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The cottage industry was based on workers buying raw materials, bringing them home and producing hand-crafted items to sell. The system worked, but was slow, tedious and expensive, producing goods that were affordable only by the rich.
The Industrial Revolution changed all that. The factory system brought machines and workers into factories that reliably and quickly produced mass quantities of items at a much lower cost.
You can easily see the connection to the analytical process. Too often today, the analytics process is run like a cottage industry: Workers get raw data from IT, analyze it in silos and produce predictive insights for their individual business units. Read More
After acquiring personal IoT data in part 1 and cleaning it up in part 2 of this series, we are now ready to explore the data with SAS Visual Analytics. Let's see which answers we can find with the help of data visualization and analytics!
I followed the general exploratory workflow described by the Visual Analytics Mantra):
"Analyze first, show the important, zoom, filter and analyze further, details on demand."
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Well OK, so there is an "i" in science, but being a data scientist is certainly not a lonesome job. Engagement with other team members is essential with data analytics work, so you never really work in isolation. Without the rest of the team, we would fail to ask all the right questions of the data so as to solve critical business issues. The hard-earned insights produced would also not be used or understood by the organisation we’re working with.
So, what are the key ingredients of a data science team that you should be looking for? It is, in fact, a group of employees with quite diverse roles. My SAS colleague Jennifer Nenandic highlighted these in her recent blog post, How to build a data science dream team. I’ve summarised the star players here.
The translator (AKA, the business manager)
The subject matter expert has lots of business acumen: an understanding of the issue from a business perspective. As the team focuses on one analytics effort after another, the translator’s role will change. Their role is to help the rest of the team understand the business context of the challenge. They are involved from the beginning of the project and helping to set the scene, right through to the end result when the results are presented. With the business context in mind, they can also help prepare and present the results as well as the return on investment from the project. Read More
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We often hear questions like: Are the shared service chargebacks to my business units’ cost centers accurate and transparent? Will I save any money by using a centralized shared service? Why should I consider a centralized shared service?
These are all good questions. To answer them, you need to understand your organization’s cost structure. That brings us to the age-old debate: Is traditional cost accounting sufficient to provide insight into what things cost and why they cost what they do? This in turn depends upon our ability to understand the root causes of costs as well as the cause-and-effect relationship between resources and outputs.
Many organizations today are exploring options to optimize services, particularly support and back-office services by addressing root causes of inefficiencies, including resource-sharing constraints and poorly aligned processes and supporting systems.
For example, IT services, call centers and order processing are typical candidates for process alignment and cost reduction initiatives. More complex organizations are even looking at sharing sales, field service and logistics operations across diverse business units. Many believe these improvements can be realized through the implementation of shared service centers.
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Do you “buy and build as you go” with your analytics architecture? Most companies do, and have for decades. The result is a heterogeneous environment for analytics with a variety of hardware, software, databases and analytical applications used in silos. There’s tremendous duplication of data and inconsistency in the analytical process, leading to a lot of wasted time and money.
What can be done to improve your analytics architecture? It’s much like improving a home – you can renovate your existing house (modernize), expand the existing structure (extend) or knock down what you have and rebuild (innovate). Let’s take a closer look at these three scenarios: Read More
In part 1 of this series we looked at how to acquire personal data from the Internet of Things for our own exploration. But we found that the data was not yet ready for analysis, as is usually the case.
In this part, we will look at how we can use SAS Visual Analytics to get the data in shape for our personal analytics project.
How can you use an innovation lab to be as agile and innovative as a startup? Are there different types of innovation labs and if so what is the difference?
I answered these two questions in previous posts, and now I will answer a third pressing question: how can you build the business case once you've decide to develop an innovation lab?
Providing concrete numbers is hard, as it varies from organization to organization, but there are some clear areas to research as you build out your proposal. In this post I cover four compelling arguments for establishing a dedicated platform and services for innovating with big data. My hope is that this list will help support you in building your business case for creating an innovation lab, so that you can profit from Hadoop, big data and analytics.
1. Too much time and too many resources are spent trying to justify one-off big data project investments.
As I eluded to in my previous innovation lab post, it can take an army of people across an organization a great deal of time to outline, justify, approve, deploy and try out an idea related to big data. This is especially true when using traditional approaches for proving out IT software and building a business case for investments. Read More
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Everything’s bigger in Texas and that definitely held true at SAS Global Forum 2015. The conference was bigger and busier than ever, especially for the education industry. There were so many amazing presentations and announcements, that you may have missed a few -- here are the highlights.
We had a several customer presentations on SAS Visual Analytics:
- Western Kentucky University
- University of Connecticut
- Valencia College
- University of North Carolina at Chapel Hill:
We’ve all been there. You’ve knuckled down, cleaned out the garage, the attic, and that cupboard under the stairs, thrown away a ton of stuff, only to need it again the very next week. Until recently, that’s exactly what many businesses did with their data.
Don't treat your data like garbage and lose it forever
The data explosion has radically changed how and where we accumulate data, and the way we approach the tough decisions about what data to retain longer-term and what data is of value.
Businesses and consumers are producing data at levels never before seen. In fact, IDC estimates that enterprise data doubles every 18 months. It’s claimed that as much as 75 percent of that data is unstructured, coming from sources such as text, sensors, voice and video. This is exciting for businesses as all that data presents opportunities to unlock value.
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How do they know that?
Have you noticed how your smart phone seems to know everything about you? Where you live, where you work, and even how long your daily commute will take!
A lot of that information is generated by your daily activities while using your connected devices. There is much to be found by analyzing the massive amounts of data generated by the Internet of Things (IoT), and smart organizations are starting to do just that.
Wouldn't it be cool if you had this power too? To collect your own IoT data and then to explore it to find patterns and insights? Well, you can! In this three-part series, you will find out how SAS Visual Analytics gives you the power to explore your very own Internet of Things!