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Composable Analytics - Creating Actionable Insights

Analytics and insights the way you need them are required to outperform in the market.

Composable Analytics

Introduction


Being competitive requires
next-generation tools and data
solutions.

The nature of reporting analytics and bi insights has changed significantly
in the last few years because of the changing nature of how solutions are
capturing data. Today’s enterprises have in many cases orders of magnitude
more information about how things are working within their environment than
just a handful of years ago. Data that includes customer engagement, system
log data, IoT and device based events plus location and other telemetry type
data. Those who are able to work dynamically and stitch together this data
into insights that make a difference in a short period of time will thrive beyond
what their competitors are capable of.


These massive amounts of data being collected across multiple sources
also form the backbone of how to improve process, design and delivery
along every measurable dimension in the enterprise. The challenge is not
data access or data visualization, it’s in leveraging all of the pieces together
into a common data insights platform that everyone in the organization can
leverage. Then integrating those insights into the operational model so that
the people who benefit the most can get it in real time.


Reacting to this need using traditional bi analytics where information is
cobbled together using a manufacturing assembly line approach doesn’t
scale well when the data is highly fragmented. Various scenarios are possible
to create true insight and understanding however it surfaces long after its
usefulness. Sometimes at the point of regulatory drivers or some other type
of forensic need.


Building operational models on top of this can be very risky especially when
other data streams need to be part of the picture such as transactional
records, process information and system information. Having all of this data at
your disposal yet it being difficult to stitch together can introduce risk into the
operating model and result in degrading the capabilities of the organization.


In the next few sections a look at how people can start to use composable
analytics is used to bring together just in time information forming the
backbone of a data driven organization.


The Way Forward - Composable Insights


The insight at the foundation:
approach to operations.

Often BI platforms or data warehouses, data lakes are viewed as the one place
where everything has to come together to create understanding. There’s
nothing inherently wrong with this approach if the need is appropriate for it.
Foundational reporting and after the fact analytics are served well with these
approaches.


The central question to answer in the race to improvement is: does the insight
need to be embedded in a process for it to be useful or within a report that
can be looked at 30 days after it happened?


BI platforms have a legacy in the enterprise that goes back decades and have
different levels of capability from simple databases to OLAP and OLTP based
solutions to highly specialized solutions like ODS’s then to data warehouses,
data lakes etc. Any of these can be part of an operational scenario where
composable analytics is being deployed and equally can be used as input
into it. This paper looks at composable analytics as the result of a set of
distributed and fragmented data coming together in a moment of time to
provide insight.


This traditional architectural or design approach while it has been valid and
does hold a place in the enterprise design model for certain use cases.

Creating Actionable Insights

Foundation Needs of the Insights Based Organization


Time to Action

How long does it take for an insight to be acted on?

Time to action needs to be aligned with the speed of how fast a response
is needed. Organizations create hundreds and thousands of signals
around a single event at every moment. Signals in the aggregate can
create a view into what is actually happening then be able to act on it.
The unlock here is not to just surface the insight once but repeatedly
inject it into the point where it’s needed at just the right moment then to
also use those process learnings to further improve operations.


Customer Insight Driven
Operations

How to completely utilize all of the customer data value?


Many organizations are challenged with getting the most out of their
customer data. Combined factors that make this difficult is that just as
the data is distributed across the organization so too must one navigate
through the organizational dynamics to collaborate on a specific need
- spanning operations, technology and business teams. As mentioned
above the traditional approach is to have an internal creator consumer
model of data analysis. Organizations looking to become insight driven
need to move beyond this approach into a more cohesive process.


Siloed Operational Data

Is the complete 360 view of information accessible?

Operational data needed for composable analytics is located across the
system landscape - often partially duplicated then enriched for its local
needs or embedded within a process or black box system. Bringing
together all of this data into a central repository before its use has
been tried in many organizations. However it doesn’t keep up with the
changing nature of queries or doesn’t fit into the data models easily.

The Traditional Approach - The Data Assembly Line


The data assembly line in many instances may not be as efficient as its name would suggest. Often it’s only as fast as the slowest moving step function, requires highly technical skills and tools to make it usable only for a small group of people.

Characteristics of it are:

• Proprietary flows of data and processes

• Specialized technical tools for working with the data • Complex data assembly to drive reports after the fact

• Untraceable insights back to point of origin

• Small groups of people actually get to use the information or have access to it

data process flow
data analytics

Put It Forward’s Approach - Insight at the Time of Need


Insights at the center of an
operational model that can
be embedded directly into a
process or trigger the process
itself through a number of
means.

• Siloed data works bi-
directionally between systems and processes
• Users can leverage no-code
tools to work directly with
the data
• Insights can be easily
connected into a process
for continuous improvement
• AI insights can be
integrated directly into
operations without coding
• Composable solutions can
be embedded into BI tools,
applications and analytics

data process method
Put It Forward analytics

Put It Forward for Composable Analytics - Siteline


The Put It Forward intelligent automation platform was created and built for
organizations who need to have the flexibility to work their own way. With a
single easy to use interface it integrates systems, enables process automation,
surfaces deep insights and connects them directly to the user all through a
no code platform that anyone can use. This brings teams together spanning
marketing, revenue, operations, finance, HR and IT enabling them to scale
their great ideas which make the difference.


Not everyone can be under the same roof all the time - distributed teams,
disconnected processes and data locked in applications are the limiting factors
of many organizations as they try to reach their maximum potential. Through
a common user interface and infrastructure, Put It Forward brings all of these
together. This data platform infrastructure enables people, groups and teams
to work and learn together quickly while getting the most out of insights as they
emerge.


