The foundation for a successful analytics program is data. But, without accurate and quality data, it’s nearly impossible to make informed business decisions. As data becomes an even greater asset for the credit union, the ability to store large amounts of complex data in a unified, central database, known as a data warehouse, is critical.

The data warehouse is the perfect solution for credit unions that are trying to centralize data integrated from several applications. This single source of truth is the repository for all the data that has been collected and integrated from multiple sources across the organization. It puts all of your data into one place, accumulating history, and makes your information easy to access and analyze by your team. The result is that everyone in the credit union is using the same data derived from the same source, which leads to quick, easy access to accurate data and better decision-making.

Data warehousing solves the ongoing problem of analyzing separate data and converting it into actionable information you can use. Consider these benefits a single source of truth can bring to your data analytics initiatives:

Greater Data Governance and Consistency:

Creating an ongoing set of rules, policies and procedures for collecting and managing data ensures that the credit union’s data strategy and business strategy are aligned. Without a single source of truth, data governance and data quality disciplines that govern the overall management, usage, storage, monitoring, and protection of the is data is nearly impossible. Consistent, high-quality data leads to strategies that create enhanced member service, greater employee productivity, data-backed decision-making, and better business outcomes.

Improved Efficiencies:

With a data warehouse you can process, integrate and consolidate large volumes of complex data into a single stream of data. This eliminates the need to rely on multiple data sources for reports and analytics. Employee time is saved, and consistency and accuracy are greatly improved across the organization.

Enhanced Business Intelligence:

By integrating data from various sources into a single source of truth, managers and executives will no longer need to make business decisions based on limited, inaccurate data or gut instincts. In addition, data warehouses and related BI can be applied directly to business processes including marketing segmentation, member retention programs, financial management, and sales and other business reporting.

Time Saving Reporting and Process:

Data warehouses are also designed with speed of data retrieval and analysis in mind. You are able to store large amounts of data and rapidly generate results. Since data analysts and business units within the credit union can quickly access critical data from a number of sources—all in one place—they can confidently make informed decisions on key initiatives. They won’t waste time retrieving data from multiple sources and guessing which data is updated and most accurate. A single source of truth also allows your credit union to analyze the data effectively as well as trust it! It creates accuracy, dependably and unrestrains the data so that more than just the analysts and CDO/ data scientists can access it. All employees can use the data.

Positive Return on Investment:

Having a single source of truth for your data enables the credit union to generate higher amounts of revenue by making more timely, accurate and informed business decisions. As data warehouses produce greater efficiency, employee productivity and time savings, credit union can often see a clear cost savings due to the implementation of a data warehouse.

Don’t be encumbered by a number of common challenges that stem from not investing in a data warehouse for your data analytics program (e.g., impaired decision making, inaccurate data, slowed workflows, negative business outcomes, inefficient processes.)

It is clear, when a data warehouse is implemented and designed properly it leads to significant advantages for your organization. Do you need guidance on selecting the right data warehouse for your credit union and ensuring it is properly implemented? The Knowlton Group is ready to help! Contact us today.

It has been a long journey from the early days of credit union business intelligence solutions. As your credit union leverages the benefits of a data analytics program, you will need all the capabilities required to make it easy for management teams, data scientists, and analysts to store data and extract information of any size, shape, and speed across multiple platforms. When it comes to planning and budgeting for the right tools, it’s important to know what products and tools are available and the differences between them.

Two key terms you may have been hearing:  Data Warehouse and Data Lake. Both have many definitions from various business savvy techies, but let’s dig deeper to help you understand what they are and how they are different.

What is a Data Warehouse?

Credit Unions use reports, dashboards, and analytics tools to extract insights from their data, monitor member transaction activity, and to support decision making. These reports, dashboards and analytics tools are most effective and efficient when powered by data warehouses which store modeled and structured data efficiently to deliver results quickly.

The data warehouse integrates data from multiple data sources including the core, loan origination systems, online banking platforms, CRM systems, and more  into one centralized, single source of truth. The data that is uploaded each day to the data warehouse is then made available to run complex queries fast and efficiently.  Information stored in a data warehouse is historical, spanning member and transaction information that has occurred over time. The data warehouse aggregates and structures information to provide a 360-degree view of your membership including their products, services, online banking utilization, credit and debit card usage and so much more.  With a data warehouse, the credit union can instantly gain insightful information for better decision-making, leading to improved business outcomes.

What is a Data Lake?

