The Economist Intelligence Unit surveyed over 600 business leaders worldwide and across different industry sectors about the use of Big Data in their organizations.
The research confirms a growing appetite for data and data-driven decisions and those who harness these correctly stay ahead of the game. The report provides insight on their use of Big Data today and in the future, and highlights the advantages seen and the specific challenges Big Data has on decision making for business leaders.
Key findings:
75% of respondents believe their organizations to be data-driven
9 out of 10 say decisions made in the past 3 years would have been better if they’d had all the relevant information
42% say that unstructured content is too difficult to interpret
85% say the issue is not about volume but the ability to analyze and act on the data in real time
Data Governance, the foundation for building a succesful data managementTentive Solutions
This Whitepaper clearly explains how the Data Governance function plays a key role and which factors are of great importance in successful data management. Also available in Dutch.
Data Governance, the foundation for building a succesful data managementTentive Solutions
This Whitepaper clearly explains how the Data Governance function plays a key role and which factors are of great importance in successful data management. Also available in Dutch.
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalSubrahmanyam KVJ
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
Businesses face a multitude of challenges in today’s environment. The overall speed of business is constantly increasing. Decisions are made within minutes and channels are diversifying rapidly. Perhaps most importantly, face-to-face interaction has started to become a luxury, rather than a necessity or consequence of everyday behavior.
How can banks maximise the value of their customer data?Ben Gilchriest
Almost all banks say that being customer centric is important to them and yet only a small proportion of customers believe that their banks really understand their needs and wants well enough (only 37%). This may be surprising given how much data banks have on their customers - a figure that has only been increasing over the past few years as more and more interactions become digitized. Add to this new sources of data which are available now on preferences,via social media, and increasingly available on location and physiology (see; http://bengilchriest.tumblr.com for more on this)....and the opportunity for better customer understanding becomes huge.
With a 90% of banks citing "big data" as key to long term success, where's the disconnecting coming from? In this study it's clear that the main challenge is that data is not sufficiently well pooled to realise the benefits of cross-referencing to gain insight. Coupled with the fact that not enough time is spent on analysis and the gap between the intent and customer's view becomes clearer.
So what can banks do about this? This paper describes some of the key challenges, which may be familiar to you, and some insights into how to scale up to the next level of customer analytics.
It includes a high level tool to assess your big data maturity.
Big Data Open Source Tools and Trends: Enable Real-Time Business Intelligence...Perficient, Inc.
Most organizations still rely on batch and offline processing of data streams to gain meaningful analysis and insight into their business. However, in our instant gratification world, real-time computation and analysis of streaming data is crucial in gaining insight into patterns and threats. A trend is emerging for real-time and instant analysis from live data streams, promoting the value of logs and a move toward functional programming.
This shift in technology is not about what and how to store the data, but what we can do with it to see emerging patterns and trends across multiple resources, applications, services and environments. Log data represents a wealth of information, yet is often sporadic, unstructured, scattered across the enterprise and difficult to track.
These slides provide insights into some of the most helpful Big Data tools used by the largest social media and data-centric organizations for competitive trends, instant analysis and feedback from large volume data streams. We show how how using Big Data tools Storm, ElasticSearch and an elastic UI can turn application logs into real-time analytical views.
You will also learn how Big Data:
Contains data that is elastic, minimally structured, flexible and scalable
Helps process live streams into meaningful data
Promotes a move toward functional programming
Effects the enterprise data architecture
Works with real-time CEP tools like Storm for functional programming
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalSubrahmanyam KVJ
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
Businesses face a multitude of challenges in today’s environment. The overall speed of business is constantly increasing. Decisions are made within minutes and channels are diversifying rapidly. Perhaps most importantly, face-to-face interaction has started to become a luxury, rather than a necessity or consequence of everyday behavior.
How can banks maximise the value of their customer data?Ben Gilchriest
Almost all banks say that being customer centric is important to them and yet only a small proportion of customers believe that their banks really understand their needs and wants well enough (only 37%). This may be surprising given how much data banks have on their customers - a figure that has only been increasing over the past few years as more and more interactions become digitized. Add to this new sources of data which are available now on preferences,via social media, and increasingly available on location and physiology (see; http://bengilchriest.tumblr.com for more on this)....and the opportunity for better customer understanding becomes huge.
With a 90% of banks citing "big data" as key to long term success, where's the disconnecting coming from? In this study it's clear that the main challenge is that data is not sufficiently well pooled to realise the benefits of cross-referencing to gain insight. Coupled with the fact that not enough time is spent on analysis and the gap between the intent and customer's view becomes clearer.
So what can banks do about this? This paper describes some of the key challenges, which may be familiar to you, and some insights into how to scale up to the next level of customer analytics.
It includes a high level tool to assess your big data maturity.
Big Data Open Source Tools and Trends: Enable Real-Time Business Intelligence...Perficient, Inc.
Most organizations still rely on batch and offline processing of data streams to gain meaningful analysis and insight into their business. However, in our instant gratification world, real-time computation and analysis of streaming data is crucial in gaining insight into patterns and threats. A trend is emerging for real-time and instant analysis from live data streams, promoting the value of logs and a move toward functional programming.
This shift in technology is not about what and how to store the data, but what we can do with it to see emerging patterns and trends across multiple resources, applications, services and environments. Log data represents a wealth of information, yet is often sporadic, unstructured, scattered across the enterprise and difficult to track.
These slides provide insights into some of the most helpful Big Data tools used by the largest social media and data-centric organizations for competitive trends, instant analysis and feedback from large volume data streams. We show how how using Big Data tools Storm, ElasticSearch and an elastic UI can turn application logs into real-time analytical views.
You will also learn how Big Data:
Contains data that is elastic, minimally structured, flexible and scalable
Helps process live streams into meaningful data
Promotes a move toward functional programming
Effects the enterprise data architecture
Works with real-time CEP tools like Storm for functional programming
This is a short closing keynote speech delivered at the NLDITA 2011 conference in Utrecht. In a number of publications, DITA maturity is seen as the maturity of the company that is using DITA. In my presentation, I briefly talked about maturity of DITA tools and the direction in which they should evolve to make DITA a viable alternative for a much wider, not-so-technical, audience of technical authors.
Localization Maturity is about matching your organization’s expectations on global communication outcomes with the right people, processes and technologies. As organizations grow and their localization (translation) needs grow, that organization will have to move through stages of maturity.
Our Global Communication Maturity Model™ 2.0 shows an enhanced view of the stages through which organizations will progress when implementing a global communications strategy. Each stage represents a point in time when an organization faces unique challenges that must be met and built upon in order to move forward on their global business path. Click on the Maturity Model to enlarge and ask yourself… where would you place your organization?
Various groups in your organization may have different goals but alignment of those goals is on the maturation model. All groups are working toward moving the organization further on the model – toward efficiency, cost savings, quality in communications, less risk exposure……a better handle on International.
Click on the image of our model below to print and ask your group where they feel your organization is RIGHT NOW in their current stage.
Be the one to bring in the map to start the discussion, to find a strategy, to move forward, to make decisions. Be the one who shows how to get a handle on International…
Multimedia and Big Data are closely related topic. Big data enables solving some important challenges in multimedia and basic principles of multimedia are the key issues in multimedia.
Incentive Compatible Privacy Preserving Data Analysisrupasri mupparthi
Now a days, data management applications have evolved from pure storage and retrieval of information to finding interesting patterns and associations from large amounts of data. With the advancement of Internet and networking technologies, more and more computing applications, including data mining programs, are required to be conducted among multiple data sources that scattered around different spots, and to jointly conduct the computation to reach a common result. However, due to legal constraints and competition edges, privacy issues arise in the area of distributed data mining, thus leading to the interests from research community of both data mining.
In this project each party participates in a protocol to learn the output of some function f over the joint inputs of the parties. We mainly focus on the DNCC model instead of considering a probabilistic extension. Deterministic Non Cooperative Computation needs to be extended to include the possibility of collusion.
This tutorial/lecture addresses various aspects of the graph traversal language Gremlin. In particular, the presentation focuses on Gremlin 0.7 and its application to graph analysis and manipulation.
