Friday, 22 of February of 2019

Category » Communication

News galore

Data Mining for Dummies, my epic tome for beginning data miners, is available now.

Here’s the scoop:

Data Mining for Dummies, an easy-to-read new book for beginners in data mining, published by John Wiley and Sons, and available through your favorite bookseller.
Data Mining for Dummies is for business people, information technology professionals and students who want to…
• Know what data mining is all about
• See what’s really involved in data mining, icky parts and all
• Find friendly expert guidance for getting started as a hands-on data miner
Data Mining for Dummies is written in a light, yet no-nonsense, style for readers who are new to data mining. You won’t need any special expertise to read and understand this book.
Beginners can learn the basics of data mining, including
• Understanding data mining concepts
• Embracing a comprehensive data mining process
• Planning for data mining
• Gathering data from internal, public and commercial sources
• Preparing data for exploration and predictive modeling
• Building predictive models
• Selecting software and dealing with vendors
Author Meta S. Brown is a hands-on data miner who has educated thousands of beginners from industry, government and academia in the fundamentals of data mining. She’s known in the analytics community for her articles, books and talks on data mining, text mining and classical statistics, reaching out to audiences from novices to working professionals.
Here’s what Tom Khabaza, pioneering data miner and Founding Chairman of the Society of Data Miners has to say about Data Mining for Dummies:
Meta S. Brown tells it like it is, more than anyone else in the field.
Data Mining for Dummies is the first data mining book for beginners which gives an accurate picture of what we data miners do. This is a landmark for the profession, and an essential tool for anyone learning or teaching practical data mining. I will be recommending it to everyone I meet: business people, students and teachers alike.
Where to find Data Mining for Dummies:
Your favorite independent bookseller (find one on Indiebound
Powell’s City of Books
Barnes and Noble
• Ask your local library to get it. ISBN: 978-1-118-89317-3

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Storytelling for Data Analysts

“Storytelling with data is critical. But the emphasis is on data, not story.”
— Richard Hren, marketing strategist

Storytelling for Data Analysts

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Are your metrics too sentimental?

New piece on All Analytics: Beware the Sentimental Metric

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Risks of product loyalty

3 Tips for Sustaining Your Analytical Software, an All Analytics post by Bryan Beverly, left me with some doubts. Speaking as one who has interacted with many highly product-partisan software users, I must suggest a few cautions here.

Referring frequently to a product name, rather than using common technical or plain-English terms, has consequences. For one thing, it is often difficult for people who are not experts or familiar with your subject to understand what you are talking about. Use of such jargon also contributes to the analyst’s image as a geek whose concerns are not important to the bottom line.

Product-loyal analysts take other risks as well. They may become so attached to their tools that they are unaware of, or unwilling to use, easier or more cost-effective alternatives. They may appear inflexible to current employers, untrained or untrainable to prospective employers. They may have difficulty collaborating with others who use different products.

I have worked with organizations burdened with managing multiple products and multiple versions, creating serious inefficiencies in purchasing, training and support. Product loyalty implies much more than just believing a certain tool is the best one to do your job. It’s also a roadblock to interacting with others who have differing needs or preferences. Snobbery tied to product preferences is a common thing. It is the profession’s class warfare.

I love the ideas of creating in-house user groups, letting management know about the value of your tools to the organization, and doing all we can to learn and help others learn about good tools. In the end, though, a tool is just a tool.

Noreen Seebacher asked: “What if an analyst has a very specific reason for favoring one piece of software over another? Is it fair for him to promote that product or does it create risks for the organization in terms of cost controls, etc?”

Analysts always have very specific reasons for preferring one product over another!

It’s important to separate the reason – what we need to accomplish – from the product. Often, analysts become attached to specific products, believing them to be better than all others for performing specific tasks. When that happens, and it happens often, there are many possibilities to consider.

    The product may not actually have been superior in the first place
    A real product advantage that existed at one time may not persist as other products change and improve
    A new product may have better capabilities, or eliminate the need for certain tasks or methods
    The benefits of standardizing methods or tools across an organization may outweigh the specific benefits that drive the preference for a specific product
    Challenges of using some products, or integrating them into the business, which affect many stakeholders, may outweigh advantages to a subset of the organization
    The costs (think total cost of ownership as well as pricetag) may not be justified by the reasons behind the analyst’s preference
    and so on…

If you have a good reason for preferring a specific product, you must state it in terms of the business. What do you need to do, why is it necessary to the organization, how does it translate into dollars or some other meaningful business metric? If you can only state your preferance in statistical jargon, you won’t be persuasive.

