Splice Machine Digital Transformation 2.0 white paper

Digital Transformation 2.0
An Application Modernization Journey
WHITE PAPER

3 | Digital Transformation 2.0 An Application Modernization Journey
EXECUTIVE SUMMARY
Oxford Dictionary defines transformation as a “marked change in form, nature, or appearance.” Businesses undergo
similar transformations with significant advancements in technology. For example, the invention of the production
line fundamentally transformed the productivity of companies. The use of digital technology or computers was
another transformation that allowed businesses to digitize their data for immediate access and sharing across the
organization. Businesses will not fully realize the promise of digital transformation until they inject their legacy
applications with intelligence and new sources of data.
We are now in the midst of such a transformation. AI, Big Data, and
advances in storage and computing capacity have delivered the
capability for big gains for businesses and consumers alike. Most
companies haven’t realized these gains, though. Most companies
have migrated their infrastructure to “the cloud” and claimed their
transformation complete. This is misguided. Cloud migrations
undoubtedly provide enterprise agility but their ability to provide
overall cost savings is less clear cut. Up-front capital costs are
often simply shifted to ongoing operational expenses that don’t
contribute significantly to overall business value or cost savings.
More importantly, until businesses modernize their custom-built
applications that represent their unique intellectual property to be
enriched with new sources of data and “intelligence”, they will not
fully realize the promise of digital transformation.
In this white paper, we walk you through the various cycles of digital transformation and then outline an objective
approach to make your custom-built applications agile and infused with intelligence. This allows your apps to
utilize new and more substantial data sets as well as apply artificial intelligence and machine learning to take
in-the-moment actions. This approach delivers demonstrable gains for companies and the clients they service.
DIGITAL TRANSFORMATION 1.0
About 30 to 40 years ago, with the advent of the mini-computer, businesses began to transform themselves by
converting their manual processes into automated ones. The accounting business ledgers and bill of material
records began to find their way into computers to be efficiently stored, accessed, and shared within the organization.
The widespread adoption of the PC and client-serving computing made this ubiquitous in the 1990’s. At the end
of the 1990’s the internet exploded, companies raced to the web with new e-commerce business models, and
most recently in the 2010’s mobile computing exploded. This automation represented the first wave of digital
transformation for businesses - Digital Transformation 1.0 if you will.
Throughout this journey, a question that many businesses faced is to build or buy software to give them a competitive
advantage. Companies apply this criterion to two different classes of software. In the first category is the software
that companies use to automate their support functions. These include accounting, finance, inventory management,
human resources, sales, etc., - software that is essential to running your business and transferable across companies
but not necessarily a source of competitive advantage. It is now estimated that on average a large enterprise has
about a couple of hundred applications ranging from billing, payroll, supply chain and security.
In this white paper, we
walk you through the
various cycles of digital
transformation and then
outline an objective
approach to make your
custom-built
applications agile
and infused with
intelligence.

In the second category is the software that companies needed to automate their core operations - activities
that were unique to each company and represented the source of its competitive advantage. For a bank or an
insurance company, this may be the underwriting process to approve or deny loan and insurance applications,
or for a manufacturer, the supply-chain planning that forecasts demand, plans production, and promises orders
to customers, or for a telco, utility, or network of oil platforms, the process of monitoring and servicing capital
assets to avoid outages. There are hundreds of business processes in enterprises that are highly customized
and unique to how the company does its business. We call the software that automates these unique business
processes purpose-built applications. Purpose-built applications are an integral part of any company’s mission-
critical business processes. They are the crown jewels that differentiate a business from others in its industry. See
below a list of banking business processes that can be automated using purpose-built applications.
Cross-Sell/
Up-Sell
Personalized
Marketing
Risk
Profitability
Analysis
Customer
Onboarding
Customer
Lifetime
Value
Credit
Risk
Customer
Churn
Dispute
Resolution
Anti
Money
Laundering
(AML)
Fraud
Detection
Prevention
BANKING
Purpose-built applications are an integral part of
any company’s mission-critical business processes.
They are the crown jewels that differentiate
a business from others in its industry.
Figure 1: Partial list of typical custom business processes for banking.

