Tag Archives: data analysis

99-Spreadsheet modeling, Excel power users, Excel resources

Introducing…. Power Pivot (with Excel)

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I think everyone can agree that pivot tables are great excel tools which make analyzing and sorting massive amounts of data a life saver. However, over the course of the past couple years I have been introduced to Power Pivot. Yes, Power Pivot. Pivot tables on steroids. At first, I was pretty intimidated because of the sheer size and complexity on the surface. But after spending some time learning and taking “lunch n’ learn” course at work, Power Pivots and I are becoming friends. My favorite part is being able to run pivot tables essentially off other pivot tables. At work, we’ve also created many tools that allow our leaders to simply and easily run their own reports for specific requests, which saves everyone time. (The analyst teams refresh the data regularly as needed).

I’ve included a couple links below about Power Pivot, and would encourage all to consider using the valuable tool the next time you are looking to crush some colossal data. It’s available as a free add-on for Excel 2010 and now comes standard on Excel 2013.

Happy Pivot Tabling!

 

WJEM

 

https://www.youtube.com/watch?v=URy_uQYS49s

http://info.110consulting.com/blog/bid/374825/Top-10-Benefits-of-Using-Excel-PowerPivot

http://blogs.office.com/2010/10/01/top-5-ways-powerpivot-helps-excel-pros/

99-Spreadsheet modeling, Excel power users, Excel resources, Excel shortcuts

Every Spreadsheet Has An Error

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If your job is anything like mine, you’ve had to work on a massive data dump, sorting and manipulating to find “a story.” The size of the data files can sometimes be intimidating and there sometimes is that concern in the back of your mind that some formula or reference within your Excel workbook went rogue.

As we strive to be more proficient in our Excel skills and more efficient in our tasks, coming across a headline such as “Every Spreadsheet Has an Error” can certainly sound alarming! However, as I read the article in greater depth, I found the points to serve as good guidelines to help check my work.
http://www.forbes.com/sites/billconerly/2013/04/25/every-spreadsheet-has-an-error-7-lessons-motivated-by-reinhart-and-rogoff/
Some of the tips suggested in the link are:

1) Use assumption variables – Similar to what we learned in Decision Analysis class, create a section within your workbook with all your assumption variables. Create links within the workbook to these assumptions. This will ensure that when you have to change your assumptions in the future you will not have to search all your data for every occurrence that may be affected.

2) Link, don’t copy – Create links to raw data so that you can go back and check your manipulated data against the original document.

3) Create double-check formulas – “If, then” and “true, false” statements are great for checking your work!

4) Format to Tell Differences – Conditional formatting helps to highlight differences in the data. They also help to identify trends and patterns in your data. I’m a big fan of using colors when doing conditional formatting.

5) Graph Your Data – When possible, graph your data. A line chart for instance will give you a quick visual to not only spot any irregularities in your data, but also to help find “the story.”

6) Document – Create notes of the steps or sources that you used to create your end product. This habit can save a ton of time when you have to do an update. Notes are also helpful for others who may have to replicate your report. We have a saying at my workplace, “a detailed source line is not for the client, it’s for us!”

7) Be Suspicious – Check your work. See if you can find an error.

Hopefully, by following some of these tips, you found all of your errors!

01-Structuring a problem, 04-Managing a project team, 05-Creating & constructing good solutions, 06-Building a case, creativity methods, evaluating evidence, problem statement, scoping, team dynamics

How Effective Leaders Solve Problems

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Effective leaders tend to find a strong balance between data analysis and intuition. Many times, the aspect of intuition comes from recognizing patterns or trends. A big part of problem solving is recognizing these trends and finding ways to minimize its reoccurrence. Additionally, it is important for leaders to understand the true essence of the problem rather than finding a temporary fix that will likely rise again in the near future. This gives leaders more time to focus on other areas of the business rather than continuing to fix the same problem over and over again.

Understanding the intricacies of every aspect of the business is one way to develop a strong sense of how your decisions can affect specific areas of the business. Effective leaders don’t look at problems as a nuisance; effective leaders see problems as an opportunity for ongoing improvements.

Forbes has come up with four characteristics that make an effective leader:

1) Transparent Communication: The main takeaway is that transparent communication allows for other people to be heard. It fosters an environment where people are willing to speak up if there is an issue.

2) Break Down Silos: The importance of this characteristic is to eliminate boundaries. It is important to solve problems that affect the overall business rather than one segment of the business.

3) Open-Minded People: Effective leaders are ones who are not discouraged to find innovative ways to solve a problem. They are individuals that do not avoid the problem; they are not afraid to face problems head-on.

4) A Solid Foundational Strategy: As we have learned in our Strategy course, a business without a strategy is dangerous. Effective leaders go beyond figuring out the problem; they find ways to implement a strategy to solve the problem. This includes resource allocation and budgeting.

