How to Tackle the Data Problem in the Cloud

Simit Patel is a trader, writer, and technology entrepreneur. Simit blogs via

The biggest economic impact the Internet has had thus far is in enabling a greater degree of data-driven decision-making, which has been shown to increase productivity.

The result? Companies heavily invested in the infrastructure for Internet-based data transmission and storage  — like Rackspace — can leverage an abundance of data to design businesses processes with an unprecedented amount of efficiency, the result of which is innovations that translate to greater profits for technology companies and lower costs for consumers. The ultimate win-win.

That’s the basic story, but for technology companies, the challenge remains: how can firms leverage the data they have to create greater efficiencies within their respective organizations?

Here’s a road map to help with conquering that challenge:

1. Know Thyself.

The ancient Greek aphorism isn’t just for philosophers and mystics — it’s for technology businesses as well. Specifically, knowing thyself means two things: knowing the mission of the company and knowing its core competences — what the organization is designed to do.

For instance at Rackspace, our mission guides us to offer high quality services enabled at helping each of our customers solve their unique data challenges, and ensuring our team members are capable of delivering that high quality service.

By having a deep understanding of its mission and its core competence, upper management is positioned to understand what type of data they will have and how they can use it. Which leads to the next step….

2. Property Rights.

Understanding the mission statement and organizational capabilities can help firms craft information rights policies consistent with their operational strategies.

Property rights pertaining to information in the cloud is a major issue in cloud computing and the whole “big data” frontier; accordingly, companies may benefit from ensuring that managers across all major departments understand how the firm views property rights to the information it accesses.

Getting the legal team involved early on to ensure the strategy is legally sound may also be a prudent form of planning — see Digital Inspiration’s blog entry on laws and the cloud for an introduction to the intersection between big data and the law.

3. Ensure Data Operability.

Once the organizational context and property rights policies are clearly understood, the next step is to ensure the technical architecture of the cloud enables all data to be easily aggregated and re-mixed.

The burden here typically lies on the Chief Technology Officer or Product Managers within the respective firm; basically those who outline the vision of the applications that firms use to gather data and create their clouds will benefit from ensuring that their applications are built on the same technical standards.

It is with this in mind that many large technology companies have called for a formalization of cloud computing standards, and projects like OpenStack have started to gain traction.

4. Creating Feedback Loops.

After upper management establishes the vision, the legal team approves, and technologists identify which applications are best for creating an infrastructure capable of leveraging big data/cloud computing, the stage is set for feedback loops to be created.

In other words, data can be queried and experiments against this data can be conducted. Most seasoned Internet marketers will be familiar with the concept of A/B testing, which allows marketers to conduct experiments and examine the data results.

If the cloud computing infrastructure has been set up properly, a system will be in place to collect a wide variety of data and easily create experiments to see how changes in the business affect sales, marketing, costs, and virtually any dimension the business wishes to make more efficient.

Companies that can create feedback loops based atop a cloud infrastructure designed specifically for their business are well-positioned to use all the data at their disposal to increase their efficiency/productivity — a win-win for all involved, save competitors.




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