For the first category, a majority of the companies decided to invest in enterprise software packages, commonly
known as enterprise resource planning (ERP) or customer relationship management (CRM) suites available from
companies like SAP, Oracle, Salesforce.com, among others. These software suites also came with basic functionality
to report on the data they collect and provided a set of pre-built reports as a starting point for management reporting.
For custom applications, businesses either built them from
scratch or bought off-the-shelf software and then customized it
significantly to meet their specialized requirements. Each of these
applications was accompanied by operational or OLTP database
that powers fast lookups, inserts, and deletes of data. Additionally,
these operational databases are often the ultimate source of truth.
They possess capabilities, like transaction semantics, that ensure
that they can be trusted with tracking data that has immediate
real world value. Inventory management, financial records,
customer reservations, etc. are stored in these kinds of systems.
As managers begin to ask more sophisticated questions about
their businesses, a new piece of infrastructure appeared in the
IT landscape - an enterprise data warehouse or EDW or Data
Mart (DM) for analytics. The EDW provided a decision support
mechanism that brought data from many sources, including ERP,
CRM, and purpose-built applications into a centralized repository
and Data Marts were specific to an area of the business. These workloads required new storage and compute
methods to perform analytics across large volumes of data versus the lookups and updates performed by operational
systems. The type of questions that an EDW or DM would answer include “what was the best selling product last
month?” or “who were my top 20 customers based on the order size?”. Data warehouses and data marts consumed
the data produced by purpose-built applications and became an essential part of the IT infrastructure.
It is now common to have two separate types of platforms – an operational database for an application and an
analytical data mart for that same app. An operational database powers the application and the analytical data mart
provides visibility into and reports on business performance. But since these technologies are built for different
purposes, organizations need a team of expensive data engineers to duct tape transactional databases with OLAP
data marts. This duct tape often takes the form of complex ETL routines that introduce latency into the operational
applications and analytical use cases because the data has to be physically moved and transformed from one source
to another. This pattern of specialized databases to support subsets of the overall use case’s data needs also exists in
state-of-the-art data architecture implementations. Lambda architecture is an example of using specialized data
technologies that are stitched together through one-off custom software that is often costly to implement and
maintain because of the tricky problems it must solve.
Additionally, these
operational databases
are often the ultimate
source of truth. They
possess capabilities,
like transaction
semantics, that ensure
that they can be trusted
with tracking data
that has immediate
real world value.

ENTER CLOUD – AND WHY MOVING TO THE
CLOUD IS NOT ENOUGH?
The emergence of cloud services was a major milestone in digital
transformation. Public cloud providers offered data storage and
computing services to customers, as a service, over the internet.
By moving to the cloud, businesses were able to bring their
applications online faster without worrying about the capital
expenditure of building data centers. Additionally, they could
elastically adjust the computing resources they consumed both
on demand and independently because of cloud architectures
that allowed the separation of storage and compute.
Cloud-based applications are more agile and advances in
container orchestration and microservices have made developers
more productive, but porting existing applications to the cloud
often is often an exercise in infrastructure optimization, it doesn’t
fundamentally change or improve the application itself.
Cloud migration and containerization is certainly important, but true transformation that improves the quality of
business outcomes often requires improvements to the application itself.
The reason cloud providers are often not successful in moving the needle on business outcomes is that they have
essentially replicated the duct tape that existed in their on-premises IT infrastructure into the cloud. To make matters
worse, the addition of new workloads, like data science, has further complicated the enterprise data infrastructure
landscape. To modernize their new or existing mission-critical applications with machine learning businesses are
required to duct tape together multiple pieces of infrastructure - an OLTP database, an OLAP database, and data
science algorithms and tools. When companies move their data infrastructure to the cloud, the duct tape doesn’t
go away. For example,consider a company that is interested in building a data infrastructure comprised of an OLTP
database, an OLAP engine, and data science tools and algorithms deployed in the AWS Cloud. This setup would
require subscribing to Amazon S3 (storage layer), Redshift or Snowflake (data warehouse), RDS or Dynamo (OLTP
database), and one of at least nine machine learning engine options like Amazon Sagemaker depending on the
particular use case. They would then need to integrate all of this together by using Glue, Amazon’s ETL tool, and
somewhere between a little and a lot of custom code. This is a complex architecture that is expensive to build,
operate, and maintain and applies to all public clouds. Additionally, it requires data movement across platforms
that can result in poor business decisions because insights are drawn from stale data or increased costs through
the data movement itself being metered and charged.
Cloud migration and
containerization is
certainly important,
but true transformation
that improves the
quality of business
outcomes often requires
improvements to the
application itself.

