Machine learning (ML) – the ability for computers to gather, analyse and interpret data based on patterns and inference rather than explicit instruction – can have an immense and tangible impact on how enterprises conduct business.
Increasingly, companies are using the technology to enrich their understanding of key daily tasks, for example in engaging with their customers more effectively. And recently, ML has become a must-have for organisations who want to revolutionise their internal company practices, to improve processes and expand opportunities for innovation. But, as with any new technology, adopting ML for your business successfully is no simple task – it requires specific capabilities, the right external partners and, perhaps most importantly, a shift in company attitude and culture.
We can break down the integration process into three key stages that can help drive successful company adoption of ML: data acquisition, data understanding and company acceptance.
1. Acquire the data
Before leveraging ML capabilities, organisations need to successfully gather and house the large amounts of data that will become the foundation of the future use of ML. Many organisations we work with begin the process of integrating ML by building a strong data foundation that includes governance & easy access to clean data. Ingesting new data sources should be a repeatable process that provides data scientists access to the raw data as well as aggregates. Well governed data also allows an organisation to restrict access to personally identifiable information (referred to as PII) where necessary and implement strong privacy solutions to reduce the threats of data leaks before they can occur.
2. Understand the data
Almost all modern enterprises generate an immense amount of data, and likely have a good understanding of it – what it means for the business, how to leverage it successfully and so forth. But a simple understanding of the data does not mean that it is ripe for ML projects, and moreover, a simple analysis of data is not the same as ML. The technology requires an entirely different set of expertise that extends beyond baseline analysis, and a thorough understanding of how data is generated, what it could be used for and whether it needs to be prepared for modelling.
3. Drive company-wide acceptance
To successfully implement a new technology into a company, leaders need to get employees on board – and the most effective way to do so is through showing the tangible value the tool can have on their daily activities. What this value is will vastly differ from organisation to organisation and from department to department, but in order to get quick buy-in, it’s best to start with smaller tasks that can produce fast results. This could mean leveraging ML to quickly produce a financial report that would typically take an employee a few hours, or forecasting retail sales to effectively manage an inventory order. When employees directly interact with a new tool and see how it can improve their workload, acceptance and adoption follow suit.
This process can differ per organisation – whether it happens in-house, through an external partnership or via an actual acquisition of a tool that manages this process. A survey from Deloitte found that 60 percent of companies first leveraged ML through the latter option – the purchase and use of a software that had the advanced tech baked in.
Once companies successfully implement a standardised pipeline to use ML into their businesses, they can start to reap the benefits of the technology, such as cutting down the time it takes to gather and analyse data, or automating tedious tasks that free employees up to manage more complex issues. ML has the potential to completely transform how organisations do business, both internally and externally, but successful adoption requires thorough planning. Start small and take the steps needed to ensure success. Be transparent about how it will improve your bottom line but also, the day-to-day lives of your employees. In no time, you’ll have successful implementation, adoption and excitement for the possibilities ahead.
Brandon Bagley
Senior Data Scientist, Cloudreach
