Data Analytics: We’ve Come a Long Way, Yet There is So Far Still to Go

Since the advent of IBM’s punch cards in the 1920s, the information technology industry has fostered improved decision making through data analysis. But the pace of progress has accelerated dramatically over the last decade, with storage costs dropping at an incredible pace (the price for a gigabyte of storage has fallen 99.5 percent since 2000), and internet connectivity becoming essentially ubiquitous. These developments have enabled a massive increase in the amount of data generated and captured by an increasingly diverse world of devices. In fact, more data is generated now on a daily basis than was produced in the entire year 2000 (EMC).

It is now entirely possible for a business to perform very extensive and complex analytics across almost infinite data sources, which has opened up the potential to ask a wide variety of questions that were not possible before: What if our organization could combine quantitative with qualitative information? What if we could look at all the data, not just samples or averages? What if we could get results/reports in near real-time? What else is possible?

But while recent data capture and storage rates have grown with extraordinary speed, improved techniques for data analysis have lagged. With the overwhelming amounts of data produced, decision makers often find it difficult to distill the data signal from noise. According to CIO Insight, 41 percent of global executives admit to being either somewhat or completely inadequate at collecting and analyzing data. A recent Gartner survey reveals that 56 percent of IT executives feel that determining how to extract value from data remains a major challenge.

These hurdles have not deterred interest in “Big Data.” 63 percent of IT executives believe that allowing workers to harness data will help their companies to make better and faster decisions (CIO Insight). However, 55 percent of all “big data” projects fail because organizations cannot figure out how to connect the dots or derive true insights that will be meaningful to their business. But while this hard work can be frustrating and unsuccessful at times, it is worth the effort. At Ascent, we have witnessed the power of well harnessed data analytics for many years now, with one current example in ClickFox.

At our B2B IT Forum in September that focused on data analytics, ClickFox COO Bill Hawley explained how the company can analyze cross-channel customer behavior trends and issues across all service touchpoints of a business. ClickFox can then generate powerful insights on how to drive new revenues and reduce costs, saving major brands more than $100 million annually, he said.

With so much progress and the potential for such tremendous value, why are companies either slow to adopt or failing to execute on advanced data analytics projects? Because, in short, it is a tough nut to crack. And that is one of the reasons “big data” is so attractive from the perspective of a venture investor like Ascent. We believe the inherent challenges mean new innovative solutions are required, making it ideal for nimble startups to tackle. Those early stage companies that show evidence of scalable solutions are a terrific fit for venture financing.

At Ascent, we have had the good fortune to work with some of the leading emerging vendors in this category. On the infrastructure side we have invested in and work closely with companies like Interactive Supercomputing (acquired by Microsoft), Terascala and ScaleBase. Each addresses a different part of the overall Big Data challenge, and each enables analyzing data in increasing volumes and at increasing speeds.

We also have considerable experience on the application side of data analytics, working with companies such as ZoomInfo, Bizo, Cymfony (acquired by TNS), Network Intelligence (acquired by EMC), ClickFox and CargoMetrics. We continue to see major opportunities for these types of companies: solution providers that pull data sources together – sometimes public, sometimes proprietary – and then clean, filter, normalize and analyze it all before presenting it to customers in ready-to-digest form.

And the opportunities continue to grow. While many companies are still trying to figure out how to achieve success with analytics, the potential sources of data continue to expand rapidly. One area where we expect dramatic increase in data volumes is the Internet of Things. Machines, devices, sensors, and even household items such as coffeemakers or thermostats are becoming integrated. These connected devices are expected to grow to more than 25 billion by 2015, enabling these “things” to communicate with other objects and yes, people, within the next few years. While this has implications for individuals – you will know where your children are at every moment, and also what the temperature is in your living room – it is also an enormous opportunity for businesses. Imagine the knowledge that can be gained and the new services that can be created around such rich data.

Today Ascent remains focused on new opportunities to harness data in previously impossible ways that combine the volumes of new data sources with new algorithms and analytics, to drive unconventional and deep business insights. It’s the kind of intelligent analysis that it hard to do well but can provide an organization serious strategic advantage.