ENTER DIGITAL TRANSFORMATION 2.0
Major forces are ushering in a new age of digital transformation - DX 2.0. It is no longer viable for enterprises to
operate exclusively based on artifacts like invoices, receipts, payments, and customer interactions - business
records that are tracked by enterprise applications like ERP and CRM systems. Let us call them Systems of Record.
Businesses now have access to entirely new types of data. This data is generated by IoT devices, sensors, servers,
and third-party data including weather and social media. Business visionaries like Geoffery Moore call this data
signals1
. There are nuggets of insight hidden in this new data, but you have to look at the right places to find them.
These devices produce data at a much higher frequency and a much lower granularity. In other words, a sensor can
generate a reading every 5 seconds or even continuously, and that reading may only consist of a string of numbers.
The data generated by signals is the major contributor to the total amount of information that we have at our disposal
as humanity. This data is also vastly different from the system of records that keeps track of typically human-
generated business transactions that sporadically occur - spanning over days, weeks, or months at a much higher
level of aggregation than signals that could be continuously generated by machines at great volumes and frequency.
In DX 2.0, businesses must make the transition from systems
of records to the system of signals. The cost of not making
this transformation is just too high. Companies that do
not transform could suffer the same fate as those that
have become extinct during DX 1.0 such as Blockbuster,
Tower Records, Polaroid, and Borders. Signals provide the
raw material for enterprises to build artificial intelligence
(AI) and machine learning (ML) algorithms and these
algorithms use the same signals in production to make
in-the-moment decisions. For the first time in history, we have
the capability to not only store all of the data, but to analyze it
in its entirety instead of the selective sampling we resorted to
in the past. The ability to more comprehensively analyze the
data we collect, leads to better predictive modes that drive better business decisions. Companies that solely rely on
systems of records are only aware of what has transpired in the past. On the other hand, businesses with a system
of signals have visibility into what’s happening in the moment, and potentially, the future. These organizations can,
therefore, take advantage of opportunities or avoid risks as they present themselves. It is the difference between an
organization that has to wait for its books to close to find out whether it has met its numbers or not compared to the
one that is agile and intelligent enough to offer appropriate promotions and discounts during the quarter to prevent
a potential revenue shortfall. This is a shape of things to come in the future, but we already have a sneak peek of
AI-powered competitive advantage in companies like Google and Netflix that have made ML-powered
recommendations part of their business model.
Every app must undergo a transformation in order to become “signal” and AI-enabled. Let us turn our attention to
the purpose-built applications we mentioned above and investigate their DX 2.0 transformation. The loan officer
who is now using a modern application to process a home loan would make the decision, not just based on comps
but also on rich data sources. By tapping into the latest county records, satellite pictures, earthquake and flood
data and even information from local agents over social media all available to her through the app, she can arrive
In DX 2.0, businesses
must make the
transition from systems
of records to the
system of signals. The
cost of not making
this transformation
is just too high.
1
Records vs. Signals: The Landscape of Digital by Geoffrey Moore, January 29, 2018

