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HOW COMPLEX DATA GAVE RISE TO DIGITAL-FIRST COMPANIES

August 9, 2021

JESSE NOYES

VP of Marketing



It was supposed to be more fun than this. 


Big Data — sweet, rich, untapped data — was supposed to be the great equalizer.
McKinsey declared it the “next frontier for innovation, competition, and
productivity.” Every organization would have unparalleled insight into the
opportunities and risks within their business.


A decade later and the signs of frustration are mounting. A recent report found
that only 41% of firms are competing based on their analytics, while 39% are
managing data as an actual business asset, and merely 24% claim to have built a
data-driven culture.


What happened? How did the big promises of Big Data turn into lackluster
results?


There are no shortage of databases, dashboards or even alerts. Anyone who wants
to visualize the state of their business can. But what do you do when those
visualizations fail to show you what to do next??


All the dashboards and prediction algorithms in the world are ineffective unless
they produce actual money either flowing into or kept by the business.
Ostensibly, that’s what Business Intelligence and Analytics (BI&A) is for. 


But stand up in a meeting armed with only charts that say “sales are down,” and
you quickly recognize there’s no direct line to ROI. At the end of the day,
analytics are about searching for answers to questions you’ve thought to ask,
not about turning data into dollars.


That is changing. AI, particularly unsupervised learning, has emerged for the
masses to provide a different way to analyze even extremely complex data. This
is leading to the rise of a new category of analytics — Data Capitalization
Management (DCM). Unlike traditional BI or analytics, DCM platforms are focused
on the tangible, realizable value for the businesses using them by automating
the analytics process . 



THE COMPLEX DATA EXPLOSION

Let’s go back, way back.  One of the earliest and still widely used analytic
tools, the spreadsheet, was invented in the era of the floppy disk with a
whopping 1.44MB storage capacity. Fast forward a few years to business
intelligence reporting and dashboard tools that arrived when 25GB of storage was
revolutionary.  


The exponential growth of data makes those days seem quaint. How much data
depends on who you ask, but let’s go with IDC’s projections that more data will
be created between 2020 and 2023 than in the previous 30 years.


That kind of volume is hard to contemplate, much less analyze. Yet, volume isn’t
the problem. It’s part of the bigger problem - data complexity.


As more channels and data sources have come online, the vast majority of the
data generated isn’t well-structured or well-understood. Most of the world’s
data is unstructured from text, audio, images which is incredibly complex.


Plus, it’s spread out. The average company is pulling from 400 data sources.
That’s average. Many are spread out across 1000 data sources.


Buried in the data are the insights needed to not only run the business, but to
change the business. You’d have to dig through an exponentially growing layer of
complexity to find insights— one by one. 


Using traditional AI and tools, this has largely been done by an overworked and
under-resourced centralized data team. Most of their time is spent on preparing
the data. As much as 80% of data scientists time is spent on massaging the data,
which just so happens to be the part of the job they find least enjoyable. 


This generates a frustrating scenario for most organizations: the data teams are
frustrated with the high quantity of demands from various teams and business
teams are frustrated with the lack of insights to act on.


That’s bad enough. As data complexity exploded, something else was happening.


THE RISE OF THE DIGITAL-FIRST COMPANIES

It’s tempting to say the data complexity problem is a universal problem. The
reality is that a select few organizations got ahead of the complex data curve.


These high-performing companies saw what was coming, and invested deeply in
turning that complexity into their competitive edge within their respective
markets. These weren’t companies with huge digital transformation projects in
play; they were the ones transforming the digital world. And they’ve greatly
outperformed the market.


Digital-first companies built a culture around uncovering hidden insights buried
in the most complex data, permeating the entire organization — from which
products they make and where they sell them to how they market and how they hire
— with insights that guide human decision making, not replace it.


What gave them that leg up? Did they have better tools? Well, not really.


They created armies of data workers. There are more than 1,000 open data
scientists roles at Facebook currently posted on LinkedIn. There are thousands
of data scientists at Microsoft and Amazon, according to LinkedIn.


The digital-first advantage is not purely due to resources. It’s cultural. These
are organizations that look at complex data as a business asset, rather than a
liability where most data sits unused with the majority of the investment going
into managing, storing and securing it.


The investments in data science are difficult for even large enterprises to
match. But it’s the culture of ensuring each team — sales, marketing, supply
chain, finance, customer experience, etc. — have real-time access to truly
actionable insights that holds even the best intentioned organizations back.

There needs to be a different way.



THIS IS WHERE DATA CAPITALIZATION MANAGEMENT COMES IN.


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