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Top data trends for 2018: GDPR, IoT & the rise of the chief data officer

By Julia Gabel
Thu 16 Nov 2017
FYI, this story is more than a year old

With the year almost over, Infogix has compiled a list of the top game-changing data trends for 2018.

“Metadata management and ensuring data privacy for regulations such as GDPR joins earlier trends like AI and IoT, but the unexpected trend of 2018 will be the convergence of data management technologies,” says Emily Washington, senior vice president of product management at Infogix.

While big data has been the next big technology phenomenon for a long time, businesses are looking for ways to streamline their overall technology stack, says Washington.

And as a result, companies are able to “successfully leverage big data and analytics to create a better customer experience, achieve business objectives, gain a competitive advantage and ultimately, become market leaders,” adds Washington.

The major data trends for 2018, assembled by business leaders at Infogix, include:

2018: The Year of Converging Data Management Technologies

Use cases have proven that leveraging data requires a multitude of separate tools for tasks like data quality, analytics, governance, data integration, metadata management and more.

To extract meaningful insights and increase operational efficacy, businesses will increasingly demand flexible, integrated tools to enable users to quickly ingest, prepare, analyze, act on, and govern data - while easily communicating insights derived.

Increased Importance of Data Governance

The deluge of data is growing, government regulations are increasing and teams have much greater access to data within an organization.

Add to this the increasing need to leverage advanced analytics, and data governance has become more critical than ever.

Data governance capabilities have evolved in a way that provides complete transparency into a business’s data landscape - allowing them to combat increasingly complex regulatory and compliance demands and the shifting tides of business policies and business alignment.

The Continued Rise of the Chief Data Officer (CDO)

In today’s data-intensive environment, a CDO is more important than ever to navigate regulatory demands, successfully leverage data and manage enterprise-wide governance.

A CDO helps businesses manage unstructured and unpredictable data, while successfully leveraging advanced analytics and maximizing the value of data assets across the business enterprise.

Ensuring Data Privacy for Regulations such as the General Data Protection Regulation (GDPR)

When GDPR goes into effect in May 2018, it will strengthen and unify data protection rules for all organizations processing personal data for European Union (EU) residents.

Through analytics-enabled data governance, a business can not only locate personal data enterprise-wide, but monitor compliance, usage, approvals, and accountability across the organization.

The Proliferation of Metadata Management

Metadata is a growing trend for 2018. This “data about data” contains the information necessary to understand and effectively use data such as business definitions, valid values, lineage, and more.

Using such ontologies, organizations can understand the relationship between data sets, as well as enhance discoverability in metadata.

Metadata management is critical in enterprise data environments to support data governance, regulatory compliance and data management demands.

The Monetization of Data Assets

Organizations recognize that data is either a liability or an asset. Metadata can be used to enable a deeper understanding of the most valuable information.

We are seeing more organizations using a combination of logical, physical, and conceptual metadata to classify data sets based on their importance, and businesses can apply a numerical value to each data classification, effectively monetizing it.

The Future of Prediction: Predictive Analytics to Improve Data Quality

With the continued concerns with data quality and the volumes of data increasing, businesses are enhancing data quality anomaly detection with the use of machine-learning algorithms.

By using historical patterns to predict future data quality outcomes, businesses can dynamically detect anomalies in data that might otherwise have gone unnoticed or only found much later through manual intervention.

IoT Becoming More Real

Each passing year marks an increase in the number of connected devices generating data and there is a steep rise in focusing on extraction of insights from this data.

We are starting to see more and more defined IoT use cases leveraging data - from newer connected devices like sensors, and drones for analytics initiatives.

With this, there is a growing demand for streaming data ingestion and analysis.

“As more data is generated through technologies like IoT, it becomes increasingly difficult to manage and leverage. Integrated self-service tools deliver an all-inclusive view of a business’s data landscape to draw meaningful, timely conclusions,” said Washington.

“Full transparency into a business’s data assets will be crucial for successful analytics initiatives, addressing data governance and privacy needs, monetizing data assets and more as we move into 2018.”

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