Tag Archives: Database Management

Data Literacy 101: What is Data?

Whenever the topic of data comes up at meetings or informal conversations it doesn’t take long for people’s eyes to glaze over. The subject is usually considered so complex and esoteric that only a few technically-minded geeks find value in the details. This easy dismissal of data is a real problem in the modern business world because so much of what we know about customers and products is codified as information and stored in corporate databases. Without a high level of data literacy this information sits idle and unused.

One way I try to get people more interested in data is to make a distinction between data management and data content. In its broadest sense, data management consists of all the technical equipment, expertise, security procedures, and quality control measures that go into riding herd on large volumes of data. Data content, on the other hand, is all the fun stuff that is housed and made accessible by this infrastructure. To put it another way, think of data management as a twisty, mountain road built by skilled engineers and laborers while data content is the Ferrari you get to drive on it.

Okay, maybe that’s taking it a bit too far. Stick with me.

At its most basic, data is simply something you want to remember (a concept I borrowed from an article by Rob Karel). Examples might include:

  • Your home address
  • Your mom’s birthday
  • Your computer password
  • A friend’s phone number
  • Your daughter’s favorite color

You could simply memorize this information, of course, but human memory is fragile and so we often collect personally meaningful information and store it in “tools” like calendars, address books, spreadsheets, databases, or even paper lists. Although this last item might not seem like a robust data storage method it is a good introduction to some basic data concepts. (I’ve talked about the appeal of “Top 10” lists as a communication tool in a previous post but I didn’t really address their specific structure.)

Let’s start with a simple grocery list:

Data101_List_1

Believe it or not, this is data. A list like this has a very loose data structure consisting of related items separated by some sort of “delimiter” like a comma or — in this case — a new line or row on our fake note pad. You can add or subtract items from the list, count the total number of items, group items into categories (like “dairy” or “bakery”), or sort items by some sequence. Many of you will have created similar lists because they are great external memory aids.

The problem with this list is that it is very generalized. You could give this grocery list to ten different people and get ten different results. How many eggs do you want? Do you want whole milk, 2%, or fat free? What type of bread do you want? What brand of peanut butter do you like?

This list really only works for you because a memory aid works in concert with your own personal circumstances. If someone doesn’t share that context then the content itself doesn’t translate very well. That’s okay for “to do” lists or solo trips to the grocery store but doesn’t work for a system that will be used by multiple people (like a business). In order to overcome this barrier you have to add specificity to your initial list.

Data101_List_2

This is a grocery list that I might hand over to my teenage son. It is more specific than the first list and has exact amounts and other additional details that he will need to get the order right. Notice, however, that there is a cost for this increased level of specificity, with the second list containing over four times as many characters as the first one. At the same time, this list still lacks key attributes that would help clarify the request for non-family members.

If we are going to make this list more useful to others, we need to continue to improve its specificity while making it more versatile. One way to do this is to start thinking about how we would merge several grocery lists together.

Data101_List_3

Here is our original list stacked on top of a second list of similar items. I’ve added brand names to both of them and included a heading above each list with the name of the list’s owner. The data itself is still “unstructured”, however, meaning it is not organized in any particular way. This lack of structure doesn’t necessarily interfere with our goal of buying groceries but it does limit our ability to organize items or find meaningful patterns in the data. As our list grows this problem is compounded. Eventually, we’ll need to find some way of introducing structure to our lists.

Data101_List_4

One step we can take is to break up our list entries and put the individual pieces into a table. A table is an arrangement of rows and column where each row represents a unique item, while each column or “field” contains elements of the same data “type.” For this first example, I’ve created three columns: a place for a “customer” name (the text of the list’s owner), an item count (a number), and the item itself (more text). Notice that the two lists are truly merged, allowing us to sort items if we want.

Data101_List_4_sorted

Sorting makes it a bit easier to pick out similar items, which will help a little on our fictitious shopping trip. However, we still have a problem. Some of the items (like the milk, butter, and peanut butter) are sorted by the size criteria listed in the unstructured text, which makes it harder to see that some of things can be found in same aisle. Adding new fields will help with this.

Data101_List_5_sorted

By adding separate columns for brand name and size, the data in the “item” column is actually pretty close to our first list. All the additional detail are included in new fields that are clearly defined and contain similar data. We’ve had to clean up a few labeling issues (such as “skim milk” vs. “fat free milk”) but these are relatively minor data governance issues. Our final, summarized list is ready for prime time.

Data101_List_6_Summary

And that, my friend, is how data is made.

