and unstructured.

It is Industry Intelligent because our experience knows the kinds of questions an institution needs to answer—that is why over 40% of the data in our model doesn’t exist in the raw customer data. These new data points are called Smart Features.

Over 40% of the data in the Daybreak model doesn’t exist in the raw customer data. It is engineered using Smart Features.

Data Enriched with Smart Features Provides Actionable Insights

One huge advantage that Daybreak offers banks and credit unions is automated data enrichment, through the use of Smart Features. Smart Features are newly calculated data points that didn’t exist before. Daybreak utilizes AI to generate these new data points which allow you to answer more questions about your customers than ever before. For example, Daybreak automatically converts unstructured transactional data into structured Smart Features—converting a long, confusing text string to a transaction category. A transaction that looks like this in the raw data…


Is converted to this data point, which can be easily analyzed or used to filter and analyze:

Customer Number Account Number Destination Category Destination Name Recurring Payment
123 456 Credit Card Chase Yes

Another category of Smart Features are new values that are calculated based on existing data. For example, Daybreak’s AI scans transactional data and determine which branch is used most often by each individual. It can look at person’s home address and determine which branch is the closest to their home. The AI also scans transactional data for anomalies, and flags any unusual activities, which may indicate a fraud attempt, or a life change. For example, if an account suddenly stops showing direct deposits, perhaps that customer has changed jobs, or is in the process of switching to a different bank.

Since these Smart Features are automatically created, you can start asking actionable questions right away, without waiting for complicated analysis to be performed.

Daybreak has pre-built connectors to most of the major core systems, CRMs, loan and mortgage systems, and other heavily utilized financial industry applications. This means we can get access to your data faster, including granular daily transactional data, and automatically serve it back in a format that you can use to make data-driven decisions. We’ve figured out the difficult foundational part so you don’t have to spend months of development time building a data warehouse from scratch—you will begin getting insights right away.

Daybreak aggregates data from multiple sources, and allows you to receive actionable insights right away.

Scale your Team’s Industry Experience with Daybreak

One challenge that organizations face with any new initiative is how to transfer knowledge and collaborate across departments or locations. If one team member creates a useful analysis or process, it can be difficult to share with others who may want to look at the same type of information. Daybreak’s Query Wizard makes it easy to share intelligence across your business by providing pre-built queries for common banking questions and insights. We also update the pre-built queries regularly with new business-impacting questions as they are formulated. Lastly, in a click of a button, any query is available in SQL code, providing a huge head start to your IT team in more advanced work that they would like to do.

The Daybreak Advantage

Unlike other “one-size-fits-all” technology solutions, Daybreak has financial industry intelligence and AI built into the platform itself. It allows banks and credit unions easy access to relevant data quickly, and without investing excessive time and money to make it happen. With Daybreak, business users have access to actionable data, enriched with Smart Features, in order to start answering impactful questions they’ve never been able to before. Your entire organization can utilize industry-specific insights and collaborate on data analysis.

Daybreak is a game-changer for banks and credit unions. Start making better business decisions by effectively leveraging your data today.

Financial Services

Solving Data Challenges for Mid-Market Community Banks

Mid-market community banks and credit unions are facing increased expectations from customers and members. As technology advances across all industries, people expect a highly-personalized experience. To provide a customized customer journey, banks and credit unions must utilize the wealth of data that is available to them—basic account information, CRM data, transactions, loan data, and other useful sources.

However, this requires an institution to overcome a number of challenges, including siloed data, poor data quality and governance, use of stale, non-actionable data, manual retrospective reporting, and overall lack of necessary analytics tools and talent. Building a strong, scalable, and automated data management foundation is necessary to realize a digital transformation and ultimately meet customer expectations through actionable analytics insights. Thankfully, these challenges are not insurmountable, and we have outlined the various challenges and put forward guiding principles to help your organization overcome them.

Asking the Right Questions, at the Right Time

Having access to data is powerful, but only if you are asking the right questions. Some organizations have hundreds of dashboards, but few of the insights are actionable. Others have specific questions they’d like to answer, but no easy way to create custom queries or visualizations to dig deeper into the data. It is important to arm all users with the tools necessary to analyze data in order to take action. Users need to be given the ability to utilize relevant pre-built queries or build their own, filter data, segment customers and members, and create meaningful lists. This is also dependent on having a strong, centralized data repository, built for analysis, that gives access to timely and accurate data.

Choosing the Right Technology

While modern core systems and other business applications have their uses, they are not equipped to handle enterprise-wide analytics. These databases systems of record and are meant to handle transactions, and while ideal for collecting and modifying records in real-time, they are not able to meet the needs of querying and analytics. Furthermore, when data is spread across multiple systems, there must be a central repository to aggregate all of this data. A cloud-based next-gen data lake or data warehouse is the ideal option in this situation. They are easily queried and the structure lends itself to analytical applications, including machine learning, predictive analytics, and AI. By building a strong foundation, for BI and analytics, community banks and credit unions make a huge leap towards digital transformation and more closely competing with their larger industry peers.

