Knowledge Management & Big Data - Making Smart Enterprise a Reality March 2018 - Deloitte
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Knowledge Management & Big Data Making Smart Enterprise a Reality March 2018
Knowledge Management & Big Data
Contents
Foreword02
Message from Confederation of Indian Industry 03
Digital Transformation through Data and KM 04
Digital Manufacturing and Supply Networks 07
Customer Knowledge: to Serve them Better 13
Learning and Skilling: Data Empowering the People 18
About Confederation of Indian Industry 22
Acknowledgements23
Contacts23
References24
01
Knowledge Management & Big Data
Foreword
The paradigm shift towards the digital transformation among businesses is the need of the hour. Firms
are making continuous efforts to digitize their operations and investing huge amounts of money to
achieve the same resulting in increased revenue, cost reduction, improved customer satisfaction and
enhanced differentiation, and mitigation strategies for the risk of digital disruption.
The transformation is rapidly broadening the range of technologies to be used in the workplace. Among
the many, Knowledge Management (KM) is one of the key driving vehicles for the digital transformation.
Digital data needs to be appropriately used considering the company’s critical knowledge assets: its
core competencies, intellectual property rights, market and industry comprehension, and customer
understanding and expectations.
KM is the art of transforming information and intellectual assets into enduring value for an organization’s clients and its people. The
core objective of KM is to provide the right information to the right people at the right times to help people share experiences and
insights, and to improve the productivity of teams.
With the help of technology, businesses have developed robust software platforms to leverage KM strategies. These software
continues to evolve in response to new demands and challenges. Various data science techniques are being used to accomplish KM
objectives. In addition, recently, there has been an increasing interest in how organizations can leverage augmented and virtual
reality (AR/VR) in incorporating KM strategies in line with the objective of going digital.
Businesses need to map the strategic and critical knowledge for complete digital transformation. This helps in identifying those
knowledge assets that digital transformation can leverage, as well as highlights gaps in an organization’s knowledge network. KM
prevents staff from constantly reinventing the wheel, provides a baseline for progress measurement, reduces the burden on expert
attrition, makes visual thinking tangible, and manages effectively large volumes of information to help employees serve their clients
better and faster. KM, in the current scenario, is a necessary game changer.
Hemant Joshi
02Knowledge Management & Big Data
Message from Confederation of
Indian Industry
The world of knowledge management has always been exciting, with the move from data to information
to knowledge to wisdom holding out great promise for the future of companies, societies, and the whole
world. Today, the exponential search for new knowledge made possible by the proliferation of the
internet and smart phones and the dominance of the tech giants – Alphabet, Amazon, Apple, Facebook,
and Microsoft has made it imperative for the science of knowledge management to lead new thinking
and opportunities for global transformation!
The opportunity is not restricted to technology. The world is seeing rapid advances of cyber-physical
systems, changing the processes of manufacturing, distribution and logistics. It is widely believed that
Industry 4.0 will lead to the digitization of all physical assets and integration into digital eco-systems
with value chain partners. McKinsey & Co has called out four disruptions which make up 4.0 – data volumes, computational power
and connectivity, business intelligence and analytics capabilities and human-machine interaction advances like touch systems and
augmented reality. Each of these disruptions has enormous potential for change in corporate thinking, but together they create a
new knowledge—the world will need new ways to manage the technologies, processes, data analytics and culture needed for people,
companies, and societies to compete and succeed in the future!
Companies like DHL, Walmart, and Amazon are already practicing anticipatory logistics where demand is being forecast and
sometimes even created by intelligent suggestions to customers. Artificial intelligence has a key role to play in this anticipation
process with the entire sequence of demand forecasting, manufacturing, transportation and storage planning and maintenance of
transportation equipment riding on the ability to use AI well and deploy machine learning to provide adaptive knowledge through
the supply chain. Self-learning logistics processes, enabled by algorithms that recognize patterns and initiate action across the
logistics chain, will enable dynamic changes in volume and timing of shipments, inventory and stocking suggestions and pricing to
optimize product offtake and movement across the supply chain.
The pace of change is disrupting old jobs and creating new ones. As systems get more complex in every economic sector, the
opportunities for human intervention in the design and implementation process will always exist and indeed grow! The demand for
customer experience designers, customer behavior analysts, and new knowledge creators and disseminators will grow. We are in
for exciting times and the winners of tomorrow will be those who use both human and artificial intelligence and machine learning to
build cognitive value chains in all industries. KM practitioners of tomorrow will be at the center of digital transformation and must
prepare to lead!
Dr Ganesh Natarajan
Chairman, CII KM Summit
03Knowledge Management & Big Data
Digital Transformation through
Data and KM
Digital Transformation This spawned an entire generation of business opportunities using tools such
The usage of digital tools and tech-savvy consumers who are adept as IoT, AI/ML, etc. thereby transforming
technologies to transform business in using digital tools for discovering themselves and their product/factor
operations has steadily increased over products and experiences, while also markets.
the years. While the earlier generation sharing instantaneous feedback through
of enterprise business applications such social media channels. For businesses The key challenge for such
as enterprise resource planning (ERP), and brands, such consumer technology organizations to harness the real
customer relationship management platforms are not only an important potential of digital transformation is
(CRM), etc. helped digitize organizational distribution channel to invest, but having an integrated strategy and a
data and streamline information also an important source of customer holistic approach towards Knowledge
flow within large organizations, feedback, providing real time insights Management (KM) in the digital context,
advancements in technologies such in to the consumption patterns of their building internal systems and processes
as cloud computing, cost effective products and services. to streamline information exchange
communication systems, and business and data analytics, along with a
model innovations such as Software- This intersection of digital technology strong culture of data driven decision-
as-a-Service (SaaS) truly democratized across producer and consumer markets making. Traditional KM practices and
the access of digital tools and made brought enormous amount of structured platforms focused on capturing and
them viable even for small and medium and unstructured data into the realm of codifying explicit knowledge in to
enterprises (SMEs). This eventually business operations which, if harnessed digital artefacts, making the collective
brought entire industry value chains into properly, has a potential to transform organizational knowledge accessible
the digital fold, enabling shared data business models, generate new sources to the larger section of practitioners.
