Category: Apex 1 on 1

The world of banking, financial services, and insurance organizations have faced a complete paradigm shift in the last 24 months. FinTechs, TechFins, Digital Transformations, and more are disrupting the industry and the key to maintaining the competitive advantage is unlocking the value of data sprawled across the institution.  Maybe you’re a CDO, CIO, CTO, Head of Data Science, etc. – regardless of the title, you’ve been asked to “weaponize your data”.   Where do you start?  

 

• Help! I’m lost in an alphabet soup and don’t know where to begin – AI? BI? ML?

 

Data isn’t new and neither is the idea of business intelligence.  Data warehouses, reporting, dashboards, scorecards – you name it – have been around for a long time.  However, the modern experience requires a new approach because of how much has changed and evolved.

The explosion of data and the speed of business has created a nexus where companies need to know what’s going to happen before it occurs.  Data science driven solutions are being sold on every corner, and every business feels they need an AI strategy to compete – but you can’t put the cart before the horse. A robust data strategy is required to build intelligence before you can support hyper intelligence and science. 

Look for ways to push your data into active decisioning rhythms.  Accelerate the intelligence of your business around data and let the natural evolution towards machine learning and artificial intelligence occur.  The basics haven’t changed – the right information to the right people at the right time has been the foundation of data intelligence.  Start small, review the impact, get more comfortable with the impact of data-powered intelligence, and look at ways to create more impact. 

 

• Ok, but I don’t know where my data is. My analysts spend half their time just trying to find everything and then bring it to Excel – how am I supposed to get the right data if I don’t know what I have or where it is? 

 

Data sprawl is real, but there’s a solution. In a word?  Governance. It’s easy to say everyone needs data governance, but the actual implementation of robust governance programs tends to fail more than it does succeed. First and foremost, when you look up at the data mountain, it gets overwhelming, and many groups tend to kick the can for more ready wins.  Second, a lot of companies just fail to understand what it means – is it just cataloging? Is it data quality?  Is it compliance and access? 

At its core, data governance is really meant to be a means for the business to take ownership of its assets.  Technology teams can shift into an enablement mechanism, and the business can take the lead in defining the people, processes, and definitions of what the data represents. 

New technologies in the cloud enable agile execution and implementation of data governance solutions that take a lot of the grunt work off a business’s plate.  Starting small and letting the value of governance present itself sooner rather than later is a great first step to getting rid of sprawl and getting your data in a usable state.

 

• This sounds like a lot and my IT department is understaffed and underwater.  Don’t they have to do everything? Who owns and manages our organization’s data – the business team or the technology team?

 

So, you know you need to get back to basics of defining the “why” of my data, and you need to get your arms around what data you have and what it means. Now the question becomes, who’s responsible?  For a long time, data was seen as a technology system and asset – database systems were technology team purview, processes generating data were applications maintained by technology, reports being created for the business were created by the technology team.  

As Lee Corso might say, “not so fast, my friend”. Data capture, use, maintenance, definition, analysis, etc. are now all the purview of “the business user” more so than the technology.   Data is a business asset. Period.  As a business asset, the business is responsible for defining the data, governing the data, and maximizing the ability of the data.  That doesn’t mean technology teams aren’t involved – compliance, security, operations, BCP and DR, evolution and scale – are all facets the technology teams must partner with the business to execute. However, to truly recognize the value of data, the business must own it as its core asset. 

 

• Ok, but what about data that isn’t even in my department?  What if I want to use our partners’ datasets? How can I use other datasets more easily as a 2nd or 3rd party when the data contains sensitive information?

 

Data sharing is being thrust to the forefront of “what is old is new” regarding data trends.  The increase in both direct regulatory actions (think CCPA and GDPR) and the sensitivities around consumer information privacy and transparency have created a paradox.  On one hand, businesses need to be able to share data in an ecosystem to create a fully fleshed out view of their customers’ profiles and needs. On the other hand, businesses are loath to share their data for privacy concerns and exposure risk. 

So how to kill two birds with one stone? 

Leaning into the cloud and what it does best – sharing compute and storage in a secure manner.  Modern cloud technologies have enabled a business to share data without sending data and do-so in a manner that still protects consumer identity.  There is power in data, and there’s power in data exchange.  

Data exchanging shouldn’t be about moving data around (extract to csv, zip file, encrypt file, send file, receive file, decrypt file, unzip file, load file…).  Data sharing should be about storing once, creating points of secured access, then enabling connectivity.  Modernizing your data means bringing the model to data and not the other way around. 

 

• This sounds like a lot of work, and I don’t exactly have a lot of staff. How am I supposed to build and retain a data team?

 

“The War on Talent” is at the top of almost every organization’s mind right now.  The last 24 months have flipped the world on its axis and we’re seeing large migrations of talent move every direction.  To double down, the speed of cloud and data technology has outstripped the ability for most companies to maintain the knowledge, skills, and certifications required to harness the new capabilities that become available. 

