(c)iStock.com/Erik Khalitov

The vast majority of companies who are moving to cloud applications also have a significant current investment in on-premise operational applications and on-premise capabilities around data warehousing, business intelligence and analytics. This means that most of them will be working with a hybrid cloud/on-premise data management environment for the foreseeable future.

Moving at ‘cloud speed’, and setting up a new application in a matter of hours, is a big advantage in terms of business agility, but one cost is that managing data becomes more complicated.

It seems that more often than not, a hybrid architecture isn’t planned – it just happens. The ‘typical’ use-case pattern starts with organisations integrating a cloud application, perhaps for CRM or HR, then, they add cloud database before finally adding a cloud data warehouse and/or analytics capability.

A lot of the time it’s the business that drives this pattern in an effort to solve a particular problem. Most frequently, IT is hugely involved in the effort, but the cloud analytics decision is made by the business side. As a result, IT inherits significant new data management complexity.

Implementing data governance

It can be difficult to retrofit governance into existing systems. Often, the focus is on the initial data migration to the new operational application or analytics, where a simple bulk data loader is employed in the interest of speed and agility. This has several downsides:

• There is no metadata. Using a data integration tool with metadata support, instead of a bulk loader, will give you an end-to-end view of your data lineage throughout your environment. This will be critical as the complexity grows and you need to make changes, quickly and without errors
• The opportunity to do a data clean-up while moving the data is lost, and you risk populating a new application with ‘bad data’
• You miss the opportunity to think about ongoing data security and data governance on a broader scale

Once the new applications have gone live, focus shifts to ensuring data consistency. Moving between cloud and on-premise systems and cloud-to-cloud brings new challenges, and leave fewer resources dedicated to overall data management.

If you don’t want to slow down the business initiatives that are driving the new applications, but still want to prevent that data complexity or chaos, it will pay to have a data management architecture and best practices in place before-hand.

The hybrid data management checklist

Data management in a large organisation is challenging. But this gets even more complex when it’s hybrid. The key is to plan ahead so that you’re not a roadblock to the business. Key considerations for a successful programme should question:

• Does your software provider offer out-of-the-box, high-performance connectivity to all of the on-premise and cloud sources?
• Does your software provider have compatibility across their on-premise and cloud data integration capabilities including shared skills, shared code (mappings), shared management tools?
• Do they support multiple integration patterns: batch, real-time, API integration?
• Can they smoothly enable you to grow your data management capabilities as you need them covering data quality, data governance, master data management, metadata management, security, B2B?
• Do they support wizards and template-drive development for ‘citizen integrators’?
• Do they have metadata management tools for data lineage and business meaning and context. This is critical for reducing errors, enabling self-service, and speeding changes to the environment
• Do you have a central point of operational management for data?
• Can you manage quality and governance of data across a hybrid environment?

At the end of the day, the business challenge is to deliver value faster than the competition. The IT challenge comes with meeting the speed and quality requirements of the business while enabling them to accelerate their business agility. It can be done, but it takes careful planning and a good, forward-looking data management architecture.

(c)iStock.com/Matej Moderc

Data today is moving and multiplying at pace across boundaries, platforms and applications. With the growth in cloud adoption, social media and mobility, information very rarely stays within the secure parameters of the enterprise anymore. 

More and more businesses are looking to reap the benefits of the cloud. A recent study from the Economist Intelligence Unit found that the most mature enterprises are now looking to cloud strategies in order to expand sales channels. IDC also forecasts that total spending on cloud IT infrastructure will reach $53.1 billion by 2019, accounting for 46 per cent of the total enterprise IT infrastructure spend. However, with more critical data than ever before now residing outside of the corporate perimeter, the task of locating, securing and controlling this data will continue to be a challenge for organisations. The traditional fortress-building strategies that businesses have used to de-risk company data are no longer the viable option they once were.

A data-centric approach

The ability to roll out and maintain an effective data regulation strategy begins with an organisation’s ability to identify sensitive data at the source, wherever that may be. In today’s digital era it is essential that IT security professionals can visualise where their sensitive or confidential data resides. This involved putting strong data governance practices in place, which ensure the delivery of trusted, secured data.

Organisations also need to make it their business to understand the risks that are posed to their data, staying up to date with the constantly evolving threat landscape. Only once this is done, can the right security measures be applied to that information, ensuring it is safe. This is a big shift in security posture and is essential for future data strategies, but in light of a threat landscape that is rife with skilled and vigilant cybercriminals, businesses need to make sure that they are equipped to fundamentally re-architect security approaches to be data-centric. Security has to travel with the data, no matter where it goes. For IT  professionals this means adopting an approach that focuses on managing and securing all end users and tying them to the data they create. By identifying and analysing sensitive data, such an approach can then be applied to help thwart data theft, whether it’s from internal or external sources.

The key to making this happen is helping business users easily integrate, consume and analyse all types of data. From there, the organisation can understand where applications create sensitive information in databases and how the information is proliferated to other data stores for use by line-of-business applications, cloud services and mobile applications.

The age of compliance

The recent introduction of EU data protection regulations has once again hammered home the need for organisations to ensure compliance with data regulations. Failing to manage and protect sensitive information can result in hefty fines of up to 4 per cent of global revenues, a sum that could jeopardise business viability. Of course, financial penalties are just one element of non-compliance. Another powerful incentive for businesses to adequately protect their data assets comes from the risk that data breaches pose for customer trust and corporate reputation.

No company wants to be known for their failure to protect confidential information. There is a growing list of organisations with first hand experience of the impact that a breach can have on brand equity. Consumers’ loss of confidence in business services can take a long time to repair. In fact, the latest research from Informatica into the State of the Data Nation reveals that security fears stop half of UK consumers sharing personal data with brands and businesses alike. What’s more, over half are reclaiming access and plan to share less data over the next three years, while a third claim nothing could incentivise them to share data at all. 

There’s no doubt that effective data security comes at a cost, but by implementing the correct tools and adapting data postures, data security needn’t be the burden that companies seem to fear. When implemented effectively, a solid data strategy can provide the protection that all corporate assets require to master data protection and breach resiliency.