Companies using artificial intelligence (AI) techniques such as deep learning have learned these capabilities position them among the digital elites whose business models are powered by data. Left behind are the others who fail to leverage the analytical capability of AI and consequently miss out on opportunities to improve customer experience, achieve cost reduction, develop cross-selling capability and asset optimization.
While top performers can make the useful application of advanced analytics look easy, the truth is that the practical and profitable application of machine learning, deep learning and other AI techniques is a complex and multistep process that only a handful of companies have successfully deployed at scale.
Many organizations have launched successful single intelligent machines. However, focusing on the deployment of a single model at a time ultimately leaves organizations failing to compete with the leaders in data driven advanced analytics. The fact is, successful companies will create frameworks that enable the deployment of hundreds or even thousands of models — at any time, for any analyst, written in any language and operating on a joined-up view of their business data. These new frameworks will be built using Analytic Operationalization, or ‘AnalyticOps.’
When it comes to AI and machine learning, businesses will have to ensure they can operate at scale in order to compete on an enterprise level with other companies invested in advanced analytics.
AnalyticOps – What is it and why should companies care?
If “big data” was the buzz phrase a few years ago, AI was all the buzz in 2017 and is becoming mainstream in 2018. However, companies are waking up to the realization that AI and other advanced analytic approaches quickly become unmanageable without adequate automation in place, and this is where AnalyticOps comes in.
In short, AnalyticOps is a framework that oversees development of automation and consumption of analytics across the enterprise. It’s a series of steps, integrated processes and technologies that are must-haves for companies that want to successfully deliver business value from AI and advanced analytics-based models. AnalyticOps frameworks help break silos and accelerate time to value by bringing together data science, software engineering and the business.
For example, as data scientists work to address unique business use cases with AI-driven machine learning prototypes, they must bear in mind that prototypes alone do not create value. Key business leaders are all too aware that they need to move quickly from experimentation to production in order to generate real business value. Case in point, a prototype model designed to detect credit card fraud is useless until it is tested and put into production in order to detect genuine, real-time fraud cases.
So why do some organizations struggle to achieve business value from AI-based machine learning or deep learning models?
The truth is that no hard and fast solution can be applied to every case. Many businesses lack a framework to process models and have to execute manual approaches to put each analytical model into production, one at a time.
Maintenance is frequently a problem for data scientists since, after a handful of models are put into production, data science teams spend so much time doing maintenance that they don’t have time to experiment and innovate. Models require a lot of attention and have to be constantly looked after, since they will degrade over time if they are not refreshed. Even after they are put into service, in order to remain competitive, models must be refreshed with new data to self-adapt and continuously improve.
Governance is another key challenge. Even if companies do manage to implement models, they tend to be produced and managed in silos, which makes it difficult for teams across the business to recognize potential business value. When one of these mission-critical models fails — without a commonly understood platform, data ingest pipelines and production processes — IT and data teams can run into trouble providing support.