Numerous enterprises now recognise the value of implementing analytics. Reports indicate that the global “analytics as a service” market size is likely to touch $12.1 billion by 2024, which represents a Compound Annual Growth Rate (CAGR) of 23.2 percent from 2019.
Several factors are driving this growth. These include greater adoption of IoT devices, the growing volume of organisation data, as well as the growing uptake of analytics solutions that help businesses improve decision-making.
Analytics benefits organisations by helping them enhance their decision-making abilities significantly. It also helps them formulate accurate business models whether they are month to month or quarter to quarter. It can help considerably improve operational capabilities.
But getting the expected details from data can be reasonably complex. The data journey typically starts with the capturing of data, cleaning and processing it for modelling, training data, and then ultimately using it to conjecture demand. These processes can be relatively time- and resource-intensive, taking months to have a multitude of the right talent to come up with a reliable solution
Fastest and most accurate operations solutions
Predictions are based on historical data and rely on data-capturing strategies to verify patterns, and then test assumptions. Nevertheless, demand patterns are likely to change based on factors such as unexpected weather conditions, untoward incidents, political developments etc.
The recent coronavirus pandemic is a case in point. COVID-19 has forced several countries into lockdown, affecting production, manufacturing, supply chain, and logistics, and impacting organisations and businesses across sectors around the globe. It would be accurate to say that any plans or predictions made before the pandemic simply won’t make sense in the changed global realities. This drives home the importance of insights needing to be dynamic and timely.
Having said that, use cases are abundant for the application of analytics insights. The most popular example is probably that of Netflix and its ability to provide viewers with the most relevant recommendations on shows that they might enjoy.
A retailer might use predictive analytics for demand forecasting, i.e. understanding which products are likely to be in demand for the coming quarters. For instance, an Amazon gift card loyalty programme might use predictive analytics to conclude whether they could increase reward reclamation by 20 percent and spend 10 percent more on groceries for frequent buyers. Amazon could predict that grocery buyers are more prevalent in the market and, based on past reclamation rates from other specials, would raise reclamations in-line with that conjecture.
Predictive analytics aids in having an expert forecast of products at the lowest granularity, informed decision-making on collateral, which geographic markets and demographics to target – all while ensuring timeliness of the insights.
For instance, we’ve all seen the unprecedented rush to stock up on hand sanitisers and liquid soap in the face of the coronavirus crisis.
Automation and AI for the last mile of analytics
Automation and Artificial Intelligence (AI) are exploding into our daily lives in unimaginable ways. By enabling the automation of redundant tasks and using the technological prowess that AI brings to the table, enterprises can use the potential of analytics in a sustainable, timely and reliable manner.
Over the last few years, AI/ML-driven solutions have made it easier than ever to automate insights for business users. AI can simplify the process of connecting all the data produced within an organisation. It can also help data professionals understand the data to determine how it can be best leveraged. We can call this the last-mile analytics approach.
When AI is used in conjunction with analytics, it offers several avenues for generating new solutions to stimulate higher revenue. For instance, it can help with sales planning, marketing planning, and deciding which new products to launch.
The last-mile analytics approach can help make the data journey much more efficient by acting as a bridge between the technology expert and solutions expert.
Last-mile analytics can prove to be a one-stop solution for enterprises to reliably adopt AI analytics for their organisation in a manner that is efficient, accurate, sustainable, and timely.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)
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