The Cost of Low-Quality Data for Decision Intelligence

In the quest to achieve unrivaled business growth, organizations show increasing interest in Decision Intelligence (DI). Whether you use DI to augment, recommend, or automate decisions, the effectiveness of your DI endeavors heavily depends on the quality of data that powers it. As financial services and global businesses alike expand the use of DI, weeding out poor quality—within both internal and alternative data—isn’t a luxury. It’s a necessity.  

Why data quality matters in Decision Intelligence

The strength of your decision-making process hinges on the caliber of your data. When you operate on low-quality or outdated data, you risk steering your business down a costly path.

The proof? A 2018 Gartner survey revealed that organizations attribute $15 million per year in losses to poor data quality. What’s more, 60% admitted to not measuring how much bad data costs their businesses. As a result, that $15 million figure failed to capture a significant portion of likely financial losses at the hand of bad data.

Given that organizations have only increased dependence on data in the ensuing years—and poor quality remains an issue—the costs continue to skyrocket.

5 factors that contribute to poor internal data quality

While quality control for internal data falls under the purview of your organization, lapses remain all too common. Just breaking down the silos to improve data availability takes a concerted effort. Maintaining the integrity and usefulness of internal data demands even more. Here are some factors that typically contribute to the degradation of internal data quality:

1. Inaccurate data entry: Human errors during data input can compromise the integrity of a dataset. Typos, incorrect categorization, and skipped fields are common culprits.

2. Data decay: Failing to update records with the latest information can lead to stale data, which can skew decision-making processes. For instance, B2B marketing contact databases decayed at a rate of 30% a year in 2018, however the combination of a global pandemic and the ongoing ‘Great Reshuffle’ among employers has led to a 50% bounce rate in 2022.

3. Inconsistency: Different departments or teams using different formats or units of measurement create inconsistencies, making the data unreliable for analysis.

4. Lack of standardization: Without a consistent set of standards and guidelines for data collection, organizations open the door to errors and inconsistencies.

5. Contextual gaps: Even the most well-maintained internal data can lack external context. This gap can be filled by supplementing internal data with third-party news, regulatory, legal, marketing, and industry data.

MORE: Eight ways to use alternative data to improve your data modeling

5 considerations for sourcing external data

Alternative data brings in the added layer of granularity that traditional data often misses, optimizing the performance of your DI tools. It turns decisions from being merely data-driven to being exceptionally insightful. But while it can offer additional dimensions and perspectives that internal data may lack, the quality of alternative data can make or break its value for DI. Here’s what you should be alert to when identifying a data as a service provider:

Source variety

A narrow range of sources—within a specific geographic region, for example—could lead to potentially misguided strategies for organizations with a global footprint. Similarly, relying on data that favors a particular viewpoint can lead to skewed insights.

What should you look for? A wide range of data from news, legal, regulatory, and other sources. For example, look for aggregated global news data from sources spanning an entire spectrum of viewpoints—conservative, liberal, and neutral.  This ensures that your DI tools have comprehensive, well-rounded data to support confident decisions.

Broad chronological span

Data that lacks historical depth or real-time updates could result in decision-making that is either reactive or outdated, missing crucial trends and patterns.

What should you look for? Datasets spanning historical and current data, providing the necessary temporal context for insightful analysis and forecasting.

Data usability

Manually cleaning and structuring messy data is time-consuming and diverts valuable resources from analytical tasks, prolonging your time-to-insight. In fact, data professionals spend 40% of their time checking data quality. And all that wrangling adds to the costs of low-quality data.

What should you look for? Clean, semi-structured data that requires less wrangling. Not only does this enable faster time to insight, but it also frees your data scientists to focus on higher value work.

Data searchability

Finding relevant data is a monumental task given high volume. If the data you bring in lacks tagging and metadata, it perpetuates challenges with searchability.

What should you look for? Datasets that feature useful enrichments, including topic tags, sentiment, and additional metadata can streamline the search process, allowing you to integrate more targeted datasets into DI for more effective decision-making.

Data delivery

A cumbersome data delivery system can lead to integration challenges, delayed access to data, and consequently, slower decision-making.

What should you look for? A flexible, easy-to-integrate API allow for quick and seamless ingestion of data into your existing systems, thus accelerating the decision-making process.

Recognizing these factors can help you focus on improving both internal and external data quality for more effective Decision Intelligence applications.

MORE: Using third-party data to empower decision makers

Alternative data and its crucial role in Decision Intelligence

Alternative data spans diverse sources such as global news data, legal data, regulatory data, patents data, and more. This third-party data lends a broader perspective, an essential factor in today's age where information comes at us like a torrent. Notably, news data for Decision Intelligence serves as a timely resource for monitoring market trends, understanding consumer patterns, and being aware of geopolitical shifts, thereby adding layers of context to your existing data reservoir.

When alternative data is of subpar quality, you run the risk of muddling your DI tools' efficiency. Inaccurate or inconsistent data can distort longitudinal analyses, skew real-time insights, and ultimately affect your Return on Investment (ROI). You're not just dealing with poor decision-making; you're also incurring financial losses.

Investment in high-quality alternative data can dramatically reduce these costs. It enhances your Decision Intelligence, thereby ensuring each decision you make is not just robust but also strategically advantageous.

Alternative data isn't merely an add-on; it's a prerequisite for optimizing Decision Intelligence. Ensuring quality in this data type is crucial for informed, financially-sound business strategies. With Nexis® Data+, you gain access to a wide range of high-quality, enriched alternative data that can elevate your DI tools and, consequently, your business decisions. Don't compromise on quality; make it the cornerstone of your Decision Intelligence strategy.

Ready to get started making high-quality decisions from high-quality data? See how the right data can take your decision making to the next level in our new white paper, "The Future of Decision Making.”