Data professionals are so valuable to their companies.
They determine which data streams are most important to specific business
projects, challenges and initiatives.
They identify key goals for the future and they give their organizations the
ability to take meaningful action by re-imagining processes and improving
operations. To do all of this, data teams require individuals with diverse
skills, knowledge and interests.
Therefore, they're countless different data focused roles, responsibilities,
and project types which are further differentiated by the industries and
businesses they support.
Among all of these possibilities, data careers can be loosely categorized into
two complimentary types of work, technical and strategic.
In this video, we'll investigate both.
First, let's find out about the folks whose work requires a heavy emphasis on
technical skills. Some examples of these professionals are machine learning
engineers and statisticians.
These people provide high effort solutions to specific problems.
Through their expertise in mathematics, statistics and computing, they build
models and make predictions using tools such as R and Python, they help their
teams extract value from business data sets.
The result is a solution that has a direct positive impact.
Another highly technical role is the expert data analyst whose work involves
exploring vast and complex data sets to identify directions worth pursuing in
the first place. They ensure that an organization's data science efforts are
directed as efficiently as possible.
Bridging the gap between other technical data professionals and the
strategic work we’ll cover shortly.
Like most technical data professionals, you'll learn how to acquire, scale,
organize, structure, and manipulate data so that it's packaged in a way that
others can work with. In other words, you'll know how to transform raw data
into something useful for decision making.
Okay, now let's consider data professionals on the more strategic
side. These people include business intelligence professionals and technical
project managers, to name a couple.
Strategic data professionals use their skills to interpret information that
affects an organization's operations, finance, research, and development and
so much more. Their work aligns closely to the overall business strategy and
involves seeking solutions to problems through data analytics.
In short, strategic data professionals maximize information to guide how a
business works. Sometimes you'll find a company has roles that blend specialist
technical knowledge with strategic data expertise often in unusual and very
creative ways. Soon we'll learn more about some of these opportunities as
well as the more specialized technical and strategic roles, and of course we'll
discover some proven ways to tap into them as a data professional.
Lots more to come. Nowadays, it seems that data is generated everywhere.
Computer functions have been integrated into a multitude of everyday
technologies, smart home voice assistance, step trackers, TV streaming
apps, even some coffee makers are connected.
All of these touchpoints create data that businesses can use to understand
trends and advance their products and services.
And with every update, the ability to gather environmental and user data
expands even further.
Within this massive reserve of data is a wealth of untapped potential awaiting
data professionals and the organizations they support.
That's why these people are so in demand.
Companies need them to use data to refine business strategies, meet
consumer preferences, react to emerging trends and realign internal efforts.
Let's consider some examples of how data professionals use their expertise
to transform industries.
First, the world of big finance was an early adopter of the power of data
science and with the way information drives this industry, it's easy to
understand why. Data professionals help financial institutions assess investment
risks, monitor market trends, detect anomalies to reduce fraud and create a
more stable financial system overall.
Hundreds of millions of financial transactions occur in the financial
world each day and data is part of each and every one of them.
As another example, data analytics is key in healthcare and here the data
benefits can actually be in lifesaving.
For instance, the information collected by smartwatches is making a huge
difference in the lives of many people.
Sensors in these devices record biodata such as heart rate and oxygen levels,
and of course all this information can be shared with healthcare professionals.
Together, the patient and the physician can better understand sleep trends,
stress levels, and much more.
Then individualized wellness plans can be created and modified for the
patient's wellbeing. Plus, on a larger scale, data analytics is helping
healthcare organizations process large amounts of clinical data, which supports
the early detection of a health condition and leads to more precise
diagnoses. Thirdly, data has a big impact in manufacturing.
Data professionals predict when to perform preventative maintenance to
avoid production line breakdowns, use data to maximize quality assurance and
defect tracking and artificial intelligence models help respond to
logistical issues and reduce delivery truck miles on the road, advancing key
sustainability goals.
And in a time when supply chains reach every corner of the world, data enables
clear and near real-time communication with suppliers, retailers, and other
network partners. It also helps supply chains maintain optimal levels of
inventory to avoid stockouts and empty retail shelves.
Data professionals are also advancing approaches to agriculture.
With data insights, farmers develop new ways to approach crop production,
livestock care, forestry and aquaculture.
The inclusion of autonomous machinery, tractors and irrigation systems is
improving harvesting technologies as well.
If you'd like to keep learning about how various industries use data
analytics, refer to the course resources on this topic.
And here's a little piece of advice from me: don't miss an opportunity to
learn from someone in real life.
I love asking business owners, store managers and client support
professionals about how they use data each and every day.
Who knows, one of these conversations could open a door to a future
opportunity for you.