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.

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.

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.

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.

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.

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.

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.

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.

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.