Organizations across industries are discovering the power of data science. And the technologies to launch data science initiatives are becoming more and more accessible. In the following chapters of our Getting Started with Data Science Guide, you will learn how you can become a data-driven organization and deliver business value with data science and analytics.
Organizations today are amassing huge amounts of data from a variety of sources, including mobile apps, social media and more. Yet, the organizations with the most data don’t necessarily prevail. Rather, it’s the ones that place data at the heart of all their decision making that compete the most effectively.
So how do you transform your organization from one that simply collects data to one that is a data driven? To become an analytic enterprise, you have to overcome two major challenges:
Changing an organization’s culture is a huge endeavor. It requires addressing the underlying factors that drive employee behavior, acquiring the necessary analytic skills and talent, and developing a strategy for embedding analytics end to end, throughout the entire organization.
As you transform into a data driven organization, you might find this white paper from SAS® that breaks down the process of becoming an analytic enterprise helpful.
In the last chapter, you read resources on how to transform your company into a data driven organization. When organizations look at their options for analytics, there is a lot of excitement about the possibilities with open source. Open source has become popular for big data and data science because of its low-cost source community for innovation.
There are multiple flavors of open source for analytics – typically a free version with no support except for community forums and an analytics package with support licensing. There are also commercial open source analytics options – such as Red Hat for Linux – with more bells and whistles, like GUIs, data preparation, and visualization capabilities.
Though excitement is building around open source analytics, many organizations still don’t know much about how to get started and whether open source is even right for them. We’ve got a great report from TWDI that breaks down best practices for evaluating open source analytics. In this report, you’ll read about:
Organizations generate and collect data every minute. Everyone – from executives and department heads to analysts and production line employees – hopes to learn from this data so they can make better decisions, take smarter actions, and operate more efficiently.
Advanced analytics and intuitive visualizations are powerful tools to help everyone at your company understand your data. Data visualizations enable people across all levels in your organization to dive deeper into data and use the insights for faster, more effective decisions.
To create meaningful visuals of your data, there are some basics you should consider. I’ve got a great white paper from SAS® on this topic that I thought you’d find valuable. This paper discusses:
In previous chapters, we shared resources on becoming an analytics enterprise, trends around open source, and data visualization techniques. There’s one topic we haven’t explored that research shows is a key factor to successful data science initiatives: operationalizing analytics. By integrating analytics into operational processes and systems, organizations can make analytics actionable and produce business value.
For example, manufacturers can use analytical models to predict when maintenance for a machine or part will be needed, which is significantly less costly than machine failure and unplanned downtime. For retailers, analytical models can reveal what offers will appeal to specific customers and when these customers might be most receptive to these offers.
We’ve got a great research report from TDWI that you might find helpful as you explore how to implement and operationalize data science initiatives at your company. This report dives into:
A lot of our customers came to us with data science and advanced analytics ambitions, but were at a loss of how to start. They knew the pains they needed to address – sales forecasting, fraud detection, object recognition and gesture analysis – and they suspected data science could help, but they didn’t know how to get from where they were to where they wanted to be.
That’s where we came in. With our data science expertise to help them, our customers were able to eliminate the pains they were facing, making them a hero at their company.
And it all started with our Pinnacle Data Science Quickstart to jumpstart their quest. We’d welcome a chance to hear about what’s on the horizon for your company and help you become the hero of your company’s data science initiatives. If you’re interested in talking further, please feel free to reach out at 317.423.9143 x1 or fill out a contact form.
In the meantime, we encourage you to check out our Pinnacle Data Science Quickstart factsheet and to visit our Pinnacle Data Science Quickstart page to learn more.