The Importance of Data

Collaborateurs   |   Mel Erbes   |   8 oct. 2020

The concept of “data” isn’t entirely new. If you trace it back through history, you’d find that the earliest records date back to over 7,000 years ago when accounting was introduced in Mesopotamia to help record the trade of crops and herds. What appeared to be crude markings on clay actually served as an innovative method that transformed the way we run our economy to this day.


While data has been around for centuries, it’s interesting to note that 90% of the available data in the world was only created in the last few years thanks to the Internet of Things (IoT).

Whether it’s posting on social media or checking the weather, think about the vast amount of data you generate each day — it’s mind-blowing. So much so that it is no longer just “data” — it’s become “big data”.

Gartner defines it best: “Big data’ is high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight, decision making, and process automation.”

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just cannot manage them. But these massive volumes of data can be used to address business problems you would not have been able to tackle before.

So, by now, we must all surely be experts in working with and getting the most out of data, right?

Not necessarily.

Most of the data we generate today is semi/unstructured, which means it can come in any shape, size, or format. We then have to store it somewhere (beyond markings on clay tablets).

Globally, we have become masters at data collection — storing (or in some cases hoarding) vast amounts of data. According to EMC, in 2020 there will be around 40 trillion gigabytes of data or 40 zettabytes. For perspective, in 2010, this was only a “mere” 1.2 zettabytes.

It should come as no surprise that most companies only tap into less than 10% of the available data.

Barriers to Using More Data

  • Manual processes. Time analysts waste searching for data: 51%
  • Disparate data sources. Number of data sources accessed: 6 inputs, 7 outputs
  • Outdated technology. Number of tools used to perform data activities: 4 to 7 tools
  • Delayed time to insights. Decisions made with data: Just 48%


For businesses today, it has become a strategic imperative to both capitalize on the data economy and accelerate digital transformation.
According to a report by McKinsey, “capturing the most value from the wealth of potential data begins with excellence in identifying, capturing, and storing that data; moves through the technical capability to analyze and visualize that data; and ends with an organization that is able to complement analytics with the domain knowledge of human talent and rely on a cross-functional, agile structure to implement relevant insights.”

This is the foundation for Analytic Process Automation (APA).

Analytic Process Automation is the convergence of three key pillars — data, process, and people. Successful execution requires elevating data assets, analytics, daily business processes, and people towards business goals and outcomes.

By addressing data as the fundamental starting point, businesses can build a robust foundation that helps translate data insights into business value.

Traditionally, a myriad of tools would be required to discover data, prepare data, analyze it, and apply data science algorithms and machine learning. Additionally, the business process for the analytical workflow would entail manual handoffs between teams, breaking the “flow” and slowing down the ability to achieve meaningful outcomes. Analytic Process Automation is a comprehensive solution that unites those disparate activities into a single end-to end automation platform.

Driving insightful, action-based answers from data now becomes your daily vantage point to achieve strategic business priorities. It’s leveraging data analytics, data science, machine learning, and AI to create predictive analytics versus retrospective views.

How APA Solves the Data Problem

  • Simplifies self-service analytics, data science, AI, and ML via hundreds of code-free automation building blocks
  • Widens accessibility to data, analytics, data science, and process optimization to anyone in the organization without specialized skillsets
  • Delivers quick wins in hours or days, with actionable diagnostic, predictive, and prescriptive analytic outcomes

“The ultimate goal is to connect everything into one single platform for better management. Marketing data, accounting data, CRM data, operational data, and financial data can be linked up together to show a clear picture to us on how to improve our business. Alteryx is a key platform that enables us to automate the future.”

— East Asia Head of Digital Solutions, Yusen Logistics, Global Supply Chain & Logistics Provider



Learn how the convergence of analytics, data science, and process automation is driving digital transformation success.


See How Coca-Cola, PwC, and IDC Help Accelerate Digital Transformation with APA.

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