The Top 5 Data Trends CIOs Cannot Afford to Ignore

BrandPost By Radhika Krishnan
Oct 28, 2021
Artificial IntelligenceChief Data OfficerCIO

Strategically thinking IT leaders should keep their eyes on how new data technologies are redefining how we capture value from data.

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Credit: ipopba

Data is the fuel for digital business. Yet, data and its role within the organization are changing. For companies to gain maximum value from their data, they must change with it.

To that end, here are the top five data trends that will affect organizational data and that should be considered as CIOs adapt their data strategies.

  1. Data fabrics are going mainstream to deliver 360-degree views of the business.

Data silos have served the purpose of organizing and managing data. But they prevent the ability to gain a comprehensive view of all data – and make decisions that benefit the entire organization. Through an enterprise data catalog, you can unify a variety of data silos, including IT and OT, into a single data fabric. As a result, you can ingest, cleanse, curate, and semantically enrich data to deliver a single, 360-degree data view of the entire business.

  1. Data lakes and data warehouses are converging to form data lakehouses.

Data warehouses have long served as the repositories of structured data from which to glean insights to guide management decisions. Data lakes, meanwhile, have emerged as the reservoirs of the unstructured big data driving predictive analytics. A data lakehouse is built on the same low-cost storage as a data lake, but like a data warehouse, is designed for best of both worlds with data governance, transaction support, and business intelligence (BI) enablement in mind. A data lakehouse delivers timely insights while lowering storage and administrative costs.

  1. Artificial intelligence (AI) and automation are making it easier to manage all enterprise data.

Data is exploding quickly. Although there is more valuable data than ever, it is more difficult to manage than ever. An enterprise data catalog can leverage AI to help automate discovery to keep up with rapidly proliferating data. For example, you can use machine learning (ML) to enrich metadata through pattern matching, as previously identified patterns are applied to new data. By alleviating the need to perform manual tasks, AI and automation make your staff more productive and reduce the risk of your organization being overwhelmed by data.        

  1. The role of the Chief Data Officer (CDO) is evolving from data gatekeeper to driver of business strategy.

The time has come for CDOs to do more than handle data governance. They must step forward to advocate within their organizations for the strategic value of data. Astutely managed, data provides a strategic edge that differentiates a business from competitors. And because data is critical, data quality is also critical. The CDO should make it their responsibility to cut through the “noise” of excessive amounts of poorly managed data to find the most valuable insights and assure they are delivered to the right decision-makers. In short, CDOs have graduated from merely policing data usage to enabling business competitiveness.           

  1. Data quality is key for trust in data.

For data to form the basis of business decision-making, it must be high-quality: accurate, timely, and complete. Even more: it must be trusted. To create data worthy of trust requires a combination of data quality scoring and the semantic enrichment of data. The result is high-quality data that will deliver valuable and actionable insights. And trust is a virtuous circle. As data delivers value, it will prove worthy of still more trust. 

Visit to discover how to prepare your data infrastructure to meet these trends, while reaching new peaks of data efficiency.