This document discusses how big data has evolved from data warehousing in the 1990s to today's focus on big data to better understand customers. It argues that many organizations fail to leverage big data to improve customer experience and gain business insights. To succeed with big data, organizations must develop a clear strategy to deliver business value, such as increasing customer retention and growth. The document recommends that organizations focus big data initiatives on improving the customer experience through integrating customer data and feedback and providing frontline employees with easy access to customer information.

1. EXeCUtiVe SUMMaRY
Big Data is back on the corporate agenda. I say ‘back’,
it never went away for the CIO, it’s just moving with the
times, bumped along by changes in operating systems,
user technologies and digital transformation.
Big Data 1.0 was Data Warehousing, which came to the
fore in the 1990s, when the need was to extract, transform
and load (ETL) data from one-to-many disparate systems.
Many of these back-office, infrastructure systems were
proprietary legacy systems and, with Microsoft starting
to proliferate IT operations and the desktop, so the need
to consolidate corporate data became a critical business
need.
Data Warehousing was the response for the need to parse
large amounts of data, normalised, so employees had
access to connected intelligence, using Microsoft Excel,
OLAP tools such as Business Objects and data or report
mining software, like Datawatch Monarch. All in pursuit
of greater, actionable, decision support to aid business
results.
Data Warehousing broke down data demarcation, from
operationally siloed systems to deliver single layers (or
cubes) of data for deeper analysis, regardless of its pro-
venance and source. As data got bigger, so Data Marts
logically segmented datasets to meet line-of-business
needs and scalability. Job done!
Fast forward and Data Warehousing has morphed into
Big Data, today’s response to SaaS, the Internet of Things
(IoT), multi-channel data collection and the need to push
information-rich interfaces to anything from desktop PCs,
tablets and mobile devices.
BIG DATA – THE CASE FOR CUSTOMER EXPERIENCE
BIG DATA – THE CASE FOR CUSTOMER EXPERIENCE
It is Big Data for good reason. In 2013 IBM reported over
2.5 exabytes of data were created every day; by 2014
this was 2.3 zettabytes. Data is being offered up through
digital portals and interactions more than ever before, so
achieving true support for Big Data is huge. According to a
study entitled “How Organisations can Unlock Value and
Insight from the Information They Hold¹”, Pricewater-
houseCoopers (PwC) state that 43% of companies surveyed
“obtain little tangible benefit from their information” while
23% “derive no benefit whatsoever”. Such statements are
indicative of the chasm that exists between the collection
of corporate date and the presentation of monetised
business insights.
To understand why Big Data struggles to get corporate
tenure, Gartner research director Svetlana Sicular² recently
opined, “To succeed, you must develop a viable strategy
to deliver business value from a big data initiative. Then
map out and acquire or develop the missing and specialised
skills that are needed. Once strategy and skill priorities are
addressed, then you can move on to big data analytics”.
This document aims to substantiate a justification for Big
Data, when customer retention and growth is the business
value that is derived. That value can be used to deliver a
positive ROI calculation when pursuing a Customer Experience
(CX)-backed corporate initiative.
¹ PwC Report published Autumn 2015, commissioned by Iron Mountain
² POV the 2015 Gartner Business Intelligence, Analytics and Information Management Summit

2. The State of Big Data
The concerning aspect of Big Data is not how much data is
being collected, its vast and increasing, but the fact there
is a motherlode of data never mined or analysed because
organisations fail to invest and commit to technologies,
beyond storage, that truly leverage this data for business
change and customer-centricity. Yet the same challenges
that existed for Data Warehousing remain for Big Data,
decision support and intelligence should contribute to
operational efficiencies, revenue and growth.
Forbes³ recently stated that in Europe less than a quarter
of enterprises have a clearly defined strategy for Big Data
(in North America this drops to under 20%), although 76%
of all global organisations surveyed at least have a plan to
increase or maintain investments in big data over the next
2-3 years. Significantly for the development of CX in Europe
over 30% of respondents said Big Data was necessary in
order to implement and/or integrate new technologies and
methods.
Investment in Big Data will increase as organisations seek to
manage massive volumes of disparate data and introduce
new technologies to analyse additional data capture in areas
such as location (e.g. telemetry in cars) and free-form text
(Feedback, Reviews and Social Media). All of which
substantiate strategies to better manage customer
experiences and outcomes.
Organisations will have multiple goals for Big Data initiatives
but it is the complexity of any project that often slows or
stalls completion. Connecting data and processes to single
business functions can be incredibly onerous and one that
can involve high consulting costs and resourcing. Additionally
as data breaches continue to cause boardroom concern,
Big Data projects can become defocused and many projects,
up to 23%, depending on the region⁴, saw the need for
enhanced security capabilities grab funding.