Spelling and National Security

A former co-worker of mine always used to joke about our company’s customer database by posing the deceptively simple question: “How many ways can you spell ‘IBM’?” In fact, the number of unique entries for that particular client was in the dozens. Here is a sample of possible iterations, with abbreviations alone counting for several of them:

  • IBM
  • I B M
  • I.B.M.
  • I. B. M.
  • IBM CORP
  • IBM CORPORATION
  • INTL BUS MACHINES
  • INTERNATION BUSINESS MACHINES
  • INTERNATIONAL BUSINESS MACHINES
  • INTERNATIONAL BUSINESS MA

I thought of this anecdote recently while I was reading an article about the government’s Terrorist Identities Datamart Environment list (TIDE), an attempt to consolidate the terrorist watch lists of various intelligence organizations (CIA, FBI, NSA, etc.) into a single, centralized database. TIDE was coming under scrutiny because it had failed to flag Tamerlan Tsarnaev (the elder suspect in the Boston Marathon bombings) as a threat when he re-entered the country in July 2012 after a six-month trip to Russia. It turns out that Tsarnaev’s TIDE entry didn’t register with U.S. customs officials because his name was misspelled and his date of birth was incorrect.

These types of data entry errors are incredibly common. I keep a running list of direct marketer’s misspellings of my own last name and it currently stands at 22 variations. In the data world, these variation can be described by their “edit distance” or Levenshtein distance — the number of single character changes, deletions, or insertions required to correct the entry.

Actual Name Phonetic Misspellings Dropped Letters Inserted Letters Converted Letters
Kinde Kindy Kine Kiinde Kinoe
Kindee Inde Kinder Kimbe
Kindle Kimde
Kindde Isinde
Kindke Pindy
Kindl
Kinds
Kinge
Kinele
Winde
Kinae
Kincius
Jindy

Many of these typographical mistakes are the result of my own poor handwriting, which I admit can be difficult to transcribe. However, if marketers have this much trouble with a basic, five-letter last name, you can imagine the problems the feds might have with a longer foreign name with extra vowels, umlauts, accents, and other flourishes thrown in for good measure. Add in a first name and a middle initial and the list of possible permutations grows quite large … and this doesn’t even begin to address the issue of people with the same or similar names. (My own sister gets pulled out of airport security lines on a regular basis because her name doppelgänger has caught the attention of the feds.)

The standard solutions for these types of problems typically involve techniques like fuzzy matching algorithms and other programmatic methods for eliminating duplicates and automatically merging associated records. The problem with this approach is that it either ignores or downplays the human element in developing and maintaining such databases.

My personal experience suggests that most people view data and databases as an advanced technological domain that is the exclusive purview of programmers, developers, and other IT professionals. In reality, the “high tech” aspect of data is limited to its storage and manipulation. The actual content of databases — the data itself — is most decidedly low tech … words and numbers. By focusing popular attention almost exclusively on the machinery and software involved in data processing, we miss the points in the data life-cycle where most errors start to creep in: the people who enter information and the people who interpret it.

I once worked at a company where we introduced a crude quality check to a manual double-entry process. If two pieces of information didn’t match, the program paused to let the person correct their mistake. The data entry folk were incensed! The automatic checks were bogging down the process and hurting their productivity. Never mind that the quality of the data had improved … what really mattered was speed!

On the other hand, I’ve also seen situations where perfectly capable people had difficulty pulling basic trends from their Business Intelligence (BI) software. The reporting deployments were so intimidating that people would often end up moving their data over to a copy of Microsoft Excel so they could work with a more familiar tool.

In both cases, the problem wasn’t the technology per se, but the way in which humans interacted with the technology. People make mistakes and take shortcuts … it is a natural part of our creativity and self-expression. We’re just not cut out to follow the exacting standards of some of these computerized environments.

In the case of databases like TIDE, as long as the focus remains on finding technical solutions to data problems, we miss out on what I think is the real opportunity — human solutions that focus on usability, making intuitive connections, and the ease of interpretation.

Update:

  • July 7, 2013 – In a similar database failure, Interpol refused to issue a worldwide “Red Notice” for Edward Snowden recently because the U.S. paperwork didn’t include his passport number and listed his middle name incorrectly.
  • January 2, 2014 – For a great article on fuzzy matching, check out the following: http://marketing.profisee.com/acton/attachment/2329/f-015e/1/-/-/-/-/file.pdf.

Politicians Discover Data Science

During the 2008 U.S. Presidential campaign, the online design community devoted a lot of pixels to comparisons of the two candidate’s web sites (a few great examples here, here, and here). The overall consensus was that Obama won the war for eyeballs by emphasizing design, web usability, multimedia, and robust social networking. According to an in-depth study by the Pew Research Center’s Project for Excellence in Journalism, Obama’s online network was over five times larger than McCain’s by election day and his site was drawing almost three times as many unique visitors each week.

There is no doubt that the web has fundamentally transformed the way political campaigns are run. Voters are no longer tied to traditional media outlets for information and they can participate directly in a campaign in ways that were unimaginable only a few years ago. Adam Nagourney, columnist for the New York Times, summed it up nicely:

[The Internet has] rewritten the rules on how to reach voters, raise money, organize supporters, manage the news media, track and mold public opinion, and wage — and withstand — political attacks.

So, with the next campaign season gearing up, what technology-driven changes can we expect for 2012? If the rumblings are true, this election may see the ascendancy of data science as a formal part of the campaign toolkit.

