Posts Tagged With: data science

Dream Big, Start Small

long range planning

Long range planning does not mean 3 years anymore, its 6-12 months in the new digital era. Waiting any longer, is equal to missed opportunity and declining customer satisfaction.

Quick prototyping allows for real customer input that then allows a fail fast approach to a faster development cycle. This may seem challenging for incumbent companies, but it can actually be turned into an advantage, because many big incumbents have the brand recognition, the funding, and experience.

Hiring a charismatic CDO is not the only lever of creating digital transformation within large companies, the disruption should target all functions end to end to transform the IT systems and core business processes.

Internal and external data should be processed and leveraged to drive decision making analytical models. These insights provide the key to agile and customized process transformations that can make large corporation move fast with precision.

A common pitfall in digitization strategy is to drive an overarching strategy from the get-go. Every change in core business process should be thought through outside in, to be able to connect it in the larger eco system of the overall corporation, and in many cases, they may not all tie in together in the first 90 – 180 – 360 days. This is where following a ‘leader’ in the industry can be detrimental. Its important to articulate a strategy that’s bespoke for your respective company and then double click down from there to the individual organizations and functions.

Pure play disruptive models like Uber, Air BnB, have a stronger starting point because they have built their business models on data. Companies like Amazon, Google, and Netflix are digitally conceived from the get go. For incumbents, frequent concern on the prediction stage is the quality of the data. That concern often paralyzes executives and downstream organizations. This is where the IT investments, over the last few years, with the right algorithmic approach and compressive data strategies, have equipped most companies with sufficient information to obtain new insights even from incomplete and disparate data sets.

It helps to look for low hanging fruit and start small and build up rapidly. This also aids the transformation and change management process in the enterprise where operational changes may be resisted. Ability to frequently evaluate the results of how the organization or process is benefiting the machine learning outcomes will prove to be the pivotal foundation of driving enterprise digitization.

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