Like a data warehouse, a data lake is also a data storage repository. However, a data lake stores raw (both structured and unstructured) data using a flat architecture for storing data. In a nutshell, a data lake is a data storage and processing system where a credit union can place internal and external data that does not fit into a typical data warehouse.  In a data lake, vast amounts of raw data in its native form is stored.  The data lake retains ALL data and keeps it in its unrefined state that is then transformed and defined only when ready to consume.  Since the data lake stores data of all kinds, this allows highly skilled analysts and data scientists to explore the raw data in new ways, helping with projects that have diversified data. The data lake allows for complex algorithms to identify patterns and trends that will power real-time decision-making analytics and business opportunities.

Now that you are more familiar with the key basic differences between a data warehouse and data lake, the next step is to determine your organization’s needs and objectives to identify which one is right for the credit union. Stay tuned for our next article that outlines the pros and cons of a data lake.

The Knowlton Group can ensure your business goals are met with a data strategy assessment and a business intelligence roadmap.  To learn more about how data analytics can help drive your business practices, contact us today.




database design

Everyone seems to have built one and those who haven’t want one. A rational person might question whether or not this is simply a fad – a buzz word to throw around in conversation – or if there truly is a business justification for the effort and expense involved in data warehousing. For small and medium-sized companies, a data warehouse and business intelligence solution can seem to be a daunting expense. While those concerns are not unwarranted, a successful data warehouse implementation can yield significant value.

1) Time Savings

A data warehouse can save your organization a significant amount of time. Transactional databases are not designed to handle the type of analysis-oriented queries that a data warehouse is capable of. Data warehouses are primarily driven by read operations (querying against the data) as opposed to being heavily write operation driven (inserting data in the database). A data warehouse, designed for read operations, will return data much faster than your existing transactional databases. The turn-around time to produce useful information is greatly lessened allowing your organization to make more decisions faster.

2) Centralization and Data Source Integration

Integrating various sources of data can yield incredible cross-functional insights into your business operations. A retail company might integrate their CRM system with their marketing and sales databases. A financial institution might look to integrate their loan origination system with their core processing system. Both instances represent examples where organizations can integrate two disparate data sources into one centralized location. Data warehouse design allows you to analyze several measures (housed in fact tables) like counts, amounts, and averages against several dimensions (like product, location, branch, etc.).

Think of this scenario and see if your organization has a similar task. Let us assume you house your customer sales data in a Microsoft Dynamics CRM system. Your marketing survey information might be housed in another database or some other third-party application. A data warehouse would enable your organization to query your sales data AND your marketing survey results at the same time for side-by-side comparison. Perhaps you wish to compare customer satisfaction ratings of your sales force side-by-side with their sales metrics for the current month. Through the centralization and data source integration a data warehouse provides, you are able to identify unique cross-functional insights into the health of your organization.

3) Historical Analysis

A data warehouse can be developed to enable historical trending and pattern recognition. Most CRM systems, for example, contain only the current location of a customer. If your organization is interested in the migration of customers over time, your data warehouse can be designed to handle that type of request. If product categories change over time, most often they are simply overwritten in the source system. If you would like to track what product category a particular product was in this year, versus five years ago, your warehouse can be developed to handle such queries.

With more businesses understanding the value of trending and modeling, the ability to leverage historical analysis can yield valuable insights into past and future outcomes.

4) Automate Data Extraction and Reporting Tasks

Manually extracted data sets and manually run reports are a common practice in source systems. Having those basic tasks automated can yield significant FTE savings with minimal upfront effort. Leveraging SQL Server Integration Services (SSIS) and SQL Server Reporting Services (SSRS) – both included in a standard Microsoft SQL Server installation – allows you to automate data extracts and report creation. As a complement to the aforementioned benefits of a data warehouse, automating these tasks can give a low-effort ROI boost.

5) Unburden I/IS Departments

Many organizations have an IT/IS group responsible for creating data extracts from the source system. A data warehouse takes that responsibility off of the shoulders of the this group. This allows the IT/IS group to focus on maintaining and improving the source system without handling tasks that the end users could perform themselves with a data warehouse.

These are only a few of the benefits a data warehouse can provide your organization. However, a data warehouse is not a “plug-and-play” system. It requires in-depth business analysis and business process research to understand how best to design the warehouse. It takes time to develop the ETL (extract, transform, and load) and validate the data in the warehouse. If the proper planning occurs, however, your organization will have a data warehouse that is built to last and provide a significant return on investment. Your successful data warehouse will become the platform for your organization’s business intelligence strategy.

For questions about data warehousing and business intelligence solutions, contact The Knowlton Group here.

(Photo Credit: tec_estromberg via Flickr)