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.’

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.

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 – Uniting business for better results

AnalyticOps brings models into one framework and one environment so that data scientists are no longer maintaining one model at a time. With AnalyticOps frameworks, companies can schedule, manage, monitor and maintain tens, hundreds or even thousands of models at the same time. This level of automation is fuelling better services that can deliver speed in up-selling, improved customer experience and cost savings based on value across any number of business initiatives.

These frameworks also feed models new data so that they are continuously refreshed and able to self-adapt, providing dashboards out to the business, IT and analytical teams to ensure all departments have better visibility and understanding. As a result, teams are empowered to work towards common goals and business objectives.

With automation in play, AnalyticOps frameworks can also alert the right teams to become proactive in managing their analytics in production, rather than being reactive. Data scientists are alerted as models become stale; IT teams are alerted to the fact that models are taking longer to run; and business teams are alerted to sub-optimal results.

AnalyticOps enables data scientists to easily tell when a model is out of date and also demonstrate to IT teams whether models are running correctly. It also ensures key business stakeholders get visibility into the value those models produce. This is the level of collaboration and self-service capability that users across the enterprise are demanding today.

AnalyticOps – What does the future hold?

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 is making this level of scaling possible, presenting new opportunities to develop new product and service innovation faster than ever before.

Furthermore, the introduction of accelerators that are being developed by analytic experts are key to unlocking more efficient AI deployment through AnalyticOps frameworks. If organizations want to compete, they must become innovators of these new solutions in a fast-paced market where AI is positioned to dominate CEO decisions in the years to come.

Yasmeen Ahmad

Yasmeen is a strategic business leader in the area of data and analytics consulting, named as one of the top 50 leaders and influencers for driving commercial value from data in 2017 by Information Age.

Leading the Business Analytic Consulting Practice at Teradata, Yasmeen is focused on working with global clients across industries to determine how data driven decisioning can be embedded into strategic initiatives. This includes helping organisations create actionable insights to drive business outcomes that lead to benefits valued in the multi-millions.

Yasmeen is responsible for leading more than 60 consultants across Central Europe, UK&I and Russia in delivering analytic services and solutions for competitive advantage through the use of new or untapped sources of data, alongside advanced analytical and data science techniques.

Yasmeen also holds a PhD in Data Management, Mining and Visualization, carried out at the Wellcome Trust Centre for Gene Regulation & Expression. Her work is published in several international journals and was recognised by the Sir Tim Hunt Prize for Cell Biology. Yasmeen has written regularly for Forbes and is a speaker at international conferences and events.

View all posts by Yasmeen Ahmad

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