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What Every Business Leader Needs to Know About Python

Blog: The Tibco Blog

Reading Time: 4 minutes

In this blog, written for logistics and operations management, marketing professionals, and other business leaders, you’ll learn a bit about why a scripting language, Python, may be invaluable for your analysts and data scientists, and how it can help them to get more meaningful insights faster. 

What’s Python, and why does it matter to your data analysts and data scientists—and to you?

Python is one of the world’s most popular programming languages, with wide adoption that’s growing at 27% year over year. Fueling this growth are data scientists, and the tools—many now leveraging Python—they rely on. With its rich libraries and ease of use, Python makes it easier to focus on solving problems rather than writing code. Because Python is open source, its community of users can contribute directly to making it better. 

Here’s why it matters for you. Python works so well for data science and machine learning (a key to automating processes and insights) in part because it can rapidly scale up or down, making it possible to quickly build, test, and iterate models. This enables data scientists to iterate for insights faster, and to build out predictive (“what will happen”) and prescriptive (“what would be best to happen”) analytics. These insights are the highest value use cases of analytics for the business, but they are also the most difficult insights to gain.

Moving from descriptive to prescriptive analytics, gain a more significant competitive advantage and turn information into innovation. 

Faster insights lead to innovation and competitive advantage

In the following two examples, learn how the right tooling—including Python capability—provided data analysts and data scientists what they needed to accelerate time to insight and drive competitive advantage.

Anadarko, an oil and gas exploration and production company, wanted to increase value to its stakeholders by lowering operating costs and improving efficiency through new technology. According to Data Scientist Dingzhou Cao of Anadarko, “With open source, it would have taken at least 10 to 12 months to build a similar system…[now we can] see live data coming in, and we have plug and play analytics models to process the live data and generate results. Without having to make a phone call, the drillers get the message, ‘Please act in this manner.’…We believe this is going to give us an advantage, and profit for our shareholders.” With these new enhanced, interactive models, Anadarko was able to significantly speed up time to insight, directly improving operations and benefiting the company’s stakeholders. 

AA Ireland, specializing in home, motor, and travel insurance, wanted to get real-time predictions to identify the most profitable customer types, and chose to step beyond industry standard software for “something that was powerful and future-proof,” said Colm Carey, Chief Analytics Officer. “You don’t sit in an IT queue for a year and a half. You build a model yourself and generate a lot of revenue for the company. It’s that power.” As Carey noted, “Insurance has always had predictive models, but we would build something, and in three months, update it. [Now] data comes in and goes out to models seamlessly without disruption, basically providing real-time predictability.” Accelerating the building of these predictive models allowed analysts to think in real time and increase company profits. 

In both cases, combining advanced visual analytics with Python expressions and data science drove faster, more insightful decision models. Increasing the agility of their data analytics and data science teams was key to getting insights and business benefits faster.

Build a framework with your data science teams to optimize and innovate

When business leaders seek insights through data analytics and data science, a shared understanding across teams is necessary for insights and innovation to arise. Orchestrating data science, IT operations, and business operations to go beyond information into transformation requires: 

Ask your data scientists what they need to slash time to insights, and what obstacles they face in helping your business outperform and outcompete. Listen to their perspective and challenge them to help you find the highest value insights—those that can make the impossible possible. 

And invite them to join us for a special webinar with Neil Kanungo, Data Scientist at TIBCO, as he shares how data scientists can use the power of Python and its open-source libraries to speed time to insights, walking through the setup, execution, and governance of Python for advanced analytics needs.

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