It is an honor and a privilege that my colleague Dr. Keeratpal Singh and myself are quoted by MIT Technology Review. I am hereby reproducing the whole article.
You can visit TIBCO’s website to download the full report TIBCO’s website or from here.
Engines of Insight: How leading CDOs deliver top and bottom line results
A briefing paper in association with Tibco
INSIGHTS | Research
To produce this study, MIT Technology Review Insights conducted a review of business analytics technology adoption trends, as well as research on the organizational and strategic impact of analytics in decision-making. This secondary research was underpinned by a series of interviews with senior analytics professionals during March and April 2018 – typically chief data officers or global heads of analytics and insight – in Europe, Asia and North America.
The report is sponsored by integration and analytics software provider, TIBCO. The report is editorially independent, and the views expressed are those of MIT Technology Review Insights.
We would like to thank the interviewees for generously giving their time and insights:
- Colm Carey, Chief Analytics Officer, AA Ireland
- Pedro Uria-Recio, Head of Axiata Analytics Center, Axiata Group Berhad
- Dr. Keeratpal Singh, Chief Data Scientist, Axiata Group Berhad
- Sameer Gupta, Chief Analytics Officer, DBS Bank
- Ramon Morote Ribas, Chief Data Officer, Gas Natural Fenosa
- Landon Lockhart, Senior Director Data and Analytics, GE Transportation
- Wolfgang Hauner, Chief Data Officer, Munich Re
- Lauren Sager Weinstein, Chief Data Officer, Transport for London
- Dr. Katia Walsh, Chief Global Data and Analytics Officer, Vodafone
1. Executive summary
Operational efficiency has certainly been an impetus for investments in analytics; insight is used to improve customer service operations and plug leaks in supply chains. Increasingly, however, leading companies are looking for insights to build top-line revenue–and to do this, their leaders must be compelled to use analytics to inform every business decision.
To effectively drive the use of analytics and insight throughout their operations, companies must pursue a three-pronged approach. The first is to recruit and develop the talent, which most chief data officers (CDOs) regard as a precious commodity: the highly sought-after analytics team profile combines data scientists, expert business analysts and communicators, to make data useful throughout their organization.
Technology is another important capability, both in terms of building robust data architecture to consolidate and rationalize data across the organization, and in investing in artificial intelligence tools and platforms that accelerate the generation of insight. The third prong to the approach is the organizational transformation that drives the investments into people and technology, championed from the top leadership and accompanied by cultural change and re-orientation of the organization’s values to give data a place at the decision-making table.
To produce this study, MIT Technology Review Insights conducted a review of business analytics technology adoption trends and held a series of executive interviews with analytics professionals in leading global organizations – typically CDOs or the global head of analytics and insight. These leaders in analytics shared the following insights about how to drive strategic excellence through the consistent and company-wide use of data:
1. C-suite sponsorship and visibility
To make tangible change in the organizational culture around data, senior leaders must be highly visible in terms of ensuring that every business decision is informed by the results throughout the organization. On a day-to-day basis, the CEO and other top leaders must consistently perform as Chief Insight User.
2. Short-term and long-term strategy
The strategy developed by the chief data officer must include developing a common data architecture, breaking down silos, and building an analytics organization that can balance two imperatives. The first is to be a decision-support SWAT team, solving business problems and delivering immediate value. The second is to be a long-term innovation center using analytics to focus on the future.
3. Investments into talent and technology
Analytics teams are growing, both in terms of headcount and spending on tools and technology. The right people are hard to find, having a sought-after blend of data science, business analyst and communication skills. Leaders are naturally cautious of making large investments, but the executives interviewed for this report put technology at their heart of their strategy.
4. Joint accountability
Responsibility for data collection, data quality and the consistent use of insights in the decision-making process must be shared between the business and the analytics teams. Measuring the results of data-driven decisions with established business performance metrics then makes it a more straight-forward process to demonstrate results.
5. Culture change
Organizations are in the throes of full-scale transformation, which is challenging and, at times, slow-going. It involves leaders building corporate cultures that recognize and reward the efforts of all employees to engage in data and analytics initiatives
2. From data to insight
Globally, organizations – both those established companies in traditional industries, and newminted ‘digitally native’ ones – have been aggressively expanding their analytics capabilities for some years, driven by rapid technological change and the digital modes with which customers and constituents prefer to interact with them. IT research company
IDC estimates that by 2020, businesses will invest more than US$ 210 billion in big data and analytics tools and solutions, up from US$ 150 billion in 2017.1 While this represents a slim fraction of total IT spend, analytics investments are growing at treble of that pace, and are already twice as great as spending in security. According to IDC, banking followed by healthcare, insurance, securities and investment services, and telecommunications will be the industries that are leading the growth in spend.