In a recent CNN article, Micah Sifry wrote about the Obama campaign’s establishment of a “multi-disciplinary team of statisticians, predictive modelers, data mining experts, mathematicians, software developers, general analysts and organizers.” The article goes on to discuss the importance of data harmonization (a fancy term for master data management), geo-targeting, and integrated marketing.

Obama may be struggling in the polls and even losing support among his core boosters, but when it comes to the modern mechanics of identifying, connecting with and mobilizing voters, as well as the challenge of integrating voter information with the complex internal workings of a national campaign, his team is way ahead of the Republican pack.

All this has some GOP supporters concerned. Martin Avila, a Republican technology consultant, states in the same article that he doesn’t think that anyone on the opposing side fully understands the power of organizing and analyzing all of this data. According to Avila, the current GOP use of information technology is still largely shaped by its pre-Internet experience in broadcast advertising.

In some ways, this cavalier attitude toward the value of data shouldn’t come as a complete surprise. One trait that many members of the so-called “party of business” share with executives in the private sector is a strong attachment to a “gut based” approach to making decisions.

A recent Accenture Analytics survey of over 600 managers at more than 500 companies found that senior managers rarely used data-driven analysis when making key business decisions and instead relied heavily on intuition, peer-to-peer consultation, and other soft factors. According to the study, 50% of companies weren’t even structured in a way that would allow them to use data and analytical talent to generate enterprise-wide insight. In addition, those organizations that did make analytics-based decisions often depended on inconsistent, inaccurate, or incomplete data.

Savvy voters, like savvy customers, have come to expect a certain level of performance and consistency from the IT systems they use. This is bad news for businesses that still think that things like social media, data analytics, and master data management are gimmicks:

Organizations that fail to tackle the issues around data, technology and analytics talent will lose out to the high-performing 10 percent who have leveraged predictive analytics to become more agile and gain competitive advantage.

Creating a structured program for better targeting and more efficient communications seems like a no-brainer these days, but, for now, there doesn’t seem to be a lot of competition.

Further Reading:

    • 1/30/2012 – Slate recently published an article that talks about the different philosophies guiding the development of Democratic and Republican voter databases. Catalist, an independent data initiative, is focused less on profit and more on becoming “an indispensable tactical resource for the American left” with a privately-funded data warehouse containing records of the entire voting-age population combined with other commercially available data. It’s customers include many traditionally liberal groups who consider the Democratic National Committee’s database insufficient. In response, the DNC has stepped up development of its own database, the Voting List Management Cooperative (or “Co-op”). In order to take advantage of the increased desire for voter information, the DNC has also developed statistical models that are particularly valuable for candidates. Meanwhile, the Republican National Committee established the Data Trust, a private company filled to the brim with former RNC staffers and committee members. The goal of this organization is to create robust voter profiles that can be shared with political allies. However, because of concerns about outside influence, the RNC is modeling it more along the lines of the DNC’s data co-operative instead of the more independent Catalist. The Data Trust development model is also less focused on data mining activities and more on basic data.
      7/17/2012 – Another Slate article. This one covers the Romney campaign’s attempt to boost its analytics efforts. Their initial approach appears to center on trying to figure out the President’s strategy by tracking his movements and breaking down his ad buys. This seems pretty reactive to me but time will tell.
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    Relearning an Old Lesson

    Anyone who’s ever used a computer has — at some point — lost a carefully-crafted sequence of ones and zeros to the unforgiving gods of the digital realm. Every time it happens, you mourn, you rage against the sky, you re-write and you move on. Each time, you add another rule-of-thumb to the mental checklist designed to minimize the losses or at least ease the reconstruction effort of the next minor catastrophe. In the end, it all boils down to one simple rule. That’s right, ladies and gentleman: save often.

    I was reminded of this basic lesson when I arrived at work on Monday and was presented with an odd little e-mail from the data warehouse team. Why had I changed the user profile for the data feed to the Marketing server? Hmm … I hadn’t actually done anything to the profile and, when I checked the database, it was more than altered, it was completely missing. Not a big deal, I thought, because I could always re-instate the permissions from another source and then we’d be ready to continue with the upload process. Easy peasy.

    But upon closer inspection of the tables, it became clear that something was slightly off. First of all, the tables were old — not really old but still missing a few months of data. Then I checked a few structural updates I’d made the previous week and they weren’t there. Several new procedures were missing, too, as were a couple of new tables and even recently stored files on the shared network. Not good. It was becoming pretty apparent that the DBA team had done something major over the weekend and that everything on our server had been rolled back to July. Further investigation suggested that they had done their overhaul without a backup.

    It took two days of effort for everyone to finally accept that the information was just plain gone. I began the re-building effort rather reluctantly but it soon dawned on me that this whole incident was really a blessing in disguise. Our department had been working without a net for too long — our server was a creaky old SQL 2000 box leftover from a failed project and we’d never really had any official technical support. Now, we’ll probably be able to switch the whole thing over to a full-fledged production server with upgraded software, a routine maintenance plan and — best of all — a robust data backup procedure.