Into one that looks like this:

The columns have been transposed to rows, and the former column name is now a value in each row.

Why would you want to do this? One very common reason is to prepare data for olap tools such as Tableau or Qlikview- Having each individual country as a column will not allow you to take advantage of their slice and dice capabilities, with Country as a column, and the country names stored in each row, you can take advantage of pivot tables and olap tools.

How its done in Datamartist

To do this in datamartist, we use a PIVOT block, and connect it to the data set we want to transpose:

Then we simply specify which columns are to be transposed, and provide a name for the the two new columns- one column will contain the former column names, the other column the values.

You can try this yourself with the free trial of datamartist- you will find you can import data from files, excel and databases, and using easy to use blocks, create powerful data transformations, making your data ready for use in great tools like Excel, Tableau and Qlikview.

With the new version, Datamartist can insert data sets easily and visually into Excel workbooks as well as having the capability of defining a template, allowing automation of dashboard generation.

As always, data transformations are created visually, using blocks and connectors- in this case, we are analyzing some sales and profitability data.

We use blocks to summarize, transform and pivot our data, and then export it into specific areas of the desired excel template.

By inserting the data onto a "data" sheet in the template that has links to charts or tables on the dashboard sheet, we can create powerful, clear dashboards.

When in datamartist, you can see clearly where the data sets are going to be inserted- and datamartist generates the excel workbook(s) needed with all the data inserted. Because Datamartist can be run from the command line, you can automate your excel reporting, pulling data from databases and files, and generating professional looking dashboards that are easy to share and use.

The Excel export capability is far from the only enhancement that V1.6 brings. I'll be blogging, and providing examples of new functionality in the coming weeks.

Sometimes, we make decisions NOT to set a goal, because we don't want to break it.

You might be thinking you really should step up your data quality monitoring- get some data profiling underway to help identify the data domains and areas you most want to tackle in 2012. But you might be also thinking that with all the pressures and cutbacks that many companies are facing, you don't have the resources to implement a full scale profiling and monitoring effort, and so might decide to delay.

Don't wait. Just do it. The perfect is the enemy of the good.

Rather than worrying about how much of your data you are going to be able to cover, or that you can't devote enough resources to tackle all of your reference areas at once, work at the problem from another direction.

First, start with master data.

Master data is the data that all your other data is made from. It's the data everyone uses to view the massive piles of transactional data, so one bad row in a master data table, and the impact is felt across perhaps hundreds of reports, and multiple time periods. If you have a product in the wrong category, then every transaction, across perhaps hundreds of customers, and all time, will be mis-catagorized, and every total, sub-total and calculated metric using it will suffer.

While bad transactions are bad, bad reference data is deadly. Bad reference data takes a good transaction and messes it up.

Worst first!

Make a list of your reference tables/area. Customer, Product, Chart of account, etc. etc. What are the most important for your business? This isn't something I can tell you- you have to think about what is most critical.

If you are a company that purchases large amounts of materials from many vendors, and purchasing decisions are fast paced and critical, then maybe it's your vendor master, and your accounts payable.

On the other hand, if you have lots of interaction with your customers, and errors in the customer master cost you business, then start with that.

The key is to first make the list, and then think to yourself "if I have bad quality data, where am I most afraid it will be?" Start profiling there. You want to find the worst first, and fixing that will have the greatest positive impact.

Get to know your data

Don't worry about setting complex or work intensive goals right away. Data profiling is about data discovery sometimes. You need to wade into your reference data, play with it, tease out patterns and relationships. As you get to know your data, you will be able to better identify where there are issues to tackle, and where root causes might lie for data quality issues.

One approach might be to simply resolve to spend an hour a week, every week, profiling some data. If you aren't do that now, you will find that even just a bit of time set aside will give huge insight- sometimes we get too busy to do the basics, and we miss opportunities to make significant improvements with relatively little effort in our data.

This version of Datamartist brings with it some useful new functionality, including new functions that can be used in expressions, new capabilities in terms of exporting to databases, and a new data block.

In this post, we'll look at two new features, the Pivot block, and the enhanced database export capabilities.

Pivot Block

Our beta testers loved this new block. The pivot block lets you do the equivalent of a cross-tab query, rolling up a measure, and distributing the value in a new set of columns, where the column names are provided by the input data set.