If you make the decision to build your own team, then it’s paramount to partner with your people organization and create a robust learning and development program, centered around building and using knowledge capital.  Having leadership that can draw career paths and trajectories, facilitate active projects to create points of exposure to the new technologies, and building compensation and variable bonus pools around learning are critical to stimulate ongoing skill development.  Retaining talent requires a culture that supports the same and speaks to talents’ desire to work with purpose. 

A lot of that is easy to type, but hard to execute.  It’s why I have always leaned into strategic partnerships that allow me to balance my organization’s needs with the budget, plan, and overall strategic goals around people and abilities.  Finding the right partner(s) allows your organization to focus on your strategic goal and trust the partner is focused on their talent, skills, certifications, and training.  Look for partners that want to build and partner with you to build an effective data journey vs just putting bodies in your office. 

 

• Ok. So, let’s assume I get all my ducks in a row – I still must “weaponize the data”. How can I use data to innovate my business?

 

Once the right data architecture – technology and resources – are in place, you’ll see the gains in knowledge capital, efficiency, collaboration, risk mitigation, productivity, and overall performance.  Once you’re able to use data, then you can *use* data.  Making data pervasive throughout the organization and more accessible has tangible results.  For example, increasing the usability of data in Fortune 1,000 companies by just 10% could create:

• $2 billion increase in total revenue each year 
• 16% increase in return on equity (ROE)
• 1.4% increase in return on invested capital (ROIC), increasing net income by $5.4 million
• 0.7% increase in return on assets (ROA), adding $2.87 million of value in fixed assets

Those are all great signposts that you’re on the right path.  The right data architecture also opens the door to the powerful combination of human and machine intelligence.  Once you’re able to use your data effectively, evolve the data and processes towards bespoke combinations, new avenues open for innovation.  Creating shareable datasets and placing them in marketplaces.  Combining insights across your organization and other organizations to rapidly evaluate new products and offerings.  A full, 360-degree view of your customer can open new patterns for retention and add-on sales.  

 

Anand Pandya, Global Head of Financial Services at Hakkoda, Inc. 

Anand serves as the Global Head of Financial Services at Hakkoda, and, as such, he is the primary leader in the industry sector to design, build, and manage modern data platforms for banks, lenders and financial institutions. With 20+ years of experience leading global teams across technology firms, Anand’s experience has nurtured him into a transformational leader with foresight and creativity. Prior to serving as Chief Data Officer at Curinos and leadership roles within Informa Financial Intelligence, Anand served as a data, analytics, and intelligence consultant to Fortune 1000 companies across the United States.  As a career long data advocate and evangelist, Anand has been responsible for transforming data management, governance, and analytics practices, developing new FinTech products and services, and rooting himself in the belief that innovative thought strikes best over a piping hot cup of coffee. 

 

Apex chats with Mir Ali, Head of Platform Engineering at The Kraft Heinz Company. Mir has 20+ years of experience leading technology teams across industries. His role includes focus on building a robust platform that supports KHC’s growth strategies, improves analytics-driven decision-making, and much more. In today’s Apex 1:1, Mir highlights organizational challenges faced within his role, explores the future of technology, and takes us through key lessons learned as a leader in the technology space! 

 

Q: What is the difference between a Chief Data Officer and a Chief Analytics Officer? Are they one and the same?

A: The Chief Data Officer (CDO) and Chief Analytics Officer (CAO) roles are relatively new roles, and their responsibilities are yet to crystalize across the industry. At the moment, the roles and responsibilities of the two positions vary vastly from one organization to another and often overlap with each other.

In some organizations, the CDO is tasked with both managing the organization’s data and extracting actionable insights from it. Data management includes collection, cleaning, storing, and making data available to analysts whenever they need access to it. Extracting insights involves data analytics, which would be the domain of CAO. To avoid confusion, some organizations assign only data management tasks to a CDO and leave analytics to CAO. 

To make matters more complicated, it’s not uncommon for organizations to appoint a CDO for the analytics tasks. At the same time, the technological aspects of data management are left to CIOs or CTOs. 

 

Q: How have you seen the role of CDO change? How do you partner with the CIO?

A: Traditionally, the CDO was responsible for data management and data governance. Today, the CDOs are increasingly responsible for data management, data governance, and data analytics, thanks to the availability of data management tools that greatly automate data management and, to some extent, data governance too. 

CDOs in many organizations own a data analytics platform that operationalizes strategy, governance, architecture, and data life cycle management. Going forward, the CDOs would be increasingly responsible for extracting value out of the data they are entrusted to manage. 

CDO and CIO can work together to create protocols and deploy the technologies required to make organizational data easily accessible to all the stakeholders – data analysts, decision-makers, or even customers – who need it. They can break down the siloes that prevent seamless data access for stakeholders, thereby transforming the organization into a data-driven enterprise.

