Tag Archives: big data

99-Applying business intelligence

Consumer Analytics and the Future of Shopping

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These days the concept of “Big Data” is becoming more and more popular.  Companies are using Big Data to get a better understanding of who their consumer is and what they value.  Some companies have even hired employees just to mine data.

Two examples I have been a part of in my career are Home Depot dotcom and bubba brands.

While I worked at Home Depot dotcom they had a full team dedicated to understanding how an online consumer shops, what their product preferences are, and how to best get them to the pages they are interested in.  Since we were operating in an online environment the data was real time and easy to obtain.  All of these analytics and upgrades have Home Depot dotcom positioned to be a top 10 online retailer by the end of 2014.

At bubba brands we do 95% of our sales through big box retailers so it is not as easy to become intimate with our consumer.  In order to get closer to our consumer we have focus groups once a month throughout the country to understand what women look for when they purchase drinkware.  We combine this qualitative data with the quantitative data to make real time decisions on product assortment and future product launches.  This analysis has positioned us as the number one drinkware vendor in Wal-Mart.

The below article provides information on consumer analytics and how it is going to shape the market place in the future.

http://timoelliott.com/blog/2014/02/the-future-of-customer-analytics.html

 

How is your company using big data to better reach your end consumer?

 

 

 

 

 

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!

99-Spreadsheet modeling, charts & graphs, Excel resources, team dynamics

My Challenges with Excel

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C.S. Lewis (1898-1963), Fellow and Tutor in English Literature at Oxford University, and Chair of Medieval and Renaissance English at Cambridge, was an intellectual giant of the 20th century. In later life, reflecting on challenges he faced in his younger days, Lewis remarked:

“I could never have gone very far in any science, because in the path of every science the lion mathematics lies in wait for you. Even in mathematics whatever could be done by mere reasoning as in simple geometry I did with delight. But the moment calculation came in I was helpless. I grasped the principles but my answers were always wrong. Yet though I never could have been a scientist, I had scientific as well as imaginative impulses and I loved ratiocination*.”

To be candid, I have some apprehension when it comes to Excel . . . made all the more pressing because of how central excel skills are to the practice of finance. So when I consider my excel challenges I draw much encouragement from these thoughts of C.S. Lewis.

Although I have a deep passion for finance, and have worked in investment management for 10 years, my position at work has been more about interpreting and leveraging excel generated data, rather than performing the actual work of creating spreadsheet models myself.

The result is that up till now, my excel skills have languished.  Thus I’m grateful that our MBA program has forced me to improve. In this spirit, (and acting on the advice of professor Noonan) I’ve set out to build my own personal top 10 excel skillset, that will be useful to my financial career.  Some of these may seem rather obvious, but excel is my self-selected focus area and I figured that I could subject my list to public opinion and scrutiny, in the hope that collaborative discussion might occur. I also thought that some of this material might be useful for those considering a financial career.

Rough draft of “Top 10” excel skills for investment management and financial planning for high net worth individuals, families, trusts, and charitable foundations:

  1. Precision tree
  2. Sensitivity analysis, useful for evaluating insurance policies
  3. Goal Seek
  4. Historical market and financial instrument analysis
  5. Excel integration with Word and PowerPoint
  6. Configuring excel for pleasant appearance and functionality
  7. Mortgage / Bond / Annuity amortization schedules
  8. Learning to manipulate and efficiently sort data – this is hugely important for many areas within our business such as ranking client positions, asset allocation decisions, analysis of individual positions, and assessing performance in order to prioritize investment decisions
  9. Graphing portfolio performance vs. relevant market benchmarks
  10. Leveraging excel with proprietary finance software used by major financial enterprises. (our firm uses Advent)

When I reflect on the challenges of the first year of MBA school, I am glad that I have improved in several of theses areas and incorporated them into different assignments and projects. Our cohort has aided much of my improvement.

For example, during first semester, I was fortunate to be on a team with Shehzad Shabuddin, who was quite generous with his time and patient with helping me make progress on excel. Shehzad’s blog post, The Excel trap, reminds us of the dangers of reducing life to data and mathematics. Excel-ing in Real Estate by Bob Caperton and the article by Barry Slaymaker on MBA level excel skills were both particularly helpful. I’m also hopeful that I can persuade Joe Song to give me some Excel lessons between now and graduation.

One of the most powerful lessons I have learned in life (and a “key content” area of MP) is the importance of surrounding yourself with others whose complementary strengths have the capacity to offset your personal areas of weakness.  My next blog post will examine a leader who intimately understood just how powerful this principal is, and became a great, and most unexpected, actor in history.

*Ratiocination, noun. – the process of logical reasoning. [1520-30]

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Data And Information Management

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My clients contact me with a variety of questions – information on our products and services, technologies provided, their project status, charges, quality metrics, data access, turnaround times, etc. There are questions I can answer right away and there are questions I can quickly look up in our information system to find the answers to. Two years ago, I was spending a significant amount of time everyday searching in individual records, logs, and online resources, calling other divisions, harassing innocent interns, and/or just simply guessing to get the answers.
Effective data and information management is an essential component for many organizations. It is especially a concern these days as the amount of digital information is exploding at an exponential rate. The consequences of poorly managed data can be significant. The following are the examples  discussed in this article:
  • Financial losses: Your organization’s headquarters are flooded unexpectedly. Your backup system is outdated, and, as a result, you lose months of data, worth millions of dollars to your organization.
  • Litigation risk: Hackers access your customer database, which includes addresses and credit card numbers. These customers are now at risk of identity theft, and they decide to sue you for violation of their privacy.
  • Excess data storage costs: Your organization has no process for data cleansing – replacing or deleting inaccurate, incomplete, or outdated information. Consequently, your data storage costs and IT resource needs double each year.
  • Inefficient workflow processes: Your team members can’t find the information that they need to do their work, because each department has its own database, and none of these systems communicate with one another.
  • Missed opportunities: Your sales reps struggle to access the inventory database, which informs them of product availability and delivery dates. Competitors win sales from you, because they have immediate access to this information.
  • Brand/reputation loss: Customers are frustrated, because departments can’t communicate effectively with one another. As a result, your organization’s reputation and sales suffer.
  • Negative press/publicity: One of your team members loses their laptop, which contains information about a well-known client. As a result, your organization receives negative media coverage and you lose a number of clients.

For me, developing an effective information system was a life saver; I did not have to spend most of my time answering emails and calls, but could actually do some productive work.

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

06-Building a case, 07c-Telling stories, evaluating evidence

Intuition + Data = Good Decisions and Compelling Storytelling

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One of my reasons for attending business school is to improve my decision making skills. I typically apply the mantra “follow your gut,” when making decisions, but have quickly found that this doesn’t fly in the business world.  Colleagues want evidence based recommendations, which in my mind means I have to work with numbers (not my favorite).  Fortunately, incorporating both data and intuition can result in good decisions and compelling storytelling.

In this article by Andrew McAfee, we learn from specific examples that human judgment alone does not trump algorithms. So is the sum of our experiences and beliefs worthless? No! It’s all about using judgment or opinions as inputs to data models. Unfortunately, many people get this process turned around and disregard data that doesn’t align with their opinions.

From there, you can use data to tell compelling stories and build persuasive business cases. In this HBR blog, Walter Frick interviews Jim Takersly on how data and stories enhance each other. At one point, data is referred to as “medicine” and the story as something that helps you consume that medicine. I’d have to agree with that metaphor, but I also understand how data can give a story shock value or credibility. There are many different kinds of stories to tell with data. Here are ten.

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