Data as an Asset: Is Your Business Embracing the Asset Mindset?

We all understand that data is becoming one of the key assets. However, how do you know whether it is an asset or a liability? How do you know whether it will continue to be an asset or it may become a liability?

These are big questions, but we must pay attention to them more often. It is similar to the share market, where everyone knows it helps you earn better, but people often trade/invest without understanding the business and the market.

I like the meaning of asset explained in Rich Dad, Poor Dad book by Robert Kiosaki

An asset is?something that puts money in your pocket?and a liability is something that takes money out of your pocket. Hence, data is a liability if it is not putting money in your pocket or saving you money.

From here on, while I will leave the business models part with you to monetize your data, on the technical front, I would like to talk about key ingredients that make your data true assets.

1. Data Governance (DG)

Data governance plays a crucial role in treating data as an asset by ensuring data quality, security, privacy and usability, ultimately enabling organizations to make data-driven decisions and gain valuable insights. An enterprise must establish a data governance framework that defines roles, responsibilities, policies, and processes for managing data across the organization, ensuring alignment with business goals and data quality standards.

We recommend data governance to stay as closer as possible with the business that generates and use the data, while the data management can stay centralized with the IT Systems.

For example, create a data governance committee with representatives from various departments, such as underwriting, claims, and customer service, to ensure alignment with business goals and data quality standards.

2. Data Quality (DQ)

While we usually first talk about data quality, without proper data governance, people start taking the "Just Do It!" kind of approach. That creates several misaligned jobs/pipelines, and data starts drifting closer to liability rather than an asset.

Given that there is a mindset and process for Data Governance, a deep culture of ensuring great data quality plays a crucial role in converting data into assets. Develop DQ processes to ensure your data is accurate, complete, and consistent all the time across the business units.

Poor quality data can have a severe impact on the data-driven (or AI-driven as we call it these days) outcome. Some of them include

• Inaccurate, suboptimal or even detrimental insights and decision-making
• Loss of customer trust, thus negatively impacting customer satisfaction, loyalty, and long-term relationships.
• Decreased operational efficiency as the data quality issues become time-consuming to identify and fix if not managed properly.
• Compliance and regulatory risks potentially lead to fines, penalties, or reputational damage.
• Finally, poor quality data make it difficult for users to access, understand, and analyze data, limiting the value of data as an asset.

By addressing data quality issues proactively, organizations can mitigate these negative impacts and ensure that their data-driven initiatives and decision-making processes are effective, accurate, and reliable. Thus converting their data into assets.

3. Data Integration/Ingestion (DI)

The democratization of data definitely recommend that the data should stay closer to the business users. However, these silos can cause limited knowledge about the overall business scenarios, and thus, the decisions may get misaligned at the business level. Seeing data as an asset and building a platform so that all the businesses within the enterprise can use the data effectively becomes imperative.

Further, using modern data integration tools or platforms that support a wide range of integration patterns, such as ETL, ELT, real-time streaming, and API-based integration, has become critical. Specifically, the iPaaS tools like Airbyte, with powerful transformation using DBT and data engineering pipeline management using Apache Airflow, are excellent options to seamlessly collect data from various sources into your data lake or lakehouse.

Use the modern approach of data integration to ensure that you are able to connect to the desired sources seemlessly.

At the macro level, data integration leads to quality issues if not done well. Symptoms like duplicate, missing, or conflicting data are examples of that. Hence, it must be given due space in strategies so that enterprises can have a unified view of data, and their people are easily able to collaborate using the data, and the scalability will be inbuilt to accommodate the future incoming data.

4. Data Security and Privacy (DSP)

Inadequate data security can result in unauthorized access, theft, or leakage of sensitive data, causing financial losses, reputational damage, and loss of customer trust. Insecure data can be exploited by competitors or malicious actors, potentially exposing trade secrets or valuable intellectual property. Further, data breaches and privacy violations can expose an organization to legal actions, damaging its reputation and potentially leading to a loss of business.

As data grows and security concerns rise, the data movement strategy should be reconsidered. Using data virtualization techniques to create a unified, real-time view of data from multiple sources without physical data movement or replication may prove beneficial.