 

Q: Have you encountered any challenges facing the CDO function?

A: As discussed before, the roles and responsibilities of a CDO are not fully established across the industry. This poses unique challenges to a CDO. For one, CDOs are often overlooked by the CEOs and CFOs for critical decision-making. Although the CDO can be a powerful innovation driver, the cost-conscious focus of the CEOs and CFOs can relegate their valuable insights to mere opinions.

Secondly, organizational data continues to be highly siloed, and breaking them down often causes friction with other departments. In many cases, the fragmented nature of data management makes it extremely difficult even to identify the data sources. Even when the data is available, pitching the importance of the insights from that data to CFOs and CEOs, with the purpose of investing into powerful data analytics infrastructure and talent, can be extremely difficult; especially because the potential benefits cannot be easily tangibilized before actually investing in analytics.

 

Q: What should be the ideal role and responsibilities of the CDO?

A: A CDO’s primary responsibility in the organization should encompass data management, data governance, and data analytics. They should be responsible for collecting and storing all corporate data safely and securely. They should make that data available to all stakeholders when they need it (role-based access). 

However, the technologies and infrastructure that operationalize these goals should ideally be the domain of technology experts like the CIO or CTO. In practice, much of the technologies should be automated to reduce human intervention during data collection, storing, and making it accessible. Instead, the CDO should lead data collection and analysis teams, giving them clear mission guidelines on what, when, and how to enable data-driven decision-making in the organization. 

 

Q: What are the current data trends, and how will they impact your organization?

A: Perhaps, the most significant trend we’ve witnessed in recent years is the heightened emphasis on analytics and artificial intelligence. Decision-makers are increasingly relying on data-backed insights to fine-tune their strategy. At the same time, AI is being used to automate much of the decision-making to make the organization more responsive to challenges. Both these goals have shone a spotlight on the cloud, which is promptly becoming the new home of corporate IT.

Although the push towards data-driven decision-making is palpable, a clear pattern of challenges is emerging. Data quality and reliability continue to be major concerns, and are expected to worsen with time. 

Also of concern is data security. Despite the enormous benefits of self-service, balancing data security and data governance mandates with self-service can be quite tricky, since the two objectives may often pit individual goals against organizational goals. However, the good news is that organizations have been more successful in embracing a data-driven culture, which can help overcome these challenges in time.

 

Q: How are you justifying the cost needed to evolve and adapt IT to support the speed and agility required by the business?

A: IT has evolved beyond its traditional support role today and become a source of innovation for the organization. So, IT’s benefits are not limited to cost-cutting alone; they also include discovering and creating new revenue sources. Therefore, justifying the cost of modern IT infrastructure and talent requires a two-pronged strategy. Strategy A is the identification of inefficiencies, and Strategy B is uncovering new opportunities.

The first step of Strategy A involves a thorough review of the business’s cost structure to identify inefficiencies so that they can be eliminated. A host of performance metrics can be presented to monitor the impact of IT on these inefficiencies. For proof-of-concept, the IT investment can be made in staggered measures, which helps win the trust of higher management more easily. 

Strategy B involves granular analysis of the enterprise’s products, services, and customer interactions through them (essentially by diving into customer data). Powerful data analytics tools and even case studies from other organizations can be used to demonstrate the impact of analytics on the company’s bottom line. Once again, innovation-related KPIs can prove handy.

Subsequently, clear timelines can be established to track, measure, and demonstrate the ROI from both strategies, while continuously fine-tuning the strategies to achieve desired results.

 

Q: What is the degree of cultural awareness of data as a corporate asset across your organization? How important is it to have a data-driven culture? Have there been obstacles to building a data culture, and if so, then how have you resolved it?

A: There is a growing awareness about the importance of data as a corporate asset for decision-making. However, we are still a long way from making it an automatic driver of and integral to the decision-making process in an organization.

Many factors have helped us get this far, and our learnings have provided us with a path forward. First and foremost, resistance to data-driven decision-making does not arise from technological challenges but the cultural kind. Therefore, data-driven culture must originate from the very top. Secondly, the entire organization’s workforce must get clear mission statements, key metrics to target, and guidelines on using data to make decisions.

Then comes the breakdown of data siloes. Analysts must know each other’s work and how their contributions and insights fit into the big picture. Also, data access must be seamless, easy, and role-based. Whenever and wherever possible, require managers and analysts to quantify potential benefits, risks, confidence levels, and so on. That reinforces the importance of data-driven decisions. Simple proof of concepts, specialized training, embracing flexibility over consistency, and tangibility (whenever possible) of the tradeoffs involved in each data-driven decision can build a robust data-driven culture within the organization. That’s what has helped us so far. 

