DanMaycock.com

Data, Strategy, Leadership, and Innovation

Month: July 2014

Thomas Alva Edison & Persistence

Regardless of what kind of person you think Thomas Edison was there is one thing he can go down in history as, and that is he was persistent. Making the several hundred attempts at getting the light bulb working, or building all the components necessary to make DC viable for people, moving this many concepts forward was not easy for him, or the grad students that helped contribute to his many patents. With each of the ideas he worked on to develop it required a long period of time to get from the concept to the invention, but he recognized when he was onto something and would work persistently to get there. All one has to do is look through several of his famous quotes to get a sense of how he felt about giving up:

“Our greatest weakness lies in giving up. The most certain way to succeed is always to try just one more time.”

“I have not failed. I have just found 10,000 ways that won’t work”

“Many of life’s failures are people who did not realize how close they were to success when they gave up”.

Thomas Edison was a man that was willing to try things multiple times until it worked, and he pushed others around him to do the same. Often you will fail the first time out of the gate getting traction with your concept, so it is important to be persistent and make adjustments each time until it works. Perhaps the initial concept you had was bad, and the idea is determined to be a failure. You are most likely still to stumble onto something great, so refine what you need and keep working on it till you strike gold.

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4 Ways to Effectively Use Data In Your Job

With all the excitement around how companies are using data today, it’s hard for anyone outside of a job specifically dealing with data to know how to effectively use it for their day to day work. Yet, there isn’t a single career that isn’t impacted by the use and understanding of data, and the more effective someone becomes at harnessing and understanding data mining, the more they can impact the things that impact their professional ecosystem.

From impacting your online brand, to better tracking variables you deal with around a given task at work, knowing how to leverage data can make a big difference in any number of careers.

1. Start with a question

Before diving into a number of articles or tools regarding data, start with the question of what you’re trying to answer. It sounds basic, but you’d be surprised how often I’ve worked with people that have said data is the answer without first having the question. Figure out what are the most pressing business problems you, your boss, or your company are facing and see how data might help provide insights to answering those important need to knows.

2. Start with a small amount of data, build from there

It doesn’t take petabytes of data to answer questions, sometimes it can be a relatively small set of data to answer big questions. With all the hype around big data, sometimes it’s hard to realize that with only 100 or so records, and a pivot chart, you can get to important answers that are far more useful than what a million records could show, depending on the type of data and the question you’re looking to solve.

3. Leverage third party data that’s free

There’s a TON of data out there that’s completely free, and useful to use. US Census is a great place to start, and there are a number of sites, such as Google’s public data directory that’ll let you explore it. Furthermore, you can download the data for free and combine it with your own internal data to add greater context for things like taking your company’s store sales by zip and seeing how demographic trends within those zip codes may impact certain buying habits.

4. Learn about Data Mining 

The key to making data useful is by learning methods that allow you to tap into data, and find useful data points that can help solve the business problems you’re looking to tackle. Data mining is the practice that helps you start to uncover trends and patterns in data, and is a great discipline to begin with, whether it’s using Excel and a little bit of data or tapping into RapidMiner and starting to dive into Hadoop, Data mining spans the gambit on complexity and data quantities. Remember the first three points to keep the right context and not go overboard too soon though, and you’ll be in good shape.

Regardless of your career, there is a way data can no doubt help you professionally and impress your co workers and higher ups in the process. Start with the fundamentals, help answer important questions, and simply build from there and you’ll be a bonafide data analyst before you know it.

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Why Everyone Should be a Data Miner

In thinking about the topic of data mining, a lot of different types of roles pop up in people’s minds. From data scientists typing away in giant data centers, to DBAs sitting in cubicles processing large amounts of corporate data, to an analyst building a spreadsheet for an annual report contribution.