at a more precise metric of risk at great speed in order to process loan applications faster and more accurately.
At the same time, the app would also generate a list of additional financial products that the bank would be able
to cross-sell or upsell to the applicant based on the applicant’s financial picture using a propensity to purchase
model embedded in the application. Similarly, a modern supply-chain planning app for quick service restaurant
(QSR) franchises would be able to customize demand forecast for each individual restaurant by capturing signals
related to local events and weather and using ML to optimize the inventory of ingredients on hand. Telcos, utilities
and oil and gas companies will now feed real-time data emitted by sensors in the field directly into an ML model in
the cloud to get a dynamic picture of asset performance and to generate a predictive maintenance schedule that
takes into account spare availability to prevent outages.
One approach that is sometimes used to attempt to harness
signals and effectuate a DX 2.0 is often referred to as Lambda
architecture. Lambda architectures use multiple specialized
scale-out compute engines for different workloads. These
scale-out engines use clusters of inexpensive computers to
parallely process data. While Lambda architectures are designed
to utilize the unique strengths of multiple specialized systems
simultaneously to avoid problems like slow ETL by streaming
data to both the analytics and the compute engines that serve
data to the application simultaneously, they suffer from a number
of limitations. These include:
§§ Complexity – This architecture is extraordinarily complex
to build and maintain because there are many separate
systems written in different languages that were not
designed to work together seamlessly. Duct taping
together systems might require an expert understanding of
extremely tricky computer science problems like distributed
transaction management
§§ Specialized Skills – The compute engines that are part of a lambda architecture require highly skilled
and sought-after developers who can program in multiple programming languages and distributed system
paradigms.
§§ Loose-coupling – The engines in a lambda architecture are loosely coupled. This means that changes to
any one layer takes time and effort to be applied to the other layer. It also means that organizations must be
extremely careful when making changes to ensure that data flowing through the layers is not processed with
subtly different rules that results in data inconsistency and corruption.
§§ Concurrency – Lambda architectures are extremely limited in their ability to handle concurrent users at the
application level which requires ACID properties.
Simply put, building lambda architectures has proven too difficult for companies as they have to constantly duct
tape a number of systems together and the skill sets of people who can do that are extremely scarce, expensive,
and hard to recruit and retain.
Lambda architectures typically leverage the cheap, scalable storage of Apache Hadoop. Apache Hadoop emerged on
the IT scene in 2006 with the promise to provide organizations with the capability to store and analyze unprecedented
...building lambda
architectures has
proven too difficult
for companies as they
have to constantly
duct tape a number
of systems together
and the skill sets of
people who can do that
are extremely scarce,
expensive, and hard
to recruit and retain.