Funding is the ongoing issue for many Big Data projects
that drives fear of negative ROI:
• Legacy System Management – Big Data can extend
use, rather than sunset, a proprietary system and this
can introduce increased maintenance costs as services
switch from turnkey to bespoke
• Skills – Big Data can increase the need (and cost)
for contracted DevOps capabilities in order to build the
connectors and apps to present data to staff and customers
in ‘consumerised’ interfaces
• Governance – the collection and management of
confidential data has a cost to ensure processes and
protocols are compliant to privacy, confidentiality laws
and policies
BIG DATA – THE CASE FOR CUSTOMER EXPERIENCE
Therefore it is imperative that gains in growth and revenu-
es from existing and prospective customers can quickly
offset the costs that could negate ROI.
Customer Experience – Big Data Success
Getting singular focus within a cross-functional project
such as Big Data may appear to be difficult but successful
organisations are doing just that by focusing on a culture
of customer-centricity. In short if an organisation can put a
value on a customer, the data held can be used to shape
experiences and advocacy so loyalty, retention and growth
can be predicted.
Variances will exist between B2C (transactional volume)
and B2B (transactional value) organisations but tangible
values should be there so an ROI goal can be set against
a greater share of wallet or budget – very quickly in B2B
where customers are known and data is available from
many channels. However, in B2C the impact may take
longer to realise as the customer may first be anonymous
even though ‘behaviours’, such as web browsing and
abandoned shopping carts, can be tracked.
No surprise, therefore, that 64% organisations see CX as
the primary ROI goal for Big Data. Of greater interest, and
also reported by Gartner in 2015, nearly a third of Big Data
projects were initiated by line-of-business heads (drawing
level with CIOs) with a vested interest in the customer.
Data strategy ownership is changing as companies shift to
a value and velocity model allowing Big Data to support
CX; monetise customer insights and drive business change.
The maturity of any Big Data management strategy, in
terms of its integration into connected business functions
and insights, could also use CX maturity models, such as
CXEvolution⁵, to assess its value to staff, Line-of-Business
(LOB) functions and customer engagement and NPS and
C-SAT scores.
³ http://www.forbes.com/sites/louiscolumbus/2016/05/22/51-of-enterprises-intend-to-invest-more-in-big-data/#2ba875553ad0
⁴ Gartner Survey Analysis: Practical Challenges Mount as Big Data Moves to Mainstream
⁵ More information: www.maritzcx.co.uk/cxevolution/

3. USiNg CX to MatURe
Big Data MaNageMeNt
So what six things should the ‘Big Data Collective’ consi-
der from the outset of their corporate CX initiative:
#1 Big Data Success Factor - Enculturate
Assemble a cross-functional team to lead a Big Data
project with a singular focus and goal; customer value –
compelling for stakeholder and shareholder value.
By democratising interests, the assessment of customer
value from the cross-functional project team brings
operational datasets into a federated state of One, enhanced
by social media postings, survey responses and web
reviews. Structured storage and collection ensures Big
Data has the facets to parse the datasets needed to
commence enhancing decision making and frontline
service effectiveness.
#2 Big Data Success Factor - Integrate
Focus on a desired customer experience outcome that a
value can be calculated, such as reversing a 10%
year-on-year reduction in lapsed maintenance contracts.
Let these calculations become the desired Big Data
objective, it will become an ROI statement. Many Big Data
projects stall or fail simply because they start without clear
business objectives.
The feedback and connected analysis of customers’
interactions and experiences will allow the B2B organisation
to use the federated Big Data dataset to assess the points
of failure.
Typical integration may include:
• Surveys and feedback may provide the qualitative
response to the overall value of the human relationship
(account managers, service engineers and the perceived
company culture)
• CRM will provide the quantitative assessment of
engagement and buying behaviour
BIG DATA – THE CASE FOR CUSTOMER EXPERIENCE
• Service Management will overlay the reliability, or
otherwise, of the products and services provided
Combined the organisation can begin to assess overall
satisfaction and the likelihood of a customer to detract and
why. The analysis begins the process of rectifying the fault
lines within the customer experience, using case management
and action planning to revise and adjust sales and service
processes.
Combined businesses will move to a more predictive state
of customer insights. For many sectors survey responses
are falling and metrics, such as NPS and C-SAT, are becoming
static moments in time and by bringing in other available
operational data that can be used to trend against different
customer events and demographics to predict the true
state of a customer – loyal or detractor.
Zero Moment of Truth, A Single Pane of Customer Truth
Gartner and Forrester have both publicly reported that
more than 85% of organisations will look to compete on
customer experience from 2016 but compete means
ensuring that every customer facing/interacting employee
or representative has simple access to all relevant information
and feedback, presented in quick and easy dashboard
and report formats.
Because what happens next is often that zero moment of
truth for a customer when frontline staff either succeed in
delighting or leave the client despairing about their relati-
onship with a company or brand.
Having reversed negative trends, such as customer churn,
the next phase is to become more proactive, listening to
the voice of the customer every time they engage, online,
in-store or on the telephone and ensure that every event
and experience is visible to act upon.