Small Is Beautiful: Summarizing Scientific Workflows Using Semantic Annotat...Khalid Belhajjame
Scientific Workflows have become the workhorse of BigData analytics for scientists. As well as being repeatable and optimizable pipelines that bring together datasets and analysis tools, workflows make-up an important part of the provenance of data generated from their execution. By faithfully capturing all stages in the analysis, workflows play a critical part in building up the audit-trail (a.k.a. provenance) meta- data for derived datasets and contributes to the veracity of results. Provenance is essential for reporting results, reporting the method followed, and adapting to changes in the datasets or tools. These functions, however, are hampered by the complexity of workflows and consequently the complexity of data-trails generated from their instrumented execution. In this paper we propose the generation of workflow description summaries in order to tackle workflow complexity. We elaborate reduction primitives for summarizing workflows, and show how prim- itives, as building blocks, can be used in conjunction with semantic workflow annotations to encode different summariza- tion strategies. We report on the effectiveness of the method through experimental evaluation using real-world workflows from the Taverna system.
The Conference Board of Canada, 52 pages, April 2013
Report by Vijay Gill, Crystal Hoganson, David Stewart-Patterson
Note - Door to Door postal service is slated for cancellation in Canada, and this "objective" report, is problematic as Canada Post's CEO, Deepak Chopra is a board member of the Conference Board of Canada, which tempers the analysis significantly.
Strengthening CMMI Maturity Levels with a Quantitative Approach to Root-Cause...Luigi Buglione
This presentation discusses and analyzes the opportunity to approach a typical TQM qualitative technique such as Root-Cause Analysis (RCA), expressed with the well-known Ishikawa (or Fishbone) diagrams, in a quantitative manner. Adding a control measure at the end of each ‘cause bone’ can help decision-makers in their determination of corrective/improvement actions to take in terms of how much to introduce into the related action plan in the way of resources. The ISO 15939 Measurement Information Model can be the appropriate technique to help derive such measures, as it overcomes the intrinsic limitations of Orthogonal Defect Classification (ODC) by providing direct process improvement at ML2 on Measurement & Analysis (ME) and at ML3 on Organizational Process Focus (OPF), in particular taking care of the specific practice SP1.4 concerning the establishment of the organization’s measurement repository), as well as on the General Practice GP2.8 (Monitor & Control the Process) across all the process areas involved in each single cause-effect analysis. The communication issue is discussed, an alternative way to express Ishikawa diagrams is proposed (Mind Maps), and how Mind Maps can facilitate the diffusion of RCA and its quantitative variant (Q-RCA) into organizations, and subsequently be used by organizations, is shown.
CMI Presentation on Organisational Change Maturity Modelkyliemalmberg
On 22 March Caroline Perkins, MD of Carbon Group and President of the CMI, shared her latest research and work from her new book. The Maturity Model supports you and your organisation in becoming more agile with clear levels that you can aim for.
The Digital Workplace Maturity Model – Going Beyond the Intranet
What does it take to move from a traditional intranet to something that supports all aspects of a digital workplace?
* How do the dimensions of community, collaboration, communication, services and structure interrelate?
How should your organization’s strategy dictate the profile of your digital workplace?
What can we learn from similar systems about how intranets can evolve?
Sam Marshall, Director, ClearBox Consulting Ltd.
From IntraTEam Event Copenhagen 2011 #iec11
Big Data, Big Deal? (A Big Data 101 presentation)Matt Turck
Background: I prepared this slide deck for a couple of “Big Data 101” guest lectures I did in February 2013 at New York University’s Stern School of Business and at The New School. They’re intended for a college level, non technical audience, as a first exposure to Big Data and related concepts. I have re-used a number of stats, graphics, cartoons and other materials freely available on the internet. Thanks to the authors of those materials.
Internet of Things Landscape (Version 3.0)Matt Turck
(Latest/revised version uploaded December 23, 2014)
It's been about 18 months since my original attempt at charting the Internet of Things (IoT) space. To say the least, it's been a period of extraordinary activity in the ecosystem.
While the Internet of Things will inevitably ride the ups and downs of inflated hype and unmet expectations, at this stage there's no putting the genie back in the bottle. The Internet of Things is propelled by an exceptional convergence of trends (mobile phone ubiquity, open hardware, Big Data, the resurrection of AI, cloud computing, 3D printing, crowdfunding). In addition, there's an element of self-fulfilling prophecy at play with enterprises, consumers, retailers and the press all equally excited about the possibilities. As a result, the IoT space is now reaching escape velocity. Whether we're ready for it or not, we're rapidly evolving towards a world where just about everything will be connected. This has profound implications for society and how we collectively interact with the world around us. Key concerns around privacy and security will need to be addressed.
Driving A Data-Centric Culture: The Leadership ChallengePlatfora
Embracing data as a corporate asset—and a source of competitive advantage—is not just a “good idea” that companies should consider. Such adoption will help determine the winners and losers across multiple markets and industries in the future.
In the last couple of years, corporate focus has shifted: first, from investing in the right technology and tools; then to acquiring the right talent and skills; and now to building the right organizational culture that can realize the business value of powerful big-data analytic tools.
Most organizations today are still focused on putting in place the right technology and talent, but others have evolved further and are working toward fostering a data-centric corporate culture.
Infochimps Survey: What IT Teams Want CIOs to Know About Big Data - Learn the top items that IT team members would like their CIOs to understand concerning their Big Data projects.
The report - CIOs & Big Data: What Your IT Team Wants You to Know - is based on a survey of more than 300 IT department employees, 58% of whom are currently engaged in Big Data projects, and aims to identify pitfalls that implementation teams encounter, and could avoid, if top management had a more complete view.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
Views From The C-Suite: Who's Big on Big DataPlatfora
he way that big data pervades most organizations today creates a dynamic environment for C-level executives to explore how it can and should be used strategically to add business value.
While each C-level executive views big data through a unique lens, a strong consensus exists among them about the need for effective big data analytics across their organizations.
This Economist Intelligence Unit report shows that senior executives are optimistic about both the capabilities of big data and the impacts such data can have on their businesses.
Download the report to get the whole story.
Data driven culture in startups (2013 report)Geckoboard
Results of a global survey of 368 startups carried out by Geckoboard and Econsultancy into startup organisations use data to drive their business and establish a culture.
Data-driven decision-making is an incredible process that helps data science professionals boost their businesses. Explore DDDM in detail and learn how you can master it in 2024
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalCapgemini
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
Survey Results Age Of Unbounded Data June 03 10nhaque
Enterprises today can generate, collect and consider more data than ever before. New types of data can provide insight into previously opaque processes and motivations, but prodigious quantities of data present opportunity, as well as complexity and distraction. nGenera Insight’s 2010 Leading in an Age of Unbounded Data survey garnered responses from over 70 major organizations, including many global corporations, to provide a cross-industry pulse of the state of enterprise data.
COVID-19 heightened chronic challenges within the global healthcare industry. It became a catalyst amid fierce competition and tight regulations for health providers and payers to focus on digital health, cybersecurity, patient data transparency, and a variety of customer-centric and operational enhancements. As a result, we found the 2022 trendline pointing to improvements in access and quality of care.
Healthcare challenges such as optimizing the cost of care while simultaneously enabling personalized interventions and consumer-friendly shoppable services are long-standing − but, historically, the industry has been slow to react.
Read our Top Trends 2022 report to examine the lingering ramifications of the pandemic, responses from medical and insurance organizations, and the worldwide impact of ever-changing regulatory standards and mandates.
A combination of factors − the pandemic, catastrophic weather events, evolving policyholder expectations, and insurers’ drive for operational efficiency and future relevance − are sparking P&C industry changes.
In a post-COVID, new-normal environment, the most strategic insurers are building resilient, crisis-proof enterprises poised to take advantage of emerging and future business opportunities. They are leveraging advanced data analytics and novel technologies to assure agility and achieve positive revenue and customer satisfaction outcomes. Competitive advantage will hinge on accelerated digitalization and faster go-to-market. Therefore, win-win partnerships and embedded services with InsurTechs and other ecosystem players are critical.