flow and information exchange across of revenue, optimize resource usage, While KM practices and tools achieved
the supply chain, such as producers, and maximize stakeholder value. considerable success across industries,
suppliers, distributers, and retailers, Successful organizations will be those more specifically in knowledge driven
thereby streamlining the delivery with a thoughtful and mature approach industries such as Consulting, IT, legal,
experience and bringing efficiencies and to business process re-engineering, pharma, life sciences, etc. they mostly
transparencies in the value chain. As design thinking, and technology focused on codifying explicit knowledge
‘data’ assumed center stage in decision assimilation to take advantage of such artefacts and providing search and
making, this resulted in a virtuous cycle opportunities. At the same time, a strong retrieve capabilities to discover the
of increased investments in technology culture of a data driven decision-making artefacts from the repository. The
and data infrastructure, and the vibrant is equally important to effectively missing element is the availability of
demand environment bringing down harness the power of data, generate contextual knowledge, with predictive
costs of digital tools further, accelerating information and build knowledge. and prescriptive suggestions, which
their mainstream adoption across the Exponential technologies such as can connect the dots specific to
ecosystem. Artificial Intelligence (AI), Machine the given problem, and suggest
Learning (ML), Internet of Things (IoT), suitable solutions. For organizations
On the other side, commoditization Robotics, etc. provide more advanced to maximize the benefits of digital
of consumer technology and the tools to accelerate such transformation, transformation, this capability assumes
emergence of web-scale IT systems and with their ability to collect, and process significance, as they now have access
e-commerce platforms brought digital large volumes of data at a faster rate. to large volumes of structured and
technology closer to the end consumers, Digital first organizations having an unstructured data across the value
supported by contemporaneous integrated approach towards technology chain. An efficient cognitive engine
advancements in smartphones and driven business operations are more supplementing the KM system that
the penetration of mobile internet. likely to focus on new and emerging can gather meaningful insights from
04Knowledge Management & Big Data
the data and provide for a seamless to perform a task. At the core, ML solutions faster. The value of such
discovery and delivery of contextual helps create a prediction engine for a tool increases manifold especially
knowledge to effectively address business applications that maximizes when the delivery of such contextual
business problems is the missing accuracy and minimizes errors. This knowledge is seamless. For example,
element in the KM value chain, which is achieved through experience (as displaying indicative solutions when a
can enable organizations achieve the algorithms get exposed to more practitioner searches the enterprise
success with digital transformation. and more training data) and feedback. knowledge base for a specific problem,
ML could be beneficial for any AI enabled chat-bots to help suggest
Knowledge Management and application that involves prediction, suitable approaches, etc.
Artificial Intelligence such as medical diagnostics, image
This is a domain where tools such as recognition, autonomous driving, AI/ML can also help organizations
advanced analytics, machine learning predictive maintenance, drug discovery address the missing link in their
(ML), artificial intelligence (AI), and etc. Whereas the focus of AI/ML is knowledge flow, which is in codifying
cognitive technologies could have towards building an accurate prediction tacit knowledge. Traditional KM tools and
maximum impact1. Early developments engine to address specific business processes are very effective in codifying
in AI started in the 1950s and evolved problems, the focus of KM tools is to explicit knowledge artefacts such as
over the years with progresses in help practitioners discover solutions operating procedures, know-hows,
areas such as natural language through search and retrieval of the etc. but fall short of brining the tacit
processing (NLP), text recognition, codified knowledge base. Essentially knowledge, which is more unstructured
speech recognition, robotics, etc. The AI/ML and KM are two sides of the and people dependent, to the benefit
field really took off with the advent same coin. An advanced KM platform of the larger organization. AI tools such
of machine learning (ML) and deep with a built-in AI engine can bring as natural language processing (NLP),
learning, which is teaching programs to contextual knowledge and predictive speech recognition, text processing
learn for themselves rather than giving models for a business problem and can be beneficial in sifting through
them exhaustive set of instructions help practitioners discover effective unstructured data sources such as
05Knowledge Management & Big Data
emails, community portals, enterprise forecast by Gartner2, IT spending in constituency. As programs such as
social networking platforms, etc. to India is projected to reach $87.1 billion BharatNet bring villages and remote
identify patterns and build a knowledge in 2018, which is 9.2% increase over locations to the broadband connectivity
map of the tacit expertise, which can 2017 numbers. While devices ($31.4 network, custom-built KM applications
then feed into the AI algorithms to billion) and communication services along with sufficiently trained work force
build predictive models for respective ($32.4 billion) contribute majority of can significantly improve desired social
business applications. this spending, IT Services ($14.3 billion) outcomes in the target areas.
