Data and analytics continue to be a top investment priority for business and technology leaders in 2020, with nearly 70 percent of global enterprises planning to increase their analytics spending this year. Undoubtedly good news for everyone in the data domain–it’s just that the analytics scenario within organizations will not be very simple as we advance.
Many existing analytics and data projects are big in size and scope while highly ambitious about results. But, if we probe deeper, we will see a different picture.
According to Gartner’s last year estimation, almost 80 percent of analytics insights will not deliver business outcomes through 2022. About the same number of AI projects will likely not surpass the experimentation phase this year.
What has gone wrong? The answer isn’t necessarily provided by technology. Organizations need to adapt to the changing data ecosystem in the coming days and prepare to convert the growing amount of data into an opportunity rather than a problem.
That isn’t going to be very easy.
The mounting challenge called data
There are a number of reasons behind failed data projects – siloed approach, lack of vision, skills gap, and many more. But one of the most prominent challenges organizations face today is the explosion of data, leading to massive pressure on organizations due to space crunch and the inability to store data for practical analysis. To add to this stress, unstructured data is growing unprecedentedly. Consider the case of IoT, for instance.
IDC estimates that 41.6 billion connected IoT devices will generate nearly 80 ZB of data in 2025! This is certainly huge and presents a massive challenge for organizations. Video surveillance applications will generate the bulk of this data, while industrial and medical devices will add to the growth.
This is in addition to traditional unstructured data sources like text documents, voice recordings, videos, and social media posts. As much as 80 percent of the enterprise data is unstructured. Connected systems and devices will be inevitable components of organizations’ digital transformation journey, and so will the growth of unstructured data.
Converting these growing data sets into tangible business insights will perhaps be the greatest undertaking for organizations in the new decade.
The scale-up conundrum
Often while working with large data sets, traditional approaches, and legacy systems come in the way of arriving at the desired results. These approaches will likely shift in 2020 because data sources are now highly scattered, and new applications are emerging —behavior analysis, fraud prevention, or real-time edge analytics.
As the volumes of data go up, will the existing systems be able to manage and scale up? How will the present technologies and systems work on the ever-growing unstructured data sets? These are some critical questions that need answers in 2020.
For some of these answers, organizations are turning to the Public cloud, which is emerging as the de-facto standard for storing and processing vast amounts of data. Cloud-based analytics effectively addresses critical challenges like storage, scalability, and elasticity for enterprises, ensuring tighter integrations and centralization of data lakes. Most importantly, organizations do not have to worry about future data growth and unanticipated demand on a cloud model.
The trend in the market is quite evident today. Organizations on the verge of starting an analytics journey are already considering a cloud-first deployment. And those with existing on-premise systems are going for a hybrid deployment model rather than a mere capacity extension.
As organizations look at supercharging their data and analytics projects with AI, cloud will be a strategic investment for most organizations this year.
Cloud is predicted to change the whole data dynamics in the coming days. The fate of a data analytics project –whether it will be a grand success or fall flat—will depend primarily on how effectively cloud fits into the whole equation.
By Amit Gupta, Co-Founder & CEO, Rapyder Cloud Solutions.
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