2020 business priorities: the top 7 trends in data governance
As businesses venture further into the data-driven age, the rate at which they create, store and ingest data continues to climb exponentially. This means more insights, which means more value.
But it means something else too. More challenges surrounding effective data governance.
With data governance becoming a critical business practice, organizations are investing in programs and technology to help achieve quality, compliance and security at scale. To Gartner, it’s all too little, too late: looks like four out of five organizations investing in data governance over the next three years will struggle to adapt to these new realities of digital business.
But by putting key governance practices in place sooner rather than later, organizations will be better placed to respond effectively to new opportunities and challenges as they arise. For those getting started today, foundational best practices in data governance will be the first point of focus.
So, we looked at the latest governance trends popping up amongst the leading analysts. Here’s what we found.
1. Follow the leaders: adjust to regulatory compliance
In the wake of the enactment of GDPR and California’s new data privacy mandates (CCPA), consumers will expect robust compliance and transparency – including clear and consistent privacy standards and policies – when companies handle their personal information. (Your complicated and wordy Terms & Conditions statement will no longer cut it.)
According to principal analyst at Forrester Michele Goetz, data has reached the stage of being too “dynamic, federated and ownership-complex” for traditional rule-based compliance practices to be universally effective. Instead, companies should aim for an “ambient” data governance model, which avoids hard distinctions between people, process, digital, analytics and data. Critical to this shift will also be smart data governance services that use smart decentralized technologies and artificial intelligence to help people source, curate and utilize data.
2. Keep one eye on privacy at all times
Protecting the personal data of individuals is an increasing challenge when it can be distributed across business units, geographies and third parties. As privacy laws continue to evolve, companies risk significant fines and reputational harm for non-compliance, making it critical that they continuously self-assess for privacy “gaps”.
Gartner recommends that organizations make an inventory of their most critically exposed or at-risk data and analytics assets, and use it to build a “trust model” that supports current and future business needs, along with the data and analytics ecosystem it resides within. It can then be determined whether the trust level for the data and analytics assets is acceptable, and what actions are needed to gain this trust.
3. Improve the general quality of your data
We couldn’t have said it better. Relevant, accurate data is the only data that matters, and as such, certain quality assessment mechanisms need to exist within any data governance strategy.
A data cleaning process which identifies and corrects inaccurate, inconsistent or incomplete data will help to prevent problems during its later use or analysis. Once the root causes of data error have been identified, a data quality plan should be established that aims to address data quality issues at the source. Given that data quality is itself dynamic, it’s also important to invest in the tools, time and experience needed to measure the accuracy of the data in real time.
4. Lock down a data governance framework
And according to Deloitte, an effective data governance framework hinges on standards. Things like metadata management, data quality, data security and compliance, and data modeling, which ensure data serves and fits its intended purpose.
This is where a data governance framework is useful. It sets the processes and procedures which guide these standards into execution and determine how enterprise data is acquired, managed and archived. It includes approved plans for tasks such as data management, data sharing projects, regulatory compliance and risk management. This allows business and IT departments to achieve a shared, company-wide approach to data governance.
5. Accelerate innovation with automation
Governing decentralized data effectively at scale only becomes more challenging when autonomous AI systems such as chatbots and virtual assistants are added to the mix. The large volumes of data needed to train machine learning models can make the job even harder.
Automation with digital agents can mitigate this risk by onboarding many of the fundamental aspects of data governance. For instance, machine learning and natural language processing can improve overall data quality by removing duplicated or irrelevant data. Similarly, data profiling can apply statistical analysis to data to eliminate uncertainties about its identity – thus ensuring the right governance policies are applied to the right data.
6. Classify your master data with metadata
Metadata is essential to giving master data its context. In an IMB interview, Principal Analyst at Forrester Michele Goetz said, “only 8% of companies are fully capitalizing on their existing data in storage. Metadata is the key to powering digital with data.”
How do you do that? A data catalog. This enables users to identify important data sources, data usage, and data lineage from origin to final use. It should be user-friendly in a way that effectively translates technical information into a business lexicon.
As well as helping to reduce ambiguities and improve transparency, a data catalog can help employees discover data more quickly, giving them more time to analyze it for insights. Additionally, automation using ML and NLP can help data librarians categorize and catalog data with greater efficiency.
7. Extend governance beyond your business systems
As the open data economy emerges, systemic data governance will be a given for organizations looking to capitalize on the big-time benefits of this movement. Namely, new insights and innovation. By investing in a platform that can to run governed data sharing projects with external partners, it’s no longer just about minimizing risk, but harnessing data’s potential.
The most comprehensive data collaboration platform? Data Republic’s Senate Platform. It solves the complexity of multi-party data sharing with end-to-end data governance from legal terms, to user access and dataset licenses.
Want to find out more about data governance and its impact on the enterprise? Download our whitepaper ‘The Data Governance Effect’.
To learn more about Senate and how your organization can leverage data sharing to gain bigger, better insights, visit our website.