Read Capgemini’s Top P&C Insurance Trends 2022 for a glimpse at the tactical and strategic initiatives carriers are undertaking to boost customer-centricity, product agility, intelligent processes, and an open ecosystem to ensure profitable growth and future-readiness.
This analysis provides an overview of the top trends in the commercial banking sector as they shift to technology high gear to boost client efficiency and battle a volatile, uncertain, competitive, and evolving landscape.
First, it was retail banking. Now, advanced technology is shifting to – and disrupting − the commercial banking space. Many commercial banks, known for paperwork, red tape, and branch dependency, were unprepared to support clients during their post-COVID-19 ramp-up. But now, the digital pivot to new mindsets, partnerships, and processes is in overdrive.
As commercial banks grapple with competition from FinTechs, BigTechs, and alternative lenders, their inability
to fulfill SME demands and pandemic after-shocks necessitates transformative process changes and a move
to experiential, sustainable, and inclusive banking models. We expect banks to strive to meet the demands
of corporate clients and SMEs by digitally transforming critical workflows and improving client experience.
Additionally, incremental process improvements in the middle and back-office that leverage intelligent
automation will keep the competition at bay because engaged clients are loyal.
Adopting newer methods to mine data and moving to as-a-Service models will prepare commercial banks
to flexibly respond to newcomers and find ways to co-exist through effective collaboration. The time has come for commercial banks to put transformation on the fast track as lending losses in wallet and market share could spill over to other functions!
How incumbents react and respond to 2022 trends could determine their relevancy and resiliency in the years ahead.
The Covid-19 pandemic necessitated the payments industry undergo a facelift, sparked by novel approaches from new-age players, fostered by industry consolidation, and customers’ demand for end-to-end experience. Crossing the threshold, the industry is entering a new era – Payments 4.X, where payments are embedded and invisible, and an enabling function to provide frictionless customer experience. As customers make a permanent shift to next-gen payment methods, Digital IDs are critical for a seamless payment experience. The B2B payments segment is witnessing rapid digitization. BigTechs, PayTechs, and industry newcomers are ready to jump in with newfangled solutions to help underserved small to medium-sized businesses (SMBs).
As incumbents struggle with profits, new-age firms are forging ahead to take the lead in the Payments 4.X era by riding the success of non-card products and services. The new era demands collaboration, platformification, and firms can unleash full market potential only by embracing API-based business models and open ecosystems. Data prowess and enhanced payment processing capabilities are inevitable to thrive ahead. The clock is ticking for banks and traditional payments firms because the competitive advantage is not guaranteed forever. As industry players seek economies of scale, consolidations loom, and non-banks explore new territories to threaten incumbents’ market share. While all these 2022 trends are at play, central bank digital currency (CBDC) is emerging globally and might open a new chapter in the current payments landscape.
As we slowly move out of the pandemic, financial services firms have learned the criticality of virtual engagement to business resilience. Wealth management firms will need capabilities to cater to new-age clients and deliver new-age services. This report aims to understand and analyze the top trends in the Wealth Management industry this year and beyond.
A year ago, our Top Trends in Wealth Management report emphasized how the pandemic sparked disruption and digital transformation and changing investor attitudes around Environmental, Social, and Corporate Governance (ESG) products. As we begin 2022, many of those trends continue to hold as COVID-19’s wide-reaching effects continue to influence the wealth management industry.
As wealth management (WM) firms supercharge their digital transformation journeys, investments in cybersecurity and human-centered design are becoming critical to building superior digital client experience (CX). Another holdover trend − sustainable investing – is gaining mainstream attention and generating increasingly sophisticated client demands. Data and analytics capabilities will become ever more essential for ESG scoring and personalized customer engagement. As large financial services firms refocus on their wealth management business while new digital players make industry strides, competition is becoming historically intense. Not surprisingly, client experience is the new battleground.
This analysis provides an overview of the top trends in the retail banking sector driven by the competition, digital transformation, and innovation led by retail banks exploring novel ways to create and retain value in evolving landscape.
COVID-19 caught banks off guard and shook legacy mindsets to the core. With 20/20 (2020) hindsight, firms are more aware, digitally resilient, and financially stable as they head into 2022. The trials of the past 18 months forced firms to shore up existing business and consider new models and revenue streams.
Customer-centricity remains at the top of most FS agendas and is a 2022 focal point. Banks will focus on achieving operational excellence as diligently as delivering superior CX. In 2022 and beyond, it will be paramount for FIs to explore and invest in new technologies to remain relevant and resilient.
Banking 4.X will arrive in full force in 2022 with platform-supported firms monetizing diverse ecosystem capabilities and aggressively harvesting data to create experiential customer journeys through intelligent and personalized engagements. The new era will compel future-focused banks to finally abandon legacy infrastructure and collaborate with third-party specialists to solidify their best-fit, long-term roles. Increasingly, open platforms will make banks invisible as banking becomes embedded into customer lifestyles. At the same time, banks will shed asset-heavy models and shift to the cloud for greater agility, speed to market, and faster innovation. The shift will act as a precursor to adopting new technologies on the horizon – 5G and Decentralized Finance.
The recent past was filled will extraordinary lessons for financial institutions. Now is the time to act on those learnings and move forward profitably.
While COVID-19 has sparked the demand for life insurance, it has also exposed the operating model vulnerabilities in distribution, servicing, and customer retention. In a post-COVID, new-normal environment, insurers need to enhance their capabilities around advanced data management and focus on seamless and secure data sharing to provide superior CX and hyper-personalized offerings. Accelerated digitalization and faster go-to-market are vital to remaining competitive, and win-win partnerships with ecosystems are critical in the journey.
Read our Top Life Insurance Trends 2022 to explore the tactical and strategic initiatives carriers undertake to acquire competencies around customer centricity, product agility, intelligent processes, and an open ecosystem to ensure profitable growth and future readiness.
Property & Casualty Insurance Top Trends 2021Capgemini
The Property & Casualty insurance landscape is evolving quickly with the changing risk landscape, entry of new players, and changing customer expectations. The ripple effects of COVID-19 on the P&C insurance industry and natural disasters such as forest fires have adversely impacted insurance firm books.
In this scenario, to ensure growth and future-readiness, the most strategic insurers strive to be ‘Inventive Insurers’ – assuming a customer-centric approach, deploying intelligent processes, practicing business resilience and go-to-market agility, and embracing an open ecosystem.
Read our Property & Casualty Insurance Top Trends 2021 report to explore the strategies insurers are adapting to remain competitive amidst the evolving business landscape and how they can explore new ways to enhance their profitability.
A combination of factors such as demographic changes, evolving consumer preferences, and desire to become operationally efficient were already spurring changes in the life insurance industry. Enter 2020 – the COVID-19 pandemic is having a significant impact on the industry.
At the peak of disruption, the focus was on ensuring business continuity, but new initiatives are cropping up to tackle the challenges as the industry is adapting to the new normal.
Furthermore, COVID-19 has acted as a catalyst, pushing life insurers to prioritize their efforts on improving customer centricity, developing go-to-market agility, making processes intelligent, building business resilience, and embracing the open ecosystem.
Read our Life Insurance Top Trends 2021 report to explore the strategies insurers are adopting to manage the changing market dynamics.
The uncertainty of 2020 is setting the global tone for the immediate future in the financial services industry. So it is no surprise banks are laser-focused on business resilience, emphasizing both financial and operational risks. The need to adapt quickly to new normal conditions through virtual customer engagement is clear.
Customer centricity continues to drive commercial banks’ solution designs. And, the pandemic compelled products that deliver immediate client value ‒ quick digital onboarding, seamless lending, and support for small and medium-sized enterprises (SMEs). The onus is now on banks to go to market more quickly, which requires the implementation of intelligent processes and integrating corporates’ enterprise resource planning (ERP) systems with banking workflows.
To achieve go-to-market agility, banks across the globe are investing in and collaborating with FinTechs. Many of these partnerships are focused on boosting digital lending and providing seamless support to anxious small-business clients in need of assurance.
With newfound impetus for FinTech collaboration, commercial banks have picked up their step on the path toward OpenX. COVID-19 made it evident that survival during turbulence is manageable through collaboration with ecosystem players.
Read our Top Trends in Commercial Banking 2021 report to explore the strategies banks are adapting to transform their businesses from a product-led, siloed model to an experiential and agile plan.