and Enterprise Software ($5.7 billion)
This combination of AI and KM assumes are expected to grow at a faster rate, at Digital transformation has the potential
greater significance in the Indian 13.8% and 15.3% respectively. As Indian to disrupt existing business models,
context, as we move from being a data enterprises embrace technology led bring new sources of revenue, and
poor to data rich economy. Technology business processes, AI and KM can be redefine value chains. Successful
adoption in India is witnessing an an effective way to bring faster internal organizations will be those with a
increasing growth, due to the evolving transformation within the enterprises, thoughtful and mature approach to
market dynamics and favorable providing an opportunity to leapfrog business process re-engineering, design
policy environment. The market is the challenges faced with traditional KM thinking, and technology assimilation to
also witnessing the emergence of tools and processes. take advantage of such opportunities.
new age enterprises and startups In addition to this, a strong culture of
that use technology as a competitive Another important application of AI data driven decision-making is equally
differentiator to optimize operations and led KM is in social sector use cases, important to maximize the benefits from
provide better quality of experience to specifically in areas such as education the ever-increasing internal and external
consumers. Supported by the domestic and healthcare, which faces significant data sources, and transform the way
technology ecosystem through flexible challenges in the last mile delivery due organizations approach customer, talent,
business models delivered through to the bottlenecks in infrastructure and supply chain ecosystems. Effectively
cloud computing, digital technologies and man power. A cognitive KM can utilizing the organization’s explicit and
are witnessing widespread adoption act as an effective decision support tacit knowledge, accelerated through
across the industry value chains and system for primary care workers advanced cognitive tools such as AI/ML
for businesses across industries, working in remote locations to access will help them address the emerging
technology is becoming a core element the larger institutional knowledge base opportunities better and maximize
of business strategy. According to a and provide better services for their stakeholder value.
06Knowledge Management & Big Data
Digital Manufacturing and
Supply Networks
Manufacturing 2.0: Renaissance
in manufacturing
With the advent of Fourth Industrial
revolution (4IR) – the concept of
Manufacturing 2.0 is back to life.
Convergence of both physical and
digital world with the integration of
communication and collaboration
applications has led to creation of
a connected set of platform/s for
manufacturing applications.
Manufacturing 2.0 laid the foundation
for creating “Smart manufacturing”
and “Industry 4.0 architecture” in
operations. This is a quantum shift
from the conventional automation to
a fully connected and flexible system
– which involves horizontal integration
of all operational systems – enterprise
planning, design, warehouse etc. within
the organization and vertical integration
of manufacturing ecosystem.
Figure 1: Manufacturing 2.0
Manufacturing 1.0 Manufacturing 2.0
• Manufacturing Execution System (extended • Value driven manufacturing
to Manufacturing Operations Management – Web • Collaborative manufacturing ecosystem
MOM) • RFID and Sensor network across assets
• Paperless manufacturing • Smart and Mobile workforce
• Silo manufacturing • User centric interfaces
• Focus on Quality, Cost, Delivery Enterprise • Multi styled manufacturing set ups
Increasing manufacturing complexity and value proposition
Source: Deloitte analysis
07Knowledge Management & Big Data
This integration has led to a factory Figure 2: Managing digital data flow: Physical – Digital – Physical loop
gathering constant stream of data
from connected systems. This data
is further synthesized to learn and
adapt to demand, maintain assets,
track inventory and digitize operations,
thereby, creating an efficient and agile Information
factory. Capturing
Data Driven Transformation
It is estimated that 1.9 billion units
will be connected under IoT by 2020
Information Transfer via
in India3. The first major change Physical to Action Systems Digital
these devices will bring is a barrage
of information to add to the existing
data-complexity challenge. Every
interaction and transaction between
the supply chain leads to multiple data Synthesis and
Analysis
points, which has to be captured and
processed effectively to derive tangible
outcome.
With digitalization at the core of
every smart factory this end-to-end Source: Deloitte analysis
data / information flow needs to be
captured digitally. This is where Big
across all nodes in the supply chain. The products, these insights can result in
Data analytics plays a pivotal role of
next stage is the analysis of data where productive growth.
processing high volume, high velocity,
Big Data plays a significant role–it not
and high variety of data 4 . When coupled
only processes information but also has This change in the way technology
with cloud storage and IoT, which
to ability to automate process, innovate has increased the value of digital
translates physical data to digital form
based on findings to provide insights. information and the related potential to
and back to cyber physical systems,
When translated into action items either maximize enterprise value has resulted
organizations are handling tremendous
via human intervention or automatic in organizations evaluating their key
amount of data at higher rates at never
feedback loop across people, assets, and areas:
analyzed level of granularity for various
applications.
Business Process flow • Convergence of business IT and manufacturing IT
As shown in Figure 2, Big Data coupled • Product life cycle management for rapid development
with Industrial internet of things (IIoT) • Incorporation of latest Web and Enterprise applications
assist in continuous flow of information • Shared economy
from physical and digital worlds. This
Application architecture • Manufacturing architecture embedded on existing
entire process starts from information
assets
and data capturing. This process in
• Intelligent sensors, RFID tags, platform enabling virtual
enabled by digital interfaces, channels
simulations to see impact in real time
etc. across all stakeholders including
• AI driven machine learning to identify patterns for
suppliers and customers for end to-end
automation
integration.
Delivery and support • Augmented reality enhanced operations
In case of a manufacturing set up, models • Proactive issue identification and resolution
this real-time data capturing happens • Reduce time to market with minimized cost
via sensors and interlinked systems. Performance areas to • Inter-company and intra-company platform for
Cloud storage assists in storing high address complexity coordination (suppliers, out sourcing etc.)
volume historical and current data in a • High Product variety mix - Modular structure with ultra-
single system. It also supports in digital delayed differentiation
transfer for this information for access • Flexible work base
08Knowledge Management & Big Data
Smart factory: Integral to the fleet monitoring, fuel efficiency carry out predictive analytics, large
broader digital supply network tracking and translated them into real scale real time simulations, and cloud
In this evolution of current organizations time data for customers computing for distribution of analytics
to become a smart factory, it is clear to various nodes in the chain. And
• An electronics supplier uses IIoT to
that data and technology are major for a smart factory strategy which
connect in house devices, so that they
components in the path to creating a encompasses suppliers to customers,
learn from mistakes and produce
network. This smart factory is an integral cyber risk will present a great concern
intelligent algorithms independently
element in the broader digital supply and hence, cybersecurity is a priority
network which will integrate physical enabler.
