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Analytics is currently a buzzword in the tech industry, and it has been so, for a while now. Ever since data started flowing in volumes that could no longer fit into conventional spreadsheets, analytical processes have found a way into everyday conversations at some of the top firms in the world. As a result, the spread is ever-expanding, and today, they have made their way into our homes with optimized solutions and smart features.
This famous quote of recent times echoes the importance of Data. It also highlights the crucial role analytics and data sciences play in decoding and understanding what lies within its infinite complexities. Yet, for all the glamour that we associate with the word, analytical solutions often find themselves stranded midway to business adoption.
The key purpose of analytics and data science centres is to serve business or organizational needs; otherwise, there is no meaning to any kind of cutting-edge algorithm that we might develop. No avant-garde data scientist can survive for long unless a business adopts the solution and records any kind of gain out of it. Just to clarify, gains for a business are not limited to higher profits or revenue. It can be associated with error reduction, efficiency gains, cost optimizations etc. All these can be influenced by the ‘magic’ of analytics and data sciences. But for that, hurdles need to be identified and overcome.
What hurdles are we talking about? Let’s take a closer look.
> Lack of clear Business Objectives* – Some time back, the UK National Health Service initiated a project to integrate all patient medical records into a central database1. It was estimated to cost €6.4Bn. It was finally shut down in 2011, recording an expenditure of more than €10Bn. However, there was no clear communication to individual healthcare providers on the benefits of a central repository. Hence, they did not have any impetus to move to an integrated system. They also mistakenly identified the NHS as the customer, whereas the actual customers were the patient and the providers.
> Lack of Expertise* – The above example also suffered from inefficiencies within the analytics and data sciences team. There was no prior experience in building necessary systems, which made it difficult for them to grasp all intricacies. This led to disputes and a lack of progress.
> Technology – Often, business entities suffer from inadequate infrastructure, which is crucial for analytics and data sciences to be quick and precise. Legacy systems, systems in isolation, lack of integration cause major hindrances to development as well as deployment at a large scale.
> Black Box – In many scenarios, analytical solutions end up being too complex for the business to digest. At an operational level, they need to be simplified and broken down as much as possible to ensure easy interaction with all stakeholders. In addition, the models should be interpretable and aligned to the core business questions. However, the final output often deviates from the path and ends up at the bottom of a pile of good ideas and nothing more
> Inertia – Traditional organizations have thrived and grown over decades of hard work and established practices that have been driven by a dedicated workforce. However, these scenarios can often become a hostile environment for analytics. Tried and tested processes take precedence, and gut instincts developed over years of experience play a bigger role than recommendations from an analytical system leading to eventual low adoption.
Does this mean analytics and data sciences can never be truly adopted? On the contrary, analytics has already played a big part in businesses. New age start-ups and tech giants have emerged as torchbearers and continue to contribute to the development in enormous ways.
We at Rakuten believe that drive for innovation, flexibility and an appetite for risks can empower analytics. Following the age-old adage, ‘We reap what we sow’, true business impact via AI can be achieved if we are bold enough to build the right team and the right infrastructure. Analytics and data sciences are an integral part of the Consulting Team. They work closely with individual business units to understand their current strategic priorities and pain points, develop tailor-made analytical solutions, deliver actionable insights and recommend solutions using explainable AI. This ensures that recommendations are relevant and easy to comprehend, which helps in quick implementations leading to higher adoption and success in driving business impact.
As part of the bigger analytics and data sciences community, all of us have a unique opportunity to share and learn from each other. The Rakuten Product Conference (Theme: Applied AI) is the perfect stage to come together. In our shared vision of progress, discussions on recent trends and products by industry leaders can help us understand the best ways to push the fold. It also sows seeds of ideas that can grow and become unicorns of tomorrow.
Look forward to sharing and learning as part of the Rakuten Product Conference (Theme: Applied AI). Hope to meet you there very soon ☺
Click here to register and join us on August 19 and 20, 2021.