When we published the Top Trends in Wealth Management 2020, little did we foresee the pandemic that would sweep through the world and disrupt life as we knew it. Yet, when we reviewed last year’s trends, we found that many still hold and some have taken on even greater relevance. One such trend is sustainable investing, which had begun to gain prominence as investors became more aware of ESG considerations, and firms rolled out more sustainable investing offerings. Another trend that has accelerated in the post-COVID world is the importance of investing in omnichannel capabilities and technologies such as artificial intelligence (AI) to enhance personalization and advisor effectiveness. The pandemic has driven wealth management firms to accelerate their digital transformation journey, with some immediate focus areas being interactive client communications and digital advisor tools.
There is no denying that time is of the essence. Yes, budgets are tight, but the Open X ecosystem offers wealth management firms opportunities to reimagine their operating models and deliver excellent customer experience cost-effectively.
Top trends in Payments: 2020 highlighted the payments industry’s flux driven by new trends in technology adoption, innovative solutions, and changing consumer behavior. The pandemic has tested the digital mastery of players, who are already grappling with transition. Non-cash transactions are on a robust growth path, accelerated by increased adoption during COVID-19. Regulators are working to instill trust and address non-cash payments risk amid unparalleled growth as players collaborate to quell uncertainty. Regional initiatives, such as the P27 (Nordics real-time payments system) and the EPI (European Payments Initiative), are gaining traction in response to country-level fragmentation and competition.
Investment in emerging technologies is looked upon as an elixir to mitigate fraud, data-driven offerings are being considered for providing value-added propositions, and distributed ledger technology is in focus for digital currency solutions, efficiency enhancement, and cost gains. New players, such as retailers/merchants, are integrating payments into their value chains while technology giants are upscaling their financial services game by weaving offerings around payments as a center stage. Constrained by budgets, firms consider business models such as Platform-as-a-Service (PaaS) to provide cost-effective and superior customer experience.
A combination of factors, including demographic changes, evolving consumer preferences, and regulatory and compliance mandates, were already spurring change in the health insurance industry. Enter 2020 and the COVID-19 pandemic, which is having sweeping implications for the industry.
At the peak of disruption, the focus was on ensuring business continuity, but new initiatives are cropping up to tackle the challenges as the industry adapts to the new normal.
Furthermore, some changes are here to stay, and it will be prudent for the industry players to be resilient to the market shifts by being agile, improving member centricity, making processes intelligent, and embracing the open ecosystem.
Read our Health Insurance Top Trends 2021 report to explore the strategies insurers are adopting to manage the external pressures.
The banking industry’s resilience is being tested as banks navigate through a remarkable 2020 filled with uncertainties. The impact of COVID-19 has been about setting the tone for future operational models. Retail banks have shifted focus towards integrated risk management with a more holistic view of operational risks. Adapting to the new normal, banks have prioritized cost transformation while engaging customers virtually. Incumbents sought to be more responsible within fast-changing environmental conditions and ESG remained a critical focus.
To provide more experiential services, banks are leveraging techniques such as segment-of-one to hyper-personalize offerings while aiming to humanize digital channels for increased engagement. Banks are also revamping middle and back offices, going beyond the front end leveraging intelligent processes. Open X is enabling banks to play on their strengths and use the expertise of ecosystem players. Going forward, banks are poised to become an enhanced one-stop shop by providing consumers value-adding FS and non-FS experiences.
To acquire customers in cost-effective manner, retail banks are tapping value-based propositions ‒ such as POS financing and mortgage refinancing. Further, Banking-as-Service provides incumbents a way to provide their high-value offerings to other players. In preparation for the future, banks will be looking to improve their go-to-market agility by leveraging the benefits of cloud. This analysis outlines the top 10 trends in retail banking for 2021.
Explore how Capgemini’s Connected autonomous planning fine-tunes Consumer Products Company’s operations for manufacturing, transport, procurement, and virtually every other aspect of the supply-value network in a touchless, autonomous way.
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Capgemini EIU Big Data Study
1. Business Analytics The way we see it
The Deciding Factor:
Big Data & Decision Making
Written by
2. The Deciding Factor: Big data and decision-making
Foreword
Big Data represents a fundamental shift in business decision- The survey also highlights special challenges for decision-
making. Organisations are accustomed to analysing internal making arising from Big Data; although 85% of respondents
data – sales, shipments, inventory. Now they are increasingly felt the issue was not so much volume as the need to analyse
analysing external data too, gaining new insights into and act on Big Data in real-time. Familiar challenges relating
customers, markets, supply chains and operations: the to data quality, governance and consistency also remain
perspective that Capgemini calls the “outside-in view”. We relevant, with 56% of respondents citing organisational silos
believe it is Big Data and the outside-in view that will generate as their biggest problem in making better use of Big Data.
the biggest opportunities for differentiation over the next five For our respondents, data is now the fourth factor of
to ten years. production, as essential as land, labour and capital. It follows
that tomorrow’s winners will be the organisations that succeed
The topic of Big Data has been rising rapidly up our in exploiting Big Data, for example by applying advanced
clients’ agenda, and Capgemini is already undertaking predictive analytic techniques in real time.
extensive work in this area all over the world. That is why we
commissioned this survey from the Economist Intelligence I would like to thank the teams at the Economist Intelligence
Unit: we wanted to find out more about how organisations are Unit and within Capgemini, along with all the survey
using Big Data today, where and how it is making a difference, respondents and interviewees. I believe this research will do
and how it will be used in the future. much to increase understanding the business impact of Big
Data and its value to decision-makers.
The results show that organisations have already seen
clear evidence of the benefits Big Data can deliver. Survey
participants estimate that, for processes where Big Data Paul Nannetti
analytics has been applied, on average, they have seen a 26%
improvement in performance over the past three years, and Global Sales and Portfolio Director
they expect it will improve by 41% over the next three.
2
3. The Deciding Factor: Big data and decision-making
About the Research
43%
Capgemini commissioned the The Economist Intelligence Unit
Economist Intelligence Unit to write The conducted a survey, completed in
Deciding Factor: Big data and decision- February 2012, of 607 executives.
making. Participants hailed from across the
globe, with 38% based in Europe, 28%
The report is based on the following in North America, 25% in Asia-Pacific
research activities: and the remainder coming from Latin
America and the Middle East and of participants are C-level
Africa. The sample was senior, 43% of and board executives
participants being C-level and board
executives and the balance—other
high-level managers such as vice-
presidents, business unit heads and
department heads. Respondents
worked in a variety of different functions
and hailed from over 20 industries.
Of the latter, the best represented
were financial services, professional
services, technology, manufacturing,
healthcare and pharmaceuticals,
and consumers goods and retail.
To supplement the survey, the
Economist Intelligence Unit conducted
a programme of interviews with
senior executives of organisations
as well as independent experts
on data and decision-making.
Sincere thanks go to the survey
participants and interviewees for
sharing their valuable time and insights.
3
4. The Deciding Factor: Big data and decision-making
Executive summary
When it comes to making business At the same time, practitioners query unstructured data, such as text
decisions, it is difficult to exaggerate interviewed for the report—all analytics and sentiment analysis. A
the value of managers’ experience enthusiastic about the potential large number of executives protest that
and intuition, especially when hard for big data to improve decision- unstructured content in big data is too
data is not at hand. Today, however, making—caution that responsibility difficult to interpret.
when petabytes of information for certain types of decisions, even
are freely available, it would be operational ones, will always need Although unstructured data
foolhardy to make a decision to rest with a human being.
without attempting to draw some
causes unease, social media
meaningful inferences from the data. Other findings from the research are growing in importance.
include the following:
Anecdotal and other evidence is Social media tell companies not
only what consumers like but, more
indeed growing that the intensive use The majority of executives importantly, also what they don’t
of data in decision-making can lead
to better decisions and improved
believe their organisations like. They are often used as an early
business performance. One academic to be “data driven”, warning system to alert firms when
study cited in this report found that, but doubts persist. customers are turning against them.
controlling for other variables, firms Forty-three percent of respondents
that emphasise decision-making based Fully two-thirds of survey respondents agree that using social media to make
on data and analytics have performed say that the collection and analysis of decisions is increasingly important.