These examples illustrate
assets and human assets to drive transformation across industries have
digitization of complex operations. Many Real time analytics: Virtual simulation
evolved from linear supply chain to a
sectors have already adopted the smart – to break down complex activities and
dynamic network which is digitalized.
factory concept in many areas. create a simulated environment for real
time e-learning. Augmented/Wearable
Digital Supply Network: Linear
Examples of smart factories: glass based enhances effectiveness of
to dynamic
operations compared to conventional
• A large global retailer analyses social As seen in the previous section, as
paper/e-paper instructions in areas
media chatter to optimize local every node in the supply chain becomes
such as product prototyping, remote
inventory assortment and enhance digitally connected the conventional
assistance during manufacturing/
inventory planning linear supply chain moves to an
maintenance, warehouse and logistics
interconnected dynamic supply network.
• High-tech semiconductor management.
Figures 3 illustrates the set of enablers
manufacturing company uses Smart
which will enhance the network to
Glasses to remotely support off-shore Digital manufacturing: Next gen
operate digitally to deliver key set of
manufacturing and assembly through additive manufacturing, smart materials
attributes.
on-demand knowledge sharing5 and nano technology coupled with IT
• A machinery and equipment convergence will drive the architecture
Data & connectivity: Base for supply
manufacturer uses technology for of dynamic manufacturing.
chain integration with IT. Big data to
Figure 3: Digital supply network
Data & Connectivity Real-time Analytics Digital Manufacturing
Enablers
• IIoT (Industrial internet of things) • Virtual simulation • Advanced robotics
• Big data analytics • Augmented reality • Nanotechnology
• Cloud computing • Open source desig • Additive manufacturing
• Cyber security • Smart materials
Intelligent Supply
Digital Supply Network
Integrated Smart
Planning Factories
Digital
Core
Digital Dynamic
Development Fulfillment
Connected Customer
Attributes
Connected products Rapid/Agile systems Connected community
Smart workforce Transparent value chain Omni channel distribution
Source: Deloitte Insights and Deloitte analysis
09Knowledge Management & Big Data
The interconnected network with
interactions from each node to every
other point of the network, allowing
for greater connectivity among areas
that previously did not exist, is what
differentiates the new digital supply
network (DSN). With digital at its core,
DSN can minimize all inefficiencies and
eliminate risks inherent in a linear supply
chain6.
Organizations approach towards
strengthening the supply chain
Internet of Everything (IoE) is defined
as intelligent connection of people,
process, data and Internet of things7.
This IoE will transform passive supply
chain into a live digital supply chain to
create transformational solutions. By
connecting people with process, data
and things (which are the core of DSN)
organizations have started realizing and
embracing the value potential this digital
ecosystem brings along. The overall IoE
market potential from investments and
savings on people, process, data and IoT
by the industries (VAS8) is estimated to
be INR 25000 billion by 2025.
Figure 4: Market potential for IoE in India (in INR billion)
4812 3980 1721
2210 2100
Smart factories Connected Security Time to Supply chain
marketing solutions market efficiency
19% 16% 9% 8% 7%
4851
1542
1516 1241 1203
Future of work Business process Connected gaming/ Connected Others
optimization entertainment commercial vehicles
6% 6% 5% 5% 19%
10Knowledge Management & Big Data
Smart factories, connected marketing, supply chain efficiency They have started building the required capabilities in the
and business process optimization contribute to ~ 50% of network based on the strategic lever they wish to focus and
the total potential opportunities in the private sector. Many pursue. For example, use of big data analytics for real time
organizations have realized that a digital business model and a demand driven forecasting seems to be one of the major focus
supportive ecosystem will help organizations achieve tangible areas across all sectors of business.
benefits in the near term.
Figure 5 provides an illustration of key transformation areas
Though overall digital transformation of the business is the followed by companies to strengthen the supply levers to
vital objective, companies have started their migration into the create a positive impact.
journey by strengthening multiple levers in the supply chain.
Figure 5: Key transformation areas
Supply chain levers Transformation areas Impact
• Data as a product or • High tail with delayed
service differentiation
Product • Prototyping with 3D
printing
• Sensor/data-driven • Rapid prototyping
design enhancements • Segmentation of
Process • Open innovation/ supply chain using
crowdsourcing analytics
• Big data analytics- • POS-driven auto- • Holistic decision making
driven demand replenishment • Improvement in
Planning forecasting • Advanced scenario Products and Service
• Dynamic real time planning levels
inventory fulfilment • Flexible Supply chain
• Inventory , Warehouse,
Logistics – Lower
• Augmented reality- • Sensor-enabled labor operating costs
enhanced operations monitoring • Collaborative
Manufacturing • Automated production environment – asset
• Predictive maintenance sharing, transparency
• Reduced supply chain
risk
• Dynamic/predictive • Digital documentation
Network, routing
Logistics & • Contracted and Spot Of
distribution logistics services
• Inventory-driven • Segmented/Targeted
dynamic pricing marketing
Sales • Sensor-driven • Predictive aftermarket
replenishment pushes
Source: Deloitte Insights, Deloitte analysis
11Knowledge Management & Big Data
Total Factor Productivity Though the degree of transformation may vary across
In India, manufacturing gross value added (GVA) has been industry sector and company, creating a collaborative digital
growing at a rate comparable to major economies, but supply network can assist in significant productivity benefits
productivity as a whole is a concern. With technology growing and gaining competitive advantage. While the impact of
at exponential rates effective foray into the evolving stream digitalization and intent to implement is evident across
would increase the Total Factor Productivity (TFP). segments, DSN is still playing a minor role in 2018.