Using data to drive the top line
Operational efficiency has certainly been an impetus for analytics investments; insight is used to improve customer service operations and plug leaks in supply chains. Increasingly, however, leading firms are looking to insight to build top-line revenues – and to do this, their leaders must be compelled to use analytics to inform every business decision.
A McKinsey global survey of senior executives found that those in high-performing organizations are twice as likely to have built strong analytics capabilities, such as platforms that enable data visibility across the organization, and the use of machine learning to support advanced modeling techniques, than their low-performing peers.
Quantity is not quality
Being data-driven is a necessary precondition for success, but it is also insufficient. Data volumes are expanding, at times outpacing the ability to interpret the information. As well, re-organizing business and technology processes by centralizing unstructured data into lakes or embedding analytics teams across complex businesses, is costly. For those investments to be transformative to the business, rather than simply efficient, the data needs to be utilized by decision makers consistently, and constantly.
3. The Data-driven CEO
‘Organizational change must be driven by its top leaders’ might be a business-speak bromide, but it is a founding principle upon which leading companies begin aligning their data analytics process with their overarching business objectives. “Having our CEO as a champion is key to the success of data analytics in the customer experience process,” says Chief Data and Analytics Officer Dr. Katia Walsh at Vodafone.
Chief executives need to be seen supporting the role of analysts, data scientists and other members of the analytics team. In the McKinsey study cited earlier, senior management engagement in analytics activities was found to be the primary driver of their success. “You absolutely need the CEO’s team’s support,” to make analytics work for the organization, according the Landon Lockhart, Senior Director Data and Analytics at GE Transportation in Chicago who oversaw a transformative initiative to redesign service contracts for the company’s locomotive management business with the support of GE’s Vice President of Services, a member of the CEO’s staff. But Lockhart notes that senior leadership support must be accompanied by tangible results in real-time, which his analytics team “has to deliver in twelve months or less.”
Pedro Uria-Recio, Head of the Analytics Center at Malaysia-based digital and mobile conglomerate Axiata, notes that “Cultural change requires a topdown approach: the first people in our organization driving analytics change have been our regional CEO and CxOs.” Chief executives must hold their organizations to a commitment to insight through their every action and must cascade that management philosophy down to the day-to-day operations.
Leading the culture change
A common theme that links all data-centric organizations is their willingness to embark upon, and sustain, significant change management projects which integrate the use of analytics into the overall business operations. CDOs at these organizations speak of the fundamental importance of making the use of data compulsory for every business decision. They are also building analytics departments that work to achieve two (seemingly contradictory) objectives simultaneously: to harmonize and streamline the disparate data assets generated by departments across the organizations, while having autonomous analytics teams embedded in every department to support business unit-level initiatives. Implementing such change often takes years and is usually seen as a continuous improvement process.
Building on a data heritage
While all insight-driven companies have come into to the era of analytics with a deep-seated appreciation of data, at Transport for London this approach is a genuine part of the culture. “We have always had a heritage of using data to make decisions,” observes Lauren Sager Weinstein, Chief Data Officer at Transport for London (TfL), responsible for harnessing the data used to optimize route design and planning for the government body overseeing the city’s public transportation grid. She references a 1939 photograph recently unearthed in a photo archive, showing staffers sifting through mountains of paper tickets; “an understanding of patterns has always been important to us.” Other industry sectors with similar historical data proclivities include insurance and logistics.
Data heritage can be an important cultural asset for insight-driven firms, but it is not a necessary precondition. Moreover, in many cases, the historically data-centric organization must wrestle with the implications of that legacy: data is collected and organized in disparate ways across an organization over the years, and often comes to rest in multiple siloes. Technology and customer needs also evolve. All of this means transformative insight can be hard to generate with legacy data processes.
We have to ask, ‘Who is making the decision?’ And then it has to be well
thought through as to exactly what data they need.”