volume of data using commodity hardware. Companies rushed to migrate or offload their data warehouses onto
data lakes enriched with new “signal” data. Data lakes were largely IT-driven projects whose value to the Business
was not very clear from the onset. They were built on the premise of “build a centralized data repository and they
will come.” Data lakes did deliver on the promise of cheap storage. Schema-on-Read, the practice of applying a
schema only when data is read, not when it was originally stored also came with Hadoop. As a result, businesses
started ingesting data into their lakes without worrying about how this data would be organized or accessed and
terabytes of structured and unstructured data began to flow into the data lakes.
This approach has proven to be one of the missteps of Hadoop-based data lakes. Data lake projects began to fail
because of the complexity of Hadoop and the expertise required to operate numerous engines that operate on top
of it. Also the lack of structure applied to data in the data lakes largely made it useless for analytics. As a result,
this data never found its way to a real business application in the operating fabric of the enterprise.
As enterprises transition from on-premise enterprise applications
to cloud computing and now to AI-enabled apps, their
focus has been on building brand new applications. Existing
purpose-built applications have been largely overlooked during
this transformation. This is ironic because in most cases,
purpose-built apps are the very applications that would deliver
the most benefit from being made agile and intelligent. Part of
the reason that the purpose-built applications have been left
behind is because of the complexity of rewriting and migrating
them to new data processing systems that are more specialized
than the legacy systems that they were originally built on, require
new complex architectures, like lambda to power in-the-moment
intelligent decisions and actions. This approach represents an
expensive, risky, and lengthy proposition.
DX 2.0 is a timely development for the enterprises because many
purpose-built applications have become dated. They are built
on older platforms that lack agility and intelligence, and these
applications are now struggling to scale in the era of Big Data and business signals. Businesses have also been
demanding that their IT departments support data science and predictive techniques such as ML so they can be
integrated into purpose-built applications to help them make data-driven intelligent decisions and actions. In
response to these demands, IT departments have started implementing stand-alone data science workbenches. This
has further complicated the enterprise data infrastructure landscape because to modernize their existing mission-
critical applications or to create brand new ones, businesses now require all three pieces of infrastructure - OLTP
database, OLAP data warehouse and data science workbench - to be integrated.
We believe that merely moving your IT infrastructure into the cloud is a rather simplistic and narrow view of digital
transformation because the factors that are the source of latency in your data IT infrastructure continue to be present
in the cloud. DX 2.0 is different from some of the initiatives mentioned above as its focus is on modernizing business
applications rather than data management and analytics. Enterprises therefore need to supercharge their apps to
capture new data signals and take intelligent actions using ML models for predictive reasoning.
Part of the reason
that the purpose-built
applications have been
left behind is because
of the complexity of
rewriting and migrating
them to new data
processing systems that
are more specialized
than the legacy
systems that they were
originally built on...

In DX 2.0 the secret sauce is not in building the predictive model,
but rather in embedding the predictive model in the applications
to take intelligent actions operating on real-world data in the
moment. By modernizing these applications to a scalable platform
and injecting predictive technology such as machine learning into
them, enterprises can significantly enhance the viability of their
digital transformation initiatives.
In DX 2.0 the secret
sauce is not in building
the predictive model,
but rather in embedding
the predictive model
in the applications to
take intelligent actions
operating on real-world
data in the moment.
Converged
Platform
Early Migration
to Cloud
Early Implementations
of Lambda Architecture
Emergence of
Relational
Databases
1980s 1990s
BusinessValue
Low
High
2006 2008
Use of multiple
specialized
scale-out
compute
engines for
different
workloads.
Implementations
of Apache Spark
& Cloud begin to
accelerate
Emergence of
Hadoop Data
Lakes
Store and
analyze large
volume and
variety of data
using
commodity
hardware
Focus on
modernizing legacy
business
applications by
capturing new data
signals and using
ML for predictive
reasoning.
Release of Splice 2.0
and the first hybrid
OLTP/OLAP/ ML
capabilities built on
open source
ecosystem
Use of an
OLTP
database to
power an
application
Simple data
storage and
computing
as a service
2017
DX 2.0
DX 1.0
Use of DBMS for
Data Warehousing
Use of an
OLAP data
warehouse for
decision
support &
management
reporting
2013
Figure 2: Evolution of custom-built applications