 

Q: How have DevOps and cloud services changed how you design, build, deploy, and operate online systems and secure infrastructure?

A: DevOps has become the preferred strategy for most organizations, with an estimated 90% of cloud development projects employing it. The benefits are numerous, including faster application development, accelerated delivery of features and updates, stable work environment, quicker infusion of user demands into software, drastic improvement in product quality, automation of repetitive tasks, agility, improved organizational responsiveness to challenges, and lower costs for development.

 

Q: What is the current state of Big Data and AI investment, and do you sense the pace of Big Data and AI investment changing?

A: In the pre-pandemic era, investment into big data and AI was increasing at a steady pace. However, the pandemic restrictions compelled businesses to accelerate their digital transformation projects, and double down on big data and AI. The explosion of AI-driven chatbots, for instance, during the pandemic offers vital clues on where this niche is headed.

The pandemic offered big data and AI technologies to demonstrate their usefulness and effectiveness in shouldering various roles, from marketing to operations, and they have delivered. Naturally, the investment into these technologies will only grow, if not accelerate, in the coming years.

 

Q: What are some of the important leadership or business lessons I have learned?

A: Here are my top five leadership lessons that I’ve learned in my career:

• Have Vision and Clarity: Your effectiveness as a business leader relies heavily on the clarity of your vision and your ability to communicate it to your team.
• Don’t Be the Smartest Person in the Room: The best leaders rely on the intelligence, expertise, and problem-solving skills of the team they lead. So, try to bring together people who are smarter and differently skilled than you.
• Talk Less, Listen More: As a business leader, it’s important to be circumspect about everything you say. More importantly, be more receptive to what others have to say. The more you listen, the more you open yourself to better solutions.
• Give 100% To What You Believe In: Be authentic, be passionate, and be emotional about what you believe in. Commitment is infectious. The best leaders infect their teams with their own burning passion.
• Don’t Second-guess Every Decision: Leadership is complex and not for the faint-hearted. So, be open to alternate solutions, but don’t doubt your every action. Be bold and stick to your values.

 

Mir Ali – Head of Platform Engineering at The Kraft Heinz Company

20+ years of experience leading technology teams across industries has nurtured Mir Ali into a transformational leader with foresight and creativity. Mir Ali is currently with Kraft Heinz (KHC), where he is the Head of Platform Engineering and helms the responsibilities of maintaining, developing, and innovating KHC’s Digital Revolution Platform to support internal and consumer-facing products. His role focuses on building a robust platform that supports KHC’s growth strategies, improves analytics-driven decision-making, and aligns the vision across stakeholders, including IT, business, and external partners.

Apex chats with Karen Holmes, the Vice President and CISO at global staffing company True Blue. Karen is on a mission to drive automation and orchestration, she is focused on improving overall seamless and secure user experiences while creating an innovative atmosphere of “I have a crazy idea that just might work.” Here Karen shares her experience as a CISO and offers her own advice on managing an enterprise team.

 

 

Q: What is IT security doing to support innovation in the enterprise? 

A: We just achieved an ISO 27001 certification for the organization, and this was both a heavy lift and a huge achievement. We knew we were following best practices, and felt it was time to “put our money where our mouth is” and get certified. This shows the Enterprise commitment.

 

Q: How has the role of the CIO/CISO changed over your career?

A: CISOs are more forward and customer/client facing than ever before. We can no longer stay in the background and practice “defense against the dark arts” potential clients and their teams want to know us and hear from us directly. We have a public face more now than ever before.

 

Q: What advice would you give an early stage CIO or CISO joining an enterprise organization?

A: Define success first. Naturally, you’re going to want to put your own ideas in motion, but don’t change things just to make your mark. Think of it as a jigsaw puzzle. You know what success looks like, then you build the corners, building in from that vision. You don’t throw away the box and try to find the center as a place to start. And listen to your engineers, the people with their hands in the chompers every day. Don’t assume you  know everything, because you don’t.

 

Q: What are your top data priorities: business growth, data security/privacy, legal/regulatory concerns, expense reduction? 

A: All of the above actually. Security cannot be a speed bump, and we have to move, advise, respond and guide from a cyber and compliance perspective if we’re slowing the business moving forward. We can’t be a speed bump or a toll booth, we have to keep up with traffic

 

Q: What are some of the personal experiences — or compelling arguments — that have influenced your thinking around gender and technology and have motivated you to get involved in being an advocate for change? 

A: Everyone has unconscious bias, but the best advice I’ve given women coming up in Technology is to take their own bias out of the equation before you walk in the room. Don’t assume every man in the room is going to consider you “less” because of your gender, or that they’ll be making assumptions about your skill level . Walk in as the technology professional, not the female technology professional. Own what you know, and if they want to underestimate you? Well, let them. It’ll be fun to show them they’re wrong.

