Data Modernization: A Thought Process

Effective use of an organization’s data can provide business opportunities, insight into infrastructure problems, the ability to predict and prevent security threats, increased employee satisfaction, and a competitive advantage in a rapidly changing world. It should come as no surprise that businesses want to do more with their data. It should also come as no surprise that engineers want to modernize their old infrastructures and rethink their approach to handling the proliferation of data throughout the enterprise.

So how does one rethink data management in the enterprise?

Ultimately, the success of such a transformation depends on the trifecta of technology, people, and processes that make up this ecosystem, and the mindset of incorporating all these aspects into the new approach.

Know Thy Org: The Assessment

Like all journeys, yours begins with knowing the current data landscape and getting a sense of where you want to be. This assessment is crucial for success as it helps formulate a new data strategy that includes the right priorities, processes, and tools.

Part of the assessment is to understand the data consumption and access patterns along with the maturity of the current data platform.

Knowing whether users interact with a central data platform or have their own silos can help understand the need and the gaps — providing inputs for a flexible architecture.

Cover All Bases: Think Holistically!

Even though the following high-level categories may be described separately, they’re all connected. For instance, good execution cannot fix bad architecture, nor can good architecture work without a good execution plan.

The design process should consider the following categories.

Data management

Inefficient data management can be a roadblock to unlocking the full potential of all the available data. Part of being a new age organization is taking a modern approach to data management, which calls for data to be precise, accessible, and available to the appropriate individuals in a timely manner.

Certain key ingredients must be in the recipe to achieve effective data management.

Common language: Common and standard definitions allow the entire organization to speak the same language. Identifying main domains and subdomains in order to develop and normalize data definitions must be an important part of the data strategy. Invest in creating a data catalog that provides a repository of all the data, processes, definitions, and metadata.

Data unification: There may be various methods for ingesting, storing, processing, and exposing data, but the end result should be a unified experience for users. Users should be able to use the data without worrying about where it came from or how or where it was stored.

Quality: Quality is the main ingredient in the data management recipe. Getting this right is critical for success as poor quality data will erode trust in the platform. Quality refers to both objective and subjective views of datasets. It is essential to manage data quality as a well- monitored process.

Data governance: Data is now everyone’s business, and it follows that governance is not a single team’s responsibility. All levels of the organization, both vertically and horizontally, need to be a part of it. Defining levels of governance for assets can help clarify priorities and build a roadmap for future enhancements without stalling innovation in the organization. But how much to govern depends on the company’s needs, size, urgency, and maturity.

DataOps: DataOps is the flavor enhancer of the recipe. It refers to the automation of the entire lifecycle from ingestion to analytics to improve productivity and reliability. It borrows from lessons in standard application development practices with a focus on managing the data operations, development environments, and analytics for a smoother release to production.

Empowerment

Limited access is frequently a barrier to gathering insights. In most organizations, technical or non-technical employees, including executives, rely on data engineers, or power users, for data prep and report generation. Across the entire organization, empowering employees with a self-service model will remove the barrier to data exploration and insights. Training or upskilling is a critical aspect of this empowerment initiative. Always budget time in the plan for training.

Monitoring and Observability

Strive for an environment where monitoring and data observability components co-exist to understand when and why something goes wrong. This investment is essential in the ever-changing world of data and ML model pipelines.

Data quality frameworks and tools can help in identifying the issues, but data monitoring and observability can help in identifying, troubleshooting, and resolving the issues as soon as they are found. The best way to achieve this is by using DevOps best practices and applying them to data, which is essentially what DataOps is.

Technology

Technology must be treated as a facilitator, not as the driver of the process. When assessing technological options, take into account nonfunctional quality characteristics such as scalability, performance, security, extensibility, and maintainability. By making the appropriate technological investments, data may be more easily collected, cleaned, integrated, and harvested.

Today, machine learning attracts greater attention in every industry, and data is no exception. This, however, needs to be complementary to the traditional data pipeline process and must evolve and mature with the data ecosystem. Data quality is one area where AI/ML can be leveraged.

Execution

The move to a new data platform cannot be a migration but instead has to be a transformation. Migration could result in repeating past mistakes in the new environment. On the other hand, transformation means creating an environment with less code, reusable modules, robust pipelines, better security, and a flexible access model.

The execution of the transformation plan should take a phased approach with demonstrations of incremental value. After all, Rome was not built in a day.

Parting Thoughts

What happens after the transformation is complete? Or is there even such a thing as “complete”? Given the speed at which organizations want to tap into new opportunities and gather insights, the mindset should be one of agility. The ability to adapt, monitor, and make continuous improvements and adjustments as you go along should be the cornerstone of any data initiative.

To reiterate — it’s all connected. The vision to leverage data is accomplished when data from various silos come together harmoniously like a symphony, and that should be music to a business’s ears.

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Article has been created by Viji Krishnaprasad and Prashant Mediratta