Here is a simple example showing how it works:

Say we start with a set of data that has mutiple rows for each date, and different values in the color field, and a quantity measure:

Then we can connect one of the new pivot blocks to this data set like so:

The pivot block lets us select which columns to include (this defines the level of detail to roll up to), which string column to use to generate the new column names, and which measure to use as well as the rollup method (sum, average, min, max)

The result? The output of the pivot block looks like this: now we have a summary by color for each date, with a column for each color value.

Database export enhancements

Now, when exporting to a database, there are a number of new options.

One of the most interesting is the capability to execute SQL commands in the database either before and/or after the data is exported into the table.

This provides the capability of running stored procedures, or launching follow on database side processing after Datamartist writes the data into the DB.

This is a powerful new capability, and makes it even easier to integrate datamartist into various systems, and get your data quality and profiling data where you need it.

If you haven't checked out datamartist yet, we're not sure what you are waiting for- download the free trial, and give it a go.

Data quality super powers.

Fighting the anti-data forces of evil.

Data silo fun.

The joys of a moving target

Data migration tools.

While it's a complex subject, some basic rules of thumb can avoid the deeper rabbit holes.

The first thing to understand about Data Quality rules is they aren't as easy as they may look. Data is in theory something in the ordered world of computers, but in reality is in the "flexible" world of humans. A huge amount of data is entered by members of the group "Homo sapiens" (or mutilated by software written by members of that group) and as a result is not as ordered as we would all like.

The challenge for data quality practitioners is to remove the chaos injected by those highly involved primates (us) and make the data the sterile, ordered, never any question about anything type that we all imagine in our fantasies.

But how?

In the end, it is amazing how powerful and complex the various solutions to this problem are.

But I suggest that there are some basic principles that can help guide us.

First- do no harm.

One of the risks of any data quality initiative is that it actually screws up the data more. Don't define rules that are so complex, and so sure of themselves that they actually make the data worse. Be humble. Don't change data unless you are pretty sure it's a good idea. Err on the side of not screwing up the original. And keep a copy of the original- so if things do go off the rails you can undo- or at least try to understand what when wrong.

Go out and talk to the people

Don't sit in your ivory tower and speculate as to what the data means. Go out there and watch people enter it in. See what real world type things are happening that never make it into bits and bytes.

Attack the basics first

Focus your first efforts on dealing with the basics- they will resolve the vast majority of the issues- don't chase after the outliers until you have the "easy" cases taken care of- the tough stuff is a case of diminishing returns- look first at how to fix processes and train your people to make the majority of typical data entry cases more accurate before you start looking into artificial intelligence based hyper-multi-semantic-algorithmic-learning-matching-holistic-flux-capacitor data quality systems.

Less is more- the fewer rules the better.

So whats the rule about making rules? Try to make less rules, and test them in a pragmatic way. It is possible to have so many rules that the rules themselves have data quality issues- don't go there.

Sometimes the simplest things will bring the greatest benefit.

In the coming weeks, I'll be posting about how to design, implement and monitor Data quality rules using the Datamartist tool.

We've had lots of great feedback from our customers and are thrilled with how people are using Datamartist, not just for powerful and flexible data profiling, but for data migration, data quality work and ad-hoc datamart creation.

As always we're committed to continually improve our products- Here are just a few of the features added in this latest version;

Block definition import and export

Datamartist adds a new level of reuse-ability and collaboration capabilities with the addition of block export/import.

This lets you export a block configuration (or a number of blocks, with all their connectors) to a file that can then be imported into any other Canvas, either by yourself or by your colleagues that are also using Datamartist. Just select the blocks you want, and right click to export- just right click anywhere on any canvas to import an existing block file.

We've found this particularly useful in saving filters, segmentations, and even full data profiling blocks to give us a library of useful blocks and block groups that we use again and again.

Improved database connectivity and connection management

We've also added some features and improved how we connect to databases in Datamartist

Datamartist can connect to SQL Server, Oracle, MySQL, MS Access, Text files and Excel files, as well as having and ODBC driver that lets you connect to many other databases. Now its even easier to manage a large number of database connections for multiple database types- keep all those servers and connections at your fingertips when you are combining all that data!

Data profiling in an affordable, graphical, visual environment

Find out why people are loving the combination of an ETL and a Data profiling tool in one- using Datamartist not just for data profiling, but for data migration, data quality audits, and ad hoc datamart creation. Try the free trial today.

Having technical things like tools work is useful, and good. But just because you build it, does not mean they will come.

