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Establishing Organizational Digital Trust

Why Your Data Products Are Only as Good as Your Data

With big data powering the optimum business decision-making in this century, organizations need to generate trust in their data sources which otherwise proves to be a source of risk. Data is now ubiquitous — according to Statista, the aggregate data volume generated was 64.2 zettabytes in 2020, and it is only predicted to shoot upwards to reach 181 zettabytes by 2025 (hbs). Such metrics make one thing clear: business networks are now becoming increasingly saturated with information.

In a modern business operation, there has been a shift from experience-based decision-making to a greater reliance on data-driven strategies. Robust business and data analytics enable organizations to uncover unique trends and patterns from complex data sets. These data-based insights ultimately end up propelling ideal business strategies.

Do You Trust Your Organization’s Data?

Despite data’s pervasiveness in decision-making across industries and organizations, our relationship with data remains complex. For what good are our data sources if we can’t trust them? More than often, data reliance poses a risk for businesses. We see that legacy data infrastructure for data management, i.e., data warehouses, often cannot scale to the volume of big data. People also make mistakes, and machines aren’t always foolproof. Furthermore, since data is channeled through complex pipelines coded by multiple developers: there is always a risk of buggy code introducing errors.

The consequences are evident: according to a survey, 60% of business executives don’t always trust their company’s data, and more than a third don’t even base their decisions according to data and prefer to go with their gut instinct (talend). Thus, we observe that in organizational management, most executives who have had decades of experience in their area of expertise are now reluctant to switch from experience-driven decision-making to data-driven decision-making. Although this may be credited to the fact that data-driven decision-making renders experience irrelevant, which may undermine the executives’ role, a more foundational reason is the trust in data itself.

This presents a crisis for organizations worldwide. If a decision-maker doesn’t trust their organization’s data sources, what are the decisions being based on? Often, executives rate their overall companies’ data capabilities poorly. This reasoning may be understandable since even a tiny amount of insufficient data can lead to catastrophic business results. However, in an age where most businesses are likely to hold their data to the highest standards, a lack of trust in data means that the organization’s credibility is weak, alongside a widening communication gap between different teams.

This is why building trust in data should be central, which permeates across the organization. Trust in corporate data allows its teams to design exceptional customer experiences, improve general operations and drive innovation. It also gives them the confidence to base decisions on a complete, accurate, and timely picture of the natural world which is likely to result in more favorable business outcomes.

Trust in data means that an organization has confidence in its data sources and is prepared to act according to the insights observed through analytical mechanisms. However, this trust cannot be taken on faith and must be earned and quantified. In building this trust in data, organizations need to ensure that everyone in the data supply chain understands its critical role, alongside incentivizing employees to input the data accurately. The second step is finding a better fit: businesses need to update their legacy data infrastructure to choose tools that can better fulfill their data needs. The analytical teams also need to understand the ML model that is being implemented and ensure that results can be replicated and proven to build confidence and transparency in the process (Forbes).

Data Trust Framework

The power of data-driven decisions clearly outweighs its risks. In the age of big data, its risk can be converted to trust by implementing a robust data trust program. A data trust infrastructure can be understood as a legal framework that lays the foundation for successful enterprise analytics programs. Between third parties, citizens and government, it promotes collaboration. Moreover, it enables businesses to securely connect their data sources and create a new shared repository of data, facilitating the transparent use of data. To simplify the framework, data trust can be considered stewardship that manages someone’s data on their behalf. The infrastructure is built on the following three foundations (EY):

  1. For data trust to be generated, the fundamental step is understanding all the data collected, stored, and processed by the organization. To bridge the communication gap, executives must interact with process owners to identify how data flows across the organization.

  2. Data trust also requires that human processes be considered alongside technology. Therefore, individuals are brought to the center of the conversation, guiding decision-making and ensuring that stakeholders benefit from corporate data processing.

  3. Alongside humans, trust in technology is required as well. Before updating to emerging technologies, existing data management tools must be analyzed to retain the ones that help you generate trust.

A data trust framework not only boosts business credibility but is also now an increasingly legal requirement. The majority of data trusts owe their existence to solid open-source technology, a way to de-monopolize the internet by enabling users to store data on private servers. Building on this legal framework, just like lawyers, organizations now have access to confidential information about their clients and are legally able to act in their best interest. We can consider the UK, where trust law is widely applied, boosting their AI sector. Although big tech companies have long recognized the value of data trust infrastructure, why it became a legally accepted practice can be owed to the fact that policymakers too now value data analytics for good governance. We can consider Canada’s bill C-11, which proposes establishing public data trusts to allow recycling data for social welfare purposes (Cambridge). Furthermore, as more and more governments aim to boost their AI sector, there is a growing need for large-scale quality data sets, and subsequently, more bills to be passed to make data trusts a prerequisite.