Some final takeaways are:

  • Always step back and assess the situation; never take a blind guess when solving a problem.
  • Find ways to solve the cause of the problem to eliminate it from reoccurring.
  • Learn from your previous failures and use those lessons learned to solve future problems.
  • Don’t avoid problem solving; challenge yourself to solve the problem head-on.

To read more about becoming an effective leader, feel free to visit the article: http://www.forbes.com/sites/glennllopis/2013/11/04/the-4-most-effective-ways-leaders-solve-problems/.

05-Creating & constructing good solutions, 99-Applying business intelligence, business databases, evaluating evidence, library research

Iron Maiden and Data Analysis: How one Heavy-metal band used data to profit from a revenue-stealing platform

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Applying data analysis definitely isn’t always the most exciting field- certainly not as fun as seeing a heavy metal band say Iron Maiden live for instance. With the constant shrinking revenues from traditional album recordings many bands are increasingly reliant on live shows- especially older bands who’s catalog of albums can be easily downloaded in a matter of minutes at no profit to the band or label. This leads them down a road of never ending farewell tours in the same reliable but boring locations.

Enter Iron Maiden: The international super group undoubtedly has fans all over the world but has struggled with their selection of where to tour, despite being one of the most iconic acts in industry. In an innovative use of data analytics for the music industry the band now weighs illegal downloads by location to help determine demand. This has paid off huge in their recent South American tours whereas prior data say that it would have been a disaster and complete opportunity loss. Their most recent tour gained them the distinction of “One of six groups that outperformed the industry” including live documentary sales and one concert alone in Sao Palo that grossed them over $2.5 million. South American attendance and revenue also trumped their previous averages in NA and the EU too.

Hail the Iron Maiden data wonks!

http://www.rollingstone.com/music/news/iron-maiden-using-bittorrent-analytics-to-plot-tours-20131226

05-Creating & constructing good solutions, 99-Applying business intelligence, 99-Spreadsheet modeling, Uncategorized

Tiny Data: Not An Excuse

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When I got my first job as a Process Improvement Engineer for an industry leading company in their flagship facility, my first question to their production manager was: “Where’s the historical data on the process we need to improve?”. His answer was: “Well I know how many pounds of potatoes we usually put in, and I know about how many bags of potato chips come out the other end.”

How could such a sophisticated, industry leading company have so little knowledge about their own processes? Four years later, reflecting back on all the companies I have worked for and had exposure to, few have had the ‘big data’ that is such a popular topic of today’s data analysis discussions.

How do those us us who have only, ‘Tiny Data’ or incomplete data use it to make better decisions and improve out businesses? The first article posted below cites an Army Colonel’s experience:

“Look,” said the colonel, “if I’m on a battlefield trying to defend a hill and I get a piece of intelligence, even if I’m not 100 percent sure that it’s accurate, I will make decisions based on that intelligence.” He strongly believed that it’s better to have some information than none—and that you’d be a fool to disregard it just because it falls short of being definitive.

There are many ways to utilize small amounts of data, incomplete data, and varying quality data. You must find ways to fill in the gaps, determine the variance of the quality, and find ways to draw meaningful conclusions and areas to investigate more fully with small amounts of data.

Branch out and be creative, because a little bit of information is better than no information and is no excuse for simply accepting the status quo.

When Big Data Isn’t An Option

Small Data Analysis

How to Analyze Data With Low Quality or Small Samples 

99-Applying business intelligence

Healthcare & Business Intelligence

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I took a sociology class in undergrad that focused on the healthcare industry, specifically evaluating the efficiency and quality of delivering services. Most of what we focused on was from the patient perspective and how the system either did or didn’t support patient needs. This article focuses on applying business strategy and intelligence to the healthcare industry from the perspective of the supplier/organization.

With the mounting scrutiny on healthcare costs and quality, especially with the implementation of the ACA, it will become increasingly important for innovative leaders to bring business savvy to the industry. I think it’s the moral debate at the heart of healthcare and whether it’s a human right vs. a service rendered that makes the issue even more interesting. Leaders who effectively balance business acumen with responsible decision-making (keeping patient needs in mind) to turn hospitals profitable, or at least sustainable, are in short supply.

With the vacuum of innovative leadership, the industry is in drastic need of smart reform so it’s nice to see the skills we’ve already been practicing in class outlined in tangible ways within healthcare. I wonder, as places like Emory start to merge their business schools and schools of public health or medicine, if the right solutions will tend to arise more prominently out of public policy initiatives or business practices? Systems like the National Health Service in the UK arose out of necessity through public policy after WWII. Without a national crisis such as that, the transition of our system to predominately government-based organizations rather than private will be far more gradual. In that time, the application of the cited business intelligence will hopefully highlight the best parts of competition and business practices while embracing the benefits of greater access.

http://www.healthleadersmedia.com/content/TEC-90944/Applying-Business-Intelligence-to-the-Needs-of-Healthcare-Organizations.html##