Increasing the TFP would require rapid improvement in two In order to transform the existing network to digital network,
areas: organizations can create a digital blueprint for the organization
1. Creating a mobile workforce which can enable resource and start with laying a KM and data foundation where
reallocation from low productive sectors. technologies can be leveraged for quick wins.
2. Increasing productivity through technology.
Figure 6: Manufacturing GVA and TFP
Manufacturing GVA (in $ billion) Total factor productivity
1.89
1.73
+7.5% 1.65
313 1.48
290
242 262
219 231
1999-00 to 2007-08 to 1999 -2012 2011-12 to
2012 2013 2014 2015 2016 2017 2007-08 2011-12 2015-16
Source: RBI
12Knowledge Management & Big Data
Customer Knowledge: to Serve
them Better
Customer experience is defined as customers for the purpose of enhancing
the aggregate of all of a consumer’s sales, customer retention and customer
experiences with a company’s products, engagement metrics.
services, and the brand in context,
or brands9. While a strong customer Relevance of Customer Data in
experience has been shown to produce Digital Transformation 2.0
significant results—more customers, ustomer data is essential for retaining
C
more sales, and more loyalty—many existing customers and for converting
companies still struggle to identify the prospects into new sales. Implementing
plan of action that will best achieve new initiatives on delivering an
them. A strong and focused customer exceptional customer experience
knowledge management can help in this would require a comprehensive view of • Personalizing customer experiences
regard. customer data. • Creating deeper relationships through
data analytics
Customer Knowledge Management is ig data has often been shown to
B
the systematic process of collecting, create value and generate new revenue rom 2000 to 2017, the number of
F
preserving, sharing and utilizing streams for organizations. In the case of internet users rose from 400 million
customer data in order to build up customers, it can help in making better to 3.7 billion, nearly half of the world’s
meaningful customer experience decisions involving customers, through population.11 Not only are more
journeys and maintain enduring processes such as: customers online now, they are also
relationships. It refers to the methods switching between different devices and
• Mapping comprehensive customer
that may be used to capture, store, channels to get the experience that they
journeys
organize, access and analyze data about want.
With the increase in channels, more customer data is available than ever before.
Then Now
tact Center • In-
• Con Sto
ial re
oc •
S
Ca
•
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ile
ob
og
Web • M
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E-Mail • Dire
ce •
tpla
ct
e
M
k
ar
ail
M
•
Te
lev er
isio o-Pe
n•R -t
adio • Peer
Source: Deloitte Analysis
13Knowledge Management & Big Data
Data, in this century, has become as valuable as oil was in the last century. IDC research predicts that the ‘Digital Universe’ (i.e., the
data created and copied every year) will grow to be ~180 zettabytes in 202510 (from ~3 zettabytes in 2012). To understand the scale
of data flow, in the duration of 60 seconds on the internet in 201711:
• 3.6 million Google searches were performed
• 4 million YouTube videos were watched
• 18 million weather searches were carried out, and
• 100 million spam emails were sent
Data is taking new forms, increasing the variety, volume, and velocity of data.
Then Now
Geo-Spatial Data CRM Facial Recognition
Location Device Usage Social Listening
Survey
CRM Mobile
Crowdsourced Data
Sales Voice of Customer Data Sentiment
Location Media Monitoring
Online Sales Third Party
Source: Deloitte Analysis
In the increasingly customer-centric world, the ability to capture and use customer insights to shape products, solutions, and the
buying experience as a whole is critically important. Gallup research shows that organizations which leverage customer behavioral
insights outperform peers by 85% in sales growth and more than 25% in gross margin12.
The customer of today is increasingly in control of their own path and a review of the end-to-end customer journey can help
identify cut-in points for analytics opportunities.
Pre-Purchase & Discovery Purchase & Receipt Service & Maintenance Ownership & Community Repurchase
Awareness, Consideration Purchase Experience Advocacy Loyalty
and Evaluation
Likely customer High probability
defection? follow-on sale
Engage and create Actively engage loyalty
understanding of Make a Purchase programs, communities
Engage & company
community Decision Warranty
Make Use / Support
Compare capabilities of Product services Reassess
to wants and needs
Maintain / landscape &
Pick Up & Repair Product Shop for repurchase
Review & make a
Receive complimentary
recommendation
products
Next best offer X-sell / Up-sell
Exposure to opportunity? opportunity?
Client X brand
• See product / add • Interact w/ sales staff • Order parts • Join community / loyalty • Evaluate product
• Research product • Compare / negotiate • Book appointment program • Engage loyalty program
• Visit stores / dealers price • Drop-off product • Receive / make • Research competitors
• Visit website • Financing • Make payment comments • Compare prices
• Test product • Select / order / pick-up • Receive product • Refer potential • Visit stores / dealers
product or service customers
• Shop for related
products
Source: Deloitte Analysis
14Knowledge Management & Big Data
It has become important to get closer to customers
and re-evaluate the way the data about them is used.
By going beyond CRM systems and developing smarter
ways to learn more about customers, organizations
are taking the extra step in assimilating and analyzing
customer knowledge (e.g. putting sensors around
interactions to detect the subtlest shifts in customer
behavior).