Implementing robust data security measures to protect sensitive data (especially master data), such as personal information and associated transaction history, has always been critical. However, ensuring data security for every possible data contributing to decision-making, should be guarded well. Using encryption, access controls, and regular security audits to ensure data confidentiality and compliance with relevant data protection regulations can be a good step towards ensuring that data becomes an asset.

Poor data privacy practices can lead to violations of data protection regulations, such as GDPR or CCPA, resulting in fines, penalties, and negative publicity.

5. Data Analytics and Business Intelligence (DA&BI)

While there is no debate that we need DG, DQ, DI and DSP to ensure that we are taking data towards ensuring that it becomes an asset, ultimately, it will become an asset when you put them to effective business use.

Depending on your industry, leverage advanced analytics and business intelligence tools to gain insights from the data, driving better decision-making and improved business outcomes. For example, if you are in the insurance industry, then use predictive analytics to identify high-risk policyholders or potentially fraudulent claims, enabling proactive risk management and fraud detection.

This means that you need to implement DA&BI well to create a data-driven culture in your organization. You need to ensure insights are accurate, resource utilization is optimal, and user adoption is easier. Otherwise, it will result in suboptimal decision-making and reduced business performance, and that will take data away from becoming an asset.

Inadequate analytics and BI capabilities may result in organizations overlooking valuable insights, missing opportunities to optimize operations, reduce costs, or identify new revenue streams.

6. Data Accessibility and Usability (DAU)

Providing easy access to relevant data empowers stakeholders across the organization to make data-driven decisions, innovate faster, and eventually improve business outcomes and performance. Hence making the data easily accessible to authorized users while also considering data privacy and security requirements is critical towards maximizing the value of data as an asset.

On the other side, the lack of a proper DAU cause hindrances in adoption. People may not be able to share insights easily and collaborate effectively, and the decisions may be delayed and potentially uninformed.

Accessible data allows organizations to explore new ideas and opportunities, fostering innovation and driving growth. For example - easy access to customer data enables organizations to better understand their customers' needs, preferences, and behaviors, allowing them to deliver personalized experiences and improve customer satisfaction.

By focusing on data accessibility and usability, organizations can ensure that their data is effectively leveraged for decision-making, analytics, and innovation, transforming data into a true asset that drives business success.

7. Data Standardization

A big barriers to effective data usability is the lack of data standardization. Without standardized data, integrating data from disparate sources becomes more complex and time-consuming, potentially resulting in incomplete or fragmented data views.

Establish and enforce the following to ensure effective usability and interoperability across systems and processes.

• Naming conventions
• Data formats
• Data storage formats
• Data models
• Data dictionaries
• Data validation rules
• Data transformations and
• Reference data management

The above data standardization recommendations will also help ensure data consistency, improve data quality and facilitate smooth data integration.

8. Data Provenance and Lineage

Data provenance and lineage play vital roles in converting data into an asset by providing information about the data's origins, transformations, and relationships. It helps users trust the data, increasing its credibility and ensuring stakeholders' confidence in their data-driven decisions.

Understanding data lineage allows organizations to assess the potential impacts of data changes, system upgrades, or process modifications, ensuring that data assets remain reliable and up-to-date.

In addition to improved trust, credibility and reliability of data, the provenance and lineage also help with the following:

• Identify and resolve data quality issues by tracing back to the source or transformation step where the issue occurred
• Provides audibility and demonstrates compliance with data protection regulations and industry standards, such as GDPR or HIPAA, by providing transparent documentation of data handling processes

Conclusion

Embracing the data as an asset mindset is crucial for businesses to thrive in today's data-driven landscape. Organizations that treat data as a valuable asset can unlock its full potential to drive informed decision-making, enhance operational efficiency, and foster innovation. To achieve this, businesses must focus on various aspects of data management, such as data quality, integration, security, privacy, analytics, accessibility, usability, standardization, and provenance.

By adopting a holistic approach to data management, organizations can establish a data-driven culture where employees across all levels effectively utilize data for decision-making and problem-solving. Furthermore, robust data governance practices are essential to ensure data alignment with business goals, compliance with relevant regulations, and continuous data quality improvement.

To fully embrace the data as an asset mindset, businesses must be committed to investing in the right technology, tools, and skilled personnel to manage their data assets effectively. By doing so, organizations will be better equipped to harness the power of data to achieve their strategic objectives, gain a competitive edge, and drive long-term success in the rapidly evolving digital landscape.