Improving the Scale and Reliability of Your Data Management Infrastructure

With digital transformation initiatives well underway, data utilization and its efficiencies are starting to increase in priority. As more and more companies uncover the value of collecting, organizing, and analyzing data at a massive scale, the need for proper intelligence to create functional benefits out of these processes has only strengthened.

That is where the role of the chief data officer (CDO) comes into play. This data czar leads the data strategy, prioritizing data initiatives that require a reliable and scalable data management infrastructure. Increased data adoption and consumption require processes and techniques to improve data quality and enforce the right level of data governance across the data management infrastructure. 

Since innovation is leading the charge in data initiatives, companies are turning to "speed to value" solutions that often introduce new requirements that can get overly complex without proper leadership. With the dynamic nature of data, CDOs are challenged with assembling the right combination of technology, process and expertise to manage data transfer, assimilation, transformation and delivery of data intelligence. Due to this complexity, many organizations have data implementations that have organically developed around certain functional areas or are limited to a certain number of data sources.

Functional Considerations to Address in Your Data Management Environment?

More than 50% of Fortune 500 companies have introduced and relied upon the services of CDOs for data management. That places a lot of pressure on these leaders to deliver data sets with consistency and reliability to their functional stakeholders. This means designing a data management infrastructure with proactive error identification, controls over data transformation as well as responsive problem resolution, all embedded with a robust communication process. 

While every data infrastructure may differ depending on the needs of the organization and the niche industry requirements, four significant areas should be top of mind today for CDOs responsible for creating and managing an enterprise data management solution at scale: data discoverability, data quality, data observability, and data security.

Data discoverability

Data discoverability is the ability to identify data elements, attributes and metadata across multiple sources. This function helps end users responsible for developing or creating insights into understanding the source of relevant data. Data cataloging with an intelligent search and display feature helps by showing relevance between new relationships and insights. That means supporting the analytics engineers with a data discovery solution that is efficient and highly accurate in relation to the final product.

The challenge is unifying the multiple data definitions aligned with various assets across multiple systems. Data needs to be easily searchable and relevant to the users conducting the inquiry. The analytics engineer can reduce time spent digging into the in-depth data foundations of a data scientist, nor does the engineer need to waste time on insights unrelated to their current goals.

A CDO must leverage modern automation engines and analytics calculations to overcome these challenges. With clear data definitions, a data product can be created and managed with greater reliability for any user at any access level. This is often achieved through the introduction of metadata management solutions that are agile enough to connect several different data sources into a clean and user-friendly interface with search and data discoverability features.

There are many important factors to consider in building such data discovery or searchable platforms. Some more critical points include:

• The ability to successfully activate services within a few weeks using standard connectors to key data sources.
• Having a system with the flexibility to ingest a wide range of data source data types. This should include raw formats of well-known data types like CSV, TSN, RTF, PDF, JPG, and more. The goal is to enrich the discovery process based on the specific data intake of the organization.
• Leveraging existing LDAP (Lightweight Directory Access Protocol) infrastructure to support more significant user authentication and privacy measures based on who can access what data at any given time.
• Finding a data management infrastructure solution that allows for regulatory and privacy controls regarding sensitive information by assigned data access levels based on LDAP-defined roles.

There are many other considerations, but with this list, a CDO can better improve the value of the data discoverability integration being leveraged for an organization.

Data quality

There is no point in trying to leverage data insights without being able to assure data quality. Therefore, a significant part of a CDO’s data management infrastructure will revolve around how data is transformed, curated, and manipulated from every stage, from raw collection to final insights and reporting.

This area has several considerations because of specific business rules (industry or internal), privacy protection routines, data definition alignments, and essential formatting synchronization. Each of these factors must be initiated systemwide. For example, you cannot rely on a data asset from one resource that is not formatted similarly to another.

Most of the time, these operations are achieved through various software-driven routines involving schema changes. That can be challenging as multiple touchpoints throughout the data transformation lifecycle must be carefully managed to ensure improved data quality.

An essential function of the CDO is to manage the data quality through sophisticated data validation routines. Considering the intense public and marketplace pressure to protect privacy at every stage of collected data lifecycles, it only makes sense for a CDO to implement measures that reassure everyone, from internal team members to external stockholders.

This type of data validation architecture should include data content threshold checks, data drift identification, dependency routines monitoring, and anything else that ensures a more reliable data set for the various users and consumers. That is not an easy task and requires a bit of experience, given the fluidity of data, the unpredictability of sources, sudden data ingestion changes, and all the other variables related to a potential drop in data quality.

