Data Quality: The Heartbeat of Analytics

At the very core of decision-making and business performance, lies data quality. 

When a company’s fundamental data is unreliable, of poor quality or just plain incorrect, even the best technologies and most accomplished professionals will struggle, potentially making catastrophic errors.

Fortunately, technology can considerably enhance an organization’s data quality, ensuring that the right people receive accurate analysis whenever and wherever they need it.

To employ the most suitable technology, a provider must establish a close and trusting relationship with its clients. Through this process, it is possible to gain a deeper understanding of the client’s technical and business requirements, resulting in a strategy to capture and analyze the highest quality data. Consequently, clients can make the most insightful data-driven decisions.

With the appropriate data architecture and governance in place, advanced machine learning can work wonders

This is a collaborative effort. Architects engage in data modeling to optimize extract, transform and load (ETL) processes for data collection, integration, cleaning, mapping, and aggregation. All of this takes place under a data governance and management regimen, which sets standards for data integrity.

With the appropriate data architecture and governance in place, advanced machine learning can work wonders with both structured and unstructured data, uncovering hidden insights and generating predictive models.

To deliver the utmost business value to a wide array of stakeholders, analytics can also be visualized through dashboards and various business intelligence (BI) tools. As always, a deep understanding of client requirements is crucial to ensure that the right technology is implemented.

In many cases, we at EffectiveSoft integrate data quality assurance within our broader service offerings. These data quality assurance and assessment services involve identifying and eliminating any data anomalies, guaranteeing that a client’s data is relevant, accurate and consistent.

 The Business of Data

Data’s value is directly linked to its business relevance. Therefore, ensuring that an organization has easy access to the best analytics requires understanding the unique needs of a business, many of which are industry-specific. 

For instance, organizations in the healthcare sector are heavily regulated by the Health Insurance Portability and Accountability Act (HIPAA) and collect vast amounts of heterogeneous data. In this context, having meaningful access to the right data is crucial. Missteps can expose an organization to liability and even be a matter of life and death. However, getting it right leads to significant rewards: data-driven intelligence in healthcare can support personalized medicine, early diagnosis and intricate care plans, among many other scenarios.

A deep understanding of client requirements is crucial to ensure that the right technology is implemented

Impeccable data quality is also vital to financial services, another highly regulated industry where anomalies can create inefficiencies and increase risk. Many solutions need to be designed to comply with the Securities and Exchange Commission (SEC), Commodity Futures Trading Commission (CFTC), Dodd-Frank requirements and the Financial Industry Regulatory Authority (FINRA). Depending on the business model, other regulatory agencies’ recommendations might also apply.

Compliance and the assurance that the best data can be accessed and visualized efficiently allows business analysts and other internal end-users to work confidently while providing a competitive edge to the entire organization.

In less regulated industries, such as transportation, media, and consumer goods, data quality is determined by how a company’s unique business model aligns with industry characteristics. Here too, being able to utilize multiple data models that pull the right data from various sources lowers the total cost of ownership and accelerates growth.

For example, in trucking, geofencing supported by accurate data keeps drivers safe and creates efficiencies. In law firms, the correct information can be protected by a permanent chain of custody for legal documents. Online stores, with access to trusted data, can bulk edit products to maintain a competitive edge. In sales, marketing, and manufacturing –among many other industries–, dashboards that draw on the correct data can ensure communication and accountability along the delivery chain, with numerous key performance indicators (KPIs) monitored in real time.

Trust: The Foundation for Success

I began this blog by emphasizing the significance of trust between a provider and its clients. This trust fosters clear and honest communication. At EffectiveSoft, this is a critical first step in any engagement, as it gives us the assurance of knowing which data is most relevant.

With this trust established, determining high-quality data involves a detailed analysis of business needs and how these will shape the future system. Once business requirements have been collected, a provider can conduct an investigation and develop a minimal viable product (MVP) that includes an outline of the project scope.

 After auditing the solution, both the client and the provider should have a clear idea of the most valuable data, the context in which it matters and how accessing and analyzing that data can improve the business. This knowledge leads to an enhanced architecture that supports a more meaningful data analysis system.

 The relationship between our clients and us should be as flexible as the technology solution itself. However, barring a radical transformation in the business landscape, understanding which data has core value ideally remains consistent, as it aligns with business priorities.

Determining high-quality data involves a detailed analysis of business needs and how these will shape the future system

 When fully implemented, the client should be able to automate loading files from different sources. The system should be able to deploy filters that prioritize certain data, target where it is sent and determine how it is represented. All of this should involve error-free communication with a database that eliminates frustrations due to missing or incorrect data. No information is lost, and all data remains searchable to support internal investigations and audits.

 Even upon completion of the project, the system should be adaptable, with the capability to add third-party services.

 After all, there’s always more data out there, and much of it holds immense value indeed.