Understanding the big picture about big data

Your data has a story to tell and it holds the keys to cutting costs and unlocking operational efficiencies. But to achieve those results, you need to have more than descriptive analytics around your loan transactions. Keeping a broader data journey in motion will provide many benefits in the long run, including competitive pull-through rates, faster time to close, best in class productivity, and more. To take advantage of these business opportunities, you need to truly make your data work for you. Begin by assessing and implementing foundational data tools, including options for data processing, transfer, warehousing, and  business intelligence. These tools will provide deeper organizational insights and establish a solid analytics foundation. 

Our new eBook, The Big Data Revolution details the steps lenders can take to fully optimize their data journey, and select the right technology solution. In this post, we break down some key elements of this eBook.  

Beginning the data journey

Data has proven to be a key competitive differentiator in the mortgage industry. Top-performing lenders understand the importance of having access to all of their data and ensuring that data is clean, well-performing, cataloged, and secure. Data maturity is a measurement of how advanced a company’s data analysis and data practice has become. Achieving data maturity is not a “one and done” proposition, it’s a journey that can take years to complete. The reward is increased organizational sophistication that will put you in a position to grow your business and become more operationally efficient. 

As organizations develop their use of data and improve their adoption of advanced analytics, they develop characteristics that we have identified as the four stages of data maturity. These four stages show how companies can look at their overall data processes, ask difficult questions, and start a discussion about how to do more with data. These stages can also help organizations make long-term plans to develop their data and analytics team resources.

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Approximately 40% of large lenders are in the predictive or prescriptive stages of their data and analytics journeys as show in the graphic above, compared to 22% of mid-size and 28% of small lenders.

Ellie Mae, now a part of ICE Mortgage Technology, recently surveyed nationwide mortgage lenders, and have outlined where they stand along the data journey continuum: 

Descriptive:

37% of lenders have just begun the process and can see simple facts about past business performance. For example, they can look at mortgage loan status data over a given time frame to determine what happened, but they have limited insight into why the results occurred. 

Analytical:

36% of lenders have reached the stage where they understand not only what happened, but why.

They have agreed on what is measured and resolved inconsistencies in their data sets. They have also developed robust data governance models to ensure data consistency and predictability. 

Predictive:

24% of lenders have taken this a step further and are using data to see patterns and meaningful trends that affect their business. They can also predict additional originations and closed loans based on demographics, marketing spend, and/or region. As a result, they can forecast “what happens next?”.

Prescriptive:

Only 3% of lenders are far enough along in their data journey to conduct the type of prescriptive-level analysis that can inform how they should make future decisions. These lenders can utilize artificial intelligence tools, such as Ellie Mae AIQ™, to automate underwriting decisions and other business functions. 

Data management best practices

No matter where your company is today on its data journey, these best practices will enable you to advance your data and analytics strategy:

  • Recruit a C-level sponsor. Their support within the organization can help properly resource and prioritize initiatives.
  • Invest in team and technology. Acquire the right tools and deploy people with the right skill sets to use, manage, and customize those tools.
  • Go beyond technology. Pay attention to internal cultural changes and ongoing employee training needs. 
  • Create a common data model. A common data model will help ease implementation with consistent calculations, nomenclature, and data reporting, all of which goes through a common approval process.
  • Implement proper governance policies and procedures. All key stakeholders need to agree to use and follow this model to achieve repeatable and predictable results.

There’s much more to learn, so download the full eBook now to gain insights on how to plot out a meaningful data and analytics strategy.  

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