They may have spreadsheets full of data and use simple data models to extract limited value, but how can they take this further? The answer lies with greater accessibility of machine learning through user-centric platforms. For the first time, this enables business problem owners—those with intimate knowledge of specific problems and their impact on operations—to connect advanced machine learning capabilities to business value.
The benefits are available to all
Machine learning has traditionally been viewed as requiring extensive resources, time and technical expertise, which often includes hiring data scientists – a highly specialized field where talent demand currently outstrips supply. Beyond this, data scientists are often too separated from a business problem to contextualize it and understand the full impact it has on operations.
Enter the citizen data scientists—employees not operating in dedicated data science or analytics roles, who can use a humanized machine learning platform to explore their data and easily deploy models to unlock the value it holds. Thanks to user-centric platforms, current employees can enjoy access to machine learning technology without the need for specialist training. This is a significant milestone in empowering data owners to quickly master their own data and complete operations at scale, without significant investment or expertise. At the company level, this puts advanced machine learning solutions into the hands of small and mid-sized organizations and their employees, who may be lacking data science expertise. But the increased accessibility of machine learning also generates fresh opportunities for data scientists, freeing up their time to get closer to business problems and focus their skill set on innovation for digital transformation projects.
New business capabilities
A machine learning platform provides citizen data scientists with greater accessibility to the capabilities required to quickly prepare and visualize data, and subsequently build, deploy and manage a suitable model. Whether this involves suggesting actions to clean and correctly format data or recommending the most suitable model for a data set, a humanized platform is designed to guide users through the process from start to finish.
A core aspect of this approach is reducing the volume of mundane data preparation tasks. Think of business processes that are repetitive and involve analyzing data in a similar way on a routine basis, such as budget forecasting. Instead of tying up senior management resources for several weeks to finalize budgets based on expected business outcomes, managers can use an intuitive machine learning platform to quickly identify and set up a model capable of being reused to revise budgets annually – dramatically cutting the time investment in this process going forward.
Alternatively, take an advanced manufacturing company that develops and produces precision components. They may have machinery experts with decades of industry experience and a deep understanding of the data produced by equipment sensors – but they can’t identify patterns and areas for optimization without a dedicated data science team. With humanized machine learning platforms, these experts can input, cleanse and visualize data in minutes, then select an appropriate data model to uncover previously unseen insights.
Man meets machine: complementary capabilities
Machine learning platforms are intended to amplify existing employee skill sets. They remove a large amount of the time and resources traditionally invested into applying machine learning to business data, yet ownership and control of the process still lies with the user. This is key to successful use of machine learning technology.
Machine learning applications are excellent for risk assessment and management, and making data-driven judgement calls, but lack the intuition and creativity required to contextualize and problem-solve for human affairs. This is where humanized machine learning platforms draw the line between ‘human’ tasks and ‘computer’ tasks. They take on the labor-intensive, repetitive tasks such as data cleaning, data-driven model discovery, and model validation, and empower problem owners to focus their time and resources more directly on the business problem at hand.
Ultimately, the computer will always have to collaborate with a human when applying machine learning. To ensure project success, machine learning needs to form part of a human team, augmenting human skills, intelligence and capabilities. Humans have the unique capability to contextualize data and associated errors. Take a simple example where error codes are present in a large data set. A machine learning platform will struggle to contextualize this, but a human who is close to the business process can quickly provide an explanation, such as sensors being out of range.
Beyond the immediate benefits, machine learning platforms solve the issue of legacy once a citizen data scientist leaves the company. These employees can develop machine learning solutions to solve specific business problems, secure in the knowledge these accomplishments will still be operational, intuitive and reusable by colleagues once they have moved on.
Machine learning is now viable for every business
Machine learning is set to become increasingly common among businesses of all sizes as they push to optimize their daily operations. Don’t forget, business problem owners will always have a unique and intimate knowledge of a specific problem and its relevance to existing business priorities. For the first time, they can directly identify and enhance the value of their data by quickly harnessing machine intelligence at scale.
Applying machine learning to data no longer needs to be an arduous, resource-consuming project spanning several months. The rise of citizen data scientists is bringing significant opportunities for smaller and mid-sized businesses to quickly harness advanced machine learning capabilities to unlock greater insights and business value from their data.
Read more: 10 Steps to Disciplined Innovation