| | JUNE 20228CIOReviewAnalytics, Data Science and Artificial Intelligence has made it to the mainstream vocabulary of every senior executive and becoming "data-driven" are amongst the most commonly stated term in the slide deck of most organizations. Yet there has been a constant struggle in achieving success in these initiatives and based on predictions by Gartner nearly 80% of these projects will never reach deployment. If you ask yourself, "Why?" -- one of the most commonly cited reasons is "Culture" (HBR article cited below) which is true but there are other factors that influence as well.As a Data Science practitioner, one of the reasons I feel why these initiatives fail is because "Doing Analytics vs Scaling Analytics" -- are different ball games. If you ask about the origin of the "Analytics team" in any big organization, you'll hear a familiar story of 3­4 data scientists/data engineers working in a siloed team trying to build a data lake, exploring use-cases for data science and developing Proofs-of-Concept (PoCs) to show business the value of data. While this exploratory way of working is a great way to start, when it came to embedding these algorithms into the workflow of these organizations, most teams faltered. This is because Doing Analytics is largely an analytics problem that a Data Scientist can solve building models/algorithms whereas Scaling Analytics needs a lot more than a team of Data Scientists which I'll elaborate below.AI/ML can indeed do remarkable things with a lot of data, but to scale analytics the first question is, `Do you have the "right" data?'A common acronym you'll hear among Data Scientists when you start talking about "right" data is GIGO (Garbage In, Garbage Out). Most organizations are sitting on top of a multitude of data coming from various sources and they usually rave on how many bytes of data they collect. However, petabytes of data does not translate to success in Data Science and most companies only realize this after they start their investigation to scale analytics. The problem occurs when you try to combine data across various sources, say combining sales data with marketing, or supply chain data with sales; that's when the cat comes out of the bag. Though, these IT systems for Sales, Marketing etc., in isolation work like a charm, when you try to build pipelines to combine them -- Data Quality/Integrity issues hamper your progress to scale analytics.When it comes to problems with data quality, a frequently suggested way to solve is having a "Data Governance" model in the organization. While having that is important, you have to ask yourself two more questions --i) How "Digital" is your business and how much of your data collection process is automated?If you take businesses like Uber, Facebook, Amazon and Airbnb -- their entire business process is digitized and in some way trackable. Scaling Analytics in companies like that is easier than doing it in more traditional businesses -- like manufacturing, automotive, aerospace, etc. The reason is that there are still certain elements in the business process that aren't completely digitized (or) has elements that rely on the manual data input. Hence, when more traditional firms like the above want to jump into the bandwagon of using Data Science, the results are not great since the data collection process isn't standardized (or) streamlined yet.By Ram Thilak, Global Head of Data Science and Analytics, Inchcape PLCIN MY OPINIONSCALING ANALYTICSDOING ANALYTICS
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