Senior Director, Data and Analytics
“Our business model for 130 years has largely been concerned with generating policy process changes at a high level,” says Wolfgang Hauner, Chief Data Officer at German reinsurance firm Munich Re, “yet these processes are no longer sufficient. We need holistic analysis that looks at data far beyond our core insurance business.” Hauner explains that this means incorporating data beyond information that immediately informs policy pricing and risk mitigation processes. “Now, we are incorporating unstructured data that allows us to increase visibility to the consumer level” and specifically, Munich Re is enhancing services in motor insurance by using telematics analytics in a ‘center of excellence’ for motor insurance.
CDOs: defining data and analytics strategy
Business research firm Gartner has been charting the rise of the chief data officer role in leading organizations. Its third annual Chief Data Officer Survey found that 57 percent of companies had a formalized CDO role in 2017, up from 50 percent the year before. CDOs held an average budget of US$ 8 million and oversaw federated organizations with a mean headcount of 34 full-time staff.
Many of analytics professionals interviewed for this study emphasize the need to ensure that analytics teams live and breathe the same business challenges as their operational colleagues. Vodafone’s Dr. Walsh, for example, oversees an analytics organization which involves embedded teams in 15 of the company’s country market telecom operating companies, or ‘opcos’, and plans to have them in all 25 opcos worldwide by the end of March 2019. These teams have dotted-line reporting to both her central analytics and their respective opco chief commercial officer. But Dr. Walsh makes it clear which is the more essential relationship: “I work for them,” she says, noting that she must help create a working environment that empowers team members in the opcos with the authority to effect analytics-driven change across many disparate working environments.
As well as solving business problems and delivering results, the analytics organization has a powerful role in looking to the future and focusing on the long-term success of the business. Analytics must be one-part skunk-works-style innovation center focused on the long term, the other a decision-support SWAT team ensuring a rapid flow of insights to enable better business performance. Holding fast both of those imperatives can seem like a phenomenal task of corporate cognitive dissidence: insight-driven change is a complex, often slow and exacting process, while serving the needs of front-line teams requires more immediate, tangible results.
Engines of Insight – Case study: Data and analytics drive customization at GE Transportation
At GE Transportation, Senior Director Data and Analytics, Landon Lockhart notes that the role of an analytics champion is to be constantly cognizant of the decision-making processes of the business, and holistically address the needs of the decision-makers themselves. “We have to ask, ‘Who is making the decision?’” he says, “and then it has to be well thought through as to exactly what data they need – and maybe there’s some information they need to fall back on. You must keep it simple, give them exactly what they need to make a good decision. And you have to track what they decided and if they deviated and understand why.”
Lockhart explains a simple business maxim with an example of an analytics project focused on the locomotive engine service contracts redesign mentioned above. Customers typically brought their engines in for servicing every seven to eight years, a process which Lockhart felt was being undertaken with too much standardization. “We were applying the same plain vanilla overhaul,” for each engine, which he reckoned needed modification with “an understanding of the usage and lifecycle parameters for each engine. We did some data extrapolation, some stitching, and definitely some modeling, until we said ‘we are doing work for no reason’.” In the first year that analytics were used in GE’s overhaul business, Lockhart reports “we’ve generated US$ 60 to 70 million of profit just on doing custom work scopes.”
- CEOs are leading the drive for better decision-making. To make a tangible change in the organizational culture around data, senior leaders must be highly visible in terms of ensuring that every business decision is informed by the results of an analytic process that is fully integrated throughout the organization. On a day-to-day basis, the CEO and other top leaders must perform consistently as Chief Insight User.
- CDOs build the architecture and strategy. The strategy developed by the chief data officer must include developing a common data architecture, breaking down silos, and building an analytics organization that can solve immediate business problems while also looking at the long term.
4. The insight organization
To effectively drive the use of analytics and insight throughout their operations, companies must pursue a three-pronged approach. The first is finding and developing talent, which most CDOs regard as a precious commodity. Data scientists are not only hard to recruit in a globally competitive market, but they also must be multi-talented to boot, making excellent candidates even rarer. The highly sought-after profile combines data scientist, expert business analyst and communicator, to make data useful throughout their organization or, as Vodafone’s Dr. Walsh puts it, “if they can’t tell a compelling story with their data, the data itself cannot be used. Data science skills need to drive business impact.”