With the growing number of sources of data over the
years - circumstantial data, situational data, behavioral
data, etc. ‘Big Data’ now presents endless opportunities
to uncover patterns about the different types of
customers and how they could be serviced in a more
efficient manner.
Customer Experience can now be supported by
analytics at various points such as:
Customer Journey
Pre-Purchase & Purchase & Receipt Service & Maintenance Repurchase Ownership & Community
Discovery • Real-time interaction • Cost to serve • Marketing mix • Customer lifecycle
• Customer management • Customer lifetime optimization engagement
segmentation • Targeted promotions value • Promotion • Social media analytics
analysis • X-sell / up-sell • Customer sentiment management • Experience gap
• Marketing ROI • Next best offer analysis • Follow-on sales identification
• Customer portrait • Product bundles • Prioritized product • Price setting • Loyalty management
• Customer growth innovation
Source: Deloitte Analysis
15Knowledge Management & Big Data
Businesses are increasingly using customer knowledge to generate insights, by harnessing and applying data analytics at every
opportunity to differentiate its products and customer experiences, across the entire customer life cycle. Some examples of
businesses differentiating and winning using insights from customer knowledge are noted below16.
Examples of Businesses driven by Customer Knowledge
• Travel – analyzing customer data to increase sales, ROI and customer satisfaction
Tracking customer behaviors, preferences, purchases and demographics. Customer insights are used to power marketing,
redefine the frequent flyer program and broaden business offerings13.
• Hospitality – personalized, unforgettable experiences for guests
Utilizing master data management, analytics and data governance towards a solution that caters to users across geographies
and across the company—from sales, marketing, real estate, finance, and hotel operations. For example, customer
preferences and experiences are shared across all hotels, leading to highly valued customer experiences on future visits14.
• Banking – customers get the right product at the right time, over the right channel
Transformation from a transactional bank to a relationship-oriented bank by using data mining, master data management
and marketing optimization to delight customers. Also using predictive modelling and optimization methodologies to help
business lines identify potential opportunities to improve customer retention15.
• Financial services – attractive student loan pricing for millennials
Providing options at better price points using a customer’s data and is not dependent on credit scores. Collecting up to
100,000 data points per customer and measures and records loan officer actions in detail.
• Personal shopping services – algorithms and expertise to provide customized styling at lower costs
Shipping clothing to customers based not only on sophisticated algorithms but also on feeding user profiles, preferences
and feedback into their algorithms. As a result, it can sell clothes in a highly personalized way and more efficiently than its
competitors.
• Media – customer insights for relevant articles and ads
Focusing on measuring and understanding how readers connect and engage to deliver an optimal user experience. Data
insights are used to improve newsroom workflow, give journalists an understanding of how readers engage with their work,
and allow executives to make data-driven decisions about company strategy16.
Digital Trends impacting personalized, consistent, and integrated in-store activities such as comparison
Customers by utilizing Big Data shopping experience across all points of of product pricing, obtaining product
and Knowledge Management17 contact between them and “The Digital information, checking product
With the industry rife with digital Customers”. availability, etc18 .
innovation and organizational change,
consumers have become drawn to the The key Digital Trends in Customer
Case study: Amazon Go – A check
ease and convenience of always being Experience revolve around “The Digital
out-free shopping experience
just a click away from user reviews, customer” experience as the pivot, with
In December 2016, Amazon introduced
comparison pricing, and endless merchandizing, promotions, loyalty
Amazon Go, a 1,800 square foot
aisles and have come to rely on programs as well as point of sale (POS)
grocery store in Seattle with the most
online and mobile shopping. It is no related digital solutions enriching the
advanced shopping technology so
surprise that traditional retailers are overall experience.
customers can shop and then walk out
bringing digital channels into stores to
with their products without waiting
tap those consumer preferences. Functionality across Touchpoints
in lines or checking out. Shoppers
At the same time, historically pure-play Increasingly connected and informed
use the Amazon Go app and the
online retailers are increasingly opening consumers are now digitally influenced
store is enabled with their “Just Walk
brick-and-mortar shops in high-profile across each touchpoint of their
Out” shopping experience, which
locations, seeking to capitalize on the purchase journey right from inspiration
leverages multiple technologies such
tangible experiences that cannot be to purchase validation. According to
as computer vision, sensor fusion and
delivered through a device. Deloitte’s Retail sector study, 71% of
machine learning. The virtual shopping
Indian shoppers use digital before their
cart tracks items and when leaving the
Both traditional stores and pure-play purchase journey. Nearly 70% of Indian
store, the shopper’s Amazon account
online retailers are working towards shoppers prefer digital devices (own or
will be charged.
the same goal: to create a highly kiosk) rather than sales associates for
16Knowledge Management & Big Data
Digital Merchandizing & Promotions becomes more empowered. Technology competitors’ pricing instantly but also
Digital technologies help address enabled loyalty programs have seen track prices over time and forecast
merchandizing challenges (space a significant success globally. Loyalty changes. Online retailers are following
allocation, store layout, promotion programs have moved beyond paper advanced dynamic pricing strategies
hotspots, etc.) and also hold great and plastic to mobile apps. to respond to price changes in less
promise for improving the presentation than an hour. In addition, shoppers
of store merchandise. While digitally Case study: Starbucks are increasingly seeking customized
enabled promotions have been Starbucks has been credited with engagement and personalized deals
traditionally linked to online sales, revolutionizing the coffee industry. that reflect their needs.