Breaking Down Data Silos

Financial institutions have no shortage of data. However, that data is usually siloed across many systems throughout the organization. Aggregating and integrating that data is a major challenge that in the best case scenario, can be difficult and time-consuming, and at worst, nearly impossible (such as with transactional data). It can be especially challenging when working with vendor-hosted systems, such as your core, mortgage, loan origination, mobile/online banking, and CRMs. All of this data offers key details into customer behavior, so it is important to utilize all sources to get a complete picture of individuals, as well as the institution as a whole. This is why a singular data warehouse or data lake is essential for analysis and reporting. When all the data from various sources is ingested into this system, via internal connectors and external APIs, it is far easier to query and link the data to gain a 360-degree view of each customer or member, and discover insights you’ve never had access to before.

Ensuring Data Accuracy

Having a wealth of data and insights at your fingertips is only helpful if that data is accurate. Whenever data is entered into a system manually, it is an opportunity for mistakes to be made. If a name is misspelled in one system, it may not be obvious that it is referring to the same person in another system. Valuable insights into that individual’s behavior may be lost. On the other hand, if, in the course of ingesting data from an outside system, there is an error, would it be easy to detect the data loss? One way to mitigate these scenarios is to implement quality assurance technology and best practices. In this way, data discrepancies can more easily be detected and flagged for correction. To take it a step further, data preparation automation tools can be used to correct common mistakes as the data is ingested into the platform.

Using Timely Data

There is nothing worse than making decisions based on stale data. Circumstances can change rapidly, and the ability to be proactive in customer and member relationships is key to providing the personalized experience they have come to expect. For example, if a customer is showing signs of potential churn, banks and credit unions need to know as soon as possible in order to intervene. Transactional databases are changing daily, so it is important to establish a system by which the foundational data repository is updated regularly. For this situation, automation is essential. Manually ingesting data on a daily basis is time-consuming and can be unrealistic for many community banks and credit unions. However, utilizing automated data ingestion and preparation ensures that the data will be updated as often as necessary, with minimal to no human intervention required.

Acquiring the Necessary Talent

Developing a foundational analytics platform is no easy task. It can take huge amounts of time and effort to build an analytics-ready data warehouse from scratch. From planning and strategizing, to actual execution, it can take many months just to get started with any BI or analytics initiative. In addition, it can be challenging, and costly, to recruit and hire data engineers, analysts, and data scientists to run and develop custom algorithms. One way that mid-market financial institutions can save time and effort is to utilize a data platform built specifically for the unique challenges of the banking industry to accelerate the development process. A product that also allows you to utilize pre-built queries, algorithms, and dashboards can also shorten the time to deployment and, ultimately, insights.

Granting Access to All Who Need It

Once the data is compiled, it can be a challenge to get it into the hands of decision-makers across the organization. Will people access data through dashboards? Do they need raw data to perform deeper analysis? Will everyone have access to all data, or will some only need a smaller subset? Using a tool that gives users the ability to interact with data in a variety of ways, be it through code or visualizations, and that gives varying levels of access, is key to managing corporate data governance. Having a centralized data repository also ensures that all users are interacting with the latest, most accurate data.

Daybreak: A Complete Solution for Banks and Credit Unions

While there are a number of challenges to overcome in becoming more customer- or member-centric, it all begins with a strong data foundation. That is why Aunalytics has developed Daybreak, an industry intelligent data model that gives banks and credit unions easy access to relevant, timely data and insights, as the sun rises. Daybreak is an all-in-one analytics solution that automates key tasks—data ingestion across multiple disparate sources, data transformation and preparation, and quality assurance practices—built on a secure, powerful cloud-based data platform, to ensure data is always up-to-date, accurate, and accessible across your organization. It also allows users to connect to any outside source, visualization, or BI tool of choice, or they can leverage Daybreak’s user-friendly, guided Query Wizard and SQL builder interfaces to get to actionable insights.

With Daybreak, anyone across your organization can gain a deeper understanding of individual customers and members, while also acquiring a high-level understanding of the business as a whole. With access to the right data, at the right time, your institution can make better business decisions, faster.

Financial Services

Six Stages of Digital Transformation for Financial Institutions

Many financial institutions have been around for decades. They’ve successfully implemented the technology advances necessary to stay relevant, such as using the latest core systems and implementing digital banking services for their customers. However, the journey to a complete digital transformation involves both technical changes as well as strategic and organizational changes. To truly embrace technology and prepare for the future, each financial organization must embark on a multi-phase process to reach their full potential. We have outlined the six stages of this transformation to give banks and credit unions a high-level roadmap of what needs to occur in order to realize a complete digital transformation.

1 | Business as Usual

In the first stage of digital transformation, banks and credit unions are still maintaining the status quo rather than experimenting with new digital initiatives. Some are still hosting their business applications internally and are spending significant amounts of time performing required compliance audits. They manually compile reports using pivot tables in Excel or other spreadsheet programs. While each department has its own reports, there is little to no aggregation of data across multiple departments; only a manually created deck assembled and shared once a month. This means that they are limited to basic reporting metrics rather than deep analytics.

