Top 5 Analytics Challenges Businesses Face

Many companies are leveraging data and analytics to create a competitive advantage in their industry and grow their business. The insights they glean can help manage risk, improve customer experience and develop better products and solutions faster. In fact, 75% of business leaders indicate the key source of value from data and analytics is growth, according to Chicago Analytics Group.

However, as companies sit on an increasing amount of data, many struggle with how to apply data to solve critical business needs. They want to market more effectively and acquire new customers who fit their criteria. They strive to balance risk and growth.  For example, financial institutions often want to improve consumer risk assessment in multiple segments (e.g., thin files, millennials, and subprime) in order to increase share and profitability, while maintaining acceptable risk levels. 

“Navigating these strategies is complex and can lead to significant challenges,” says Mike Griffith, SVP of Equifax Ignite Global Platforms. “When working with customers, we’ve defined five areas that prove to be troublesome when it comes to managing data and developing insights that help solve problems. Once they gather data and link it from various sources – which can be challenging – they often get stuck trying to generate insights from the data, deploying those insights and optimizing the decisions they’ve made.”  Here’s a deeper look into those top five challenges and how companies can address them.

1. Accessing Data

Businesses have access to more data than ever before. Identifying data sets that matter has become more important.  Businesses need relevant, unique data that goes beyond traditional credit data. Premier data providers like Equifax have access to the most predictive, alternative data available. This includes data about employment, income, wealth, marketing, transactional and more.  Approximately 91.5 million U.S. consumers don’t have a credit file or sufficient information in their file to generate a traditional credit score1. These “credit invisibles” range from millennials just entering the workforce to recent immigrants who have yet to establish credit. Alternative data can provide more detail around consumer transactions and financial behavior. This helps businesses see beyond traditional credit to discover emerging risk and opportunity.

2. Linking Data

Multiple data sources can lead to data integrity problems. Not only do businesses have to manage their own data, they must assess the quality of third-party data. Then they have to connect these data sets to their data and customer accounts. For example, is the data in the proper fields? And does each data set use the same types of data?

Recent studies indicate 95% of businesses need help managing unstructured data.

Working with an established data provider can help companies link disparate data across multiple sources. This includes cleansing and unifying data into a standard format so data scientists and analysts can use it effectively. Proficiency in keying and linking improves data quality, consolidates customer data, and reduces risk by using keys rather than personally identifiable information (PII) to manage portfolios. And by connecting previously unknown relationships between records and accounts (consumer and business), organizations can cut costs associated with maintaining multiple databases, inaccurate data and redundant information. 

3. Generating Insights

Once businesses have relevant data and have integrated it appropriately, they need to drive more predictive insights. That means testing and analyzing with a full population set, as opposed to a sample set. Data scientists and analysts are better equipped when they can use a single, streamlined environment rather than working from a disjointed network of data sources, internal resources, analytic tools and modeling techniques. With cloud-based, scalable computing capacity, analytics teams can manage mass amounts of data, faster. They can generate insights using techniques like machine learning and artificial intelligence to tease out the most appropriate insights for their business. 

For example:

• Attributes, sometimes called features, are critical building blocks to insights, helping users make data more consumable for downstream predictions and decisions. Equifax's Attribute Engine is a development and deployment framework that allows developers and businesses to build, test and manage attributes using a high-performance computing environment. It uses a web-based tool that directly reads attribute code to provide attribute definitions, data lineage, duplicate detection and other features.
• Advanced Model Engine, a model development and deployment library at Equifax, uses big data and distributed computing to create and seamlessly deploy models. It creates explainable AI-enabled models and scores using Equifax patented technologies on larger samples at a faster pace and with less friction. Advanced Model Engine leverages use case configuration, machine learning techniques, alternative and trended data, and processing power to test more model configurations for better performance.

4. Deploying Insights

It takes a lot of heavy lifting to move from data to insights. Oftentimes, companies build highly predictive attributes and models. But they must wait six to eight months for their internal resources to deploy the projects. How do you take an insight and quickly deploy it for decision-making?  

The key is automated deployment, which leverages multiple integrated platforms to move from analytics-to-production (A2P) faster.

Companies now have the ability to develop and deploy with minimal friction. Most players in the industry handle this level of automation by managing the recoding step. Typically, they build a model in a development code. Then the business creates specifications about the model because it cannot run in production. 

These specifications are then used to recode the model for deployment, adding significant time to the process. With automated deployment, that recoding step is eliminated, and the model can be deployed directly to production. 

Companies also can automate deployment by using a flexible, cloud-based decision management system. For example, InterConnect® from Equifax helps companies define, implement and automate decision policies at the front line – without having to rely on IT. 

With advanced tools embedded, such as Rules Editor, data scientists can define, test and edit rule policies on the fly.  This leads to quicker, better insights. [embed]http://youtu.be/y68e83SJhhs[/embed]

5. Optimizing Decisions

After deployment, businesses must monitor the performance of decisions to understand whether to alter risk and marketing strategies. How do you know that the insight you generated a year or two years ago is still relevant today? Could those decisions have been better? How would you optimize those decisions in the future?

The answer is having access to a feedback loop of your own data to enhance and update performance quickly. 

Take those decisions, feed them into an analytical environment, and append performance data. Then, analyze the outcomes to determine if the right decisions were made. Having an automatic feedback loop integrated in a decision platform helps companies quickly evaluate whether the models and decisions they are using are providing the business outcomes they expected, allowing them to rapidly adjust and continually evaluate their performance with actual results. 

 Equifax can help you efficiently move from data to deployment. 

With Equifax Ignite®, you can use a single, connected suite of advanced analytical processes, technology and tools to get to market faster.   1. Equifax Data Analysis, July 2019