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How to shift to a more
data-centric infrastructure

Organizations are producing, storing and analyzing data at a rapidly increasing rate.

It’s easy to see why – data can generate important new business insights, boosting operational efficiencies and directly improving revenue.

As businesses are faced with exponentially expanding volumes of information, and data becomes an integral part of their infrastructure, technology platforms must help harness the power of the data to catalyze better decision making, risk evaluation and customer engagement. For businesses, the data infrastructure is as important as the physical infrastructure.

We often get asked by customers and prospective customers how we can help them unlock the potential of their data as infrastructure, what the implications of the rise of data are for organizations and how the importance of data has changed over time. We have addressed some of the most frequently asked questions below.

Q: What does the term “data as infrastructure” mean?

A: At a high level,  over the last several years, increasing digitization has made data so important to society that we should be treating it as critical infrastructure — like roads, railways, telecommunications, electrical grids, sewers and water supply.

Data is the raw material that will help us meet 21st century challenges — to reduce friction in our economy, increase sustainability and create opportunities to innovate. A strong data infrastructure will increase interoperability, collaboration, efficiency and productivity in public and private sectors, nationally and internationally. Having the right conditions for data will reduce transaction costs, grow supply chains and inform citizens. A coherent data infrastructure should be a baseline condition for a healthy, progressive society, and a competitive global economy. Data infrastructure will become ever more vital as our populations grow and our economies and societies become more reliant on getting value from data.

At an organizational level, “data as infrastructure” can be looked at as “data defines infrastructure”. IT infrastructure should be designed around how businesses are capturing, managing and using their data, but too often IT teams take the opposite approach. They build an infrastructure and then try to make their data fit into that infrastructure. What they should be doing instead is identifying their data workflows and then deciding what the optimal place for those workflows is, whether it’s the public cloud, a private cloud, at a co-location site, or on-premises.

Q: Has the treatment of data changed over time?

A: Definitely. In the past, organizations focused more on the infrastructure. For example, they would move to a co-location model and just shift all their data over there. Then they might decide they didn’t like the up-front capital investment of a co-location model and move to a hosting model with monthly recurring charges instead.

But what we are saying now is the infrastructure should not be determined by cost or convenience – it should be determined by how you are using your data sets. Organizations should first look at what data problems they’re trying to solve and then build an infrastructure that lets them solve those problems efficiently and cost-effectively.

Q: Why have organizations historically taken a hardware-first approach to their IT environments?

A: If you take banks as an example, in the past, they planned around their core physical assets such as buildings, office equipment and IT hardware. Data wasn’t considered an asset until artificial intelligence (AI) and machine learning came along. Then the banks built a management structure around their data and applied new technologies to it. Now they can put a value on data and each type of data needs to be treated differently.

Q: Can you give an example of how data types need to be treated differently?

A: Driverless cars are a great example. They rely on a constant stream of data to navigate their routes. Data needs to be right at the network edge for them because it’s being accessed constantly. But for something like medical records that may only be accessed once every few years could be far off in the cloud because latency isn’t an issue.

Q: Can you give an example of how an organization could benefit from shifting to a data as infrastructure approach?

A: Definitely. A social networking platform for academics, researchers and scientists was experiencing tremendous growth and looking for ways that would allow the platform to continue its upward trajectory. The platform was creating new ways for its users to connect with each other to collaborate, host and share details of projects they were working on around the world. Instead of working in the isolation of a traditional peer review process, the network was connecting its users in real-time.

Beyond its steadily growing user numbers, being a platform for academics, the organization also needed to receive, catalogue and store millions of new references uploaded to its platform each month.

Data dictated the type of computing and processing, and storage the organization needed to deploy in order to maintain its growth. In a case such as this, the infrastructure needed flexibility and scalability so it could be quickly expanded. Analytics and machine learning were also applied to the data, which allowed these massive data sets to be effectively searched and catalogued. In this way, taking a data as infrastructure approach benefits not only the organization, which can continue to grow and build its user base, but it also benefits the researchers, scientists and academics who use it to collaborate in ways never before thought possible.

Q: How does an organization switch to a more data-centric approach? Are there steps they can take?

A: A strategy-first discussion is key. There are simple questions organizations can ask when they move to a data-centric model. For example, they need to understand who the data owners are, what their existing IT architecture looks like, how often the data is accessed, how quickly it needs to be accessed, whether analytics are being applied to the data and how resilient and secured the data needs to be. We can help walk customers through that journey.

Sometimes customers will come to us looking for feeds and speeds and then it’s up to us to look under the cover and say, “What do you really need to enable your business”? It all needs to start with the business problem and not a piece of hardware like a new server.

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