SPLICE MACHINE’S APPLICATION
MODERNIZATION JOURNEY
Splice Machine takes its customers on a journey to modernize their custom-built applications by making them agile,
data-driven, intelligent, and cloud-portable. These custom applications have been overlooked in today’s digital
transformation, yet they are the crown jewels of the company’s competitive advantage.
Splice Machine’s application modernization journey consists of four steps: migrate to scale-out Distributed SQL,
unify business analytics on the converged platform, inject artificial intelligence and machine learning, and optionally
move to the cloud. Customers have the flexibility to start their journey at any step, depending on their use case.
MIGRATE TO SCALE-OUT
DISTRIBUTED SQL
The operational (OLTP) database powering your purpose-built applications was built for another time.
These applications were built as systems of record to help answer questions about business transactions.
Now that your mission-critical applications need to transform themselves into systems of signals, the operational
database not only needs to store a lot more data but it also needs to accommodate data generated by sensors, IoT
devices, and social media. It is no wonder that your mission-critical applications are no longer able to keep pace
with volume and variety of data.
Scale-Out Distributed SQL architecture operates horizontally across the cluster by adding commodity hardware
while fulfilling the requirements of full ACID and SQL compliance simultaneously. This includes providing full
transactional support (commit, rollback, savepoints, etc.), enforcing the results of an operation to be fully consistent,
isolating the transactions from each other while they are being executed and preserving the transaction once
it is committed while ensuring full durability at all times. At the same time, applications operating on scale-out
distributed SQL architecture uses full ANSI SQL to interact with the underlying database.
Your journey with Splice Machine begins with migrating your purpose-built applications to our platform that can
scale from terabytes to petabytes and beyond. Unlike other data platforms that are exclusively used to support
analytical and decision support use cases, Splice Machine has been built from the ground up to power enterprise
custom-built applications. This is the reason we have built Splice Machine with full ACID compliance that describes
how transactions must maintain integrity in the database.
Splice Machine can dynamically scale from a few nodes to thousands of nodes to enable applications at every
scale without the expense, time, and the risk of rewriting them. With Splice Machine, your developers can continue
to write their applications using SQL and there is no need for an expensive team of distributed infrastructure
specialists to maintain the platform.
Replacing your existing application database with Splice Machine is not an overwhelming task that takes years,
often the migration can be accomplished in mere days or weeks. In most cases the business logic that you have
defined in your application can be reused with Splice Machine. We also have utilities available such as a native PL/
SQL compiler and DB2 compatibility that dramatically reduces the time and cost for companies to migrate their
big data workloads. Once you have migrated your application on to Splice Machine, it will continue to work like
before, but now it can ingest millions of data signals and take intelligent actions.

In addition, next generation scale out OLTP databases like Splice Machine have much more attractive licensing costs
when compared to legacy OLTP databases. The simple migration story that Splice Machine provides in combination
with a more attractive price point can lead to very quick return on investment for organizations embarking on a
modernization journey.
UNIFY ANALYTICS
Once you have migrated your operational data on to the Splice Machine platform, the next step in the application
modernization journey is to consolidate the analytics workloads. Your Data Marts (DM) are the repositories that
are used for decision support and management reporting purposes for the application. DMs require data to be
physically moved and aggregated from various repositories. It can take hours or even days to load data into a
warehouse. This delays the production of management reports, and decisions are made based on stale data.
In certain instances, enterprises do not deploy predictive and machine learning models into production due to the
concern that their models are built on stale data and they would make incorrect predictions when operating on
updated or frequently changing data.
If you have already implemented a data lake in your organization then Splice Machine can access data signals from
it. Existing reports, dashboards and business analytics along with operational data can now be unified on a single
scale-out platform. Customers time to insight is accelerated by removing the latency associated with transformations
required for management reports and dashboards. Plus there is no limitation on concurrency because more analytics
users are accommodated with more analytics executors. These in-the-moment decisions are the forerunner to
gaining the advantage back from the digital upstarts that don’t have the baggage of older architecture.
INJECT ARTIFICIAL INTELLIGENCE WITH
MACHINE LEARNING
The next step in the modernization journey is to make your applications intelligent by providing them the ability to
make-in-the-moment decisions with machine learning on production data. The same platform on which you have
consolidated your operational and analytics data also provides you with built-in machine learning (ML) functionality.
With Splice Machine, your data science teams are empowered to continuously adapt to market changes and
produce a higher number of predictive models. Consider a model that predicts the likelihood of attrition for retail
banking customers. In order to capture signals that would indicate whether a customer is reducing its relationship
with any of the lines of businesses and hence is at risk of going over to a competitor, data science teams will have
to access and consolidate data from a number of disparate internal repositories such as CRM system and EDW,
as well as bring in exogenous data. The data science team then embarks on building the model by experimenting
continuously with features or attributes that are useful in predicting the outcome. In this regard, keeping every
feature the data scientists have tested, trained, and deployed organized in an easy-to-access workspace is crucial
for the productivity of the data science team. After the model is built, the team will then have to hand over the model
to Devops in order to put it into production. By the time data wrangling and deployment operations are completed,
data might be already stale for the model to make accurate predictions. Contrast this with a scenario, in which all
the source data as well as the model resides in a converged platform, the data science team is able to train the
model on the latest data and then deploy the most effective model seamlessly into production.

