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If they are, you’re not alone.

The recent Rexer Data Science Survey found that only 10-15% of companies “almost always” successfully deploy analytics models.

If your organization isn’t in that top 15%, you’re probably already feeling two things:

1. Frustration caused by deployment delays
2. Pressure from above to make it happen

So, before I get into the challenges preventing rapid deployment and how organizations can overcome these hurdles, let’s answer the bigger question… why should we care about model deployment rates?

There are many reasons why a business needs to be able to deploy a new risk model quickly and easily, but there are a few that really stand out in today’s digital-first world. Rapid deployment:

1. Drives business growth—Analytics models are a key part of a risk strategy, they help drive business growth by making risk decisioning more accurate, which means more customers and lower default rates.
2. Improves customer experience—Customers now expect instant everything, risk and analytics models help businesses make real-time decisions and gain customers in increasingly competitive markets
3. Empowers competitive advantage—Companies that can test and deploy models quickly are able to make iterative changes to models using the most up-to-date data, making them better able to adapt to market demands.

One of the biggest reasons strategic analytics projects are often deployed late is the disconnection between the risk team and the development team.

The root of this developer-data scientist disconnection is that the two different groups literally don’t talk the same language. The modeling languages of choice for data scientists are generally Python, R (both open source languages), and the proprietary SAS. These are not usually the same languages preferred by developers, who favor Java, JavaScript, and variations of C such as C++.

So, typically data scientists create and test their analytics models—say a credit approval and verification application—using their languages. This work is then sent to the development teams, who then often spend a lot of time and costly effort recoding into their own languages so the model can be tested for security, compliance, impact on the infrastructure, and so on. Any changes that need be sent back to the data scientists for further review and approvals will kick off the same lengthy recoding processes, only in reverse.

The result? Fast time-to-market goes out the window. And if projects are deployed late enough, market conditions often will have changed so much that the reasons for deploying in the first place no longer exist, and the project is essentially dead on arrival.

Another culprit in the model development and deployment process is the fact that data is very often located all over the organization in protected silos. This is particularly true in highly regulated industries like financial services, where security and privacy concerns meet compliance realities. Historical data may be found in one or more silos, and transactional and production data in others. Data scientists needing elements of all these data have to root around to find and gain access to it.

But that’s not all, the digitization of many types of data has led to a huge range of new data sources, many of which can be highly useful to data scientists when predicting credit risk or fraudulent activity. As each new data source emerges it needs to be integrated into the businesses decisioning solution if it’s to be utilized by analytics models.

While integrations should be simple, many organizations struggle with creating or updating data source integrations due to inflexible technology that requires extensive hardcoding. Each new data source included in a model can result in lengthy delays to model deployment as they need to be completed before the model can be fully tested and pushed to a live environment.

It’s fair to say that many of the delays to risk model deployment are caused by processes, not people. It’s also fair to say that the rapid advancement of technology has made it difficult to keep up with new analytics models to tackle an ever-evolving model. So, what can you do about it?

Well, what if your process problems caused by technology, like having to translate models from one language to another, or manually updating hardcoded integrations, could be solved by technology?

So, instead of your risk team creating a model in one language, then your dev team translating it into another language for your risk engine, you could opt for a model agnostic risk platform instead.

For data scientists and developers ‘talking different languages’, being model agnostic effectively removes the intermediate steps of recoding between the two different teams. Instead data scientists can upload their models directly in their native languages, which allows them to fully utilize new analytical techniques.

These types of platforms help prevent the loss of analytics models that never get deployed due to prolonged development and deployment cycles.

Technology can also be an effective solution for data integration challenges, which both fintechs and traditional financial institutions still struggle with as a result of hardcoded connections that are often built to serve a specific purpose at a specific time.

Today’s digital market requires businesses to be able to create agile technology that can be quickly updated or repurposed throughout an organization to meet many needs.

For optimum flexibility and business agility it’s essential that data integrations can be created, used, reused, and updated quickly and easily. Again, integrations have traditionally relied heavily on over-burdened dev teams for what should be simple adjustments. Instead of following these traditional integration processes businesses now have the opportunity to use technology that empowers business users to handle the integration mapping process.

This means that the risk team can be far less reliant on the dev team for ongoing adjustments as they can easily map source data into analytics models.

What this really comes down to is using technology to simplify business processes and empower people to do more. By using specialized software solutions that remove steps in the model deployment process and reduce the reliance on development your risk teams are able to focus on current problems and initiatives to drive business growth. They’re able to respond more quickly, make changes more easily and implement a risk strategy much more efficiently.

The Simple Solution to Integrating Structured and Unstructured Data Sources.

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Innovation is a term that is frequently used to describe fintech businesses, whether it’s an innovative culture, an innovative product, or how they’re creating innovation within the industry. With the fintech space dominated by startups it isn’t surprising that innovation is thriving, after all, it’s much easier for new businesses with a small team and developing culture to build innovation within their business than it is for large financial institutions.

Startup fintechs have the advantage of agility over many of the established businesses in the financial industry. So, what does this mean for financial businesses with many employees and a long-established culture, is innovation just a pipe dream? Peter Nyberg, Group Director of Risk & Credit at Dun & Bradstreet doesn’t think so.