Technology is another important capability that insight-oriented companies incorporate into their business processes, both in terms of building robust data architecture (usually rooted in efforts to consolidate and rationalize data across the organization) and in investing in platforms that can accelerate the generation of insight, often involving investment in artificial intelligence. Such transformations can be substantial requiring the building of data warehouses and “a massive amount of computing power,” observes Colm Carey, Chief Analytics Officer at AA Ireland, and despite the growing reliance on analytics, leaders are wary of huge technology bills. This means that investments must be conducted in a way that makes the results business-relevant immediately: “We have to operationalize the data to get buy-in.”
Thirdly, organizations must create and sustain a culture which is passionate about data. This is a more esoteric capability than talent and technology, but one that is linked to both, and to the champions in the organization’s leadership team. This passion often takes the shape of cultural change and re-orientation of the organization’s values to give data a place at the decision-making table. “If the analytics team wants to leave its footprint, we have to be present where and when most critical decisions are made,
and demonstrate that we are a recommendations machine, not just a number crunching machine,” says Uria-Recio of the Axiata Analytics Center.
“If the analytics team wants to leave its footprint, we have to be present where and when most critical decisions are made, and demonstrate that we are a recommendations machine, not just a number crunching machine.”
Head, Axiata Analytics Center
Axiata Group Berhad
Engines of Insight – Case study: The art of the data scientist
Subject matter expertise in an analytics organization is a critical and scarce resource for all businesses, for two reasons. The first is that data science is a complicated discipline, the parameters of which are constantly shifting with the impact of innovation and technology change in the analytics industry. Moreover, the supply of talent is one which many countries have not been able to address: Ainun Najib, the head of data of the Malaysia-based ride-hailing business Grab, indicated in an interview with the Indonesian press that the lack of data science disciplines supported by regional education systems are contributing to a talent crunch for Southeast Asian high-tech firms like his. McKinsey found that more than 40 percent of organizations found analytics talent more difficult to recruit and retain than any other kind.
In a competitive market for talent, the company’s global brand and providing access to copious, interesting amounts of data is a hiring asset for Vodafone. It is a worldwide search for the best people. “Spain seems to be a good breeding ground for AI experts that combine deep scientific expertise and creativity,” says Dr. Walsh. “Italy also has pools of machine learning talent, and it was a junior team member in South Africa who first brought the value of analytics” to senior management’s attention.
Beyond core analytics knowledge and skills, Dr. Walsh also notes that to find the right insight team members, one must look for skill sets that are more akin to those of an MBA than a data scientist. “We have team members with backgrounds in physics, machine learning, social science, economics and my own background is in strategic communications,” she says.
Tools of the insight trade
Wary as executives may be of significant investments, CIOs interviewed for research firms Gartner’s 2018 CIO Agenda survey reported that business intelligence and analytics technology were the top strategic differentiator for their firms. The findings from this study have led Gartner to predict that by 2019, users of technology-enabled business intelligence processes and tools like selfservice analytics will produce more analytics data globally than will professional data scientists. 451 Research reports in its Total Data: Data Platforms & Analytics Market Monitor that by 2022, firms globally will spend over US$ 146 billion on data processing frameworks, artificial intelligence and other analytics tools.
While having the right tools is key, this should not translate into an assumption that more is always better. “Using technology and data to transform TfL is an organizational priority,” says Chief Data Officer Sager Weinstein, at the same time noting that this cannot translate into blind faith that technology will solve all problems. “Utilizing technology is at the forefront of our strategy,” she says, “but we still conduct business value analysis against our technology deployments to ensure that we realize the benefits of these investments.”
Development of a common data infrastructure can also help to drive an insight-led culture. Ramon Morote Ribas, Chief Data Officer at Gas Natural Fenosa observes that the implementation of a common infrastructure globally helps manage the standardized collection and interpretation of data. This allows for improved fraud detection, asset maintenance and measuring customer value. “This allows us to then employ a data governance tool to enforce processes, improve data collection, and make that data accessible across the organization.” Ribas oversees a company-wide data council which oversees the analytics Transformation process for the company and provides best practices and solutions for individual business units.
Technology investments to consolidate and make data more accessible are important for building the culture around insights. “We have implemented a group-wide data lake, and we no longer tolerate data silos,” says Munich Re’s Chief Data Officer, Wolfgang Hauner. “We turned the rule set around: now teams do not have to prove why they can share data, but why they cannot.” There is also additional recognition around data quality and sharing: “Now, the more you share data, the more ‘merits’ a team can earn. New data driven approaches get lots of visibility at top management level.”
