Canadian Oil Sands (Image Source: National Geographic)
Canadian Oil Sands (Image Source: National Geographic)

Chances are that you have heard the assertion “Data is the new oil”. You may have either reacted to it with an “Oh no, not again” or a “Eureka” depending upon whether you are a data skeptic or a data enthusiast. I think metaphors have a useful role to play in the adoption of new technology because they tend to help in bridging mental barriers, but at the same time I think that metaphors can sometimes be blunt instruments.

Through this post, I am hoping to provide a slightly more nuanced metaphor that is specific to Big Data [as opposed to your regular data].

If you are not a geologist or haven’t worked in the petroleum industry, the connotation of oil is something that is readily consumable, is valuable, and is an essential part of your everyday life. I think the same can be said of the data that resides in your data warehouse if you are an enterprise – it is the fuel that powers your business.

However, not all data can be equated to oil. Specifically, if data is the new oil then Big Data is the new Oil Sand. Just as Oil Sands are unconventional petroleum deposits, Big Data is unconventional value reservoir. The similarity does not end there – I think both Tar Sands and Big Data need four prerequisites to be in place before you can draw some value out of them:

1. Regulatory Framework

Just as it will be an environmental risk to recklessly pursue oil buried deep in the Oil Sands, in the same way there are several risks associated with the extraction of value buried in the Big Data. The most commonly acknowledged risk is around privacy and data security. Ambiguity of regulatory framework is a disincentive for investment and so leadership at the top of enterprises must ensure that there is clarity around what may or may not be done when it comes to Big Data.

2. Technological Infrastructure

You need the right technological infrastructure for you to be able to extract oil from Tar Sands. In the same manner, Big Data requires the right technological infrastructure.

However the good news is that your organization, source[s] of data, and technological infrastructure do not need to be in close physical proximity with each other as they can operate seamlessly over a network.

3. Business Case

Tar sands are viable (commercially) only when the oil price is above a certain level. Similarly, some Big Data initiatives may only have a good return on investment (ROI) under some very specific conditions.

See what you can do to reduce the cost of your Big Data initiative. Assess if you can go for a Capex-light solution to solving your technological infrastructure problem. You can hopefully leverage investments made by other businesses in hardware and software by renting them as services over the internet. Make smart decisions related to sourcing and they will go a long way towards making your business case positive.

4. Human Resources

No matter how much you automate, you always need skilled human resources to help you out. Oil Sands initiatives have been delayed due to lack of manpower availability and there is enough evidence of shortage of skilled resources when it comes to Big Data.

Fortunately, this is where the similarity ends. Unlike, Tar Sands, you can leverage global workforce much more easily for your Big Data initiatives. This can often solve not just your Human Resources problem but also the Business Case problem.

In Conclusion

There are many similarities between Big Data and Tar Sands but fortunately it is easier to extract value from Big Data than oil from Oil Sands. Regardless, you ought to plan carefully and execute diligently your Big Data strategy in order for it to reap benefits for your business.

What do you think of this post? I hope that you find my choice of metaphor meaningful and useful. Please share your thoughts with me on [email protected].

This post was originally published on LinkedIn. You can see the original post by clicking here.