Ranked No. 1 overall as the best job in America by Glassdoor, data scientists have quickly risen in prominence and critical importance within organizations. As technologies like the cloud, IoT and AI transform both the number of intelligence companies can access as well as the speed at which they can innovate, a strong team of data scientists is no longer just nice to have -- it's a necessity for staying competitive.
But with this expanding opportunity comes new challenges. The amount of information data scientists need to curate, organize and the process can often seem insurmountable, especially given the increasing volume of data sets being generated by sensors, devices, and users. As data-driven businesses continue to evolve, the cloud has become the common denominator that can equip these teams with the right tools to efficiently manage and share intelligence across organizations.
Making The Cloud The Foundation Of Data Potential
Access to data in the cloud is critical not only the success of data science teams but also to an entire organization. Almost every job in a company today interacts with data, from a developer who builds apps in tandem with the data science team to a logistics manager who depends on the most up-to-date inventory levels and customer trend data. Not only does the cloud provide a centralized platform for various teams to access data, but it also makes it easier to tap into the resources and other tools that data scientists need to share and communicate intelligence with different stakeholders.
Take cloud-powered notebooks for example. Notebooks are common workspaces in which data scientists, developers, and business analysts can work in tandem across different programming languages, iterating on data and building new models and features in real time. Because these notebooks easily connect back into cloud services, such as machine learning libraries or quantum computing APIs, they can help these cross-functional teams use and explore data in new ways.
We're also seeing the cloud power new functionality around data cataloging, helping to quickly standardize different sets of unstructured data into one workable data set. This capability helps alleviate much of the tedious processing that data scientists have had to do in the past, giving them back more time to actually analyze data and draw out intelligence that they can quickly share to impact business decisions.
Bringing Unstructured Data To Life
Not only can the cloud put data into the right hands, but it can also be designed so that data scientists can combine their data with services that customize and develop solutions tuned for their industries and the specific challenges that come with them.
Consider banking. Easily accessible and structured data, such as numerical information, often provides relatively little value. More often than not, the information that can give the most insight is scattered around a bank's unstructured data sources like information on financial or banking products owned by the customer, which can be nearly impossible to extract and exploit. By equipping data science teams with cognitive analytics and data cataloging capabilities on the cloud, they can pull out intelligence from this reservoir of unstructured data, helping them to support efforts such as analyzing customer transaction patterns to design and test different marketing offers.
Additionally, with more companies offering customer support online or through chatbots, the ability to store and quickly recall volumes of specific pieces of data becomes critical. Think of a chatbot for an airline company. To provide the best user experience, it needs to integrate information from a number of sources such as the distance to the nearest airport or the best price for a certain flight. The cloud makes it easy to integrate these services with stored data sets, turning a simple messaging assistant into a quick and informative customer experience.
Turning Data Governance From A Handicap To An Advantage
Data governance is another way that data science teams are embracing cloud data tools. Many industries today, from healthcare to finance, are facing increasingly complex webs of regulations such as the EU's General Data Protection Regulation, which will take effect next year.
The growing amount of rules around data can seem daunting. Fortunately, the cloud helps data teams approach data governance from a proactive standpoint. With cloud tools, data scientists can implement policies that help every employee better understand which types of data can be shared and automatically secure the data sets that need to be protected.
Giving employees - from data scientists to inventory staff to customer service agents -- the confidence and knowledge that their data is secure builds the foundation for a culture of sharing it. In turn, this builds the groundwork for more intelligent decisions, data-driven innovation, and collaboration across teams.
Embracing Data Languages And Tools On The Rise
To further collaboration between data science teams and others throughout a company, it helps to consider various languages and technologies available today that foster a culture of data sharing even further.
Take programming languages, for example. R has been a popular choice for data scientists since it was designed with statisticians in mind and has thousands of publicly released packages. But Python is quickly gaining momentum in both the developer and data science communities because it's easy to learn, versatile and useful for data visualization and building analytics tools.
While languages are a critical part of the data scientist's toolbox, it's also useful for teams to have access to data mining, integration, visualization, machine learning, and other programming tools. Tools such as this help better translate data into intelligence that business teams can actually use and allow them to evolve theories into actual functionalities.
Preparing For A Future With Data As The Focus
The growth of data sources across the globe shows no signs of slowing down. But to use this influx of information to its greatest potential, data scientists need to have access to the right resources and technology. It's not enough to have data; companies need a group of people and a set of capabilities to manage them and draw insights from it.