5 Steps for CDOs to Transform Data into a Strategic Asset
A unified approach to data management, supported by modern data management technologies, can help a chief data officer turn data into a strategic asset.
- By Ajay Khanna
- August 29, 2017
According to Gartner, a chief data officer's role combines "accountability and responsibility for information protection and privacy, information governance, data quality and data [life cycle] management, along with the exploitation of data assets to create business value."
CDOs are on the rise, especially in regulated industries. With the explosion of data everywhere, an important task for any CDO is determining which information can add business value, drive efficiency, or improve risk management to ensure the future well-being of the organization.
Whether you're one of the pioneering CDOs or newly anointed at your organization -- and your CEO is on board and has a clear vision of what the business needs to be doing -- here are five steps to help you transform your data into a strategic asset.
Step 1: Build a data strategy
The CDO role should not be a technology-only position. It's a business role that spans data acquisition, data governance, data quality, analytics, and data science. The CDO must bring all departments together to create a common understanding of the business objectives (such as business alignment or connected omnichannel customer experiences) and create a cohesive data strategy to meet those goals. Isolating the applicable use cases and understanding the desired customer experience helps you focus on finding the best strategy and data architecture, building effective teams, and identifying the appropriate tools and platforms.
This step is important for all industries, but it rings true especially in the current retail environment. Amazon is not the only driver behind the "Retail Apocalypse" -- shoppers' changing consumption habits, competition, and too many retail stores have all contributed to declining sales and a shrinking customer base. Never has it been more important to have reliable methods to learn what customers prefer to buy and from which channel.
Having a clear understanding of the business objectives and aligning your data strategy with them is critical for business success.
Step 2: Optimize operations today
Once your business objectives are clear, you must focus on improving the reliability and relevance of internal data to refine business operations. If your internal data is not sufficient, leverage data-as-a-service to bring in third-party data assets to enrich and augment information for your data-driven applications.
CDOs are spearheading initiatives to establish reliable data foundations -- these serve as a single source of truth for all operational and analytics systems across all functional groups. To achieve this, make sure you have a modern data management platform in place that can connect to all internal, external, and third-party data sources as well as blend the information by matching and merging data.
Newer graph technologies also help you uncover complex relationships across data entities such as people, places, products, and organizations. This accurate and consolidated data, with relationships understood, becomes the foundation for operational and analytics processing.
Once your foundation is ready, you can visualize the data profiles with attributes collected from all sources and complex relationships within data-driven applications, customized for each business objective and role. Moreover, you can provide this data to all other operational systems such as CRM, ERP, supply chain, and support, ensuring consistent information across all departments and systems.
A modern data management architecture provides accurate data both to your customer applications and channels for a connected experience and to your analytics systems for deeper insights about relationships, next-best actions, and improved data quality.
End users receive relevant insights and guided recommendations within their data-driven applications. They should not have to leave their operational systems and dig for insights elsewhere.
Using cognitive computing and machine learning to arrive rapidly at relevant big data insights is essential for organizational success. To build confidence in insights, focus on data quality and analytics. Analytics is meaningless without the proper understanding of data quality, and incomplete, inaccurate data will lead to poor decisions.
A unified approach to data management and advanced analytics at big data scale enables your teams to determine trends through visual dashboards with data continuously updated with aggregate counts of all attributes and available for convenient one-click access and analysis. Such timely and contextual visibility helps optimize the efficiency and cost of your internal operations, turning your data into a competitive, strategic differentiator.
Step 3: Facilitate collaborative curation
Your internal stakeholders are the end users of data, so place greater emphasis on transforming data into different types of information to meet the needs of different stakeholder groups. Data changes that drive business-process decisions (such as market campaigns, sales opportunities, compliance levels, and support contracts) are critical to your business, and changes to them require proper stewardship.
Workflow for data-change processes is an essential component of both data management for IT and business execution. Workflow processes, seamlessly integrated with data-driven applications, can accept input and data-change requests from frontline business users to keep data quality in check. Data-driven applications also support capabilities often seen in applications such as Facebook, LinkedIn, and Yelp, including voting, ranking, and rating so business users can comment on data quality and have ad hoc discussions about data profiles and attributes.
Data is a living organism and requires constant care and feeding. Fostering a culture where contributions are recognized and rewarded, plus the bonus of sharing higher-quality data, make this step a win-win for all.
Step 4: Ensure governance and compliance
For highly regulated industries (such as life sciences, healthcare, and financial services), accuracy in regulatory transaction reporting is a must. Billions in fines are levied every year and thousands of hours are lost due to mandated training and data management to generate complex reports. Beyond these financial challenges, enterprises must also be concerned about their brand reputation and their customers' security and privacy.
Data governance and compliance with required data quality and granular audit trails of data changes are essential. Easy-to-understand data lineage and quick access to profile history and audit trails make governance less challenging. CDOs must ensure adherence to the latest compliance and security standards and build a platform that adapts to global changes in laws and regulations, enhanced security audits, and computer system validation.
Your data strategy must take into consideration the global access needs, business rules that determine data access and masking, and regulatory reporting requirements.
Step 5: Monetize data tomorrow
As a CDO, just putting the process and technology in place to use data to improve operational efficiency is daunting. However, data monetization is like a diamond mine waiting to be discovered and leveraged. Just like diamonds, raw data needs to be unearthed, polished, and brought to life for its real beauty and value to shine.
By turning your data into an asset and profiting from it, your company transforms into a data-as-a-service organization, like a third-party provider that supplies data to support your internal operations. The caveat, of course, is that any technology you use to get your data ready for sale must provide full audit and lineage as to where the data originated so licensing rights are clear. The data must be reliable, relevant, segmented, secure, and, if necessary, anonymized.
A Final Word
CDOs rely on a modern data management platform that allows their organizations to cleanse, match, and merge data of any type and from any domain to contribute to its completeness and quality. However, you must also bring analytics back to master data profiles to enrich and improve the data for a complete, closed loop with insights powering continuous data enrichment.
This unified approach supported by modern data management technologies addresses gaps in legacy solutions (that often include disconnected MDM, business intelligence, and big data) and delivers demonstrable business value, enabling you to truly turn data into a strategic asset.