This is a guest blogpost by Dave Elkington, CEO, Insidesales.com
The phrase "predictive analytics" has become a trendy buzzword that seems to show up in every investor pitch in order to elicit premium valuations. Even legacy software giants, like IBM, Microsoft and HP, are investing or reinvesting in the space and jumping on the bandwagon.
Interestingly, predictive analytics is not as new as these companies would lead you to believe. In fact, it's just a rebrand of a branch of computer science that has been around for more than 50 years, machine learning. What's even more important to understand is that machine-learning algorithms are not the driving force behind the big data revolution.
real hero of this story is data. The explosion of data began with the advent of
the mainframe in the 1950s and has grown in significance thanks to today's
massive cloud-computing platforms coupled with big data storage systems.
Machine learning and mainframes
To understand the convergence that led to this revolution, it's important to note two major developments that occurred in the 1950s: Machine learning emerged from attempts to make computers act like humans, and companies began to use mainframes to collect and analyse data.
In the 1950s, Arthur Samuel (at IBM) developed the first machine-learning game system to simulate a person playing checkers. As early as 1959, advanced machine-learning algorithms were being used to solve real-world problems like when an artificial neural network was used to remove echo from phone conversations.
the mid-'50s, the IBM mainframe was born. The mainframe pulled data out of
filing cabinets, creating a central repository. During this phase of enterprise
software, the amount of data remained relatively small and access to this data
was extremely limited.
Fast forward to the 1980s, when client-server platforms emerged. These platforms, developed by organisations such as Sun Microsystems and HP, decentralised business applications and distributed them within each enterprise that used the system. The amount of data exploded because it could now be collected from multiple sources throughout the company.
While client servers improved the aggregation of data, they still faced significant limitations. Access to the data remained constrained within a company's networks. People pushed the boundaries of these limits through extensions of internal networks using secure value-added networks. Using general-purpose electronic data interchanges (EDI), these networks introduced inter-enterprise communications and data sharing.
EDI was also
the beginning of the important parallel process of normalising data sets and
classifying data communications between enterprises. The challenge was that
each company had to build a custom value-added network with each major
customer, partner or vendor.
Cloud computing and SaaS
solutions acted as a precursor to cloud computing. Hosted solutions made servers
available at colocation sites and provided open access through the Internet.
In the 2000s, cloud computing brought yet another phase of application delivery and access to the data stored within the applications as companies like Salesforce.com, Omniture and Workday began to provide software-as-a-service (SaaS). The cloud completely centralised data and offered ubiquitous access.
Cloud computing also made multi-tenancy possible. One way to understand multi-tenancy is to think of it as renting an apartment. Renting an apartment is cheaper than renting a house, which is akin to the client-server model, because you are sharing core infrastructure, like plumbing and electrical wiring, with other tenants.
advantage of multi-tenancy in enterprise software is that it not only centralises
an individual company's data but also consolidates data across multiple
companies. This creates the need for massive databases capable of storing data
from thousands and even millions of companies - hence the name, big data.
The big data phase brought MapReduce, document data stores to the enterprise cloud-computing vendors. Companies like Cloudera, MongoDB, Couchbase, Hortonworks and MapR commoditised databases that could accommodate billions of records with complex, non-standard relationships.
This new method of storing massive amounts of data was just what machine learning needed. Enterprise software vendors have employed data scientists to figure out what to do with all of the data they are collecting.
development is now coined the "predictive analytics" phase of enterprise
software. It materialised because cloud computing enabled mass consolidation
and universal access to the enterprise applications they delivered, and because
big database vendors made it possible to store massive amounts of data in a
Multi-tenancy not only brings together data across multiple companies, but it also spans domains and industries. This opens exciting new opportunities and will usher in the next phase of enterprise software.
A large number of applications are already consolidating data within specific domains, such as healthcare, dating, travel, consumer goods and anything else you can imagine. However, this data only represents a small slice of life and fails to capture the full picture.
You can't accurately predict who somebody will want to date if all you have is car-buying data, and you can't predict how many pizzas they'll eat this year - and when they'll eat them - if all you have is bowling shoe data, although it would be pretty cool if you could. You need to collect data across domains and put it into the appropriate context.
That's why predictive platforms represent the next generation of enterprise software. Predictive platforms will assemble data from CRM (customer relationship management), ERP (enterprise resource planning), the Internet of Things and other domains and systems to make real-time predictions based on a complete view of the real world.
The futuristic world depicted in Sci-Fi movies isn't as far off as some people think.
Dave Elkington is CEO and founder of InsideSales.com, a cloud-based sales acceleration technology company.