As part of the data quality area, a CDO should consider:

• Leveraging software solutions and routines that offer flexibility capable of adapting to all changing aspects of the current and future data environment as an organization grows.
• Introducing SOPs (standard operating procedures) into an organization by executing data quality checks to ensure a consistent, predictable, and reliable outcome.
• Balancing the need for flexible solutions with standardized, repeatable operations by building data validation functions through automation, processes, and resources.
• Integrating the entire data validation infrastructure with a robust data observability function for proactive incident discovery and management, assuring a more agile organization.

Data observability

Anytime an organization's data infrastructure is down, it risks losing clients and value. Ensuring the least amount of downtime by actively observing the overall health and function of the data infrastructure throughout its lifecycle is critical to success.

However, unlike the typical IT applications and infrastructure businesses have relied on for years, data management has yet to mature to the appropriate sophistication of IT Service Management. That being said, a CDO must find a data service management infrastructure that supports the complete enterprise goals with data operations.

Viewing the overall efficiency of the current data management infrastructure can be managed through ITIL-based frameworks deployed system-wide across data operations. This ensures a more consistent and reliable outcome, given streamlined data management routines. Here is another opportunity for SOP integration by following the guidance and best practices of the ITIL framework in relation to overall service strategy and implementation.

The more the CDO can maintain the health and live nature of the data infrastructure, the more valuable that person becomes to the organization's overall success. That is why a CDO should consider the following aspects:

• Eliminate or introduce processes to avoid fragmented notifications and system warnings generated across the entire data management lifecycle. Unfortunately, it has become common practice for most organizations to use multiple methods for notification of any new errors or anomalies. A CDO will see their value increase if these notifications are centralized and reduced so they can be reported alongside repairs or improvements simultaneously.
• Reducing the various areas where notifications are received on the data team and other departments. You wouldn’t want the mail team to be dealing with a notification from someone in marketing. The point is to gatekeep this information with centralized system integration so all data observability notifications pass through the same process.
• Overcoming the adoption of new technology often creates a fragmented data observability solution framework. It doesn’t matter if notifications are coming through Teams or an email server. They should be integrated into a centralized system for better management, response, and adjustment.

All these variables behind notification fragmentation create a new challenge. Being able to retrofit older systems with a standardized solution is complicated. That is why a CDO may want to consider a Data Operations Service management repository that consolidates across the entire data management lifecycle instead. This provides benefits like:

• Proactively create incidents with an easy-to-track audit trail from notification to resolution.
• Apply better systematic fixes across the data management infrastructure due to a greater understanding of trends through pattern analysis.
• Using modern solutions that are adaptable and able to digitize and apply self-healing features to the various software routines. This allows data validation to get smarter, proactively correcting actions during the process instead of wasting time, energy, and resources finding a solution after the process.

When a new data management infrastructure is introduced with a well-designed data observability function, it positions the CDO as a proactive leader. That kind of insight creates a positive ripple effect of benefits across the entire organization. This means data consumers will have a high reliance on the data sets used during their consumption process, and communication of any potential errors will be more efficient.

Data security and trust

Data security is always an interesting juxtaposition surrounding the position of CDO. This leader needs to be able and issue more access to the greater community surrounding the organization while at the same time introducing measures that proactively protect any sensitive and confidential data. The tightrope being walked here is often influenced by the niche market of the organization (medical=HIPAA, DoD=security clearance, etc.).

The demand for more access to this information has only increased as time has passed. A company wants a CDO to provide more access and appreciates that type of “team player.” The problem is it is on the CDO's shoulders to ensure any new concerns developing due to greater data access are appropriately covered. Procedures must be in place to ensure personal, sensitive, privileged, and role-based data have considerable access controls as data is democratized across the enterprise.

During this process, a CDO should keep in mind:

• What is the user's role, and what does that mean for data access?
• Design data profiles for each user based on enterprise levels of data privacy and confidentiality – internally through SOPs and externally from oversight.  
• Introducing data governance policies that allow for active monitoring of access controls, primarily through automation routines that operate continuously in the background.
• Managing data entity ownership through comprehensive policies and, if violated, be empowered to enforce controls through services.

The point is that a CDO faces many challenges, often involving working with a patchwork of data management technology. This is because an organization’s ecosystem tends to be complex, with many different technology providers overlapping functionality and capability. Unfortunately, no single, off-the-shelf technology can efficiently address the evolution of data management infrastructure in the rapidly changing landscape.

The CDO needs to integrate solutions across multiple data technologies, identify and retain advanced data expertise, and better manage an infrastructure that includes governance, quality, and reliability. All of these must be achieved while still serving the needs of the business community the organization operates within, including ease of data access, reliability of your data environment, and building trust.

Ravi Padmanabhan, president of NextPhase, wrote this article.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/ipopba