"The topic of Machine Learning/AI is all the rage across many industries and novel applications of the technology continue to bestow enviable competitive advantages on companies with enough creativity and talent to leverage its power..."
Right now, as you read these words, a company somewhere is preparing for go-live on a new ERP implementation. No matter the size of the company, the preparedness of the project team, or the quality of the solution, the days leading up to go-live are fraught with stress and anxiety. As I write this blog, my phone has been buzzing with emails concerning an imminent go-live at a company that we’ll call Company J. Let’s say they manufacture knockoff Jordache jeans and Members Only jackets, with legacy systems from the same era.
I’ve touched on the story of “Company J” before. Last we left them, they had taken ownership of a tool I developed in Python that automates data validation. They are now close to go-live and have already converted most of their master data to their new SAP solution. Every object they’ve loaded into SAP has been validated using Python. Validations that took multiple people as long as two weeks to complete with Excel (obviously not an acceptable state of affairs in a fast-paced go-live) are now completed in hours or minutes thanks to Python. The tool outputs consistent, easy-to-read data quality reports – much better than getting a verbal “it looks fine” from a business analyst who passed out at their desk at 2 AM the night before. But I still get asked, why Python?
Why Python, Indeed?
No matter who you ask, Python is in the top 5 most popular coding languages in the world. The IEEE ranks Python 3rd, and a mere tenth of a percentage point behind Java. The PYPL index ranks it 2nd, and Python has been trading places with C# for 4th in the TIOBE ratings for more than a year now. Python is used by Google, Yahoo, and NASA; it […]
On January 15, 2015, Target Canada shocked the business world by filing for bankruptcy protection. The retail giant’s Canadian stores had been open for less than two years, their operation marred by high profile failures: delayed openings, empty shelves, and annual losses that added up to 2.1 billion USD. Analysts, reporters, and Target’s own employees unanimously identified the culprit – bad data in SAP.Exclusive: Target Canada’s supply chain gridlock: how Barbie SUVs snarled traffic
This in and of itself was not a new story; the two largest food retailers in Canada – Loblaws and Sobeys – had both had significant issues with their SAP implementations. Target believed that flawed data conversions had caused these implementations to stumble. Target believed the problems other retailers faced were due to error in data conversion. To avoid this issue, Target decided to fill the Canada SAP systems with entirely new data rather than converting data from their US databases. Unfortunately for Target, this strategy did not pay off and led to many of the well-publicized failings that shuttered its Canadian stores for good.
Why was Target Canada so afraid of data conversion?
Done correctly, conversion allows for a smoother cutover between old and new systems – no telling customers they must remake their online order accounts, for example – and saves countless labor hours that would otherwise be spent populating data by hand. For many companies, especially in retail, conversion is the only sensible choice. But despite best laid plans, many implementations face uncertainty in the data conversion process. Late changes to functional requirements, large data volumes, and complicated data dependencies all present opportunities for data headaches leading up to and beyond go-live. And with the critical role data plays in modern business processes, a small conversion flaw can have a […]