Data comes in S, M, L, XL. Are your services served accordingly ?
Though we believe that organizations are right sized as per the business requirements, they are generally categorized into S,M,L and XL. We recommend that leaders at these organizations who are in charge of data should not try to tailor the quality of their services based on the size of the organization or data itself. Their approach to leverage value from the data should not be driven by a quantitative approach but rather a qualitative need. We will explain why.
Quantitatively data is measured in Bits,Bytes, Kilobytes, Megabytes, Gigabyte, Terabyte, Petabyte, Exabytes, Zettabytes and Yottabytes. More figuratively this translates to something as below.
As you would notice, with time the opportunity to store high quality of information has been increasing proportionally with the increase in quantitative capacity itself.
Most of us remember the days when we fantasized about being in charge of and running IT systems that were capable of handling few GB’s of data. If they could scale up to few TB’s, you pretty much had exclusive bragging rights in your conversation with your friends on a golf course.
These systems were considered to be the crown jewels of IT enterprises. These were the very systems that were constantly used to improve efficiency and automate the mundane tasks of business and IT staff. The very same capabilities which are no longer considered to be the differentiators of enterprises in the marketplace. Putting together these systems and infrastructure is no longer limited to the few elite organizations, the capability itself has been commoditized. Pay as you go and consume as much as you can is the mantra being employed for IT services.
This is the era of commoditizing enterprise class IT services and capabilities to the masses. Masses, as in organizations that are S,M,L and XL.This was a futuristic dream few years ago but a ground practicality these days. A reality that is not entirely enabled by the constantly reducing prices in compute, storage and tools but largely enabled by the nature in which these are packaged and made available for ready consumption to organizations of all sizes. More commonly, these IT services are served via private and public cloud infrastructure offerings.
So, addressing data challenges at organizations should then be a qualitative approach to get the best value out of the data itself and not necessarily an exercise involved in deploying IT infrastructure capabilities based on the size of data itself. After all, these days you can pay for enterprise class IT infrastructure as you would pay for your gas at the pump.
For leaders in charge of IT organizations,
The traditional thought process for addressing data challenges has been
1. What kind of problem are we trying to solve ?
2. What is the size of data that we are dealing with ?
3. Can our existing systems handle this size, if not can we upgrade them ?
4. Can we explore tools/services/vendors that can handle our data set sizes ?
5. What budget need to be allocate to the project for the next 2-3 years ?
6. How do we maintain the infrastructure and do we have the right resources ?
We recommend a different approach as below.
1. What is our business strategy and our IT goals ?
2. Do we have the required data to drive our business strategy ?
3. Can IT enable business to capture, store and secure the data required ?
4. What are the capabilities in the marketplace that can generate value from the data collected?
5. Can this capability be rapidly and securely tested and results measured ?
6. If the capability need to be taken live, how scalable and maintainable is it ?
7. Can the capability provide an transparent insight into operating expenses ?
There can be a difference in who can offer the capability itself. It can either be an internal Center of Excellence (COE) serving multiple Lines of Business (LOB’s) or an external provider offering it as a service.
To conclude, data can come in all sizes S, M, L and XL, but the services being offered in organizations to get the required value from this data should be the best in marketplace. It is possible in today’s IT enabled world. And IT leaders should make sure that they are enabling their businesses by leveraging these available best in class capabilities to their respective organization’s advantage
Oh, one more thing. Did we miss mentioning Big Data ? We see Big Data itself as a relative term. It’s the challenges that arise from this data and the opportunities it presents, that we consider as Big.