It takes more than just a data scientist to build a machine learning model! Credit all who participate in the tasks of the analytics life cycle


It is my third time in San Francisco, and again I am highly impressed by the great architecture of the Golden Gate Bridge. This time I take a boat over to Sausalito at the other side of the bay. The museum there tells about the diverse history of the village and also, of course, the construction of the bridge. The statement and picture of Eddie Souza, a worker at the bridge, catches my attention.

“… That was the Depression and lots of unemployed men out of work were hanging on the fence of the bridge waiting for one of us get fired or hurt … “

What a tough job his must have been! All the workers who fought hard to do the work and not fall down from the bridge. They were the ones who built the bridge!

The stars in the spotlight

Later on I walk over the Golden Gate Bridge back to San Francisco. In the middle of the bridge I see the large memorial tablet that names all the relevant people who built the bridge. Do I see Eddie Souza's name at the tablet? No. None of the workers is mentioned here – only those who planned and directed the construction. I understand it would be hard to print the names of all workers who ever worked at the bridge here.

Credit to all who make the analytics life cycle run

However, those who have their names listed here could only shine because people like Eddie did their jobs. After awhile I think about my job and the many data scientists who often stand in the spotlight because they built a good, colourful and interactive model. So I wanted to say thank you to all my SAS colleagues and project members at customer sites who do an extremely important job so that we can build and present our models and that the analytics life cycle flows.

Hidden Insights: It takes more than just a data scientist to build a machine learning model! Credit all who participate in the tasks of the analytics life cycle

Machine learning models need data

We need data about our analysis objects to train the models. Database administrators store the data in their source operational systems. Those responsible for data integration access and transfer the data from different systems into data marts, data lakes that are accessible from analysis platforms. Data stewards and business experts make sure that the data quality is checked for criteria completeness, consistency, accuracy and timeliness. They profile the data from technical and business criteria to make sure that we can access it for analytics.

Artificial intelligence is enabled by computer software

So we need analytics software accessible on our computers. System administrators install the software and maintain it by applying updates and maintenance releases. Legal experts make sure that we have the appropriate software licenses and usage agreements to be allowed to use the software. System administrators also make sure that we can access the software with our credentials and only have access to that data we are supposed to use.

Beyond the AI hype, the question is how data-driven innovation can be brought to life and put in action to resolve real business problems. What steps are needed to move AI out of the lab and into business operations to realize the desired outcomes? Join SAS experts at an event near you and be inspired to lead your team in the new age of analytics – from data wrangling – through to tangible business results.

Business experts are our sparring partners

Every data has its history and its stories to tell. We need to understand the background and origin of the data. Otherwise we might build incorrect or irrelevant machine learning models. Thus we depend on the input and feedback from business experts who understand the business background of the data and know the operational process that generates the data. Business experts are also the ones who help us to calibrate the models so that they can be used in the operational process to make business decisions.

IT puts the models into production

The story must not end here. Just building the model is in many cases not enough. You must put the model into production so that it generates predictions and forecasts and feeds business decisions. When we come up with a fancy model with a high lift, good explanation of customer behaviour or precise demand forecast, we know that the success of the model will only materialise if colleagues from the IT department help us to put our models into production and integrate the analytics models into the production system.

Next time when you see me presenting results from a machine learning model created with SAS® Viya®, don't forget: I had help from others to walk through the analytics life cycle and generate the results that I am going to present. I look forward to meeting you and discussing your experience and requirements of building machine learning models covering the entire analytics life cycle.



About Author

Gerhard Svolba

Principal Solutions Architect

Dr. Gerhard Svolba ist Analytics Expert und Senior Solution Architect bei SAS. Zu seinen Aufgabenbereichen gehören das Produktmanagement für die analytischen Produkte von SAS sowie die branchenübergreifende Konzeption und Durchführung analytischer Projekte. Besonderer Fokus liegt auf Kundenanalyse, Risiko-Prognose und Demand Forecasting. Seit 2004 ist Gerhard Svolba Buchautor für SAS-Press. 2007 erschien das Buch „Data Preparation for Analytics using SAS“, im Mai 2012 „Data Quality for Analytics Using SAS“. Darüber hinaus betreut er die User Group „SAS Club“. Dr. Gerhard Svolba hat einen Lehrauftrag an der Fachhochschule Steyr im Bereich „International Marketing Management“. Der promovierte Statistiker ist seit 1999 bei SAS. Neben seinem Statistikstudium erlangte er zusätzlich den Master-Titel im Bereich Betriebsinformatik. ENGLISH: Dr. Gerhard Svolba is a senior solutions architect and analytic expert at SAS Institute Inc. in Austria, where he specializes in analytics in different business and research domains. His project experience ranges from business and technical conceptual considerations to data preparation and analytic modeling across industries. He is the author of Data Preparation for Analytics Using SAS® and Data Quality for Analytics using SAS and teaches a SAS training course called “Building Analytic Data Marts.

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