Two Decades of Evolution in Data and Analytics

9 September 22

Technology-driven data analytics has a longer history than you might realize–dating back to at least 1958 when IBM employee Peter Luhn published “A Business Intelligence System.” But what we know as data sciences today focuses more on modern applications evolving over the last two decades.

We’ll start with the initial drive for business intelligence that popped up in the late ‘90s through the early 00s. It was a time when database administration was accessible only to trained IT professionals. And it took weeks to query results for a single question. All the way through today, where real-time data streams power ultra-efficient automation.

Let’s take a look at the evolution of data and analytics.

A Demand for Business Intelligence

The initial wave of digital transformation put a personal computer in front of almost every administrative computer. This technology access was the first step in a data-driven future. As businesses achieved new capabilities–collecting and storing consumer and product data, the demand for business intelligence grew.

Business intelligence can provide:

  • Insights on profit and loss data help direct strategic decisions for organizational leadership.
  • Marketing campaign data to build more efficient funnels, selling more products or services without overspending on advertising activities.
  • Build strategic sales relationships based on the cost of customer acquisition or the lifetime value of a customer.
  • Optimize supply chain efficiency and manage supplier relationships with appropriate feedback.

During the early 00s, a lot of data was being collected, but most organizations struggled to find efficient ways to use the data that they collected effectively. The quality of data collected wasn’t always useful, and even when it was, it wasn’t accessible when it would be the most useful. That spawned the development of new technologies in data sciences focused on making data useful.

The Arrival of Big Data

The call for better data processing tools to handle the volume of data was heard around the globe. An entire community of individual developers launched the “Open Source Community,” which supplemented private developments in data sciences side-by-side.

The arrival of ‘big data’ brought new possibilities like:

  • The ability to measure brand affinity for marketing purposes.
  • Remote monitoring capabilities for healthcare devices, energy, and more.
  • Complex pricing models that adapt to changing market demands.

While big data made big promises for forecasting or predicting future conditions, the early solutions were a no-frills approach that lacked customization. Early adopters of big data technology had to adapt to fit the technology instead of the other way around.

The Connection of Data-Enriched Solutions

As the need for custom solutions evolved, the specialization of data sciences companies that offered to help connect companies to personalized data solutions that were 100% unique to their business also grew.

  • Property data insights are built into a custom dashboard for insurance providers.
  • Location-based data insights built into advertising platforms.
  • Enriched data insights to help marketers build out customer profiles to serve up personalized marketing.

Data-enriched solutions put the right data in the right places, setting the stage for new technologies to drive efficiency.

The Era of Data-Driven Automation

With data now a central component of many organizational cultures, there is hardly a line between business strategy and technology strategy. What began as a push to collect as much data as possible has now evolved into a tech-enabled approach to fueling efficient automation throughout an organization.

Data-driven automation opens the door to:

  • On-demand Optimized Pricing Models
  • Reporting Automation Can Improve Compliance and Increase Speed-to-Market
  • Paperless Administrative Processes
  • In-Line Quality Control for Manufacturing

This new era of data-driven technology is transforming the world of work. We’re in the middle of a shift towards automation that will put machines at the center of efficiency, and humans at the heart of innovation. While some worry about an inevitable man vs. machine scenario, the more likely future is a world fueled by new intellectual capacities. The days of the ‘starving artist’ are over; with automation in place, we can celebrate creativity.

Final Thoughts on the Evolution of Data and Analytics

Instead of spending time, energy, and money fighting against artificial intelligence or machine learning–or worse, being too cautious and always one step behind–now is the time to embrace tech-enablement. It’s time to adopt a data-centered culture. It’s time to explore what custom tech stacks can do for your organization. It’s time to make a meaningful investment in digital transformation.

Datalysys is your go-to for personalized, custom tech solutions. Our data scientists, data engineers, and catch-all technophiles can help you find and use the right data to power highly effective, tech-enabled automation.