When it comes to innovation, if anyone is in a position to understand what it takes to transition an established business into a digital-forward, innovative organization, it’s Peter. As a leader within Dun & Bradstreet, he’s seen first-hand both the ingredients needed to adopt innovation within the organization and the steps Dun & Bradstreet’s clients are taking to create innovative change within their companies.

Dun & Bradstreet has taken an interesting path to become the company they are today, which is one of the most in-demand data bureaus and analytics firms in Europe. In its early years, Dun & Bradstreet purchased many small businesses to build the data giant that it is today, and Peter is quick to point out that creating a united team within an organization that is the child of 70 different entities is no easy task, but it’s essential for the success of their business.

“You cannot be competitive if you have 37 different ways to do the same thing, if your knowledge is found in 18 silos, or your data is stuck in legacy monoliths.”

For Dun & Bradstreet to succeed in using digitization to drive the business forward it had to take its existing competitive culture and transition towards a cohesive culture. To do this they created defined business goals that the team could be united around. So, when the usual objections and debates about how things are done occurred, they helped drive innovation instead of creating problems.

Dun & Bradstreet has worked hard to adopt a digital mindset and to empower their team to innovate, and while this is an ongoing process, Peter can identify one key step that became the foundation for all future changes within Dun & Bradstreet:

“There was a key turning point: setting down for the first time a set of key initiatives that align all the forces within Dun & Bradstreet, and highlighting how those initiatives take us towards being a different company.”

This cohesive understanding of core competencies, business goals, and the next step towards achieving these goals helped Dun & Bradstreet create an organization that was digital-forward, ready to adapt to change, and innovative. It provided the glue that formed one unit out of 70 companies and allowed them to transition from data bureau to leading data analytics business. A change that not only helps them be more innovative but also helps drive innovation within their client’s businesses.

When businesses reach out to Dun & Bradstreet it’s normally because they have a question that they can’t find the answer to. Whether it’s simply a question of using data, about efficiency, cost-reduction, risk decisioning, compliance or even the best way to adapt to a changing market, to find the solution it’s essential for a business to commit to making a change. Creating change in an organization is difficult, creating change in an organization that isn’t committed to evolving is almost impossible!

Whether the business can maintain focus on their goal. Digitization is an exciting opportunity for many businesses, and when people think about digitization and innovation they often fall into the trap of focusing on the technology. In Peter’s experience working with financial institutions he says, “Our clients jump into ‘we’re going to use this piece of software or that solution’ or ‘we’re going to hire so and so, many analysts, and a data scientist’, and often they do but somewhere in that the end objective is forgotten and not reached.”

When it comes to using digitization to innovate it’s essential for a business to look at all parts of the puzzle and what pieces are needed to reach their goal. Peter is a strong believer in digitization being as much about people and culture as it is about technology. Take for example a bank that has the technology in place to use risk-based pricing but doesn’t have the shared understanding or cultural awareness needed to implement that change. The technology’s capabilities and innovation opportunities are being wasted because the people weren’t ready for the digital approach.

While organizations fail to reach goals for many reasons, Peter says failure is often the result of, “businesses approaching change with a big bang method.” Creating a digital mindset within an organization can’t happen instantly, it needs a step by step method to ensure that all parts of the business are fully aligned with the new business approach. Peter used the analogy of an Oil Refinery as it’s been said many times that Data is the new oil of the digital economy. An Oil Refinery isn’t successful just because it has oil—its success is down to knowing how to access the oil, transport it, refine it, sell it, and get it to the purchaser. Most importantly, what the Oil Refinery very clearly needs to understand is what fuel is fit for which engine, and distill accordingly.

There are steps every organization must take in order to get to their goal and Peter is a huge proponent of using agile methodology to help businesses achieve their targets. He believes one of the key benefits of the agile approach for innovation is that you always have a functioning business.

“In terms of getting better at digitization, there is a lot to learn on the business side from thinking about gradual development of digitalization and processes, and the outcomes you seek.”

Instead of making large changes that the organization isn’t ready for, you can take incremental steps that slowly transition the business to where you want to go. It’s a gradual move forward that lets all stakeholders within a business adapt to the digital methodology and gives the team the opportunity to learn from each step and use this knowledge to improve future development phases.

“This is especially powerful when it comes to digitalization. You can, in fact, capture your inputs and your outputs. 3, 6, 12 months later you can go back, and see if what you predicted would happen actually did happen; and you can improve.” Peter again uses the Oil analogy to describe what he sees all too often: “In the late 19th century, the gasoline portion of distilled oil was often dumped into rivers. It was simply too explosive and difficult to use. Even today the norm seems to be for companies to throw away their most valuable data asset—the outcomes their digital processes generate and how they compare to what later on in hindsight would have been most valuable. And thus, their business continues to run on data diesel.”

What it’s clear to see from both Dun & Bradstreet’s transition to a digital-focused, innovative company and Peter’s experience with financial services clients, is that change is possible, even in the most divided organizations. Adoption of the digital mindset doesn’t happen overnight, it requires a long-term commitment, a clear goal, and a step by step process to move the organization forward as one cohesive unit. Adopting digitization to allow your business to innovate is a risk, but it’s also a risk you’ll need to take to keep your organization competitive.