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Why Data is Critical to ‘Transform to Digital’

Rob Kim Transform to Digital Blog FI 2

Transform to Digital

The worst kept secret in the business world is the rapid disruption of traditional workplace and workforce norms, accelerated of course by the ongoing pandemic. There is no shortage of blog posts and think pieces written that compel organizations to digitally transform their business and operations to keep pace.

Yes, technology is emerging as a true business partner, as executives across sales, finance, marketing, and other departments lean on their IT counterparts to innovate. Most organizations are even embracing a software engineering mindset, as containers and low-code options are democratizing the development space.

But, often overlooked in this digital arms race is the building block of any business transformation – data.

While much of the buzz around digital transformation has been associated with cloud-native development methods, the most visionary organizations are realizing that the true gold can be found by mining their data to extract unique informational insights.

It’s not easy, however. To uncover these unique informational insights, organizations must modernize the way they store, process, and access data. Databases, data warehouses, and even so-called modern management constructs like Hadoop and Master Data Management present velocity challenges and can be overbearing to manage and maintain.


Data’s Time to Shine

Just as traditional software development lifecycle (SDLC) and waterfall development was disrupted by agile and interpretive programming methods that utilize microservices and flexible APIs, it’s now data’s turn to “flip the script.” By trading in old methods of processing and analyzing data for a more low-code and business-centric approach, non-technical users gain quicker access to insights.

For example, hyper-automation techniques including Robotic Process Automation and increased availability of commoditized cognitive services means that organizations can build smarter, more responsible and scalable AI use cases with shorter time-to-value and little to no reliance on data expertise.


3 Data Visions– Smarter, Faster, More Responsible


Vision #1 – Imagine algorithms that get smarter by learning from other algorithms, becoming more adaptive and requiring smaller volumes of data.

It is estimated that by 2025, 70% of organizations will be forced to shift the focus away from large volumes of data, to small and wide data, with more context for analytics. This will make AI less data hungry and more turnkey for business users.


Vision #2 – What if these analytic platforms could also be more responsible? Building upon the smarter algorithms, they can reduce bias and differential privacy in an ethical manner. In essence, systems would be able to automate the normalization and cleansing of data, which has traditionally been a very manual and time-consuming process.

In fact, through 2022, manual data management tasks will be reduced by 45% through the use of commoditized and nested AI combined through automated methods and guided by policy.


Vision #3 – Finally, by requiring less data for precision performance and relying on more metadata insights as decision-making moves “up the stack,” AI can be applied more broadly throughout the business with little technical assistance required.

In just two years, it is predicted that 60% of organizations will compose a data fabric from three or more analytics solutions, including traditional platforms, to build decision-oriented apps directly integrated with analytics that pull insights to action.


As more data is modernized, the additive effect of combining the new data constructs to an already mature cloud-native development capability can really accelerate innovation and value to the point where business leaders will say, “I had no idea you could build that – and build it so quickly.


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