USiNg CX to MatURe
Big Data MaNageMeNt
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4. BIG DATA – THE CASE FOR CUSTOMER EXPERIENCE
#3 Big Data Success Factor - Federate
Assemble your datasource inventory and begin the
connection of data, processes and feedback with opera-
tional systems, such as Salesforce, to build frontline views
and dashboards.
Although this white paper is non-technical, Hadoop and
services such as the AWS S3 platform speed the norma-
lisation of data through the use of lightweight, agile and
adaptive processing frameworks. These allow the con-
solidation of both structured and unstructured data into
enterprise-class Big Data storage and out into consumeri-
sed apps and interfaces.
CX technologies can easily leverage these to commence
the presentation of these datasources for focused customer
outcomes, enhancing data further with survey and feedback
intelligence as sentiment is collected to complete the
analysis of system generated data like service tickets and
product returns.
#4 Big Data Success Factor - Propagate
Nearly there! Creating a dashboard so frontline staff can
better interact with customers does not start by evalua-
ting what data exists, it commences with understanding a
customer’s typical journey.
Journey mapping will highlight the opportunities and times
when data can be captured and/or recorded, it can even
draw in feedback that had previously been missed.
That single pane of CX truth will drive more and different
decision points, both push and pull. Mapping the customer
journey in absolute detail will allow the company to start
shaping the overall experience.
Knowing when to offer advice at investigation stages or
when a customer aborts an online transaction can increase
new business revenues. For example, in-store frontline
operatives can start to present relevant choices and alter-
natives to consumers driving a greater share of wallet.
Equally knowing when a customer is detracting by using
well-placed feedback channels will mitigate against churn
as case management ensures closed loop analysis of the
fault lines with customer management processes. Big Data
makes this possible as behind every stage of the journey
will be different systems, portals or feedback mechanisms.
#5 Big Data Success Factor - Celebrate
Publicise success! It doesn’t matter how strategic your Big
Data initiative is, Customer Experience will give focus on
outcomes that will positively impact ROI. Understanding
and acting upon Customer Experience insight and analysis
creates a better company to do business with.
Consequently the outcomes should be fourfold:
1. Increased Retention
2. Greater Share of Wallet/Budget
3. Company/Brand Reputation Management
4. Higher Levels of Customer and Social Media Advocacy
Customer Experience technologies come with compre-
hensive reporting and highly visual dashboards. Use these
internally to communicate success. Grow. Retain. Repeat.
#6 Big Data Success Factor - Incorporate
Do not get side-tracked by UX and DX!
For many industries, like Financial Services, Digital Trans-
formation is a constant strategic objective. The need to re-
duce traditional costs, like branch services, with the need
to introduce new channels for younger, mobile customers.
‘Apple-ising’ apps and adapting web services for mobile
are important for improving brand identity but it does not
guarantee customers for a lifetime. Improving access and
use through sharp HTML5 mobile apps helps but ultimately
it is not how a customer rates experience.
If CX is the cake of the customer, UX and DX is the deco-
ration. For Big Data is only as good as the data held, then
apps and interfaces only become applicable for use based
upon the content they present. In CX content is data, the
actionable insights that turn into real-time decision support
for frontline and customers.

5. BIG DATA – THE CASE FOR CUSTOMER EXPERIENCE
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MaritzCX believes organisations should be able to see, sense and act on the experiences and desires of every customer,
at every touch point, as it happens. We help organisations increase customer retention, conversion and lifetime value by
ingraining customer experience intelligence and action systems into the DNA of business operations. For more information,
visit www.maritzcx.co.uk
Big Data, Big Opportunity - Generate
In conclusion, customer experience can be defined as the
analysis of every customer record, interactions, behaviour,
transactions and delivery, support services and the many
channels of feedback (social media included) in order to
determine the likelihood of new and repeat business.
Big Data and CX is the perfect marriage to generate
growth through retention, increased share of wallet and
reputation management. Yet, in CX terms, only 3%⁶ of
organisations have attained that level of operational maturity
that ensure data is being used to proactively manage all
customer experiences in real-time.
MaritzCX combines research expertise, data and techno-
logy to make sure the right people in these organisations
can understand and respond to every customer experience
in real-time.
“Becoming customer-adaptive means having the
ability to sense change, understand what motivates
customers at a deeper level, and adapt at the right
speed to ensure persistent relevance. The customer
must be at the centre of any adaptation. Ovum’s
research shows that 97% of enterprises are at risk.”
Jeremy Cox, Principal Analyst, Ovum⁷
Notes:
1. All sources are in the public domain
2. Any source stated is purely for the purposes of this
document and are void of any endorsement of MaritzCX
3. All trade names and marks belong to their respective
owners
⁶ The MaritzCX Global CX Assessment Report
⁷ Ovum Analyst Opinion: If you are not customer-adaptive your survival is at risk. February 2016