This is not a copy of your data into yet another repository or requires some
heavy infrastructure footprint to enable. It works with the data where it is,
assembles into useful structures then delivers up to the ultimate consumer. This
controls costs, improves speed and unlocks scale of execution.
Any organization looking to scale with composable insights by leveraging their
current IT investments are given a new solution approach. Delivering on insight
scenarios from revenue creation, process automation through to supplier and
partner management.


Following are some of the use cases on how Put It Forward's Siteline can help those
looking to leverage better insights through composable analytics.

Composable Analytics for Sitecore

Revenue Operations Optimization

Delphi AI

Next Best Customer:

The flip side of the next best offer coin is identifying which customer
to focus your resources on. The configuration based AI algorithms of
Put It Forward help you zero in on which customer you should focus
on. Working with all of the engagement data across various platforms,
predictive insights surface the probability of customer acquisition and help
direct resources appropriately.

Revenue Forecasting and LTV:

Put It Forward can tap into the customer behavior of both past and
present to help accurately forecast future revenue and LTV of every
prospect. This data can be surfaced in a report or at the individual contact
level enabling the person focused on them to understand what product or
service will be the most valuable to offer. The power of this composable
analytics solution is that it gives you the ability to both consolidate your
reporting into a single location and to push out high value analytics to the
end user.

Churn Risk Identification:

Operational data needed for composable analytics is located across the
system landscape - often partially duplicated then enriched for its local
needs or embedded within a process or black box system. Bringing
together all of this data into a central repository before its use has
been tried in many organizations. However it doesn’t keep up with the
changing nature of queries or doesn’t fit into the data models easily.


Operational Process Management

Process automation

Customer Onboarding:

Working with customers efficiently at scale is critical to operational
success in every organization. Within Put It Forward there are a number
of advanced analytics options to create insight into the customer journey.
These help identify where a customer may be stuck in the process, where
bottlenecks are occurring and when you’re dependent upon the customer
fulfilling a commitment on their side. These insights can be used to
trigger process flows or come from the process flow themselves where
there is a blockage. Metrics are surfaced into reports and dashboards
which show the efficiency of operations and where opportunity for
improvement exists.

Identity Resolution:

Every operational team spends time in the middle of trying to figure
out who is who and what belongs with what. Said another way time is
spent understanding which customer is related to which account, which
employee is related to which device or issue and which support tickets are
related to common issues. Each of these points has a unique identifier
which is often not common across the organization. Within the platform,
identity resolution software helps link and maintain relationships between
all of these entities. This enables operational teams to work efficiently
across the organization in separate teams or as part of global structure.

Issue Management and Automation:

Common with identity resolution is understanding how to trace issues
across an organization. There are a number of issue management
solutions that fall under the banner of ITSM. What Put It Forward does
is tie the issues in the various platforms and the processes together to
automate the flow of information and events across them. Further using
the data mining capabilities you can uncover hidden efficiency gains
by examining the processes at the event level and turning them into
operational metrics used to manage the organization.


Finance Risk and Control Process

Finance automation

Financial planning and analysis process (FP&A)

Central to any organization’s resource allocation process is financial
planning and analysis software (FP&A). Solutions like Put It Forward
tie together the revenue teams projections, operations efficiency gains
into the FP&A process to create an end to end view of how capital and
resources are being used. This helps finance teams accurately model out
possible revenue scenarios with real pipeline data rather than working with
assumptions on a continuous basis. This data is then linked with other
operational data sources to help create a better understanding of where
revenue at risk exists and using AI models to help identify its location.

Procure to Pay:

Throughout the procure to pay process there are a number of analytical
data points to capture. Starting with supplier information, payment
terms, location and availability. By tying together this information
with operational data insights procurement analytics enable category
management to identify efficiencies in the process, supply risk and
improve supplier relationships. These composable insights can be
surfaced in dashboards, reports or process flows through the Put It
Forward Process Designer.

Fraud Detection and Control:

One of the lesser known and extremely important functions that finance
teams have is to identify and control fraud in the system. Fraud detection
isn’t just limited to point of order credit card issues but can run through
an organization’s processes. Leveraging the machine learning and AI
functions in the Put It Forward platform, teams are able to work with
massive amounts of seemingly disconnected events to identify possible
fraud in their processes then send alerts and notifications to the people
who need to act.


Advantages for the IT Organization

Put It Forward enables the IT team to make the most out of their strategy
for composable analytics by giving them a single solution to work with.
This solution can be reused across multiple departments and parts of the
organization over and over. Which in turn helps surface its own insights
and values such as:
• No code solutions for composable analytics that the business user can
leverage
• Centralized management of the systems with decentralized use
• Cost reduction of highly specialized skill sets
• Operationalization of business processes that can scale across the
organization
• Architectural fit and ability to leverage existing IT infrastructure
without new overhead
IT knows very well from their view that there are many small pieces of
information scattered across the organization, this enables being able to
work with them in a unified way and help great ideas scale.

Key Takeaways:

BI and composable analytics are two different and complimentary
approaches to understanding what is going on in the organization.
BI serves the purpose for after the event reporting and composable
analytics enables the automation of decision support.
• The central question to answer in the race to improvement is: does
the insight need to be embedded in a process for it to be useful or
within a report that can be looked at 30 days after it happened?
• Look at where the insight needs to be placed in the process for it to
have greatest effect and how is it going to get to where it’s needed at
the right moment in time.
• Think about the integration process and access to the data that
needed. Is this going to be enabled by highly centralized or hard to
find technical skill sets or is it best served by operational teams that
are close to the data and processe.
• Get alignment on all business initiatives today and potentially around
the corner to ask providers how their systems will deal with the
multiple data sources, data quality, AI models to generate insight and
how the insights will be shared.

Siteline data analytics