 

Q: Has security been more of a challenge to manage while your teams have shifted to a Work From Home structure?  

A: For parts of our organization who had been working in offices using desktops, certainly. I don’t think we’re unique there. Overall, I have always advocated a location agnostic approach to work. Hire the right person with the right skills, regardless of where they sit.

 

Karen Holmes – Vice President and CISO at True Blue

Karen Holmes is the Vice President and CISO at global staffing company True Blue, where she is responsible for Cybersecurity, Technology Governance, Risk and Compliance, Networking, and Telecommunications. On a mission to drive automation and orchestration, she is focused on improving overall seamless and secure user experiences while creating an innovative atmosphere of “I have a crazy idea that just might work.” Prior to taking on her role at True Blue, Karen gained experience at Recreational Equipment Inc. (REI), Carnival Corporation, Holland America Lines, and JP Morgan Chase. Karen also serves as a Director with Kitsap Credit Union. She is a CISA, CISM, CDPSE, and CIPM.

Apex chats with Shahidul Mannan, the Head of Data Engineering and Innovation at Partners Healthcare DAO. He is leading the next generation data analytics architecture and platforms for Partner’s Healthcare digitization and Analytics Innovation. Here, Shahidul shares with us how he plans to shift to a digital business model and the advantageous trends in DevOps AI, ML, Automation and more!

 

Q: How have you seen the role of CDO change? How do you partner with the CIO? Have you encountered any challenges facing the CDO function?

A: I have seen the role changing towards a more transformative leader. With all the leading companies racing for digitization, and becoming more data driven, a CDO is not only focused on traditional data management and analytics leadership anymore. Today’s CDO needs to focus more on how data can help transform the company to be successful in digital transformation and innovation.

 

Q: What are the current data trends and how will it impact your organization?

A: Having been in the financial, retail, high tech and now Healthcare industries in my decades of career in analytics, I think this is an exciting time in healthcare for data driven transformation and innovation. However, it is also obvious that healthcare had a pretty late start in this journey and is catching up on some of the core capabilities. Some of the key trends I am seeing are moving to Cloud analytics and driving data democratization for innovation, especially in AI and Data Science.

Cloud adoption is growing fast, especially in analytics for the flexibility, scalability and speed of innovation it provides. Mass General Brigham is also moving fast with cloud adoption. Our new cloud-based data lake driven analytics ecosystem will enable the latest technologies, batch and real time data capabilities, AI/ML and cognitive services which will enable enterprise reporting to AI/ML based innovative products and services. We are entering new horizons in data analytics to become a more data driven digital health company.

Similarly, self-service analytics is another trend that’s coming fast, and we are also empowering our various analytics users. We have moved a long way from building your own report to now having your own innovation space and being able to operationalize the innovation lifecycle. Our data scientist, innovators are excited to embrace it.

I believe that a combination of cloud data transformation and democratization with self-service will fuel our enterprise digitization efforts and help continue towards the next level of innovation with data at MGB.

 

Q: What are your top data priorities: business growth, data security/privacy, legal/regulatory concerns, expense reduction…?

A: Our overall goal is to improve patient care and quality of our services with data. So, I would say all of the above as they are all tied together. Our immediate focus points are improving operational efficiency, innovating in diagnostic and treatment with AI, and extending newer and better insights to improve quality and services. Obviously, we have to do all these ensuring compliance with regulatory obligations HIPAA and others, and meeting our patients privacy and security expectations.

 

Q: How do you balance the need to ensure that non-revenue generating data-driven transformation efforts receive the commitment and funding that are required to sustain these efforts?

A: First off, I think the overall value proposition for data driven transformation needs to be connected, and having non-revenue generating efforts are the cost of doing business, and most cases foundational, so the story needs to be told in a way that provides a complete and comprehensive strategic perspective. However, I am also a firm believer of providing incremental value that directly adds business value and hits the bottom line along the way. We need to execute with an agile and value-added mindset.

 

Q: How would you define “Enterprise AI” in a non-digital native enterprise like your organization?

A: What operating model and cultural changes have you considered as you shift to a digital business? What parts of your business would benefit the most from a greater digital foundation?

Overall, we have adopted Agility and Innovation, at the core of our culture. This helps drive the operating model to be curious, and to experiment, learn and adjust. A big part of our operating model would be driven by automation through DevOps, which would certainly benefit data customers across the system driving efficiency, speed and overall operationalization of any digital asset and service across the board.

 

Q: How has DevOps and cloud services changed the way you design, build, deploy, and operate online systems and secure infrastructure?

A: It certainly is a paradigm shift from traditional development and production release. Our strategy was to take full advantage of DevOps and re-engineer our process matching with best of breed technologies for DevOps. We opted for full lifecycle management through DevOps automation and cloud services, resulting in greater speed to market, agility with greater standardization, collaboration and efficiency. Our next phase is to incorporate similar practices with ML operationalization for greater collaboration and speed for innovation.