While they may discover some insights in their current data metrics, the insights that are gleaned from these manual reports may not be easily acted upon. Small projects may be taken on by individual departments, but there are no formal processes, and these projects are largely siloed from one another. Many times, the IT department “owns” the digital initiatives, and they tend to be tech-first rather than customer-first. Therefore, organizations are unlikely to see any significant outcomes from the small, one-off projects that are taking place during this stage, and they do not drive the overall business strategy.

2 | Present & Active

As the technology landscape evolves, it can be easy to get excited about new products and services that promise to revolutionize the field. But many banks and credit unions are not ready to go all-in until these claims are tested. Oftentimes, an experimental pilot project will get off the ground within one department. For example, they may start pulling new operational reports out of their core banking system, utilize very basic customer segmentation for a marketing campaign, or consider moving to a cloud-based system to host some of their internal applications.

However, their data continues to be siloed, insights and best practices around new technology initiatives are not shared throughout the organization, and there is little to no executive-level involvement. However, for most banks and credit unions, dabbling in new strategies and technologies is the first step to creating a sense of excitement and building a case for digital transformation on a larger scale.

3 | Formalized

As banks and credit unions begin to see momentum build from existing pilot programs, it is increasingly easier to justify investments into new digital initiatives. In the past, marketing, customer care, and transaction core databases had been siloed; separate reporting for each was the norm. However, in the pursuit of digital transformation, moving internal applications to the cloud is an important milestone on the path to creating a single source of truth and making data accessible across the enterprise.

At this stage, a clearer path toward digital transformation emerges for the bank or credit union. More formalized experimentation begins, including greater collaboration between departments and the involvement of executive sponsors. The development of technology roadmaps, including plans to move systems to the cloud and expand internal or external managed IT and security services, ensures that the bank is strategically positioned to advance its digital initiatives.

4 | Strategic

The pace really picks up in the next stage as collaboration across business units increases and the C-suite becomes fully engaged in the digital transformation process. This allows banks and credit unions to focus on long-term strategy by putting together a multi-year roadmap for digital efforts. This is the stage where an omni-channel approach to the customer journey becomes realistic, leading to a unified customer experience across all touch points—both physical and digital. Technology is no longer implemented for the sake of an upgrade, but rather, to solve specific business challenges.

However, some challenges may present themselves at this stage. As data is more freely accessible, the quality and accuracy of the data itself may be called into question. This accentuates the need for a strategic data governance plan for the bank or credit union as a whole.

5 | Converged

Once digital champions have permeated both the executive team and the majority of business units, it becomes necessary to create a governing body or “Center of Excellence” focused specifically on digital transformation initiatives and data governance across the company. This structure eliminates repetitive tasks and roles, and allows for a unified roadmap, shared best practices, and the development of a single bank-wide digital culture and vision.

Banks and credit unions can further refine their omni-channel approach to optimizing the customer experience by creating customer journey maps for each segment. This leads to optimization of every touchpoint along the way, both digital and traditional, and informs the overall business strategy. Marketing can start to run and track highly personalized campaigns for current customers and new customers.

At this point, one-off tools are abandoned in favor of an all-encompassing cloud analytics platform to gather, house, join, and clean data in order to deliver relevant, up-to-date insights. All employees are trained on digital strategy, and new hires are screened for their ability to contribute in a digitally transformed environment. In the Converged stage, digital transformation touches every aspect of the business.

6 | Innovative & Adaptive

The final stage of the digital transformation journey can be defined by continued experimentation and innovation, which, by now, is a part of the organization’s DNA. Investment in the right people, processes, and platforms optimize both customer and employee experiences, as well as operations of the bank or credit union as a whole.

Through the Center of Excellence group, pilot projects are tested, measured, and rolled out, providing a continuous stream of innovation. The data, reporting, and analytics capabilities of the omni-channel cloud analytics platform are integrated across every department, spreading from Marketing into Sales, Service, and HR, among others. Full personalization of marketing campaigns target customers that have triggers in their checking, mortgage, or wealth management accounts, or through actions taken via customer care or app. This allows the bank or credit union to make relevant recommendations on products such as loans, refi, wealth, etc.

Training programs are set up to bring all employees up to speed on the iteration and innovation cycle, and HR is closely involved in filling the talent gaps. Financial institutions may partner with or invest in startups to further advance their technology and innovation initiatives.


By embracing new technologies and setting up the processes necessary for a complete digital transformation, banks and credit unions are able to personalize the customer experience, enhance and streamline operations, and stay nimble in the face of changing times. No matter where your organization currently falls on this journey, your partners at Aunalytics will help you reach your ultimate goals by providing technology, people, and processes that can take your bank or credit union to the next level.

This article was inspired by research by Altimeter, as summarized in “The Six Stages of Digital Transformation” which can be downloaded here.