From automated machine learning to data visualisation and explainable AI, data and analytics leaders need to leverage these trends.
Technology is an enabler for real-time business transformation. Behind every Jeff Bezos with a cashier-less convenience store, there is sophisticated big data analytics developed by an army of clever data scientists who’ve turned a vision into reality. Big data analytics enables teams to handle and analyse the data, which comes in various forms, and employ it to uncover new opportunities and enhance products.
Here’s a look at some of the key trends that will influence how data and analytics will be used this year and soon.
The Rise of Automated Machine Learning
AutoML is changing the face of ML-based solutions by enabling people from diverse backgrounds to evolve machine learning models to address complex scenarios.
It is popular technology, we will see even more interest in 2021, coupled with more companies moving to evaluate and use the tools.
It has provided significant breakthroughs in financial services, healthcare and retail by increasing productivity by automating repetitive tasks, thus enabling a data scientist to focus more on the problem rather than the models. Automating the ML pipeline also helps to avoid slips and lapses, and, most importantly, it is a step towards democratising ML by making the power of ML accessible to everybody, even to people with no expertise in this field.
Japanese shopping app Mercari has been using AutoML Vision for classifying images. While Mercari’s own model achieved an accuracy of 75 per cent, AutoML Vision in advanced mode with 50,000 training images achieved an accuracy of 91 per cent.
It is very likely AutoML tools will be increasingly adopted by companies, invest heavily in building and buying AutoML tools and services, especially those that find it challenging to hire data science experts or want to have more efficient data science teams.
Also Read: How the Role of Data Scientists evolved amid COVID-19
The New Holy Grail is Machine Learning Operations
Born at the intersection of DevOps, data engineering, and ML, Operationalised Machine Learning, or MLOps, is becoming popular among large financial services and tech firms.
It is a discipline for collaboration and communication between data scientists and information technology (IT) professionals while automating and productising ML algorithms. MLOps methodology includes a process for streamlining model training, packaging, validation, deployment, and monitoring to run ML projects consistently from end-to-end.
A report by Cognilytica found the market for ML platforms will be going up to $126.1 billion by 2025, and there’s going to be an accelerated consolidation of the ML platform market with acquisitions, mergers, and IPOs. As ML platforms will expand, there will also be increased MLOps capabilities within existing ML platform solutions.
MLOps address common business concerns such as regulatory compliance, enable reproducible models by tracking data, models, code, and model versioning, and package and deliver models in repeatable configurations to support reusability.
MLOps means faster go-to-market times and lower operational costs.
It helps managers and developers be more agile and strategic in their decisions, serving as the map to guide small teams and even businesses to achieve their goals no matter their constraints, be it sensitive data, fewer resources, and small budget MLOps are practices that are not written in stone, and so businesses can experiment with different settings and keep what works for it.
Batman of data-driven organisations is Data Science as a Service
Batman is a human, but when aided by technology, becomes powerful. Likewise, the data-driven organisation has some very human matters to address before moving forward, especially the ones that want to reduce operational costs during the ongoing economic crisis or maintain the efficiency of services or quality products. Data Science as a Service (DSaaS) is the way to go, it is the outsourcing of data science activities to an external provider.
It is mostly a cloud-based delivery model. Different tools for data analytics are provided and can be configured by the user to process and analyse enormous quantities of heterogeneous data efficiently.
It’s particularly useful for temporary work, sudden or peak workloads, or standardised tasks like running analysis on monthly or quarterly reports.
There are multiple data science consulting firms, startups and even bigger cloud platforms that provide DSaaS offering in varied forms. As a part of DSaaS, meticulous delivery of production-ready predictive models and data analysis can be generated using mature methodologies.
Also Read: Real-time Data Analytics Predictions for Businesses
Explainable AI Continues to Expand
Models like ensembles, neural networks, called black-box models, are getting more advanced and becoming harder to explain.
Hence, explainability is becoming one of the first concerns of companies because people are becoming more aware and sensitive to the potential of AI-based solutions. It is a mainstream need now as data scientists work to create trust across many stakeholders in the organisation, regulators, and end-users and better understand their models. Explainable AI is driven by issues ranging from unfair bias, model instability, and regulatory requirements.
Researchers are shining a light on the unconscious inherent biases in the data fed to AI systems that humans program. Case in point: The controversy around the Apple credit card, which was said to be biased against women. Although there is no evidence that the algorithm is sexist, a lack of transparency has been a recurring theme.
It’s not just a drive toward being more ethical but about being able to showcase where decisions are being made and how they’re being made. Making it explainable makes it more acceptable to anyone involved or affected by the algorithm. Plus, it mitigates regulation issues and liability and improves governance.
This trend was strong in 2020. By all indications, 2021 will see even more focus, as the emphasis on transparency is driven in part by provisions in legislation like GDPR that require companies to offer consumers the right to ask how AI tools make decisions about them.
The New Architecture for Support is Data fabric
As data becomes more complex and digital business accelerates, data fabric is the architecture that will support composable data and analytics and its various components. A comprehensive data strategy is crucial to run a multipurpose system that takes full advantage of the value of data, bringing useful applications into production promptly. Analysts, developers, and data scientists can work with a comprehensive and consistent collection of data and add new data sources without either breaking the bank or overwhelming IT.
According to Gartner, data fabric reduces the time for integration design by 30 per cent, deployment by 30 per cent and maintenance by 70 per cent because the technology designs draw on the ability to use/reuse and combine different data integration styles. Data fabric can also leverage existing skills and technologies from data hubs, data lakes and data warehouses while introducing new approaches and tools for the future.
A comprehensive approach makes it possible to optimise resource use by avoiding duplications of hardware or system administration and simplifying how people architect a solution. For this to happen, a data fabric must first have a global namespace, where all data must be available through the single, consistent global namespace, second, it must have multiple protocols and data formats, and automatic policy-based optimisation of storage and access, and third, it should have a rapidly scalable distributed data store.
Also Read: Top 7 Data Visualisation Tools
Bringing New Meaning to Data is Data Visualisation
Traditionally, communication between machines and humans is carried out by graphs, charts, and dashboards highlighting key findings. Still, not everyone can spot a valuable insight in a vast pile of statistics. New ways of communicating these findings are constantly evolving, empowering organisations to act on data-driven insight.
Amid the current global health crisis, data visualisation tools have provided visuals of the new infections, fatalities, recoveries, and the “flattening of the curve”.
With cognitive frameworks and multidimensional imaging and intelligence, data visualisation enables users to see large amounts of complex data. It helps businesses in their decision-making, identify the core aspects impacting business results, and forecast future trends and patterns.
Gartner forecast that, by 2025, data stories will be the most common way of consuming analytics, with 75 per cent of the stories automatically generated using augmented analytics techniques.