The Technology That Provides the Pillars Of Trust

Data trust essentially allows data providers rapid consent-based data sharing at a scale in a compliant, ethical manner. It lays the foundation for data innovation with solid governance, giving data providers increased control over their data assets. Various angles of this infrastructure are:

1. Data observability

This term can refer to an organization’s understanding of their systems’ health and state of data. No matter how robust your ETL pipelines are, sometimes data just isn’t reliable, and the problem is compounded as data systems become increasingly complex. A solution to this is data observability tools which not only prevent bad data from entering the system but also provide insights into the quality and reliability of your data.

Data observability enables data monitoring (when the data set has correct parameters) and subsequently generates trust in data by maintaining predictability, consistency, and integrity of data sets.

2. Accuracy and fairness

As brushed upon earlier, the key to generating data trust is explainable AI outcomes: how your models operate and your ability to improve the model while also sharing this information with the rest of the organization. Furthermore, new challenges arise with AI, such as model degradation, which makes the outcomes less accurate over time. This is where model monitoring comes into play. It closely tracks your model’s performance, so any issue such as model bias can quickly be remedied. Serving as a quality assurance for data scientists, monitoring increases the accuracy of outcomes.

However, accuracy alone is not sufficient to generate trust. Machine learning engineers need to look out for potential biases in the model and address them to ensure that the model is fair. Once a bias is acknowledged, another ML algorithm may be implemented whilst ensuring that organizations document and share how their training data sets are cleansed and transformed, as most biases are introduced at this stage.

3. Privacy

Generating trust in data also means a trust from consumers that their data privacy rights are maintained. According to EY, nearly two-thirds of Canadians rated their knowledge about privacy rights as very good, and the majority of them are apprehensive about businesses maintaining privacy rights. Therefore, companies should seriously consider complying with privacy laws.

For instance, consider GDPR, which protects every EU citizen from having their personal data collected without their consent. CCPA is a more localized version of GDPR, and they both ensure that greater transparency on data management is maintained between companies and data subjects. Transparency breeds trust.

4. Governance

As a practice of data management, data governance enables organizations to generate value from data sets under the ethical constraints of security and privacy. At a surface level, although governance may give the impression of rules and regulations that thwart freedom to operate, modern data governance is actually freedom itself. It helps businesses understand mounds of data whilst ensuring that external and internal policies are followed, subsequently generating data trust. Some of the tools that aid in data governance are data catalog, which manages an organization’s metadata needs, and data lineage, which helps in understanding, recording, and visualizing datasets as they undergo transformations from their sources.

Governance simply means that businesses can trust their data sets to answer important questions. A lack of governance leads to data inconsistencies and information gaps, driving wrong business decisions and contributing to mistrust in data-driven decisions.

5. Synthetic data

As an alternative to real-world data, synthetic data is generated from computer simulations and is used to train various ML models across the industry. Using synthetic data means that the approach is relatively inexpensive, compliance with regulations such as GDPR is not required, and data sets can be well-cataloged and made in abundance for numerous conditions.

However, using poorly generated synthetic data also means the model fails to deliver accurate output. Completeness and accuracy checks help monitor data quality which in turn builds trust. Given that the AI sector is facing the problem of the unavailability of high-quality datasets at a scale, synthetic data is now the answer.

Looking Forward

Data preparation is one of the most significant areas where an organization’s trust in its data sources falters. When raw data is cleansed, transformed, and channeled through different processing pipelines, there is a high margin of errors being introduced into the data sets and a high likelihood of risk depending upon the methodologies utilized.

Today, many businesses are adopting feature stores. Feature stores act as data repositories, providing rich processed feature sets while enabling their trustable, auditable, and schedulable generation and updating. To sum up, it’s a place for you to allow robust compliance and fulfill all of your data monitoring needs.

Scribble Data’s modular feature store: Enrich takes care of businesses’ data trust needs. We are a privacy-aware, data-secure feature engineering platform. We aim to utilize the enriched data sets we generate to train ML and DL models and help organizations implement these data models. We believe that the cornerstone of our approach to feature engineering is trust, and at Scribble Data, all datasets are reproducible, versioned, quality-checked, and searchable.

For your business, this means that the data science teams can deploy models much faster and with much more confidence in the underlying data, making business operations more flexible and optimized.

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