 

Q: What is the current state of Big Data and AI investment and do you sense the pace of Big Data and AI investment changing?

A: I think the current state of Big Data and AI investment is healthy and many may consider it aggressive. I sense that it will not only continue on its own path, but also accelerate in embedded form in other areas of business. Also, for many, the shift will be from core and foundational capabilities to more matured applications and commercialization. Overall, Big Data and AI will become a more integrated part of business strategy and how we operate, if not already, hence related investments will grow further.

 

 

 

Shahidul Mannan – Head of Data Engineering and Innovation at Partners Healthcare DAO

Shahidul Mannan is the Head of Data Engineering and Innovation at Partners Healthcare DAO. He is leading the next generation data analytics architecture and platforms for Partner’s Healthcare digitization and Analytics Innovation. Most recently he commercialized data and AI products at TTEC Digital as Vice President and GM and at Dell EMC as Global Head of Big data and Analytics. A lifelong data and AI evangelist, Shahidul was Chief Data Officer and Vice President of Technology at Altisource and Chief Analytics Officer at Human Health Project – responsible for all data management, enterprise and advanced analytics, developing Fintech products and services, and had been in Sr. Technology Leadership roles at Bank of America, Freddie Mac.

 

Apex talks to Arijit Bhattacharya, Principal at Element22. Arijit is a strategist with success in creating Data Governance, Data Management, Data Stewardship and Data Operations. Here, Arijit speaks on using data and analytics proactively for governance and growth rather than taking a backseat.

 

Q: What is the difference between a Chief Data Officer and a Chief Analytics Officer? Are they one in the same?

A: While analytics is a large part of the CDO role today, they are different roles. The CDO is responsible for data governance, data integrity, face off to regulators as well as using the data in the organization for revenue generating needs. Analytics is a very important part of the data that an organization has in order to either course correct or give indications to new trends in the data collected. But overall, the trust in that data is very important – which is what a CDO is supposed to provide to the organization.

 

Q: How have you seen the role of CDO change? How do you partner with the CIO? Have you encountered any challenges facing the CDO function?

A: The role of the CDO has changed from just data governance and integrity to that of using the firm’s data for monetizing it or making use of it to gain more insights into customer interactions, creating new channels for sales, etc. It is not a passive role anymore and is now embedded in revenue generating processes.

Q: What should be the ideal role and responsibilities of the CDO?

A: It should be  blend of data governance (bring back the trust in data to the organization), timeliness of data availability – goes back to the trust and if you don’t make data available in a timely manner, people will find a way to get it – which may not be trusted data, regulatory, privacy and security of data. Above all, when all of this is in place, data should be helping businesses do more and not be taking a back seat. So a CDO should always have that view in mind as to what is the end goal of all this – it should always be to help businesses do more, embed the CDO office in the revenue generating process.

Q: What operating model and cultural changes have you considered as you shift to a digital business? What parts of your business would benefit the most from a greater digital foundation?

A: The operating model change has been mainly related to the governance of data – with data owners, data stewards taking care of their respective data domains. This has taken time to establish but people see the benefits to it. The trust in the data was very important as a foundational exercise. And this has been achieved with the right tools, automated processing to check for data quality, dashboarding and a modern way to do data governance. With this digital foundation, the journey now moves on to more revenue generating themes which involve analytics and AI.

 

Q: What are your top data priorities: business growth, data security/privacy, legal/regulatory concerns, expense reduction…?

A: The priority should always be the proper governance and trust in the data. Without that, any data in the organization is up for debate and waste of time. Once this is under control, the data can be looked at for business growth, cost reduction and answering regulatory queries. Data security is more of an IT function and is important. It should be monitored and reported on. For data privacy, as the requirements are there from regulators or the business, there may be technical solutions needed. This is important because any breach can lead to loss of reputation and/or clients, fines, etc.

 

Q: Did you have specific projects or initiatives that have been shelved due to COVID-19 and current realities?

A: None of the projects were shelved – in fact, if anything, projects got delivered at a quicker pace. People were more focused on the respective projects they were doing and also did not mind going the extra mile with the working from home environment. Overall, there has been no ill effects of Covid-19 on the delivery of work. However, there have been people who got directly affected and the firm took a sympathetic view to them and it all worked out in the end for the team.

 

Q: What advice would you give an early stage CIO or CDO joining an enterprise organization?

A: There should be a benchmarking exercise done to make sure the current landscape is clear to everyone in the organization. This helps prioritize the work in the next 2-3 years. The landscape should be looked at with the following in mind : trust in data, data duplication, regulatory and legal needs, expense reduction / re-work, etc.

