Cloud Economics: Avoiding sticker shock

To be adaptive and pivot in the face of unexpected events, an enterprise must have agility hardwired into their DNA. Data has a huge role to play in ensuring that companies are equipped with the ability to reinvent and redeploy. In looking to harness their data for greater insights that help them grow, retain customers and innovate, more and more businesses are looking to the cloud to see how it can transform their operations. 

Whilst there are a multitude of strategic reasons that will motivate a company to move to the cloud, a common denominator is often cost – or rather then opportunity to reduce cost through lower overheads and dynamic scalability thanks to a pay-as-you-go business model.

It can therefore be something of a shock when companies get a bigger bill than they were expecting. Sticker shock, as it’s become known, tends to arise because businesses seek to model costs at a high, rather than granular, level such as a line of business or geographical location. The issue with this method is that it doesn’t consider the many and varied use cases that will be deployed both now and in the future – all of which have a direct impact on compute power and therefore cost.

Welcome to the world of cloud economics, which seeks to help companies derive the greatest value from cloud initiatives. In particular, organisations need to assess the associated costs, benefits, and principles of cloud computing to orchestrate a compelling – and sustainable – business case for the cloud. By default, organisations expect to see return on investment and low total cost of ownership (TCO) in comparison to on-premise counterparts, but by dissecting the economics of cloud, companies can further optimise their cloud investments for greater reward.

Businesses need to be asking the right questions

This means companies need to look beyond just cost and CapEx balance sheets. What are the business requirements, strategic objectives and tactical imperatives that companies need to deliver in order to realise their data strategy? Data, business and cloud are all fundamentally linked and the relationship between the three is vital to ensuring the cloud delivers – both operationally and financially. To gain a deeper understanding, we spoke with Abhas Ricky, Managing Director, Global Head – Strategy & Transformation at Cloudera. Abhas is a seasoned strategy and ops executive and has profound cloud economics knowledge to share. In our conversation, Abhas put it very well: “Think of cloud economics as an investment thesis articulation and a holistic program plan to achieve your transformation and IT modernisation targets”. So, if you’re looking to explore, for example, skills automation, you need to create a business case that proves this to be a reasonable endeavour to deliver on your data strategy.

In particular, organisations need to consider how cloud projects might manifest. As is famously known, cost reduction is one of the main drivers behind cloud computing. However, its capabilities can also manifest in terms of business continuity, revenue optimisation, faster time to market, and more. The point is, whatever your value driver is, you need to ensure that it makes sense from each lens.

In other words, companies need to build their business case and see whether cloud is the solution, rather than opting for cloud simply for the sake of it. If indeed cloud is the solution, is it the full solution, or simply part of it? Another aspect businesses must consider is where they want their workloads, whether it be on-premise, in the cloud, and otherwise. This is where understanding the nitty gritty of their use cases comes into play in order to avoid any unwelcome surprises.

Abhas advises that businesses choose sensible use cases with solid metrics, and ask the metrics questions early on. Perhaps the most important question of all, however, is whether it will evolve moving forward.

Cloudera

With so much to take into account, it’s only natural that businesses feel a little stumped. Fortunately, Cloudera is at hand to guide its customers through their cloud economics story. 

Firstly, a little background on the company. Cloudera delivers an enterprise data cloud for any data, anywhere. What’s more, Cloudera helps its strategic customers understand and articulate the value of potential solutions, while also mapping a digital transformation roadmap as they move onto the cloud.

So what can Cloudera do for your cloud economics journey? There are two sides to how Cloudera helps its customers. The first is concerned with going to market and enabling customers to lower their overall cost from a technology and solution perspective. The second is the methodology that Cloudera uses to enable its customers to come up with the best business case, not only in Cloudera’s remit of data lifecycle management, but also for the other layers of the software/hardware stack.

You’ll be pleased to know that Cloudera won’t swoop in with the typical “here’s your product, and here’s the price” approach. Instead, they will start with the strategic problem your business is facing. As an open-source company, Cloudera’s fundamental belief is to drive customers’ strategic goals and tactical imperatives around digital and cloud, resulting in lower TCO, faster and resilient cloud deployment with shorter development cycles, increased monitoring fidelity, and ubiquitous automation that institutionalises best in class security and governance structures.

Then, they will work against that to create use cases and workloads, before deciding on the most appropriate solution architecture based on the ecosystem their customers have. Of course, each customer will have their own different vendors, numerous SIs, and unique setups, but Cloudera takes each detail into consideration. Only then will they suggest whether Cloudera – or which part of the Cloudera stack – is the best fit to optimise cost.

In terms of cloud economics business cases, Abhas outlines the five value drivers that customers need to be considering:

  • Cost reduction
  • Cost avoidance
  • Risk avoidance
  • Service improvement
  • Time to market

No matter the industry, domain, or use case, businesses can sum the value across one or more of these drivers. Cloud may have an impact, but it also may not – it depends on the specific situation. 

Abhas shares the example of a compliance use case. “Naturally,” he says, “There’s a risk associated with that, but at the same time, it helps you with cost avoidance as well.” Abhas goes on to explain that cost avoidance comes into the picture if you’re not procuring a licence you already have or something that is more expensive. Eventually, you will offload data from that product and move forward in the future, which is where cost reduction will occur. Abhas also reminds us that cost reduction actually “hits the books”, unlike cost avoidance, which largely has notional value attached to it. 

These are the nuances businesses need to be looking at. To answer the aforementioned value levers, companies must consider the technology cost from every perspective, including data processing, governance, security, environment, and so on. Furthermore, they should consider the startup costs needed to run a cluster, build it, and get it moving, before taking operational overheads into account. 

Although a cluster stand-up, upgrade, and business metadata management and security ops will have been tended to, it’s important to understand that the customer needs x amount of FTEs to enable the feature parity that either competitors or best-in-class vendors have, customised to their own specific needs. In other words, once a business models these ‘buckets’ and rolls them up to the four aforementioned levers, businesses can calculate their business case with Cloudera in tow for an economically sound cloud future.

For more discussion on cloud economics, don’t miss the EM360 podcast with Abhas Ricky and Scott Taylor, the Data Whisperer. In this episode, Abhas delves further into TCO models,  how ephemeral workloads can be beneficial, and where the complications and nuances come in around the cost of data and analytics.