Maybe it’s something far more physical, bringing up images of pick axes and hard hats and a big block of data (however that’s visualized, probably with 1’s and 0’s – all matrix like). Regardless of the image that comes to mind, it’s probably hard to fathom every business professional in some form or another becoming adept at data mining, and considering it a critical competency to keep in their professional toolbox in the years to come. Yet, when we explore the topic, we can easily see how data mining could become one of the preeminent skills that set folks apart in an era where it’s harder and harder to stand out from an increasingly noisy and competitive work climate. Lets start by looking at the six attributes that make up data mining (as defined by Wikipedia)

  • Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
  • Association rule learning (Dependency modeling) – Searches for relationships between variables. This is sometimes referred to as market basket analysis.
  • Clustering – is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.
  • Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as “legitimate” or as “spam”.
  • Regression – attempts to find a function which models the data with the least error.
  • Summarization – providing a more compact representation of the data set, including visualization and report generation.

Though the definitions seem somewhat dense, think about how you’d be able to take any job – from being able to use regression analysis to construct a real estate data model to improve pricing predictions, to using summarization to build a better financial report for your senior leaders to interpret how great of a quarter you had.

Though some methods of data mining are harder than others, and you can quickly get in way over your skis without proper learning, knowing how to sift through data, and pull out the useful stuff, will give you a greater sense of the world you work in by understanding the data that matters and it’s so easy these days to learn data mining techniques online!

Just typing in “data mining classes online” produces hundreds of leads, from Coursera to MIT open courseware. Though some options go into areas like Data Science, which is much deeper level analysis, it all starts with understanding data and how best to derive meaning from it – regardless of how deep into the weeds you want to go.

This in turn gives you a big foot up against your competitors, who are largely relying on other services / people to hand them processed data and conclusions to do something with. Going from a commodity to a distinct competitive advantage means going in a direction others aren’t, and just having a nicely worded dictionary isn’t enough these days – you need to be able to turn that dictionary into a novel, and tell a story with the data that will reveal things about your business or your industry that’ll drive better decisions through unique insights.

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Does Your Company Have a Chief Data Evangelist?

A lot of companies are talking about Chief Data Officers, but what about having a chief data evangelist instead?

Recently I was talking to a good friend of mine that works in the Business Intelligence space about the concept of a Chief Data Officer being brought up in the halls of different companies around the US (mainly of course, IT departments dealing with the onset of new data solutions to handle all their data.)

What he shared was that companies should focus less on centralizing data to get to a single version of the truth. Instead, they should focus on recruiting a chief data evangelist to get groups within a company on board with a set of standards that they can build data models around for use within their team, then grow grassroots communities within their company. This could be akin to a data “co-op” of sorts which could, in turn, enable teams to take their own data models and share data at a bottom up approach vs simply being drug along by a chief data officer from a top down approach, marching to the beat of centralized data control.

This extreme decentralization has worked in other facets, including executive leadership as characterized in the book “The Outsiders” by William Thorndike so why couldn’t it work with data?

As I began to think about it, it does make sense to have people in your organization advocating for best practices, and getting different groups on board with a set of standards but leaving the usefulness of the data to the teams using it, as no two groups of course ever have the same need for a specific data set in a specific format.

Though larger efforts like data warehousing will remain centralized activities, imagine what companies could achieve through extreme decentralization focused on evangelism of standards and organization level adoption & modeling efforts that in turn drive community activities within a company vs dragging along the enterprise one team at a time to conform to centralized data models that may or may not work for them.

Seems like a much better solution to me. In thinking about what a Chief Data Evangelist might do at your company, consider the following job description

Task #1) Strong understanding of best practices around data governance, data management, and data modeling for the purpose of leveraging corporate data for use by a specific team

Task #2) Desire to get teams within a company on board with leveraging standards for data governance and modeling, for the purpose of collaborating with other teams and sharing data within organizations / company

Task #3) Make a killer salsa

If that sounds like a great job description, perhaps the job is for you. Regardless of who has the role, be it official or unofficial, having strong advocates for standards along with proponents for data / BI communities in your company can go a long way in helping drive greater adoption of data solutions within your company and help grow data-driven solutions in the process.

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