Technological advances can also steal a march on the efforts of analytics teams to generate and use insight effectively. “The advent of 5G could provide additional predictive factors, through invehicle sensors and telematics installations, which will massively increase the volume of driving data,” observes Colm Carey at AA Ireland. Nevertheless, he adds “some data-driven insight is still far away. After years of customers investing in telematics systems, none of the data generated has proven useful – except the fact that the customer has chosen to install telematics in their vehicle.”
- Centralize data. A common data infrastructure with standardized processes around data collection is key to quality and availability.
- Democratize analytical tools and access to data. To build a culture around insights, data needs to be available across the business with the necessary tools, training and expertise to support analysis and decision-making.
- No more silos. Leading companies recognize and reward new data driven approaches and do not lightly accept internal barriers to information sharing.
5. Driving business performance
The goal for most CDOs and heads of analytics is to ensure that the fruits of their team’s labor, the insights, are used consistently, and with increasingly impact, throughout the organization. This is true for data novices as well as seasoned data-centric firms: AA Ireland’s Carey notes that as an insurer, it has always been data-driven, but committing to a systematic integration of the use of data in every aspect of the business allows them to embark on “holistic ways of using the data,” bringing together previously siloed insight across the CRM, fraud management and pricing activities.
In the first full year of deploying the analytics program, the team at Vodafone set a goal to support 25 percent of all customer communications, and finished the year at 40 percent. This led to a significant increase customer contract lifetime value, with redemption rates and ARPUs increasing on average by 25 percent across the group in the last year. Dr. Walsh indicates that in some of the company’s more emerging markets, local operators have achieved average revenue per user improvements in upwards of 60 percent.
Analytics teams at Axiata maintain, according to Chief Data Scientist Dr. Keeratpal Singh, a “playbook of the way we work.” The playbook’s critical factor, he notes, is “about solving clearly defined business challenges which are aligned with the strategy.” Axiata, like many regional businesses, is a family of local operations. “There are analytics departments in each company and each one of them is led by a head of analytics”—and not all data professionals report to him because data must be used in every single department. All decisions must be data-driven. The head must have the gravitas to coordinate all data activities outside his department with the C-level and other department heads.”
To take analytics to the next level, Axiata Analytics Centre, led by Uria-Recio and Dr. Singh, has a mission to democratize usage of data across the business by building data platforms, creating libraries for advance machine learning algorithms and visualization tools, as well as organizing training and workshops.
“Providing such support allows decisions to be made faster,” says Dr. Singh. “Doing this successfully, and repeatedly, sparks enthusiasm in analytics within business teams and then trust in data grows.” Dr. Singh notes that while Axiata is building this through a structured, systematic cultural change program, agility is still required. “Analytics teams need to create use cases initially with a simple design and then iterate progressively in order to improve and scale up these cases.”
Meaningful metrics matter
With high expectations for the capacity of data and analytics to deliver business value, measurement is clearly very important to business leaders. But “Metrics and KPIs are difficult to create and manage when you are trying to design an overall process to improve fundamentals like data collection,” says Ribas at Gas Natural Fenosa, a multinational gas, electricity generation and energy trading conglomerate headquartered in Madrid, Spain. Moreover, many of the most insight-dependent parts of the organization find it hard to establish meaningful measures: “attributing the revenue impact of customer experience metrics is not easy.”
Organizations are deploying a range of KPIs to ensure that the insights machine is fed with high-quality and accurate data, and that data is effectively and consistently used in business decisions. No one single person or team should be responsible for this, as it is a shared responsibility. “It is incredibly important that KPIs are shared across the business,” observes Vodafone’s Dr. Walsh – meaning that both analytics team members and business unit decision-makers must be accountable for using data and insights in key decisions.
Regarding measuring the impact of the insights, analytics teams at leading firms seek to co-opt or own established company performance indicators so that they can demonstrate value in business terms. Rather than developing a new set of metrics to measure data effectiveness, Lockhart describes how realizing “we needed to steal the metrics rather than come up with our own,” really accelerated investment in analytics at GE Transportation. “After we began to own part of the metrics [key performance indicators for the services business], the budget started flowing, the headcount started to flow. It’s been a quick process,” he says.