5-6% better—as measured by output data underpins their firm’s business For consumer goods and retail,
and performance—than firms that strategy and day-to-day decision- manufacturing, and healthcare and
rely on intuition and experience for making. The proportion of executives pharmaceuticals firms, social media
decision-making. Although that study who say their firm is data-driven is provide the second most valued
examined “the direct connection higher in the energy and natural datasets after business activity data.
between data-driven decision-making resources (76%), financial services
and firm performance”, it did not (73%), and healthcare, pharmaceuticals The job of automating
and biotechnology sectors (75%).
question the size of the data-sets
They may not be as data-savvy as
decision-making is
used in decision-making. In fact, very
their executives think, however: far from over.
little has been written about the use
of “big data”—which is distinguished majorities also believe that big data
management is not viewed strategically Automation has come a long way, but a
as much by its large volume as by majority of surveyed executives (62%)
the variety of media which generate at their firm, and that they do not have
enough of a “big data culture”. believe there are many more types
it—for decision-making. This report is of operational and tactical decisions
an attempt to address that shortfall. that are yet to be automated. This
Organisations struggle is particularly true of heavy industry
The research confirms a growing to make effective use where regulation and technology have
appetite among organisations for data
of unstructured data held automation back. There is, to be
and data-driven decisions, despite their
struggles with the enormous volumes for decision-making. sure, a limit to the decisions that can be
automated. Although technical limits
being generated. Just over half of are constantly being overcome, the
executives surveyed for the report say Notwithstanding the heavy volumes, increasing demand for accountability—
that management decisions based one-half of executives say they do especially following the financial
purely on intuition or experience are not have enough structured data to crisis—means that important business
increasingly regarded as suspect, and support decision-making, compared decisions must ultimately rest with a
two-thirds insist that management with only 28% who say the same about human, not a machine. For less critical
decisions are increasingly based on unstructured data. In fact, 40% of or risky decisions, however, there is still
“hard analytic information”. Nine in respondents complain that they have much scope for decision-automation.
ten of the executives polled feel that too much unstructured data. Most
the decisions they’ve made in the past business people are familiar with
three years would have been better if spreadsheets and relational databases,
they’d had all the relevant data to hand. but less familiar with the tools used to
4
5. The Deciding Factor: Big data and decision-making
This is particularly true of machine-
to-machine communication, where
low-risk decisions, such as whether to
replenish a vending machine or not, will
increasingly be made without human
intervention.
Organisational silos
and a dearth of data
specialists are the main
obstacles to putting big
data to work effectively
for decision-making.
Data silos are a perennial problem,
and one which the business process
reengineering revolution of the
1990s failed to resolve. Regulation
and the emergence of “trusted data
aggregators” may help to break down
today’s application silos, however.
Arguably a longer term challenge is
the lack of skilled analysts. Technology
firms are working with universities to
help train tomorrow’s data specialists,
but it is unlikely that supply will
meet demand soon. In the near
future, there is likely to be a “war for
talent” as firms try and outbid each
other for top-flight data analysts.
5
6. The Deciding Factor: Big data and decision-making
Introduction
26%
Moneyball: The Art of Winning an Unfair resources. Although financial services
Game, by Michael Lewis, is the story of and healthcare firms have long been
an underperforming American baseball big data users—where big data is
team—the Oakland Athletics—that defined by its enormous volume
turned a losing streak into a winning and the great diversity of media
streak by intensively using statistics and which generate it—heavy industry
analytics. According to the New York appears to be catching up (see case
is the extent of performance Times, the book turned many business study: GE—the industrial Internet).
improvement already people into “empirical evangelists”1.
experienced from big data. Nine in ten survey respondents agree
An Economist Intelligence Unit survey, that data is now an essential factor of
41%
supported by Capgemini, of 607 senior production, alongside land, labour and
executives conducted for this report capital. They are also optimistic about
found that there is indeed a growing the benefits of big data. On average,
appetite for fact-based decision- survey participants say that big data
making in organisations. The majority has improved their organisations’
of respondents to the survey (54%) say performance in the past three years
that management decisions based by 26%, and they are optimistic that
purely on intuition or experience are it will improve performance by an
is the performance increasingly regarded as suspect (this average of 41% in the next three
improvement expected view is held even more firmly in the years. While “performance” in this
in the next three years. manufacturing, energy and government instance is not rigorously specified,
55%
sectors), and 65% assert that more it is a useful gauge of mood.
and more, management decisions are
based on “hard analytic information”. One may question whether the
surveyed firms are as “data-driven”
Until recently there was scant research as their executives say. The research
to back the Moneyball hypothesis—that also shows that organisations are
if organisations relied on analytics for struggling with the enormous volumes
decision-making they could outperform of data and often with poor quality
say that big data their competitors. In 2011, however, data, and many are struggling to free
management is not viewed Erik Brynjolfsson, an economist at the data from organisational silos. The
Sloan School of Management at the same share of respondents who say
strategically at senior levels Massachusetts Institute of Technology their firms are data-driven also say
of their organisation. (MIT), along with other colleagues there is not enough of a “big data
studied 179 large publicly traded culture” in their organisation; almost
firms and found that, controlling for as many – 55% – say that big data
other variables, such as information management is not viewed strategically
technology (IT) investment, labour and at senior levels of their organisation.
capital, firms that emphasise decision-
making based on data and analytics When it comes to integrating big data
performed 5-6% better—as measured with executive decision-making, there
by output and performance—than is clearly a long road to travel before
those that rely on intuition and the results match the optimism. This
experience for decision-making2. report will examine how far down that
1
www.nytimes.com/2011/10/02/business/after- road firms in different industries and
moneyball-data-guys-are-triumphant.html Two-thirds of the executives in the regions are, and will shed light on the
survey describe their firm as “data- steps some organisations are taking to
2
Brynjolfsson, Erik, Hitt, Lorin M. and Kim, Heekyung driven”. That figure rises to 73% make big data a critical success factor
Hellen, “Strength in Numbers: How Does Data-Driven for respondents from the financial in the decision-making process.
Decision making Affect Firm Performance?” (April 22, services sector, 75% from healthcare,
2011). Available at SSRN: http://ssrn.com/abstract=1819486 pharmaceuticals and biotechnology,
or http://dx.doi.org/10.2139/ssrn.1819486 and 76% from energy and natural
6
7. The Deciding Factor: Big data and decision-making
On average, respondents believe that big data will improve organisational
performance by 41% over the next three years
Survey Question: Approximately to what extent do you believe that the use of big data has improved your
organisation’s overall performance already, and can improve overall performance in the next three years?
Now 3 Years
45%
40%
35%
30%
25%
20%
15%
10%
5%
Average CEO/President CFO/Treasurer CIO/CTO
7
8. The Deciding Factor: Big data and decision-making
Overall, 55% of respondents state that they feel big data management is not viewed
strategically at senior levels of their organisation
Survey Question: To what extent do you agree with the following statement:
“Big data management is not viewed strategically at senior levels of the organisation.”
Strongly Agree Agree Disagree Strongly Disagree Don’t know/Not applicable
100%
80%
60%
40%
20%
0% Total Financial Energy & Consumer Health & Manufacturing
Sector Resources Pharmacy
Two thirds of executives believe that there is not enough of a “big data culture” in
their organisation - this is particularly notable across the manufacturing sector
Survey Question: To what extent do you agree with the following statement:
“There is not enough of a “big data culture” in the organisation, where the use of big data in decision-making is
valued and rewarded.”
Strongly Agree Agree Disagree Strongly Disagree Don’t know/Not applicable
100%
80%
60%
40%
20%
0% Total Financial Energy & Consumer Health & Manufacturing
Sector Resources Pharmacy
8
9.
10. The Deciding Factor: Big data and decision-making
Putting big data
to big use
“A lot of people will say data is To keep customers loyal, retailers
important to their business, but I think have to target customers with
it’s incredibly important to healthcare personalised loyalty bonuses,
and it’s probably getting more and discounts and promotions. Today, most
more important,” says Lori Beer large supermarkets micro-segment
executive vice president of executive customers in real time and offer highly
enterprise services at WellPoint, an targeted promotions at the point of
American healthcare insurer. Ms Beer sale.
compares data in healthcare with
“oxygen”—without it, the organisation
would die.