retailers are increasingly using them to Starbucks Rewards is often
drive in-store sales through personalized regarded as one of the best retail Payments and checkout is a
promotions. loyalty programs in existence. They significant pain point for customers
have created a loyal following of in retail stores as they encounter long
Case study: Carrefour – Leveraging customers both with their customer queues severely impacting shopper
Internet of Things via iBeacon to experience and revolutionary experience. Globally, electronic-based
collect consumer data rewards program. payment instruments are extensively
French supermarket chain Carrefour 1. Starbucks uses geo-targeting well adopted due to advancement in
is one of the first retailers to when it prompts its customers to financial transaction technology. A
extensively pilot iBeacon networks enter a store that is close to where drastic decline has been witnessed
across its stores. Customers can use the customer is located. since 1980 in the use of cash to
mobile phones or tablets attached Effective Mobile Experience:
2. purchase goods in developed countries
to shopping carts to receive in-store Starbucks’ app makes their loyalty such as the United States, France, etc.
routes and personalized promotions. program more interactive and Electronic payments account for ~ 60%
As customers are guided around more effective. The app makes of all consumer transactions in these
the store, the beacons collect data it easy to see how many “stars” countries19.
about their behavior and purchasing (points) you currently have, as well
patterns, which the retailer uses to as make orders and payments right Instead of legacy payment terminals,
continuously improve operations from your phone. You can even Indian retailers too can utilize
and store layout. With more than use the service to find the nearest self-checkout options which can
600 beacons deployed across Starbucks location. be integrated with digital wallets
28 supermarkets, Carrefour has Collaboration and tie up with
3. or Aadhaar Pay/UPI. Initiatives
seen a 400% increase in its digital other Retail stores: Starbucks such as Samsung Pay or Apple Pay
application’s engagement rate and a was able to expand the scope of which are integrated with customer
600% increase in app users. its loyalty program by introducing smartphones can also be tapped for
points for purchases outside of faster payment. Retailers can also
Loyalty Programs their retail locations. Starbucks sells leverage some of these terminals
Customer loyalty and engagement can many products outside of their retail that are being used to revolutionize
make or break companies, and as such, locations, including: coffee beans, the Banking & Public Distribution
loyalty reward programs represent tea, K Cups, and ready to enjoy worlds by providing accessibility to
drinks. electronic banking service in Rural &
strategic investments for all types of
organizations. The breadth and variety Urban Districts.
of reward programs is vast, ranging from Pricing and Point of Sale (PoS) • Examples of Services: Aadhaar
tiered points program to upfront fee solutions Enabled Payment System (AEPS),
program In this digital retail era, retailers Aadhaar e-KYC, RuPay, Digital
can no longer rely on traditional Wallets
As choice increases, loyalty becomes pricing methods. Sophisticated price
more fragile, and “The Digital Customer” comparison engines not only display
17Knowledge Management & Big Data
Learning and Skilling:
Data Empowering the People
Knowledge Management and other critical inputs remain unutilized reducing challenging and limiting
Organization Learning not because of lack of intent, but due to features. Identifying and sharing critical
The world is moving fast with a gaps in skills to manage the information, business knowledge and developing
humongous impact of technology and which has not evolved along with the an enterprise wide knowledge base for
the workplace is changing in multiple development and implementation problem solving and business solutions
ways. For organizations, it is imperative progress of digital interventions. are vital features of a well implemented
to not only move progressively along and managed KM program. It is integral
but also to be conscious of the impact In this scenario, the criticality of KM to build and develop the processes
of this technological and digital comes to the forefront. KM has become to touch and align everyday work
interventions in the workplace. The a necessity to address the realities processes of each job role in the
onset of digitization in most business in the current world of business and organization. Knowledge Management
processes and the increasing avenues operations. This helps in channeling processes deliver measurable
of digitization has created the need the decision making capabilities in business benefits. However, very few
for businesses to respond effectively, organizations towards a stronger data organizations have actually come afar
and in an appropriate and agile driven culture. An integrated approach in utilizing and driving an effective and
manner. This digital output from the towards developing and implementing business oriented program. The benefits
business processes are considerably Knowledge Management systems and are multiple as we have seen through
larger in comparison to the hitherto processes will go a long way in data our own global KM implementation
simpler dataset, approached through analytics, and in developing and aligning providing support for a solid grounding
computations with month-to-date business strategy to achieve targeted in business strategy, operational
(MTDs) and year-to-date (YTDs) metrics business outcomes. There are multiple priorities and organizational learning.
for comparison. Most businesses are misconceptions on KM, and the primary Multiple levels and layers of connecting
yet to come to terms with this amount concern is that, it is viewed as a one and networking within an organization
of data and the necessary mechanisms off standalone project that needs to is possible for sharing information,
to derive actionable inferences. Further, be delivered and done with. Other knowledge and also standardizing the
businesses are yet to find solutions for misconception is that KM is a document ways of work and problem solving
employees to help them derive insights management or a technology solution methods.
from the available data, and are yet that is similar to that of an online
to train them on the optimal methods library. As per the definition of Gartner Knowledge Management
to sieve, understand the trends and “Knowledge management is a formal Framework
patterns, make interlinkages and learn ‘process’ that evaluates a company’s A well-established KM framework goes
to retrieve the data on an ongoing basis. organizational processes, people and a long way in building a sustainable
technology, and develops a system that and progressive organization culture.
