Digital businesses use technology to create new value in business models, customer experiences, and the internal capabilities that support core operations. Data Governance can contribute to effective digital business
According to Accenture, “Digital businesses create competitive edges based on unique combinations of digital and physical resources. They do things that others cannot and in ways that build comparative advantage.” What are five trends that can support the improvement of digital business outcomes using data governance?
1. Formalizing data collection from customers and third parties
To date, the organizations have focused on formalizing data consumption practices through distribution technology, access-based delivery mechanisms for analytics and AI functions. However, with data protection laws and positive awareness across the world, firms have extended the formalization to data collection management. This is the first life-cycle stage of data.
Managing data quality at sources: Across the multitude of native and digital channels, harmonizing data quality rules will bring consistency in sourcing correct customer data. There will be an increase in the use of AI-based discovery of data rules that will make it much easier for data offices to achieve the scale of fixing bad data. As an example – validity rules for mobile numbers can be consistent across all channels that curate this data from customers and partners. This approach removes ambiguity in monitoring for quality even though data is siloed.
- Measuring data accuracy using AI can support digital business
- Clean data is crucial to achieve an outcome from machine learning capabilities. Scale and diversity in data is another important aspect to encourage data governance support.
Classifying and labeling legitimate data: An important aspect is classifying data that is curated from customers or third-parties as private and zero-copy data. Data protection best practice suggests minimizing data curated from customer to reduce the threat surface area for data security risk.
Extended trust to customers: As the organization builds trust with customers, they would provide additional zero-copy data to improve the services and products they receive. For example, a traveler can provide dates of travels, places of travel so a bank can provide enable international transactions and increase limits on credit cards.
Ownership and data stewardship: A successful data ownership should traverse divisional siloes. Data Ownership is often not a full-time job for most data owners, but Data Stewards may serve in a full-time capacity. Initially, data context should be recorded by stewards in a central facility (a data catalog). Data Governance is a methodology that helps implement individual and shared ownership of data across the organization. Often without context to a data element (metadata), it can be irrelevant to consumers or to support AI & analytical models. Deriving accountability for privacy will be an enabler for improved data management
2. Increased data awareness & literacy
Knowledge of data-in-context, data processes, best techniques to provision data effectively, and use of tools enabling these methods of self-service is crucial to democratize data. However, with technology advancements including virtualization, self-service discovery catalogs, and data delivery mechanisms, internal data consumers can shop and provision for data in shorter cycles. In 2020, it took organizations a week to 3 weeks to provision complex data that included integration from multiple sources.
Also, an increase in data awareness will help data consumers explore additional dark data that can provide predictive insights to create new user-stories that can propel customer journeys. Measuring benefits from data management and aligning to value-chains: The lack of focus is common across organizations as they assume data governance as an extension of either compliance or a risk function. Data Literacy will change the attitude of business owners towards actively managing and governing data. There are immediate and cumulative benefits from actively governing data either by defining data or fixing bad quality data. But there is a need for a value-realization framework to actively manage the benefits of data management services.
Data Ethics: Blending data privacy and data sharing can bolster innovation in the business ecosystems while un-locking the economic value of data. The first step for any organization is to build a controlled environment that can govern and manage data effective and according to best practices. This activity will increase trust in the internal data hosted by various functions like marketing and create a culture of sharing for ‘digital and customer-centricity’.
Data Governance is known to have a cascading positive impact on corporate governance. Concurrently, people outside the organization start trusting the organization as stewards of their data. A well-matured organization can be called a ‘data trust’ where the control of data is held by its customers. Though organizations are either controllers or processors of data, the group can be viewed as having an ethical element with a fiduciary duty to maintaining the integrity of people’s data.
With an increased focus on data protection and governance policy in governments, awareness of these methodologies will assist organizations to prepare for compliance with the laws.
Data Protection: Harmonizing data privacy and data sharing can bolster innovation in the business ecosystems while un-locking the economic value of data. The first step for any start-up or a well-established organization is to build a controlled environment that can well Govern and Manage data. This activity will improve trust in the internal data hosted by various functions like Marketing and create a culture of sharing for ‘digital-centricity’. In short, governing data will bolster digital transformation.
Consumers have started embracing digital handshakes with marketers at an increasing rate. U.S. consumers have spent more than $66 billion online in July 2020, 55% more than one year earlier.