“After we began to own part of the metrics [key performance indicators for the services business], the budget started flowing, the headcount started to flow. It’s been a quick process.”
Senior Director, Data and Analytics
Proof of concept
The analytics executives spoken to for this report realize that working towards specific, often immediate, business goals to generate insight ‘use cases’ for other business unit heads to emulate. DBS Bank’s Gupta says he did not wait until all his team’s processes were in place to deliver value. “Analytics teams are encouraged to showcase value even if it not completely efficient,” he says. “They work on an MVP [minimum viable product] principle with three-month iterations, with lots of surprises along the way, and these teams are constantly pivoting and iterating.” Gupta reports that these initiatives are already having an impact across many parts of the bank from consumer banking, HR, risk control, audit, finance operations and others, allowing for cost-saving promotions, increased cross-selling activities, higher productivity, better employee engagement and more efficient management of operational risk.
Driving through these short cycles has been a long change process. The bank began planning its analytics restructure began three years ago. “As of last year, we have articulated a DBS-wide Data First strategy and are anticipating to crystalize it in 2019.” And yet, Gupta admits, “There is still a long way to go. Technology and culture change is hard: it will take us two to three years just to all bring our data o the new platform in-house.”
“There is still a long way to go. Technology and culture change is hard: it will take us two to three years just to all bring our data to the new platform in-house.”
Chief Analytics Officer
Engines of Insight – Case study: DBS Bank’s federated approach to analytics
Analytics excellence is “enabled by a big culture change initiative,” at Singapore-headquartered DBS Bank, according to Chief Analytics Officer Sameer Gupta: “to put data first, maximize the value of data, and use data in every decision.” This core tenet is bolstered by “enabling data accessibility everywhere and building a technical infrastructure for our own data lakes,” referring to an initiative to consolidate all the banks ‘raw’ data in a common depository, which along with getting the culture right,
is a crucial step in building an efficient analytics organization.
DBS adopts a federated approach in incorporating analytic teams across the business. “Each business must identify three to five analytics projects and execute them throughout the year.”
This involves the heads of DBS’s six country market operations, as well as the heads of business and operational units such as treasury and portfolio management, risk and control, credit lending, HR and the audit committee.
Each member of the management committee has data accountability baked into their performance criteria: their ability to foster analytics initiatives are measured on their respective performance scorecards, which evaluate them on a four-by-four matrix assessing the project’s feasibility and business impact. Cross-functional teams are tasked with end-to-end initiatives to solve complex business problems with analytics, and their ability to support business objectives are recorded in each team’s goal sheet.
- Focus on business metrics. Leading CDOs focus on a select number of meaningful business metrics around which to solve problems and drive change.
- Share accountability. Responsibility for data collection, data quality and the consistent use of insights in the decision-making process must be shared between the business and the analytics teams. This is key to the cultural change.
- Pivot as necessary. CDOs do not have long before they need to demonstrate tangible business value.Adopting an MVP approach is one way to build an agile and innovative analytics organization.
This report, Engines of Insight: How leading CDOs drive top and bottom line results, has examined some of the ways in which leading companies are establishing analytics functions and embedding their methods and findings into the fabric of the wider organization. The executives interviewed for this briefing paper come from different industries, regions, and from companies with varying degrees of data heritage that might give them a head-start in terms of modern analytics. Several common themes have emerged:
- Leading an organization to become insights driven requires a C-level mandate, typically with the CEO adopting the role of Chief Insight User. The analytics teams delivering on this mandate comprise a unique mix of technical, consulting and communication skills to be able to deliver the right recommendations across the business.
- Successful organizations put equal responsibility on the business for making sure that decisions are based on the best available data and analysis. This shared responsibility between data teams and their business counterparts is key to the cultural change.
- Breaking down silos with a common data architecture and using technology to democratize analytics are necessary elements of the transition to an insights-driven organization. This also, as Hauer at Munich Re pointed out, involves changing the discussion from “teams proving why data should
The executives interviewed for this report acknowledge that their organizations are ona journey, with the role of data still evolving. “Corporate transformation is both an exhausting process – and an exhilarating one,” says Dr. Walsh at Vodafone. This assessment underscores the most important task that chief data officers must oversee in their organization: effecting an overall culture change that puts data as the driver of all decisions and harnesses machine learning to solve business problems and create new opportunities.
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