Getting Started with Data Science

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:

1. Installing the requisite technology infrastructure
2. Establishing an analytic culture

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.

If you have any questions or would like to discuss this further, please call us at 317.423.9143 x1 or fill out a contact form.

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:

• open source opportunities and challenges of open source
• open source technologies that support analytics
• the differences between open source technologies

If you have any questions or would like to discuss this further, please call us at 317.423.9143 x1 or fill out a contact form.

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:

• basic issues of data visualization
• how to address data visualization issues
• unique challenges that come with big data
• potential solutions to big data challenges

If you have any questions or would like to discuss this further, please call us at 317.423.9143 x1 or fill out a contact form.

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:

• the model operations process.
• the necessity of model management.
• deploying and monitoring models in production.
• retraining models.
• using open source to operationalize analytics.

If you have any questions or would like to discuss this further, please call us at 317.423.9143 x1 or fill out a contact form.