Let me give an example of an organization that was deeply affected by this issue. A state government agency was planning an organization-wide operating system update. The staff used several different products, and often several versions of each, running on several operating systems. Through many lengthy and detailed discussions, we found that all the analyses that they needed to perform could be addressed with just one product family.

Standardization offered an advantage in purchase cost, but it also addressed many other issues. Technical support would be simplified. Training weaknesses could be addressed in a manageable way. Users could understand and help each other more easily with a common platform. Sharing of work would become practical. And the new tools offered valuable capabilties which had not been available to the organization before.

Some of the staff was quite open about their dislike of the change. They were certainly faced with some legitimate challenges – they would have to learn to use a new product, and they may have had code written for their old tools which would now have to be replaced. But in my discussions with them, those concerns never came up – instead they grumbled – publicly -that the old stuff could do everything the new stuff could do. That simply wasn’t true, and since they constantly made such claims during public presentations, I was forced to contradict them in front of their coworkers. It made them look stubborn and foolish.

This wasn’t an isolated case. Very similar things go on with every organization that explores standardizing tools. The people most resistant to change are usually the analysts with the most sophisticated statistical training – that is, the same people who should be the best equipped either to make a good business case for their preferred tools or learn to use the alternatives, and learn well. When they choose to do neither, they look like highly educated babies.

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Is There Madness in Your Methods?

New article on All Analytics: Is There Madness in Your Methods?

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Do data scientists really need IT? Yes!

My post, Kiss & Make Up With IT (, has kicked up a lot of discussion.

One reader asks “Do data scientists really need IT?” He thinks not. He’s dead wrong. You can read the article and discussion on All Analytics. Here is a bit about why it is important for data analysts to work constructively with IT:

Let’s consider what data analysts in corporations are expected to do. It is their responsibility to provide corporate management with information that supports decision making. These executives are empowered, and obligated, to manage the business in the interest of shareholders, and within the law.

The most relevant data to any business is internal data. This data is the private property of the business, and not available through any other source. The responsibility for maintaining and controlling such data falls to the IT organization within the business. This is no trivial matter. There are significant legal and financial concerns tied to the handling of data.

If the data is properly managed, there will be no route to obtain it other than through the proper channels, and those channels are controlled by the IT organization. This is not merely the way data is managed in the business world. Nonprofits and government agencies use similar processes.

I have encountered many data analysts over the years who resorted to any means they could devise to avoid dealing with IT. They often get away with this, because their results are viewed only by their own sympathetic team members and by executives who do not question them on matters of data management. That is, they get away with it until the work comes under serious scrutiny. That scrutiny commonly happens when the business becomes involved in a lawsuit or other legal action, or when a new manager enters the business asking questions and taking no prisoners.

Early in my career, I discovered an issue with some of the data that I and my team depended on. My manager was unwilling to take action to correct the problem. There was a loud public shouting match between us over that. But the problem got fixed in the end. Why? Because an auditor came in to examine our records, spotted the issue and brought it to the attention of upper management.

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Kiss and Make Up with IT

New post on All Analytics today.

Kiss and Make Up with IT

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Big words

You’d think that people would have learned by now that using big words doesn’t make you look smart.

Whenever I write a new article for a site such as Smart Data Collective or All Analytics, I plug it by posting messages in social media, on relevant list servs, and so forth. I know which groups are likely to refer readers, because I always use tracking links when posting, and I have spent a lot of time reviewing the data for those links.

Yesterday, I had a new article up, and posted messages about it as usual. I got a note from one of the groups where I post often – my post was rejected. The moderator (no name in the note, but clearly it was not the usual moderator), demanded that I “un-obfuscate” the link. He or she, whoever he or she may be, says that he or she doesn’t trust posts like mine.

When I learn the name of this moderator, he or she will be forever filed in my memory under John (or Jane) Q. Moderator, Ignoramous at Large.

So, what’s the lesson? There are several:

    If something is unfamiliar to you, take at least a few minutes to read about it and learn the major talking points and vocabulary before you open your (real or virtual) mouth.

    Understand that if you use the words “don’t trust” to describe any person’s actions, you are insulting them. And insulting people has consequences.

    When you take a new role, paid or volunteer, shut up and observe for a while. Ask questions, learn why things are done the way they are done. If you come in criticizing the work of others without understanding it, you’ll brand yourself as a fool. And who does their best work for a fool?

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Secrets of a Software Vendor

New piece on All Analytics today: Secrets of a Software Vendor

Think of it as “True Confessions” for the industry.

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Better Writing for Data Analysts

New piece on All Analytics:

Better Writing for Data Analysts

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