 

 

Arijit Bhattacharya – Principal at Element22

Arijit Bhattacharya is a financial Services Leader in the Data and ESG space developing and executing on long term strategies. Specific emphasis on enabling new capabilities via improved time to market, data quality, automation, streamlined processes and reduced costs. Strategist with success in creating Data Governance, Data Management, Data Stewardship and Data Operations. Designer of top-quality technical applications that deliver strategic solutions to meet real world business needs.

 

Apex talks to Harini Sridharan, Head of Analytics and Data Management at MoneyGram. She is a growth-focused digital business leader, leveraging deep, multi-industry analytics and data strategy experience to build, scale, and lead global analytics teams in generating multimillion-dollar revenue growth and cost savings. Harini shares valuable insight into building out and integrating a strong data driven business strategy.

 

Q: What are the current trends impacting data?

A: On the offense side, you have Increased Digital Adoption and Personalization and on the defense side, you have Data Privacy. 

Increased digital embrace by customers is driving digital transformations in companies and good data is key for such transformations to be successful.  You will know more about your customers and personalize their journey to make it easier for them to shop around in the digital world if you have their journey and friction related data.  Therefore, it is reasonable to expect digital adoption and personalization to further drive big data and machine learning needs.  

On the defense/privacy end, collecting, storing, and using data in a responsible manner will be critical since customer expectations and data privacy regulations are changing worldwide. The regulations are challenging us to change the way we measure effectiveness of programs and channels and to use the collected data in a more responsible manner.

All these changes enhance the need for machine learning in order to showcase the right products and services, to algorithmically make decisions on approving customers for financial transactions, and to make marketing investments more effective. 

 

Q: How do you balance the need to ensure that non-revenue generating data-driven transformation efforts receive the commitment and funding that are required to sustain these efforts?

A: The executive team supports data transformation efforts if you can articulate that those efforts will drive understanding about customers and lead to optimal decisions that would in turn drive up revenue and profitability.  So, it is important to have a list of projects that can deliver value because of the data transformation effort. 

As with any transformation, you need to show sustained progress to ensure that there is continued enthusiasm. It is important to start with small POCs and show quick effective wins. I would approach it like any new product development. Similar to a new product introduction in a market, you need to start with a critical area and find your strongest supporters and adopters who can use the product/service (in this case data service) and illustrate the value of your efforts. As you would for any transformation, you need to have milestones along the journey that show value improvements along the way. This will lead to sustained interest among executive sponsors and will keep the team’s morale high. 

 

Q: Have you developed a business driven data strategy; is there support for it and is your organization becoming more data-driven? What steps are you taking to ensure all areas of the business are data driven?

A: Any data strategy should be built around a company’s business strategy. A number of companies are going through digital transformation these days. If digital transformation is a goal, then first we need to collect data in a way that addresses immediate needs. This includes everything that we can reasonably get about the type of customer we are trying to attract and retain. Also, it is important to understand the current factors impeding adoption by these customers, learn about the kind of channels that can bring the types of customers and so on. But on a longer-term basis, we also need to ensure that data strategy can support tomorrow’s business models which require faster machine-learning driven decisions and recommendation engine driven content among others. 

It is easier to convince adoption when data strategy is built around supporting business growth and it will help your case if you can demonstrate it with a few proof-of-concept examples. Also, by now, most firms and boards see the value and there is significant investment across most industries in data. 

Again, it is very important to find a few strong supporters and pilot use cases that visibly impact their teams. Once you have some strong wins and have started scaling it with a few more partners in the company, it is easier to convince partners who are holding out.

 

Q: What is the current state of Big Data and AI investment and do you sense the pace of Big Data and AI investment changing?

A: Customers are now getting used to split second AI driven decisions in many spheres of life and are starting to expect it more everywhere. What used to be customer delight and competitive advantage five years ago is table stakes today. To support this kind of instant decisioning, optimization, and ML driven pertinent content etc., requires a tremendous amount of data and ML capabilities. As such, investments are accelerating significantly and should continue to accelerate over time. 

 

Q: How important is it to have a data driven culture? Have there been the obstacles to building a data culture and if so, then how have you resolved it?

A: To win in the digital space, first, companies need to be data driven as opposed to merely being data informed. That takes a huge cultural shift. Often the argument that we hear is innovation and data driven decision making don’t go hand in hand. Yet some of the most innovative firms are also the most data driven ones – Netflix, Amazon and so many more. Concepts like data driven innovation are catching up. 

Secondly, you will see that a lot of successful digital firms test customer preferences before they invest. In a digital world with good data and technical infrastructure, customers’ likes, and dislikes can be quickly identified. Therefore, it is important not just to build products, but have a feedback circle in the form of A/B and multivariate testing as well. Surveys and user testing are important. In addition, before going live, it is also vital to test in small volumes before you invest or make a large change wherever possible. That again requires a culture change in decision making from being opinion based to one that is data driven. It makes it vital to hire personnel with a data driven mindset across functions and for the CDO or CAO to consistently make the case for having analytics at the table.

