Digital Transformation and Data
Digital Transformation provides a focus on updating processes and technologies. Every communication, every purchase and every interaction leaves a trail of data. Organizations are revising their business models to take advantage of these new data streams. There are steps you should take even before the transformation begins
The good news is that with cloud compute and storage, we can retain and process all the data we want. Reporting and Business Intelligence are mature capabilities that provide historical insight using that data. However, for data science, what has been collected in the past may not be sufficient.
Business Intelligence helps answer questions we already know: How many cars did we sell? Which manufacturers sold the most profitable models? What was the average price?
Data Analytics provides the answers to deeper questions: How should we divide the sales territories to improve the availability of vehicles? What trim levels should be offered as options to maximize sale revenues?
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Asking Better Questions
Digital transformation optimizes current processes for new technology. It also releases opportunities for new products and markets. The job of the analyst is now to ask better questions.
Let’s identify the growing segment of people that do not want to own a car? What is the most useful way to sub-segment this group? What would be profitable mobility solutions to offer them in the future?
Knowing that we will be asking deeper and broader questions provide additional requirements for new systems implementations.
What might have been a simple outbound marketing communication is now an ongoing dialog with the consumer in general: What incentives can we provide to consumers who have never owned a car to share their travel habits? Perhaps we offer them a travel pass for a month on public transport and rideshare in return for their data.
We will find that every touchpoint with a consumer or a device is an exercise in data sharing. Data sharing will now be part and parcel of user experience design.
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Data needs to be high quality in terms of completeness and accuracy for effective data science and programmatic business (i.e. processes driven by machine learning).
Data acquisition must be forward looking. People without cars will walk, cycle or get rides in other people’s cars, how should we capture or account for missing journeys? What does a working day versus a weekend or holiday look like? How do we factor weather and availability of public transportation?
3 Simple Steps
- When specifying a new transactional system, consider the machine learning processes that could be enabled and the profitable data products could be created
- Make sure that your data collection mechanisms are capturing everything you might need, at the granularity you will need it at and make sure it’s timely, accurate and complete.
- Where consumer data is being collected make build consent, trust and privacy into the entire user experience
