Ask Me Anything

Transforming the enterprise at startup speed, with Nitish Mukherjee

Digital Transformation

October 22, 2021
·
5
min read
An image of Nitish Mukherjee, CIO of Bluetor.

Nitish Mukherjee, CIO of Bluetor, on achieving startup speed at the enterprise level.

This AMA was edited for brevity.


Nitish Mukherjee is the CIO at Bluetor.


How can enterprise-level organizations achieve startup speed when it comes to their digital transformation efforts?


Today’s enterprises need to adopt startup culture to achieve startup speed, especially in the IT department. In startup culture, all stakeholders can share their opinion and there is no rigid matrix. They’re open to new technology and most importantly, every person in the organization is aligned with the business objectives. Changing an organization’s culture is a big challenge though, so it needs to be driven from the top. It should be encouraged at every step because it will help enterprises reduce costs and make their customers happy.


For example, startups implement new features, expand capacity, and introduce new policies at a much lower cost and in less time than it would take in an enterprise. Large enterprises have a long history to deal with in terms of technology and their bureaucratic approach to upgrading or implementing new features and functionality. They have a lot of legacy systems and applications that need to be re-designed and architected with entirely new technology to take care of current customer needs, new processes and loosely coupled architecture. Some of these applications have unnecessary features implemented because they’re commercial off-the-shelf (COTS) products supplied by the vendors, which are developed with the intention of satisfying customers from all domains. 


“[S]tartups implement new features, expand capacity, and introduce new policies at a much lower cost and in less time than it would take in an enterprise.”


Documentation and knowledge building are also big challenges for an enterprise: upgrades can’t be done quickly because there is no real documentation process where all the changes are noted. Their approval processes are too long because there are several stakeholders. Sometimes new feature implementation takes so long that by the time these features are completely ready, they may not be important to the organization anymore. On the other hand, in a startup everything is very well-defined from the beginning. Any change in features or capacity expansion has a process with a clear responsibility matrix, which is delivered by a mostly in-house IT team in collaboration with the business team. Implementing something new or adding a feature to an application can be just a few hours of work. 


Do you think large enterprises would benefit from adopting startup culture? Comment on this post in the Pulse community.



What advice would you give to enterprises regarding data strategy?


At the enterprise level, data is another piece that will benefit from startup processes and policies. In general, if there is 100 GB of data in an organization, 30 GB of that is not required at all—it’s duplicate data that’s been copied for a backup or some other purpose. Retention data are stored for a much longer period and in a much more efficient disk, which is beyond regulatory or organizational requirements. Backups are generally taken multiple times in different formats, which creates a lot of confusion in terms of restoration or modernization work. 


“People cannot think innovatively in today’s large organizations because they know that there’s a lot of work required for [new feature] implementation.”


A lot of applications, data and configurations within the huge data center platforms are never even used. It's just in the system storage; nobody’s used it or bothered to clean it. There has to be some data cleaning and application processes, as well as strict measures to follow and automate most of those processes. Have a team sort out what is required or not; then the system will have less data. That means we'll use the machines faster, applications will be faster, user experience will be good, and NPS will be high. People cannot think innovatively in today’s large organizations because they know that there’s a lot of work required for implementation. And this thinking creates a blockage: "I cannot work on this, there is no value. Why bother thinking about this?" Then nobody implements anything new.


All the large enterprise companies need to adopt the startup-style approach to workforce and architecture—organizational architecture, as well as system and data architecture. From one organization to another there are different processes but if a large organization can do these things, they will benefit in terms of their employees, costs, customer satisfaction and digital transformation.


What do you think enterprises should change to improve their data strategy? Comment on this post in the Pulse community.



Does strict data compliance mean compromising the speed of business transformation?


If you keep your system, applications and data thin and lean, it actually makes security implementation very easy. If we go from having 100 machines to 70 machines—or even just 13 machines—then security implementation is easy and you have less data to protect. The likelihood of a data leak is lower than if you secure just 1% of the data when it’s also copied to another hard disk, which was left open after somebody copied it for testing or some migration. That data is still laying there open because the job was to copy it, not to delete it from the old device.


“If you have less data and applications, then compliance is very clearly defined.”


GDPR and other data retention policies are clear about how long you have to retain the data and all industries are more or less standard so there is no ambiguity. But right now nobody deletes the old data. For years and years after the required period, all that data is stored in their system, maybe in two or three locations—that is where it gets complicated. If you have less data and applications, then compliance is very clearly defined.


Do you think data compliance will always slow down business transformation? Comment on this post in the Pulse community.


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