Business activity data and point-of-sale data are
WellPoint has 34 million members, and considered most valuable across the consumer
making sure their customers get the goods & retail sector
right diagnosis and receive the right
treatment is vital for keeping costs
under control. But getting to the right
Survey Question: Which types of big data sets do you see as adding the most
information to make the right decision
value to your organisation?
in healthcare is no mean feat. There
are terabytes to sift through: millions [select up to three options]
of medical research papers, patient
records, population statistics and
Total Consumer goods & retail Top 3
formularies, to name a few types of
needed information. Using that to make
an effective decision requires powerful 68.7% 32.0% 27.7% 25.2% 21.9% 18.6% 15.5% 15.5% 10.2% 8.1% 4.3%
computing and powerful analytics (see
WellPoint case study). 57.9% 7.9% 42.1% 71.1% 18.4% 21.1% 13.2% 10.5% 5.3% 7.9% 0.0%
There is near consensus across
industries as to which big data sets
are most valuable. Fully 69% of survey
respondents agree that “business
activity data” (eg, sales, purchases,
costs) adds the greatest value to
their organisation.The only notable
exception is consumer goods and retail
where point-of-sale data is deemed to
be the most important (cited by 71% of
respondents). Retailers and consumer
Business activity data
Office documentation
(emails, document stores)
Social media
Point-of-sale
Website clickstream data
Website clickstream data
Geospatial data
Telecommunications data
(eg phone or data traffic)
Telemetry - detailed activity
data from plant/equipment
Images / graphics
Something not on this list
(please specify)
goods firms are arguably under more
pressure than other industries to
keep their prices competitive. With
smartphone apps such as RedLaser and
Amazon’s Price Check, customers can
scan a product’s barcode in-store and
immediately find out if the product is
available elsewhere for less.
10
11. The Deciding Factor: Big data and decision-making
42%
Office documentation (emails, media to express their anger at the
document stores, etc) is the second charge. Verizon Wireless was prompt
most valued data set overall, favoured in responding to the outcry, possibly
by 32% of respondents. Of the forestalling customer defection to rival
other major industries represented mobile operators.
in the survey, only healthcare,
pharmaceuticals and biotechnology But not all unstructured data is as easy
of survey respondents say differ on their second choice. Here to understand as social media. Indeed,
that unstructured content is social media are viewed as the second 42% of survey respondents say that
too difficult to interpret. most valuable data set, possibly unstructured content—which includes
because reputation is vitally important audio, video, emails and web pages—is
in this sector, and “sentiment analysis” too difficult to interpret.
of social media is a quick way to identify A possible reason for this is that today’s
shifting views towards drugs and other business intelligence tools are good at
healthcare products. aggregating and analysing structured
data whilst tools for unstructured data
Over 40% of respondents agree that are predominantly targeted at providing
using social media data for decision- access to individual documents (eg
making has become increasingly search and content management).
important, possibly because they It may be a while before the more
have made organisations vulnerable advanced unstructured data tools, such
to “brand damage”. Social media are as text analytics and sentiment analysis,
often used as an early warning system which can aggregate and summarise
to alert firms when customers are unstructured content, become mass
turning against them. In December market. This may be why 40% of
2011 it took Verizon Wireless just one respondents say they have too much
day to make the decision to withdraw unstructured data to support decision-
a $2 “convenience charge” for paying making, as opposed to just 7% who feel
bills with a smartphone, following a they have too much structured data.
social media-led consumer backlash.
Customers used Twitter and other social
40% of respondents believe that they have too much unstructured data to support
decision-making
Survey Question: Looking specifically at your department, how would you characterise
the amount of data available to support decision-making?
Too much Enough Not enough Don’t know
Structured Unstructured
7.0% 42.1% 49.8% 1.2% 39.6% 30.8% 27.6% 2.0%
11
12. The Deciding Factor: Big data and decision-making
Enough data or too much?
Structured or unstructured, most
executives feel they don’t have enough
data to support their decision-making.
In fact, 40% of respondents overall
Case study: Big data at the bedside
believe the decisions they have made For WellPoint, one of America’s In January 2012, WellPoint began
in the past three years would have been largest health insurers, the problem training the supercomputer for the
“significantly better” if they’d had all of of ensuring the right treatment plan is first phase of the project. The pilot
the structured and unstructured data provided for its members is becoming system helps WellPoint nurses review
they needed to make their decision. increasingly complex. “Getting and authorise treatment requests from
And, despite the fact that respondents relevant information at the point- medical providers. It is an iterative
from the financial services and energy of-care, when decisions are getting process where the nurses follow
sectors are more likely than average to made, is the holy grail,” says Lori Beer, the existing procedures, examine
describe their firm as data-driven, they executive vice president of enterprise the response the system provides,
are also more likely than the average business services at WellPoint. and then score it based on how well
(46% from financial services, and 48% it does. The feedback is used to
from energy) to feel they could have By some estimates, the body of educate and fine-tune the system
made better decisions if the needed medical knowledge doubles every so that it will eventually be able to
data was to hand. five years. Coupled with an explosion authorise treatments without human
in medical research papers is the intervention.
At first blush, this may seem rapid conversion of medical records For the second phase, WellPoint
contradictory, given the surfeit of data to electronic format. A physician has has partnered with Cedars-Sinai
and the difficulty organisations face in a pile of digital information to sift Samuel Oschin Comprehensive
managing it, but Bill Ruh, vice president, through yet, according to Ms Beer, Cancer Institute in Los Angeles to
software, at GE sees no contradiction. most healthcare providers spend develop a decision-support system
“Because the problems we address are very little time with each patient and for oncologists. It is hoped that
going to get more and more complex, only see “a slice of the information”. physicians will be able to review
we’re going to solve more complex WellPoint wants to provide all the treatment options suggested by the
problems as a result,” he says. “What we relevant information that a healthcare supercomputer at the point of care.
find is the more data we have, the more provider needs, in digestible format, Critically, the system won’t just provide
we get innovation in those analytics and at the patient’s bedside. an answer; it will show the oncologist
we begin to do things we didn’t think we the documented medical evidence
could do.” “If you look at the statistics, evidence- that supports the probability of why it
based medicine is only applied about believes the answer is accurate.
For Mr Ruh, the journey to data 50% of the time,” says Ms Beer. “The
fulfilment will be over when he can put issue we often face is that we’re “It is the physician who makes the
a sensor on every component GE sells not really using the most relevant ultimate decision,” says Ms Beer. “This
and monitor the component in real time. evidence-based medicine in diagnosis is not intended to ever replace the
In this way, any aberrant behaviour can and treatment decisions.” A wrong physician.”
be immediately identified and either diagnosis and treatment plan can be
corrected through a control mechanism deadly for a patient and very costly for There is no end date for the project,
(decision automation) or through human WellPoint. and various decision-support and
intervention (decision support). “We’re decision-automation tools will be
really trying to get to what we would call WellPoint had been following developed over time. The intent is
‘zero unplanned outages’ on everything the advances of IBM’s Watson that the more the WellPoint system is
we sell,” says Mr Ruh. supercomputer for some time and trained, the more accurate diagnoses
realised that the natural-language- and treatment plans will become. If
processing abilities of the machine this pans out, it will help to drive down
would make it ideal for processing the cost of healthcare in the US, where
petabytes of unstructured medical wasted health spending in 2009 was
information, and drawing meaningful estimated to be between $600 billion
conclusions from it in seconds. and $850 billion.
12
13.
14. The Deciding Factor: Big data and decision-making
The virtues & risks
of automation
58%
Data can either support a manager in With corporate clients, however, it is
making a decision (eg, information on much more difficult. “Suppose that
key performance indicators displayed a ship cannot leave a port due to
on a business intelligence “dashboard”) late payment, and suddenly all the
or it can automate decision-making bananas go rotten; from a commercial
(eg, an automatic stock replenishment perspective, this involves a much higher
algorithm). According to the survey, on risk because the amounts are much
average big data is used for decision larger,” says Mr Knorr. “The human on average use big data
support 58% of the time, and 29% of the element and review by somebody for for decision support.
