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 - Deloitte
Knowledge Management & Big Data
Making Smart Enterprise a Reality
March 2018
Knowledge Management & Big Data - Making Smart Enterprise a Reality March 2018 - Deloitte
Knowledge Management & Big Data - Making Smart Enterprise a Reality March 2018 - Deloitte
Knowledge Management & Big Data


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




Knowledge Management & Big Data - Making Smart Enterprise a Reality March 2018 - Deloitte
Knowledge Management & Big Data


                             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

Knowledge 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

Knowledge 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

Knowledge 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

Knowledge 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.

Knowledge 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

Knowledge 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
points, which has to be captured and
processed effectively to derive tangible

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
                                              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
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
                                              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

Knowledge 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
  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

                          •   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                                         Dynamic
                                                             Development                                      Fulfillment

                                                                                  Connected Customer

                               Connected products                            Rapid/Agile systems                         Connected community

                               Smart workforce                               Transparent value chain                     Omni channel distribution

Source: Deloitte Insights and Deloitte analysis

Knowledge 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

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%

                                  1516                   1241                      1203

     Future of work        Business process      Connected gaming/             Connected               Others
                             optimization          entertainment            commercial vehicles
          6%                     6%                     5%                        5%                    19%

Knowledge 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

                                               •   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

                                               •   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

Knowledge 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

                               +7.5%                                                                   1.65
                                                      313                             1.48
                         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

Knowledge 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
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
the systematic process of collecting,        create value and generate new revenue                    rom 2000 to 2017, the number of
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
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                                    •



                                                                              Web • M

                                                                                                                                            ue •
                                                                                                                                                 E-Mail • Dire
                                                                                       ce •





                                                                                                 lev                        er
                                                                                                    isio                o-Pe
                                                                                                        n•R           -t
                                                                                                           adio • Peer

Source: Deloitte Analysis
Knowledge 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
                        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
                                           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

Source: Deloitte Analysis

Knowledge 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

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
Knowledge 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
 • 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

Knowledge 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

Knowledge 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

Knowledge Management & Big Data

 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
                        implementation of knowledge management.                                Alignment

                                                                                                 4      Leader
 People &               Creation of a knowledge management culture                                      Competitor
 culture                is essential for a successful execution of the
                                                                           Technology            2      Follower Leadership &
                        KM strategy. Stimulation of the behaviors of
                        people in terms of knowledge sharing                                     1      Beginner

 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
                        management of core knowledge „assets" in
                        order to better access and exploit intellectual

                        Tooling must be in place to facilitate
                        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

Knowledge 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
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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