Do You Test Your Models Well Enough?

Numeric models and complex calculations have the unsurprising property of being quite important most of the time. That puts a lot of pressure on the developer for delivering good results. The question is how do you know the model works properly? Considering the expense and the high impact it is surprising to me that the testing phase is often being skipped or not enough efforts are put  assuring it adheres to high standards. Neglecting this phase  often results in unsatisfied and angry customers, extra time spent on the project after delivery, or bad and costly decisions based on a bad model.

An Exampel of A Successful Automation

The focus of my previous post previous post about manual processes and their automation was on the applications included in MS Office. However, the key points are applicable to processes involving all sort of software packages.

As promised, I will provide some examples for a successful automation of a manual process. Here's the first.

Automation of MS Office Tasks? What is This?

I see it over and over again - a team of expensive analysts spend big chunk of their time to process data - load, transform, copying and pasting and exporting. Very little time is left for actual analysis and getting the valuable insights. It is a total waste of time, resources and money. Not to mention the dissipating enthusiasm and energy of this highly qualified staff. I have always been amazed how otherwise clever people spend hours and hours of copying and pasting data from one source to another or from an Excel sheet to a PowerPoint presentation. Add to that the very high probability of human error and you  will have a grim picture.

In Search Of The Formula

No doubt, one of the most appealing features in Excel is the formulas. Clever usage of it could achieve excellent results in performing complex calculations, processing data, extracting and displaying information  and much more. Тhe majority of Excel users have not stepped beyond some simple operations. Мost people approach formulas as a daunting and complex task. Users get fixated on searching the formula and keep trying to find it while the real power to solve the problem with their own logical thinking. Here's my two cents to help you think of formulas as easy and fun.

Handling Formula Errors in Excel

Confucius says: there is more than IFERROR in error handling

Admit it or not, errors in formulas are a regular companion for everyone who uses formulas that are a bit more complicated than the simple subtraction and addition. There are two general types of error we make in spreadsheets - logical and formula errors. Producing error-free sheets and models is a huge topic and I will be covering it in this blog. Today I would like to focus on one the most popular ways to handle formula errors.

How To Keep Data Sources in Your Spreadsheet Under Control?

Data comes to the spreadsheet in a number of ways. It could be a short list of quarterly sales per department or a long list of customers and their balances copied from another workbook. It could be imported from a website or fed from a database query. If you are lucky, your spreadsheet will require one-off data loading. Unfortunately in most cases we need to review and update our spreadsheets. We then have to go back to the data and remember where and when it came from as well as many other details depending on the specific data source - it could be a query, report name, web-address and date extracted. The problem is even harder when you work with many spreadsheets serving different customers and purposes. This is why I think that proper management of data sources is an important step towards better models and better management of spreadsheets. Here’s how I do that.

How to Make Analytics a Success

The word "analytics" is everywhere. Its methods now appear quite often in business talk, lots of articles distribute the analytical values, there are best-selling books that describe the analytical framework and even some movies with major Hollywood stars. I believe we are witnessing levels of work occupations with some "analyst" in its titles never known before. Big data, statistics and modeling are the buzz words. However, application of analytic methods is very often far from the most-productive way to do that. There are lots of ways preventing analytics not to deliver true value..
I have spent lots of thinking on the keys for a successful application of analytics and what makes an analytical project to achieve its goals. Recently I have came across an article that illustrates some key points that I would like to share with you. The article is Big Data in the Big Apple published in Slate.  Take few minute to read it - it is a good read and good story (as if you could expect something else from Slate).

What is at the Bottom of the Horse-meat Scandal

The horse-meat scandal has been going on for some time now. There is lots of investigation, people talk and headlines. All that time there has been a question that was bugging me. So, where all that meat came from? Large herds are not roaming free the plains of Europe nor there are traditions in eating horse meats among most of the population there. Also, horse meat is relatively expensive and probably would not be the first choice if someone decide to cheat and reduce the amount of beef in a meal. So, it looks weird to me.