29%
time it is used for decision automation. larger amounts of money won’t go
For Michael Knorr, head of integration away.”
and data services at Citi, a financial
services group, deciding whether to However, the job of automating
use big data for decision support or decision-making at Citi is far from
decision automation depends on the over. Mr Knorr says the drive for more
level of risk. automation comes from the increasing
expectations of customers and
“In the consumer space, where amounts regulators for rapid decision-making. of the time it is used for
are small and if you make an error it’s “If you do not have the right level of decision automation.
easy to compensate for that error, then automation in place, that means your
automation might be applicable,” says costs have increased,” says Mr Knorr. “If
Mr Knorr. If there is a “false positive”— there is more data and you haven’t kept
that is, a loan is rejected by the system up with automating, then the number of
based on various set parameters when items you need to review manually will
it should have been approved—the have increased, which means you need
situation can easily be remedied with a more resources and people to do so.
phone call. This strengthens the business case for
automation.”
14
15. The Deciding Factor: Big data and decision-making
60% of respondents dispute the proposition that most operational/ tactical
decisions that can be automated, have been automated
Survey Question: To what extent do you agree with the following statement:
“Most operational/tactical decisions that can be automated, have been automated.”
54.1%
48.2%
45.2%
34.2%
27.7%
25.1%
13.9% 15.2%
8.2% 8.9%
6.6%
3.6% 3.9% 3.4%
1.7%
North America Europe Asia–Pacific
Latin America
Middle East & Africa
5.1% 5.1%
16.9% Total
5.0% Strongly Agree
29.1% Agree
35.6%
37.3% 48.7% Disagree
13.5% Strongly Disagree
3.8% Don’t know
15
16. The Deciding Factor: Big data and decision-making
Across all industries and regions, a
majority of survey respondents concur
that there is scope for further decision
automation at their firm. Over 60% of Case study: General Electric and
respondents dispute the proposition
that “most operational/tactical
the industrial Internet
decisions that can be automated, have If the first phase of the Internet was electric vehicle charging stations.
been automated.” This view is fairly about connecting people, says Bill
consistent across industries, although Ruh, vice president of software at “We are putting more and more
fewer healthcare and pharmaceuticals General Electric (GE), then the second sensors on all the equipment that we
companies agree with the statement phase is about connecting machines. sell, so that we can remotely monitor
(52%) than manufacturing companies Some people call this “the Internet of and diagnose each device,” says
(68%). (Respondents from the education things”, but Mr Ruh prefers the term Mr Ruh. “This represents a huge
sector also appear less certain than “the industrial Internet”. Like many productivity gain, because you used
peers elsewhere that there is much good ideas, the concept preceded to require a physical presence to know
still to be automated.) There is some the technology. But now, sensors and what was going on. Now we can sell a
regional variation, too. No more than big data analytics have reached a level gas turbine and remotely monitor its
54% of executives in Asia-Pacific believe of maturity that makes the industrial operating state and help to optimise
the job of automation is incomplete, Internet achievable. Machines are it.”
compared with 71% in western Europe. able to talk to each other over vast
distances and make decisions without “Trip Optimizer” is a fuel-saving
Mr Ruh of GE explains why automation is human intervention. system that GE has developed for
far from complete in his industry: “One freight trains. It takes into account
reason is that many of the environments “When you look at business process a wealth of data, including track
we operate in are highly regulated, so automation, the main productivity conditions, weather, the speed of the
we have to move at a speed that makes gains have been the low hanging train, GPS data and “train physics”,
sense within the regulation,” he says. fruit in the consumer, retail and and makes decisions about how
“The second is because the sensors entertainment sectors,” says Mr and when the train should brake. In
and the data weren’t really there to Ruh. “But we have not seen many tests, Trip Optimizer reduced fuel
automate anything.” automation and productivity gains use by 4-14%, according to Mr Ruh.
in industrial operations.” National With fuel being one of the biggest
Certainly decision-automation tools electricity grids, for example, are some overheads for freight train companies
have evolved from simple “if then of the world’s biggest “machines”, (at Canadian Pacific, one user of GE’s
else” programmable statements (eg, yet the fundamentals around how system, it makes up nearly one-quarter
“if credit rating = AAA, then approve the technology is used and how it of operating costs), a 10% reduction in
loan, else reject”) to sophisticated interacts with other systems have not fuel use represents a huge cost saving.
artificial intelligence programs that kept pace over the course of a century.
learn from successes and failures. But with sensors, control systems and Mr Ruh likens the industrial Internet
The more sophisticated the tools the Internet, a “smart grid” could to Facebook or Twitter for machines.
become, the more decisions that can make decisions, such as which energy Whether it is a jet engine or oil rig,
be automated. Decision automation, supply to switch to, or which part of a machine is constantly providing
however, can introduce unnecessary the network to isolate in the case of a status updates on performance. Big
rigidity into business processes. At fluctuation or disturbance. data analytics look for patterns in
times of high instability—such as the performance, and when an anomaly
current economic climate—companies In November 2011, GE showed its is identified, a decision about the
need to be nimble in order to adapt to commitment to catching up with the best corrective action is automatically
the changing conditions. Hard-coded business-to- consumer (B2C) sectors taken or a person is alerted so that
decisions can be costly and time- by opening a new software centre in a decision can be made on the best
consuming to change. San Ramon, California, with Mr Ruh as course of action.
its head. GE is in the process of hiring
400 software engineers (with 100 on “I believe that we’re in the early stages
board to date) to complement the of this,” says Mr Ruh, “and we haven’t
company’s 5,000 software workers even begun to imagine the algorithms
who are focused on developing we’re going to build and how they’re
applications for power plants, going to improve the kinds of products
aeroplanes, medical systems and and services we offer.”
Brynjolfsson, Erik, "Riding the Rising Information Wave–
Are you swamped or swimming?", MIT Sloan Experts,
http://mitsloanexperts.com/ /2011/05/18.
16
18. The Deciding Factor: Big data and decision-making
Standing in the way
The perceived benefits of The road to these riches, however, continue to do so as the overlap
harnessing big data for decision- is laced with potholes. The biggest between different regulatory authorities
making mentioned by the survey impediment to effective decision- is rationalised. “Historically, you could
respondents are many and varied. making using big data, cited by 56% of say the islands of data provided some
survey respondents, is “organisational sort of job security,” says Mr Knorr
silos”. This appears especially the case of Citi. “If different areas have their
for large firms—those with annual own vernacular, then they keep to
Perceived benefits of revenue in excess of $10 billion—whose themselves and avoid transparency.
harnessing big data executives are more likely to cite silos as That has obviously broken down, mainly
for decision-making a problem (72%) than smaller firms with through the regulatory efforts to ensure
less than $500 million in revenue (43%). that the financial services industry can
“More complete have a consistent, end-to-end data
understanding of model that’s easily understood and
market conditions The intractable silos can relate the various transactions
and evolving and products across the board.”
business trends” The business process reengineering
(BPR) movement of the 1990s— Silos may also be eroded over time
“Better business led by Michael Hammer and by what Kurt Schlegel, a research vice
investment decisions” Thomas Davenport—attempted to president at Gartner, an analyst firm,
eradicate function silos. By mapping calls “trusted data aggregators”. He
“More accurate and processes (eg, “fulfil order”) that points to aggregators which collect
precise responses to ran “horizontally” through several data that different firms (often in
customer needs” functions (sales, distribution, accounts the same industry) can access and
receivable), duplicated tasks and other analyse for their own purposes. But
“Consistency of inefficiencies were identified and Mr Schlegel believes that the trusted
decision making eradicated, and data was made to flow data aggregator model can also work
and greater group more easily across function boundaries. within organisations themselves. And
participation in BPR was given a boost by the arrival even where data protection or privacy
shared decisions” of enterprise resource planning (ERP) laws prevent a given department
software which automated a number from revealing personal information,
“Focusing resources of common business processes. an aggregator could anonymise the
more efficiently for However, while BPR undoubtedly data and make it available to other
optimal returns” improved efficiency and made the inner departments.
machinations of functions visible—
56%
“Faster growth often for the first time—the “vertical”
of my business function silos were soon replaced by
(+20% per year)” “horizontal” application silos. Before,
data was trapped in functions; now
“Competitive it is trapped in ERP, CRM (customer
advantage (new data- relationship management) and SCM
driven services)” (supply chain management) systems.
of survey respondents cited
“Common basis—one To some extent, increasing
true starting point regulation, especially in the financial “organisational silos” are
for evaluation” services, pharmaceuticals and the biggest impediment to
telecommunications industries, has effective decision-making
“Better risk begun to erode data silos and will
using big data.
management”
18
19. The Deciding Factor: Big data and decision-making
Across all sectors, “organisational silos” are the biggest impediment to using big
data for effective decision-making
Survey Question: What are your organisation’s three biggest impediments to using big data for
effective decision-making?
