The last few years have demonstrated how critical it is for businesses to maintain agility and make fast, confident decisions to stay ahead. The resulting economic uncertainty and growth of industry-wide trends, including ESG, cloud migration, and the rise of artificial intelligence and machine learning programs – such as OpenAI’s newly launched GPT-4 model and ChatGPT chatbot – are fast becoming drivers for businesses to adopt more robust data management strategies in 2023 and beyond.
Global data creation is still growing at an impressive rate and will nearly double by 2025. With more data at their fingertips than ever, businesses can make more strategic data-driven decisions. A recent study from 451 Research showed that nearly 79% of businesses report data will be more important to their organization’s strategic decision-making over the next 12 months. But to unlock maximum value from collected data, business leaders must first recognize data as a strategic corporate asset and leverage data with integrity.
What Is Data Integrity?
It’s a business imperative for organizations to trust the data they use for decision-making. But the data must have the integrity to be worthy of that trust. Data integrity refers to data that is accurate, consistent, and contextual. For many organizations, data’s in silos, stale, unstandardized, full of duplicates, incomplete, and/or lacks the insights required to make it fit for purpose. By establishing a clear framework for building data integrity, organizations can drive access to trusted data at the speed of business, empowering leaders to make the fast, confident decisions needed to drive growth, reduce costs, and manage risk.
Building a Successful Data Integrity Strategy
The framework for data integrity is built on four key pillars: enterprise-wide integration, data governance and quality, location intelligence, and data enrichment. Here is a look at the role each of these pillars plays in a successful data integrity strategy:
- Data Integration: Building a holistic view of an organization’s data requires tying multiple systems together through mapping and translation. Integrating data across the enterprise, whether in mainframes, relational databases, or enterprise data warehouses, requires a carefully considered approach to bringing the data together under one roof and in a way most aligned with the organization’s strategic goals.
- Data Governance and Quality: A sound data integrity strategy must help the organization understand the lineage of its data, how it is used by the business, and the controls around it. It also must manage and validate data across multiple systems, proactively identify anomalies or discrepancies, and trigger workflows and processes to correct those errors.
- Location Intelligence: Virtually every data point in the world can be associated with a location in one way or another. Location intelligence involves geospatial analysis and visualization to better understand the relationships in your data and use that insight to reduce risk, unlock customer behavior, and increase efficiencies.
- Data Enrichment: Data enrichment adds necessary context to your data to enable data-driven strategies. Whether you’re interested in standard or dynamic data (like demographic movement and weather changes over time), you can enrich your enterprise data with relevant context.
Unlocking Business Value in 2023 And Beyond
Organizations will be at different stages of their data integrity journey, driven by their unique business needs and priorities. Through the pandemic, many businesses accelerated the digitization of their customer experiences, supply-chain interactions, and internal operations by as much as three to four years. These dramatic shifts have made it critical for organizations to invest in improving the speed of decision-making, reducing infrastructure costs by migrating to the cloud, hardening data security and governance, and leveraging data to improve consumer satisfaction. Data integrity helps businesses to automate and accelerate decision-making with data that is accurate, consistent, and filled with rich context, uncovering hidden connections and delivering deeper understanding.
The data-driven enterprise of the future will have several defining characteristics, including productizing data for actionable insights, embedding automation in core business processes to reduce costs, and enhancing customer experiences through engagement platforms. Data integrity underpins these characteristics, which ensures programs such as artificial intelligence and data science can leverage the full value potential and ROI for the data-driven enterprise. For example, higher-quality data provide better fuel for training machine learning applications and programs, which translates into greater efficiency for MLOps and AIOps. Also, focusing data engineering efforts to improve consistencies in how data is standardized, labeled, and delivered can unlock greater collaboration and productivity with domain experts.
Regardless of the future, business leaders must ensure robust data foundations are being put in place to support the success of strategic initiatives. By building a meaningful data integrity strategy around data integration, data governance and quality, location intelligence, and data enrichment, organizations can make confident business decisions based on data they can trust.