At this junction, it is important to note leverages the relationships between There are around six critical and core
that at an individual level, adoption of these components in order to get the components that make an integrated KM
technology and its integration with the right information to the right people at framework 20. These include
individual’s work flow has improved the right time to improve productivity.” 1. Strategic Alignment
vis-à-vis the technology adoption at 2. Processes and Organization
organizations. The other thing to note Knowledge Management is a structured
3. Leadership and Governance
is that with digital interventions in and systematic process that aids the
business in retrieving current and 4. Content and Context
businesses, the expectations of change
from the end-user to the last leg of the past organizational learning valuable 5. Technology
change is yet to be managed effectively. to business in increasing positive 6. People and Culture
In most cases, data, information and and profit bearing occurrences and
18Knowledge Management & Big Data
Strategic
Alignment Success requires knowledge management
initiatives that contribute to the business
strategy & goals
Leadership & A governance structure with clear roles
Governance and responsibilities is essential to defining,
driving, controlling and overseeing the
Strategic
implementation of knowledge management. Alignment
4 Leader
People & Creation of a knowledge management culture Competitor
3
culture is essential for a successful execution of the
Technology 2 Follower Leadership &
KM strategy. Stimulation of the behaviors of
Governance
people in terms of knowledge sharing 1 Beginner
1
Processes & Processes provide a structure that allows
Organisation for consistency and standardization in the Content & People &
Context Culture
capture, organization, maintenance and
dissemination of knowledge
Process &
Content & Focus on the identification, capture and Organisation
context
management of core knowledge „assets" in
order to better access and exploit intellectual
capital
Tooling must be in place to facilitate
Technology
the knowledge management process of
capturing, organising, searching, maintaining
and disseminating the knowledg
Source: Deloitte Analysis
Revisiting the components bearing into daily work activities. Technology customer data, sales data, employee
clarity and directed towards business helps to facilitate collaboration across data, process related data, transaction
objectives is vital to drive the the organization providing the right data, operational metrics and ongoing
framework and structure. Conducting a content and expertise to the right data. Apart from converting, channeling
current state assessment, mapping and people at the right time; opting for and accessing data and outputs thereof
identifying gap analysis, developing a the right technology and digital from data through learning solutions
transitioning plan to the future state interventions is a critical organization and KM framework, there is also focus
learning and KM operating model which commitment. on how to leverage the benefits of Big
includes organization structure, roles Data in other ways including playing
and responsibilities, governance model, Challenges a role in driving business insights.
and decision-making rights. Knowledge Utilizing the data to lead an Insight
There are however many challenges
management requires tools and Driven Organization (IDO) is the
in approaching the Big Data that
technology to support the integration way forward in a world where every
organizations churn out today including
and automation of knowledge sharing organization has tried most things in
19Knowledge Management & Big Data
having changed from Soft skills to
Managerial Skills.
• Technical and Functional Learning is
primarily driven through a Skill Matrix
and Competency Model
• Managerial Skills are built through
standard programs in Effective
communication, Decision making,
Negotiation and Influencing skills
and at higher levels maybe through
executive programmes at top schools
This approach may not be effective
in addressing the disruptive changes
that the economy and businesses
are moving towards in this digitally
connected globe. In comparison, there
are organizations who have taken a
leap ahead to drive learning through
bite sized portions through mobile
the maturity continuum to thrive ahead not merely metrics and numbers but technologies that could be effectively
in business. The technical challenges meaningful to the vision and business on the job, on the go and reap
that organizations face today may be as objectives of the organization leading business benefits moment by moment
basic as not having a policy or decision to sharp questions that could create in transactions and operational
in approaching and harnessing the resolve and bring about solutions that processes.
big data. A centralized approach for are impactful.
capturing and analyzing Big Data is Skilling and learning capabilities within
yet to be formulated. Identification of an organization should be driven by
Learning through Big Data in
proper technology or infrastructure business priorities, and Knowledge
Organizations
to capture data, even identifying Management and Learning Solutions
Organizations should proactively
whether captured data as relevant that deliver customer driven learning in
develop systems and processes to
or overwhelming. There is a common agile, easier forms is the key to today’s
capture and disseminate for potential
thread of a lack of understanding learning and development teams.
learning through the rich data that it
analytics from a deep perspective, Moving away rapidly from annual
generates and circulates. The onus
though organizations have come to training calendar and scheduled training
and responsibility now lies with the
speak of Big Data, AI, Bots as everyone format to business operations training
leaders in the organization to drive this,
are doing it in the marketplace. in a digital mode is a key enabler for the
futuristic leadership has saved many a
new age workforce.
company from perishing and taking it to
Another challenge is the availability the next level of digital transformation
Going forward, the supply chain for
of talent and the capabilities required catering to a population that is born and
talent is becoming more mobile, non-
to understand and leverage Big Data thriving in a digital environment. This
structured and over time may not
to add value to the organization in a can only be driven by quickly assessing
even be full-time employees. In a gig
purposeful and meaningful manner. the organization’s current capabilities
economy the talent and employment
Acquiring Data Scientists with expertise to drive this agenda and also come up
contracts will be for fixed terms,
in math, statistics, data engineering, with a digital agenda in learning and
sharply communicated deliverables,
pattern recognition, advanced reskilling the organization in view of the
purposeful network of project teams
computing, visualization and modelling exponential change in the expectations
who may not come together again even
may pose challenges or even organizing of customers who come in myriad forms
as the business objectives are met.
business analysts’ team with strong and needs.
Knowledge Management and Learning
knowledge of company ecosystem may
Solutions have to be creative and
not be completely feasible.
Conventional and traditional innovative to update the teams with
organizations look at learning from two business expectations, making them
The acknowledgement of the criticality folds i.e. Technical / Functional learning come to speed with business outcomes
in converting data into insights that are and Managerial Skills, the nomenclature and perform as they come with no time
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