[Select up to three options]
65.8%
63.0%
59.7%
57.1% 58.2%
55.7%
54.3% 54.5% 54.3%
52.6%
50.6% 50.0%
48.4%
44.3% 45.5%
43.7% 43.5%
40.0%
36.8% 37.1% 37.0%
Too many “silos”—data is not Shortage of skilled people to The time taken to analyse large
pooled for the benefit of the entire analyse the data properly. data sets.
organisation.
47.8% 48.4% 49.1%
45.7%
41.7% 41.8%
39.1%
36.8%
34.9% 34.3% 34.2%
32.9%
27.4%
24.3%
18.4% 20.0%
17.1%
14.5%
13.0% 13.0%
10.9%
Unstructured content in big data Big data is not viewed The high cost of storing and
is too difficult to interpret. sufficiently strategically manipulating large data sets.
by senior management.
41.3%
Total
Financial Sector
17.1%
14.7%
12.7% Energy & Natural Resources
10.9%
7.9% 8.1% 7.9% 8.1%
4.4% 4.3% 4.3%
1.8%
4.3%
Consumer goods & retail
IT & Technology
Manufacturing
Big data sets are too complex to Something not on this Healthcare & Pharmacy
collect and store. list (please specify).
19
20. The Deciding Factor: Big data and decision-making
Finding the right skills
The second big impediment to making and mathematics students, has been analytics”—where data sets are loaded
better decisions with big data is the running for 12 years in the US and is used into memory (RAM), making analysis
dearth of talented people to analyse in 18,000 schools; it will be offered to UK much faster—become more refined
it, mentioned by 51% of respondents. schools, for free, from March 2012. SAS and widely deployed, decision-making
For consumer goods and retail firms it has also developed advanced analytics at the operational and tactical level, at
is the single toughest obstacle, cited by courses with a number of universities, least, is likely also to become faster.
two-thirds of respondents from those including Centennial College, Canada,
sectors. North Carolina State University and
Saint Joseph’s University, Philadelphia,
“In terms of modelling, there is to provide the next generation of data
going to be a considerable shortage analysts.
[of specialists],” says Professor K
Sudhir, James L. Frank ‘32 professor
of marketing at Yale School of
Management. “As a nation we generally
The time factor
find math and sciences less exciting, The time it takes to analyse large
and I think people have been moving data sets is seen as another major
away from this to ‘softer’ sciences. impediment to more effective use of
Clearly, there is a shortfall, especially big data in decision-making. “I think
in the analyst domain, and it is going to big data is going to stimulate the
continue unless we systemically fix it.” need for more CPU [microprocessor]
Bill Ruh of GE agrees. “There is going to power, because people are going to
be a war for this kind of talent in the next get very creative and they’re going
five years,” he says. to invent new algorithms, and we’re
going to say ‘My God, everything’s
Aside from a master’s degree or PhD in slow again’,” says Mr Ruh of GE. “We
economics, mathematics, physics, or are going to have to redo our compute
other relevant field of science, analysts and storage architectures because they
are also expected to have in-depth will not work where all this is going.”
domain knowledge—something
which usually takes years to acquire. Most of the survey respondents have
Interviewees for this report also say not experienced a slowing of decision-
that the ideal analyst should have an making due to having to process
ability to communicate complex ideas large quantities of data. Only 7% say
in a simple manner and should be that it has slowed down decision-
customer-focused. Finding people with making significantly, while 35% say
all of these abilities is never going to be it has slowed it but only moderately.
easy, and retaining them is going to be (Respondents from transport,
even harder as the benefits of big data government, telecommunications
become apparent to more firms. and education suggest a greater
deceleration of decision-making than
Technology companies recognise the other sectors.) The impediment must
problem and are working with schools be, then, not that decision-making
and universities to develop these much is slowing, but that it is not getting
needed skills. For example, SAS, a faster. This seems to be borne out by
business analytics software firm based the fact that the vast majority (85%)
in Cary, North Carolina, developed of executives believe that the issue is
Curriculum Pathways, a web-based not the growing volumes of data, but
tool for teaching data analytics to high rather being able to analyse and act
school students. The course, aimed on data in real-time. As “in memory
at science, technology, engineering
20
21. The Deciding Factor: Big data and decision-making
85% of respondents say the issue is not about volume but the ability to analyse and
act on the data in real time
Survey Question: To what extent do you agree with the following statement:
“The issue for us is now not the growing volumes of data, but rather being able to analyse and act
on data in real-time.”
Strongly Agree Agree Disagree Strongly Disagree Don’t know/Not applicable
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
Total Financial Energy & Consumer IT & Manufacturing Healthcare
Sector Resources Technology
Total Financial Energy & Consumer
Sector Resources
28.7% 21.7% 30.4% 37.8%
56.1% 62.3% 63.0% 48.6%
10.1% 10.1% 4.3% 8.1%
1.3% 1.4% 0.0% 0.0%
3.7% 4.3% 2.2% 5.4%
IT & Manufacturing Healthcare
Consumer
30.6% 36.4% 26.4%
53.2% 54.5% 62.2%
11.3% 7.3% 6.7%
3.2% 1.8% 2.2%
1.6% 0.0% 4.4%
21
22.
23. The Deciding Factor: Big data and decision-making
Conclusion
Professor Alex Pentland, director of the heavy industry, especially in areas such
Human Dynamics Laboratory at MIT, as energy production and distribution
says big data is turning the process of (“smart grids”) and transportation
decision-making inside out3. Instead of (“smart cars”, etc), excessive automation
starting with a question or hypothesis, of business processes can hamper
people “data mine” to see what flexibility. Besides, the growing post-
patterns they can find. If the patterns financial-crisis regulation calling for
reveal a business opportunity or a greater accountability requires humans
threat, then a decision is made about to ultimately make the decisions.
how to act on the information. Prosecutors cannot put an algorithm in
the dock.
This is certainly true, but improvements The financial crisis has also led to calls
in computing power and artificial for greater transparency. As the survey
intelligence systems mean that asking shows, people are increasingly wary
direct questions of big data and getting of business decisions based purely
an answer, in real time, is now a reality on intuition and experience. Even if a
(see WellPoint case study). Although sizeable minority agree that business
these systems are still very costly and managers have a better feel for
not widely deployed, this research business decisions than analytics will
suggests that the appetite for real-time ever provide, managers will increasingly
decision-making is huge. And when need to show how they arrived at their
there is a business demand, it is only decision. And big data will provide a
a matter of time before the need if post-decision review—was it a good
fulfilled. decision or not? As one of the survey
participants puts it, using big data for
Most of the executives polled for this decision-making will lead to “better
report are also optimistic about the cost decisions; better consensus; better
reductions and efficiencies that can be execution”.
had from automating decision-making
using big data. While there is certainly
much scope for decision-automation in
23
24. About Capgemini
With around 120,000 people in 40 countries, Capgemini is one of the world’s foremost providers
of consulting, technology and outsourcing services. The Group reported 2011 global revenues of
EUR 9.7 billion.
Together with its clients, Capgemini creates and delivers business and technology solutions that
fit their needs and drive the results they want. A deeply multicultural organization, Capgemini
has developed its own way of working, the Collaborative Business Experience™, and draws on
Rightshore®, its worldwide delivery model.
Rightshore® is a trademark belonging to Capgemini
More information about our services, offices and research is available at
www.capgemini.com
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