Use of AI in data protection: The development of Artificial Intelligence (AI) and Data Protection domains are largely dependent on the economic and societal needs. While Artificial Intelligence develops better customer services by using “big data” and learning from it, data protection is poised to build trust in people to share data with organizations. A recent survey from Gartner showed that over 40% of privacy compliance technology will rely on AI by 2023
Data Distribution Management
There is merit in having to drive events in customer journeys based on insights derived by a deep-learning model that crunches real-time data. This requires data to be sourced in real-time rather than mini-batches or batches to a data lake or a cloud warehouse to run artificial intelligence models.
A simple question organizations should ask: Do you want to process streams of data before a state of an application changes, or is pipe-lining data into a lake or warehouse to derive insights within a time like 15-30 minutes sufficient?
Data Architecture and data engineering are associated with having the right stack available, it is essential to ensure security requirements to move data internally through batch or real-time processes with low latency, and semi-real time with acceptable latency. Governing Data Architecture for better outcomes is crucial.
Another example – while a customer fills up data on an application form for a home loan, a back-propagation model that uses real-time data can drive decisions based on demography like a flood-insurance or a fire-insurance or prompting for other protection plans. Or it can predict fraud or typos from customers in certain co-related data like Income or place of work.
The following enablers or processes of data management have an impact on data distribution gaps:
- Data Delivery Management – How is data being delivered from sources?
- Platform Governance – Are there processes that make storage policy-relevant along with costs?
- Data Provisioning – Are sources of truth certified across the landscape?
- Metadata & meaning of data – Is there a unified data-fabric or a Business Information model to build confidence through standards?
- Integration Management – Are common standards and canonical models leveraged?
- Data Availability – Is data discoverable by rightful consumers with ease?
Gartner predicts that by 2023, organizations can accelerate time to integrated delivery by 30 percent by employing data fabrics
Platform Governance: Firms have accelerated digital transformation across multiple journeys of on-boarding and servicing customers. This has been possible by integrating and aggregating multiple sources as well as taming the ‘data swamps’ to deliver quality data.
Platform Governance is the mantra to a healthy delivery of Big Data and native data platforms. An increased number of platforms including native data-warehouses, data lakes, cloud-warehouses that are being fueled by cost parameters for compute and storage has increased the complexity of platform teams.
A formalized methodology is required to maintain authorized provisioning sources, integration methodology, redundancy maintenance, and other use-cases like deleting specific instances of customer data.
4. Future-proofing businesses with data strategy
Analyzing the organization’s data and digital strategy during periods of change to organizational strategy will assist the alignment of benefits.
Some data related goals are related to the organizational goals:
- Availability of reliable and useful data for decision making – internal balanced scorecard dimension
- Adequate use of data, and technology solutions – customer balanced scorecard dimension
- Realized benefits from data-enabled investments and services portfolio – financial balanced scorecard dimension
On performing a maturity assessment of the current state, many organizations identify the following problem statements:
- Data collections, analytics, and decisions in value chains are often time-consuming and expensive
- Data is a core factor of input into every business process and are supported by applications. Data Collection, Access, and Delivery dependencies must be defined and verified across the organization.
- Creating a data strategy in alignment with digital goals is a challenge
Data landscapes in enterprises are increasingly based on core principles of data discovery, right data interpretation, coverage, availability, and interoperability, shifting the focus of Data Management principles.
5. Governing data on modern cloud approaches
Governing data includes creating a controlled environment to support regulatory-driven processes. Governing data should enable an environment to assist the organization in monetizing data for benefits while managing regulations.
Moreover, the prime focus of leadership must be to understand the business value of data governance on the cloud. Most organizations will prefer a hybrid-cloud approach. With this mix of data spread across multiple cloud providers including Azure, AWS, GCP as well as on-premises traditional systems, the processes for governing data become more important. Each cloud provider will maintain its catalog and integrating them with the enterprise catalog using a push model could be a preferred option. Data security in hybrid-cloud is an evolving area with guidance evolving on the best approaches to encrypt and anonymize data.
Conclusion
The value of data governance to support and enable the digital transformation of any business is demonstrated by effective use of data management and data governance best practices, following each of them as faithfully as possible.