Data scientists have a workbench to manage their workflow to compare the effectiveness of various experiments
with different features, algorithms, and parameters. They no longer have to wait for recent operational data to be
moved, or to re-train the model on a separate platform and then deploy it into the production environment. With
Splice Machine, model training and deployment is seamlessly integrated with the application and native to the
database. You are now ready to leapfrog the competition by compressing the time from model deployment to taking
action. Case in point is a machine learning model to detect cyber attacks on your network, the chances are that the
hackers will change their strategy frequently, and if your model is not continuously trained on new attack patterns,
sooner or later, it will miss an important signal that will result in the network being compromised.
MOVE TO THE CLOUD
Moving the IT infrastructure to the cloud is a common part of the digital transformation. The Cloud offers businesses
the agility to provide computational resources in minutes and the flexibility to elastically scale their services based
on demand.
At any point during this journey, Splice Machine provides you with the flexibility to move your IT infrastructure to
the cloud. With our managed services on the cloud, you no longer need to worry about the complexity of operating
a distributed system that typically plagues companies. If your plans include moving your custom-built applications
to the cloud, rest assured that Splice Machine has been designed from the ground up to be portable. We leverage
the technology that enables applications and storage to be containerized, secured, and monitored with guaranteed
availability. This architecture is portable across public clouds with no-lock-in as well as on-premises infrastructure.
1. Migrate to Scale-Out SQL
Move to
Any Cloud
2. Unify Analytics 3. Inject AI/ML
Migrate purpose-built
applications to a
petabyte-scale platform
Consolidate the analytics
workloads on a unified
scale-out platform
Make in-the-moment
intelligent decisions with
machine learning
Provision computational
resources in minutes and
elastically scale services
based on demand

CONCLUSION
For the first time, enterprises have the opportunity to measure the pulse of their business by capturing the signals
emitted by IoT devices, sensors, servers, and social media and then act on future events using artificial intelligence
and machine learning. These technological forces represent the same seismic change that the invention of the
production line and information technology had years ago on the competitive dynamics of the industries. Like
previous business transformations, companies that do not transform could suffer the same fate as those that have
become extinct. No company can afford to be left behind by failing to revitalize its core business processes with
a wealth of insights from the new data sources and gets bypassed in its inability to take action based on them.
In other words, we are amid Digital Transformation 2.0 or DX 2.0. As part of DX 2.0, tactical moves like moving
the IT infrastructure to the cloud will only yield incremental improvements. Every app - new and legacy must be
transformed to become “signal” and AI-enabled. This transformation must be done using a converged platform that
minimizes data movement and integration burden and maximizes the productivity of data science teams.
Splice Machine takes you on a journey of scaling your application on a new scale-out distributed SQL architecture,
unifying analytics into one platform and injecting machine learning directly onto this app platform. Our process
can also get you there faster – in months – without needing to hire infrastructure experts.
ABOUT SPLICE MACHINE
Splice Machine is an Operational AI Platform that unlike relational databases and Hadoop distributions is scalable, real-time,
easy-to-use, and continuously learns. It combines the functionality of a operational database (RDBMS), an analytical
database (OLAP) and a machine learning workbench (ML) in one unified platform. Splice Machine can be deployed
on-premises or in the cloud and is built on open source technology.
The content of this white paper, including the ideas and concepts contained within, are the property of Splice Machine, Inc. This
document is considered proprietary and confidential, and should not be reproduced or reused in any way without the permission
of Splice Machine.
Splice Machine is a trademark of Splice Machine, Inc.
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Splice Machine Digital Transformation 2.0 white paper

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Splice Machine Digital Transformation 2.0 white paper