ShortCut: Data Analytics Transformation

SHORT CUT ALIGN THE BUSINESS How to easily master data analytics transformation It’s common sense across industries: Proficiency in data analytics (DA) helps businesses make better and objective decisions, cut costs, improve quality and reduce risk. With its potential, DA solutions using deep learning or artificial intelligence techniques, only two methods within the vast spectrum of DA which ranges from diagnostic and descriptive to prescriptive solutions, empower enterprises to turn their data into business value. However, over a third of data analytics professionals say their company never, or only sometimes, puts their piloted DA solutions to use 1 . That isn’t because these companies don’t possess a small army of data scientists or cutting-edge technology. Many companies have built up dedicated teams and initiated various DA initiatives. But it seems these aren’t the only requirement to do DA well. So, what’s going wrong? goetzpartners has identified eight root causes that are often responsible and has devised a clear strategy and operating model for organizations to overcome them and release the full power of DA. Common stumbling blocks EIGHT ROOT CAUSES FOR DATA ANALYTICS FAILURE LACK OF MANAGEMENT SUPPORT LOW RELEVANCE OF USE CASES INSUFFICIENT PLANNING 2 3 INSUFFICIENT DATA SETS 1 4 DATA ANALYTICS FAILURE To understand the root causes of DA failure, it is first important to recognize the role organizational structure plays. Typically, all digital and innovation activities – such as a DA initiative – begin in a separate area of the company from the main operational environment (e.g. data labs, innovation hubs etc.). This innovation environment is where DA pilot projects will be developed, before being integrated into the operational environment. It is at this point – the integration phase – that the challenges arise and cause many businesses to not advance their DA initiatives past the pilot phase to realize the benefits. The first and always one of the most important issues when it comes to significant 5 8 INSUFFICIENT PRIORITIZATION WITHIN IT 6 7 LACK OF COMMITMENT UNDERESTI- MATED INTERNAL RESISTANCE LACK OF RESOURCES Data Analytics and Artificial Intelligence are not tools that just need to be implemented – they completely change the way we work. changes in the way we work may be a lack of management support. Despite higher management stating their belief in DA, they are often less committed to enforcing implementation at the operational level. Another root cause lies in the fact that the DA use cases often have low relevance to the business strategy and challenges the business is facing. In fact, it’s frequently the case that an application will be developed and piloted because it is possible – not because it is needed. Insufficient planning may also be an issue which can occur in many forms: There is no realistic business case for the project; the relevant people in the operational environment or in the IT department are not involved; there is no realistic timeline in place or no legal sign-off. Another issue which can founder a project is insufficient data sets in terms of poor quality and/or a lack of quantity. Challenges also arise due to insufficient prioritization within IT. The IT department is typically required to implement the DA solutions into the operational environment, but capacity and prioritization issues can be a stumbling block. Some businesses lack a strong-enough commitment to DA, and thus business units aren’t willing to integrate the new 1) Data Science Survey, Rexer Analytics 2017: