The 5 top trends for data governance strategies in 2019

At the heart of all things data-oriented is the concept of data governance, which calls for proper protocols for data collection, storage, management, security and processing.

Technology has propelled the growth of data-based systems and operations over the past decade, especially due to the emergence of IoT, mobile, big data and analytics, AI and machine learning, and even cloud computing.

As a result, organizations and enterprises are constantly looking to improve their existing processes and update solutions to satisfy customer demand and more competitive practices.

Of course, at the heart of all things data-oriented is the concept of data governance, which calls for proper protocols for data collection, storage, management, security and processing.

It’s a rather heavy consideration for all professionals involved in IT and modern computing and will significantly impact not just the year ahead, but the foreseeable future as well. It makes sense then to assess the landscape at the beginning of each year to understand upcoming trends and patterns within the industry.

Data quality or veracity remains the priority
Data stores are useless if the information contained within is not accurate or valid. Poor or inaccurate data can make extraction efforts negligible. Furthermore, low-quality data — whether purchased or collected — is a huge waste of resources.

data gov trends.jpgSo, it stands to reason that data quality and data veracity — the measure of accuracy — remain a priority for the year ahead. The related trend is a push or movement toward systems, infrastructure and solutions that ensure quality remains top-notch.

A modern master data management solution is a great example. It puts emphasis on business value gleaned from any related data, but more importantly, ensures data quality across an entire organization. Machine learning and AI can certainly help achieve higher levels of quality by assessing data as it comes in.

Robotic process automation
As a whole, data governance and its many aspects must remain part of a continued effort with little to no downtime. This becomes incredibly challenging the larger the data volumes grow. It’s not just about the data itself, but also the number of sources, security risks and vulnerabilities, usage considerations and even privacy or regulatory requirements.

Luckily, robotic process automation has become incredibly complex and advanced thanks to AI and machine learning solutions.
It is now possible to deploy a fully autonomous system that follows a series of rules or algorithms with little to no human input. RPA is an invaluable tool for ensuring data governance across an entire organization, while also cutting down on operating costs and resource requirements.

Data and systems integration
Ask any one organization how many different systems, applications, platforms and channels they use, and you’ll get an incredibly long, if not nearly endless list. They might have one internal network, but the data being passed back and forth is moving across hundreds of different platforms.

It certainly calls for an integrated solution that is compatible with many of the disparate opportunities out there. And as it happens, several tenets of data governance touch on this concept, including availability, security, usability and consistency. Data provenance or data lineage absolutely calls for integration so historical data can be included and used.

Integrated data migration services and solutions suddenly become one of the general requirements of data governance as you need the information and digital content to be available cross-platform.

For example, Watermark’s streamlined data services allow most educational institutions and universities to achieve such a thing with existing data, and so it serves as a great example. It also highlights one of the most vital trends taking hold currently, as companies look to modernize their existing processes and systems and merge the resulting data.

Improved metadata management
Metadata can be referred to as “descriptors that attribute various facets of information to an asset extending its usability and life cycle.” You can also look at metadata as a more complex way of labeling information and sets of content.

It reveals the who, what, when, where and why of collected data. It’s absolutely necessary to understand what kind of data is available as well as how it can be used or applied. There’s no way to compile predictive insights without metadata intact. But organizing it all is an incredible challenge, which calls for unique management solutions.

A growing trend would see improved metadata management and services across the board to allow for more robust and guaranteed data applications. Think, preparation taken to the next level.

In addition, machine learning and AI can help augment the organization of datasets through metadata, further improving its value.

Strict regulation, especially stateside
GDPR set the bar, but it only applies to European countries. It’s not a stretch to predict that the United States and maybe even other countries will follow suit. The California Consumer Privacy Act which will go into effect in 2020, is a great example.

Alongside that is the rigid enforcement of existing policies and regulations, GDPR being the primary driver. Many companies and organizations will start seeing huge fines for non-compliance with consumer-protected data policies and security requirements taking front and center.

2019 Will Be an Important Year for Data GovernanceOne can see by the trends discussed here, that efficiency and productivity are improving, but it should never take away from the quality of the resulting data and the security of its handling.

Overall, it would appear that 2019 is going to be an important year for data governance and related practices. Security, regulations and consumer-demands are ramping up, putting the onus on companies to better protect the data they have or will soon have in their possession.

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