How DTNA Aligns Itself with the Evolving Big Data Trends
In the light of your experience what are the trends and challenges you’ve witnessed happening with respect to the Big Data space?
One of the biggest trends affecting us recently is the acceleration of advancements in SaaS offerings. Powerful analytics tools are now available to our data scientists and analysts as SaaS components, allowing us to rapidly adapt as new capabilities become available. Along with this, however, comes the challenge of managing a rapidly changing environment, as new tools and features are available with increasing frequency.
Data quality and data cleansing are two huge challenges that we consistently face. Developing a new predictive model can take as little as a couple of weeks but ensuring your training set of data is able to produce the results you want takes many, many iterations. It takes time and expertise to arrive at the real-world meaning of every data feature, and understand all the anomalies and their causes. Also, recruiting for good data scientists and data analysts is a special challenge for companies like ours who are in the process of building on our “traditional manufacturer” expertise. There is a huge and growing demand for these workers in the technology sector, necessitating new ways to compete for that talent.
Could you talk about your approach to identifying the right partnership providers from the lot?
Daimler Trucks has the target to setup mostly internal teams in the Big Data space, but we have partnerships with a number of key players in the Big Data space. We must protect our data and our customers’ data, so knowing exactly how our data is being handled inside every component and on every server node is highly important to us. We also need partners who are able to adapt to some of our very specific data security requirements and to integrate tightly with our legacy and on-premise system environments.
Could you elaborate on some interesting and impactful project/initiatives that you’re currently overseeing?
Autonomous vehicles: Developing the technological capabilities for autonomous trucks.
Connected and Electric Vehicles: Driving the development towards Trucks as smart and clean digital assets.
What are some of the points of discussion that go on in your leadership panel? What are the strategic points that you go by to steer the company forward?
The Daimler Trucks North America (DTNA) IT strategy aims to build the intelligent company. As a leadership team, we are focused on IT-enabled digital disruption that uses data as a key asset to drive business optimization. We champion thought leadership by bringing new technologies and methods to DTNA while simultaneously fostering innovation through successful implementation.
Can you draw an analogy between your personality traits, hobbies and how they reflect on your leadership strategy?
My career, personality, and actions encompass continuous learning, calculated risk-taking, and embracing constant change.
How do you see the evolution of the Big Data arena a few years from now with regard to some of its potential disruptions and transformations?
The near-term business value of Big Data is in automating time-consuming and costly processes. For instance, automated identification of potential quality issues can help us minimize warranty costs and dramatically improve our customers’ experience. Proactively identifying the potential for a breakdown can further help our customers to avoid disruption of their businesses. These types of solutions will drive the eventual replacement of today’s transactional systems with IoT-based systems that react in near real-time.
What would be the single piece of advice that you could impart to a fellow or aspiring professional in your field, looking to embark on a similar venture or professional journey along the lines of your service and area of expertise?
Be ready to learn, change constantly, and be visionary in your approach.
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