IT center, on-premise infrastructure is becoming increasingly complex and costly, and requires highly skilled manpower, so businesses are now moving their IT and Data Science functions to the cloud. Cloud computing promises low-cost storage facilities, 100 percent up-time, managed services, and automated analytics and BI services, leaving the businesses to concentrate on their domain expertise without having to worry about critical IT functions.

Though initially cloud service providers offered huge cost savings on storage and computing, the recent move has been to offer value-added Analytics services, resulting in competitive intelligence. Enhanced cloud platforms with AI and ML capabilities are now the battlefields of business competition. Journey to the Cloud: Unleashing the Power of Data compares the relative merits and demerits of public, private, and hybrid clouds.

While the cloud offers many benefits to business users, one nagging issue has been Data Governance and data security. Many businesses, unable to trust outsourced IT infrastructure, have opted for other solutions like private cloud, or hybrid cloud by combining on-premise and cloud resources. For all cloud users, Data Governance is a critical concern as these business users are putting their enterprise data at risk by allowing the data to travel to a far-away location.

Predictions for Data in the Cloud

According to Gartner, a leading industry watchdog, IT security incidents dominate news headlines. In a cloud-First world, the public mistrust surrounding cloud computing has led CIOs to reject cloud services.

In Is the Cloud Secure?, Gartner makes the following predictions:

• 60 percent of enterprises with proper cloud governance this year will experience 33 percent fewer security lapses.

• Through the end of 2020, public cloud infrastructure-as-a-service (IaaS) will experience at least 60 percent fewer security incidents than such incidents in on-premise setups.
• Through the end of 2022, 95 percent of cloud security failures will be the customer’s fault.

Data Governance basically provides the blueprint for business data analytics. Through Data Governance, organizations set rules to manage different types of business data, such as data related to viewer metrics, customer behavior, shopping data, and so on. The ultimate goal of Data Governance is to ensure that all data are reliable and consistent before they can be used for performance evaluations or competitive insights.

The World Reporter.com provides five strong arguments in favor of DG, which are: consistency and repeatability of data, guided Analytics, accurate analysis and reporting, cost savings, and peace of mind.

Data Governance for the Cloud

As businesses continue to move data to the public cloud, the growing concern is the best method for this data migration.

As the public cloud is a completely shared platform, and even private or hybrid clouds are partially shared, it is imperative that businesses ensure that cloud service providers have strong Data Governance practices in place. No business wants to compromise their prized asset – data. As a CIO.com article indicates, a popular Enterprise Data Management (EDM) practice of 2018 is moving data and Analytics operations to the cloud. Data Governance is also a key component in the overall EDM strategy adopted by a business.

Some of the more pressing reasons why business data in modern organizations need to be governed are:

• High volumes of multi-source data resulting in data inconsistencies
• Low quality of data
• Need for standardized policies of data access
• The rise of self-service analytics and “data democratization” across the enterprise
• Regulatory compliance requirements, such as GDPR
• The critical need for a common data vocabulary for cross-departmental data analysis
• The need for enhanced organizational metadata

How Data Governance Keeps Your Journey to Cloud on Track suggests that while a large majority of business owners are overly concerned with the technical issues of data migration to the cloud, the real enabler of a successful migration is Data Governance. Data Governance can become a bigger challenge on the cloud if a business has substantial volumes of big data.

Adapting Data Governance for the Cloud

Data Governance in the Cloud points out the common concern of all small and medium businesses (SMBs) regarding data security on the cloud. According to a University of Bournemouth survey, 54.6 percent of polled SMBs stated that the lack of data security is the number one reason for rejecting CSPs. It could be argued that Cloud services by design would probably benefit the SMBs the most. The trust deficit related to cloud solutions among smaller businesses is clearly noticeable.

With the rising pace of cloud adoption among global businesses, it is now clear that the business community needs to think of regulating and standardizing cost, performance, security, and compliance metrics of CSPs to make them acceptable to a broad range of business stakeholders.

Data Governance Strategy for the Cloud-First World

On one end of the cloud spectrum, there are small- and medium-sized businesses that stand to benefit the most from CSPs, but cannot get over their basic mistrust concerning data security. On the other end of this spectrum, there are larger businesses or organizations, ready to move their data silos to the cloud for faster, better, and more timely processing.

The members of the cloud-first world, who have made cloud services their top business priority, are increasingly opening up to the idea of risk management across premises.

12 Step Guide for Data Governance in a Cloud-First World suggests identifying a subject matter expert (SME) for developing strong Data Governance policies for data sets. The SME’s job will be to ensure balanced risk management vis a vis the needs of the business entities who access such data. It lists the following as the “7 Data Best Practices in a Cloud-First World”:

• Setting a data lifecycle with finite time limits
• Retaining the security context with the moving data to ensure uniform implementation
• Tracking of metadata for enhanced data value resulting in quick access to data
• Tracking of multiple instances of same data
• Developing policies for data integration and data transformation
• All data has an assigned SME
• Managing developed data models

Data Security on the Hybrid Cloud

As mentioned earlier, organizations are moving their Analytics operations to the cloud simply because of the cost advantages that it brings. However, data security remains a growing concern for business users who have either chosen a public or hybrid cloud environment. Data security has to be managed efficiently.

The common advantages of cloud-based security compared to on-premise security include updated software patches, regular virus scans, and physical security mechanisms at cloud centers. In truth, most of cloud security frameworks have as many limitations on data access controls, usage, and security analytics as those on on-premise facilities. As cloud service providers do not typically control data access or password updates, your business data is always vulnerable to outside attacks.

Data Protection and the Cloud: A Hybrid World Deserves Hybrid Security discusses an “alternative security technology” that can potentially cover both on-premise and cloud data stores. The implicit hint in this post is that wise business owners know they have to implement the same stringent Data Governance and data security practices to their cloud data as they do to their on-premise assets.

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