In this episode, I’m joined by Cathye Pendley an Oracle Ace and Business Intelligence expert. Cathye and I talk about all things Business Intelligence or BI. We talk about what BI is, and the skills students need to pursue a career in BI.

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Episode Transcript:
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Welcome to the Oracle Academy Tech Chat. This podcast provides educators and students in-depth discussions with thought leaders around computer science, cloud technologies and software design to help students on their journey to becoming industry ready technology leaders. Of the Future. Let's get started. Welcome to Oracle Academy Tech Chat, where we discuss how Oracle Academy helps prepare our next generation's workforce. 
  
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I'm your host here appears in this episode. I'm joined by Kathy Pendley, an Oracle AI's director and business intelligence expert. Kathy and I talk about all things business, intelligence or buy. We talk about what the AI is and the skills students need to prove and see. You'll see where I get messed up and I start over. So in this episode, I'm joined by Kathy Penley, an oracle AI's director and business intelligence expert. 
  
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Kathy and I talk about all things business, intelligence, RBI. We talk about what be AI is and the skills students need to pursue a career in by. A little bit about my guest. Kathy is a business intelligence program director at Roseann and has 30 years of experience working with Business Intelligence analytics technologies. She brings strong project management skills and a clear methodology focus to each project. 
  
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Kathy has rounded experience in all areas of business, intelligence and analytics, including product project management. Sara backed up their project management of analytics projects to valuations and selection for business intelligence tools, analysis, design, development and implementation of analytics solutions. And she has developed both large and small analytic application patterns and systems. Welcome, Kathy. So to start off, can you please tell me a bit about your background and your job role? 
  
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I am a 1992 graduate of the University of North Texas. I have a B.A. in business computer systems, and it's very similar to what most colleges would call a BBA and management information systems. I currently am a business intelligence program manager at ROSENSCHEIN, and I've been there for about a year and a half. I focused at and is to understand the nature of our business and the latest technology and then determine how the technology can best assist our businesses and make informed decisions. 
  
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My professional career has been focused on business intelligence. Some call it decision support back 30 years ago are you might call it also analytics today. So it has many different names, but it has been in the business intelligence area. I work for Rosatom, which is an electrical contracting company. The majority of my career it's been in consulting, focused on analytics and beer across many industries. 
  
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So I have experience in many industries. I would say all but about four years of my professional career has been in consulting. That is quite background. You are an expert in business intelligence. So to start off, can you give me a high level overview of business intelligence or buy business intelligence and B, I can be thought of as a superpower of turning data into actionable insights that drive better business decisions. 
  
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It's not just about collecting and storing information, but analyzing it in a meaningful way to understand your business performance, identify trends and make informed choices. The steps to do that. The first step in be AI is to understand your business needs. You need to know what is important to the business, and then you can start gathering the data. 
  
00;04;06;25 - 00;04;32;09 
So what kind of data do you get and buy? You can have what we call internal or external data. Internal data is something that is within your company. Like sales could be payroll, could be h.r. An external data is something that you're getting external from your company. That's like social media, maybe even weather data. And then there are also different types of data. 
  
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You have a structured data that sits within a database, and that's something that you'll hear where you put them in tables and you join your tables together. But then there's also unstructured data, and that's like text documents, emails. Those are kind of some unstructured data where it can be in any type of format. Now you can do your analysis in an Excel spreadsheet. 
  
00;04;57;09 - 00;05;23;21 
And and that's okay for small individual type analytics, but for more complex enterprise wide analytics, something that you're going to push out to your entire company, it's best to create a model. And a common model that is used is a star schema. And all star schema is is just some tables joined together and you have what you call a fact table. 
  
00;05;23;23 - 00;05;45;20 
A fact table is nothing more than something that has a fat sales productive. What what is the key metric that you're looking at? Then you have your dimension tables and that's basically how you want to break out your data. So you're going to break it out by time or by location. You have a dimension table for each one of those with the attributes by those dimensions. 
  
00;05;45;23 - 00;06;10;17 
A good way to think about a dimension tables. If you're looking at something and you all look at sales and you want to see it by somebody says, I want to see it by product, by time, by location. Anything after the buy is going to be a dimension, a location dimension, a time dimension, a product dimension. So that's how you kind of build a model amongst the model is designed and built. 
  
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You need to then load it with data and put data into the model, and that is called data preparation. Some people call it ETL, some people call it BLT, but basically that's where you go in and you clean the data, get it organized, and you loaded into the model. This can be a long process. Once it's in the model, now you can start doing your data analysis. 
  
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There's various tools and techniques for years to analyze the data once it's in the model. This kind of generating report, creating dashboards, performing calculations are using data visualization techniques like charts and graphs. You get that built. Then you can start looking at the actionable insights. This this is where you have the analysis. It reveals pattern trends, hidden information that helps business understand what's working and what's not. 
  
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This knowledge is being translated into actionable recommendations that can be implemented and improve performance. As you look at this, we talk about building this and building the chart data visualization, don't underestimate it. There are classes. There are books. If you are going to be working with the users and working in building analysis, understand and learn a little bit about data visualization. 
  
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A quick note is people read left, right, top to bottom. So whenever somebody looks at a dashboard, the first place they look is in the upper left hand corner, their eyes drawn up there. So you would want to put your key metric in the upper left hand corner of the dashboard that makes it stands out that allows your executives to quickly get the information without having to spend too much time digging through tables to get it. 
  
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I had actually never looked at it that way. That's really insightful. I just had an moment thinking about the tables and graphs and charts that I built that I was really a wonderful nugget that you just gave. So now on to my next question. What are some of the different industries that are used by different industries for actually every industry and every department within industry uses by for example, you have your construction things. 
  
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COLONISTS All right, look out. I'll go a little, maybe a little bit more about install rates. Retail has sales and inventory, higher education. They're looking at enrollments and salting my look at staffing. But then even departments in these industries like your h.r. Might be looking at the retention of employees, and that would be across all industries. So there is pretty much within every organization, within every department, in every industry. 
  
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I'd like to go over a couple of examples of how it's used differently at a couple of organizations. I want to start with the construction installer right? This is a metric that many construction companies use to determine how long it's going to take to install a particular product. Say, for example, a conduit in our electrical contracting company, we have conduits and we have an estimated rate of say, 5.2, five feet per hour. 
  
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And that is saying that an individual, a worker should be able to install 5.5 feet of conduit in the hour. So what happens when a given project goes down to five feet per hour? And that's just a reduction of about 10%. Not horrible, right. 
  
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Talking about multiple projects that we have with hundreds