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Trends in Data Management: Choosing the Right Data Strategy


CIOs need to be aware of three key trends as they find the best data strategy for their organization.


by Subramanian Iyer,
January 2014

Data usage and analysis can greatly impact IT costs for an organization. The wrong data management strategy can increase costs exponentially during infrastructure and software integration projects. When executives explore strategies for data management, it is critical to review the key trends that will impact data strategies.

Iyer-spotart

Data tiering and stewardship

As data increases through transformation, data tiering becomes complicated. Organizations are now beginning to focus on this area. In 2014, tiering data by criticality (to identify core data and ownership of the core data) will dominate how master data is managed within organizations.

Data tiering by criticality rarely exists in organizations that spend increasing amounts of time, effort and money in trying to secure all the data within their environments. Also, given the overall sprawl of applications, data security gets relegated to individuals who are supposed to administer the data, while the organization focuses on data access. As a result, breaches that happen within an organization go undetected until later.

A recent study estimated that average loss per incident from unauthorized access has increased to US$300,000 from US$51,000 since 2004. Given the increasing impact of security breaches, organizations will shift focus to securing data that is core to the company, and rely on improving the existing access controls to safeguard all other data.

This will require changes in the data stewardship processes as data encryption and isolation take center stage for core data. Data stewardship will entirely shift to business leaders, who will be a key stakeholder in the IT governance process. This will also reflect in cloud adoption as organizations increasingly move niche application environments to public cloud environments, while keeping their core data on premise.

The advent of big data is leading to more complex architecture deployments, increasing spend and making data retrieval a gargantuan task.

Data security

As companies identify core data and begin the process of a security overhaul, there are three areas that will be the primary focus: identity management, data exposure, and data encryption.

Identity management will evolve beyond enterprise identity management and into integrated social identity management as use of social media sites becomes more prevalent.

As organizations begin moving towards hybrid clouds, the importance of security for data in transit becomes more critical. This will require core data to be encrypted, both in situ and in transit. Standard protocols of file transfer will keep reducing in volume as web services and concepts such as data swarms become more critical to organizations subscribing to services on the cloud.

Next Steps


Onsite and offsite backups will be encrypted as part of the backup process and core data will be masked as it gets cloned outside the production environment. Within production environments, encryption technologies on silicon will become commonplace as companies look to bring in unbreakable technologies to protect their core data for business.

Database and system administrators will have their work siloed by roles even as the usage of non-essential tools to directly view data is eliminated.

Multi-structured data

The advent of big data is leading to more complex architecture deployments, increasing spend and making data retrieval a gargantuan task. As a result, extremely large databases (ELDBs) in multi-structured or multi-tenant form will dominate cost-efficiency in storage and analytics of data. Given the amount of money organizations are spending on enterprise application platforms, it is only natural that companies begin looking at various options to reduce their spending in this space.

Thought Leader


Iyer-headshot-croppedSubramanian Iyer is senior director of Oracle Insight for Data Center Technologies.

In addition, traditional investors in big data are beginning to look at alternate mechanisms to accelerate the return on investments for big data. This will lead to data analytics out of data that is multi-structured—a mix of structured, unstructured and semi-structured data. This unified data management allows for data management to be viewed through a prism of data volumes and access methodology rather than structuring or storage of data.

The emergence of ELDBs that are petabyte-sized will force this transition as companies begin structuring or semi-structuring data for cost-effective analysis. With the advent of multi-tenancy in databases, ELDBs could also be collections of smaller databases where flexibility in operations allows companies to reduce their time to market and increase operational efficiency.

The pace of evolution of the above trends will be accelerated as companies look for cloud offerings to drastically reduce costs, while cloud service firms look to improve margins. The consumption of these technologies will naturally progress, allowing for firms to focus on governance as the key to implementing the right data strategies.

 
 
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