The challenge often in Data Quality is that often what has to change even more than the technology or tools is the behaviours and perspectives of the people in the organisation with data quality issues. At the very least, the users have to use the tools. Very few data quality solutions are of the "full autopilot" bad-data-goes-in-here-good-comes-out-here type.

As much as we engineers would like to solve everything with software, people are involved in Data Quality.

While a fantastic bit of data profiling analysis or an elegant and powerful data transform would seem to be enough, the truth is sometimes how and when you present these things is key to getting the non-engineer people to buy in.

Sometimes preparing people over time, and introducing things in a step by step way helps them understand, and makes the technology and the change required less daunting.

Because I'm looking out my window at a tentative (very tentative it's only March after all) spring day here in Toronto, I'm going to use a summer barbecue analogy.

The tools and technology are the steak. The steak is key to the party. In the end (at least for me in this analogy) the steak delivers most of the value in your summer BBQ party value proposition, but you'll have more guests and be more successful over all if you package the whole.

Sometimes, part of selling the steak is the sizzle, the preparation, the things around the steak.

It's the smell of the BBQ getting ready, it's the sound of the steak hitting the grill- its the cold drink, the conversation, the games on the lawn for the kids.

In the end, even if you know that 90% of the deal was that steak, if you just put a steak on a plate and give it to each guest the moment they arrive, its just not going to get the same response.

In my usual round about way the point I'm trying to get to is that you can't solve technical problems, then drop them on people desks and say "do it". You need to invite them to the party. Prepare them for the menu, ask preferences, give them some time to hear the sizzle, smell the charcoal, enjoy the sunshine in expectation of that steak.

Steak is good. Remember to plan some sizzle too.

Obviously, we have to believe something. But today I'm asking you to be skeptical. Question your facts.

Let me give you an example. I'm a Canadian, and looking out my window right now, I can see a pretty healthy snow fall accumulating. Lots of the white stuff. Brings to mind the fact that some cultures in the far north have over 100 words for snow.

Hang on. Is that a fact? Tell me- have you heard a variation of that?

I'm sure I read that somewhere. I've heard others mention it. It makes sense- I mean, people living in the far north would see lots of snow, and would know all about it, and so their language would evolve to encompass lots of different qualities of snow.

Sounds good.

Only, is it? It's an idea that "just makes sense". People seem to just accept it as soon as you say it. People are likely to pass the idea along to others- because it makes a compelling story.

But in fact, it's wrong. I'll let you google to your hearts content if you like to find more evidence than my say so, but after reading a number of articles on the subject, (here is an example, and of course the Wikipedia entry.) it seems clear that there are not 100 words for snow in any language. In fact, English has about the same number of ways of talking about snow as languages from societies in the far north.

So the point of all this is- what myths do you have in your organisation? Things that "everyone" knows are true. Things that when they are explained to you make "perfect sense". Things that you teach to every new hire so that they "know how things are".

The insidous thing about "facts" is that once they gain purchase, any contrary evidence tends to be called an "exception", or discounted.

Use data to find out what is true. Fight to improve the quality of your data to find more and more new truths. Question the status quo if the data contradicts it. Don't assume that something is wrong with the data when "things don't make sense." Maybe they don't make sense because your assumptions are just plain WRONG.

Be very aware that you might be making decisions based on myths that while sounding so plausible, so clear, so common sense, are pure fantasy.

The good news is that all your competitors might be doing the same thing. If you look at your data, and see through it, you might show them all how wrong they are.

You know that all that data means something- but what?

One of my favorite code-busting stories is the epic victory over the Enigma code during the second world war. One of the reasons its of interest is that it was one of the early applications of computing- but the key lesson I think is from not the brute force computation done, but the strategies used to crack the code.

When you are trying to crack a code, one of the key things you need are "Cribs"- some way have samples of coded message and clear text. These cribs can radically reduce the number of possible ways a code can be decoded.

In the case of enigma, the allies would listen for German U-boat radio transmissions, while also using direction finding equipment to estimate their location. Standard procedure was for a U-Boat to first radio a weather report.

By painstakingly back tracking known weather conditions and locations of U-Boats when they transmitted it was possible to take advantage of that first weather report- there were only so many ways to say "Sunny and calm". Having this crib gave them a way to break into the code.

What is the point in terms of Data profiling? While it's critical to have the right tools to analyse the data (a data profiler like Datamartist, for example), its also important to get out there and talk to people, understand whats going on- collect some Cribs that will help it all make sense.