Always discovering, always ready
We can never know what awaits us in the future. We can be ready to act. The environment around us can and will change in unexpected ways. In order to stay pointing in the right direction, it may be necessary to perform frequent course adjustments.
Constantly making minor corrections can help us to accommodate the drift of everyday variation. But we need a strategy that
can observe and react to the accumulation of deltas into a critical mass, and to epoch-changing events rewrite the rules.
We need end-to-end automation.
Big data offers businesses many benefits including real-time insights and the ability to make more informed decisions. One of the downsides though has been the issue of data drift. Data drift hinders businesses ability to maximise the potential big data offers and can cause series technical and business challenges. Recognising this happens is the first step to dealing with it.
Data drift can be as structural, semantic or infrastructure drift.
Structural drift happens when the data schema gets changed at source ie fields are added/deleted or reorder or changed in some way.
Semantic drift happens when the meaning of the date changes but the structure has remained the same.
Infrastructure drift happens when incompatibilities are created by changes to the underlying software or systems. Frequently occurring, as multiple data source systems are independently governed thus each following its own upgrade path.
Data fidelity and end-to-end operations suffer
The fidelity of data analytic results, their reliability and productivity, are harmed by data drift. Data fidelity issues happen when data is lost or squandered or becomes corroded. Drifted data that then passes into data stores undetected creates corrosion of that data store.
- If data doesn’t conform to the expected schema, then it can be errantly not included in the data stream thus causing loss.
- If there is an inflexible ingest process, when a new field or new information is added at source, this could be not included and ignored and therefore squandered.
Incomplete and inconsistent data streams and inaccurate data all lead to polluted data stores and analysis causing data drift and low fidelity data. This then leads to inaccurate data analytics causing harm to the business. When detected, work to “clean up” is necessary.
Automating the entire life cycle is the answer
Automating the end-to-end life cycle of your machine learning solution is the only robust, scalable way to combat the data drift and model drift.
Investing in a platform that is always on, always observing, always learning: always discovering and always ready.
This new technology directly addresses the key challenges of limited data science talent availability, long project timelines, and complicated provisioning by automating complicated modelling tasks, eliminating the need for extensive programming skills, and allowing for easy implementation.
Automated machine learning enables stakeholders to gain real value from machine learning without sacriﬁcing their agility. With automation, departments develop models in days that would otherwise have taken months, increasing speed-to-insight and allowing them to be as ﬂexible as possible in response to shifting consumer behaviour.
Not only that, automation delivers the power of machine learning to the people who have deep understanding of the business, expanding the pool of people who can contribute to data science projects beyond the data scientist to analysts and business users alike. Built-in expertise makes automated machine learning tools simple to use, while inherent guardrails and best practices make it safe to engage more people in projects with the assurance that users can’t miss critical steps.
Customer-centric, contextualised experiences based on customer journey analytics will become the competitive differentiator for marketers across industries. It is estimated that by 2022, the market for customer journey analytics will reach over $12 billion in size. To take full advantage of the data at their disposal, organisations of all types are turning to machine learning and AI.
Not only does automated machine learning deliver a signiﬁcant competitive edge for organisations, it transforms virtually everything they do. Businesses that fail to effectively leverage these emerging technologies will be left in the dust by competitors who succeed. Automated machine learning is the most efﬁcient, cost-effective way to make the most of available data, technology, and resources.
The challenges on the road to machine learning success may seem daunting, but Robotica Machine Learning and the DataRobot automated machine learning platform enables every industry to overcome those challenges with its cutting-edge, easy-to-use automation capabilities.