 

Q: Have you found new vendors for your organizations that are now needed in this time of COVID-19 and remote working?

A: Yes. With COVID-19, data needs as I mentioned have only accelerated as digital adoption has scaled. So organizations have greater data needs. Some of it has been through word-of-mouth recommendations and others have been through virtual events such as yours. Some of it has also been through vendor cold calls and demos. 

 

Harini Sridharan – Head of Analytics and Data Management at MoneyGram

Harini is a growth-focused digital business leader, leveraging deep, multi-industry analytics and data strategy experience to build, scale, and lead global analytics teams in generating multimillion-dollar revenue growth and cost savings while improving customer engagement. She has worked across B2B and B2C companies in areas of product, customer, growth, business analytics, and data strategy. Her expertise includes ROI improvements for 9-digit media budgets as well as optimization and customization of US and international websites across multiple industries like e-tail, cloud infrastructure, and financial services

She is the Head of Analytics and Data Management at MoneyGram International and was previously with IBM, eBags, and Vistaprint in various roles. She is also a Senior Fellow at Wharton Customer Analytics.

Harini holds a Master of Business Administration (Honors) from the Wharton School; a Master of Engineering Management from Dartmouth College; and a Bachelor of Engineering degree.

Apex talks to Amy Nichols, Head of Technology Integrity Group, Chief Technology Organization at Wells Fargo Technology, Wells Fargo & Company. Currently she is driving oversight for key areas of control including Cloud, Segregation of Duties and Secure Development. Amy shares valuable insight into her own strategies and best practices for seamlessly moving into the cloud.

 

Q: Where are you on the journey to utilizing hybrid cloud and devops and what challenges are you facing?

A: Both moving to the Cloud and DevOps can be strategic journeys in their own right, but together  are powerful, and certainly leveraging DevOps to move to Cloud can be a huge enabler. Ultimately, both aspects come down to a specific culture change within the organization to move to a faster way of developing and delivering value into the production environments. At Wells Fargo, the journey is underway with success milestones behind us.  However, as with any large institution that has a fair amount of legacy technology, the evolution will take multiple years.

 

Key elements for consideration are:

  1. Pragmatic Strategy aligned with Business Objectives

Before moving to a Cloud Strategy, the organization top down needs to define the risk appetite and approach for the business capabilities, and more specifically, business data for hosting off premise. 

  2. Control Framework

All the existing processes for applications, network, infrastructure, etc. should be evaluated for hosting implications.  Additionally, the concept of ephemeral assets and the control environment for short lived instances of executables needs to be evaluated and understood. 

  3. Clear Deliverables and Outcomes

Depending on if your organization is largely created with new constructs, or if the organization has legacy monolithic structures, it could take months, or many years to reach the target state.  Leveraging the strategy, create interim deliverables to ensure that the organization has time to refine processes and move forward iteratively towards the target state. 

  4. Education and Training

Skilled resources can drive the transition with greater success, but training all areas of the organization on new practices and concepts is critical.  This also goes for listening to where resources have questions and tailoring different types of training and/or education to different areas of the organization (business or tech).

   5. Vendors

While there are many great Cloud providers in the market, they have different offerings, price-points, and alignment or partnership approaches.  A matrix of what is most valuable to your organization should be created, and vendors assessed based on the value grid.  Additionally, choosing multiple vendors to pilot with can give you valuable insight.  Finally, choosing more than one vendor for full implementation can provide the ability for greater flexibility if the business and financial model allows.

   6. Agile and DevOps

And finally, not to be forgotten, to be able to take advantage of DevOps, an Agile approach to delivery is a must.  The development organization needs to be re-educated to understand backlogs, user stories, quick delivery of business value, and the objective of fail-fast. The principles of Agile are varied and can be implemented for success in any shop, but become a must for a DevOps transformation.

No two organizations are the same, neither are the transformations, but these six foundational areas are key to success for both Cloud as well as DevOps.

 

Q: What factor should you consider before implementing a Cloud strategy?

A: Each organization should address it’s own business strategy for technology before just embarking on either a public or a private cloud approach. Based on the organization’s scale, business objective and risk appetite (including regulatory oversight), these all create factors that lead into objectives that have to factor into the approach.  Do not just move to cloud or a virtualization strategy because others are…it needs to be aligned and embedded into the business playbook. 

 

Q: Is there cost savings with moving to a Cloud strategy?

A: Similar to answers for the considerations, it depends on what strengths you have in your organization today (i.e. do you have data centers that are strategically placed?) on whether or not the expense to move makes sense. But let’s be clear, virtualization and Cloud can be a primary cost savings, but it needs to be factored in with other factors that are particular to your own organization. Objectively defining current costs for technology delivery alongside all the costs of transforming to new environments, tools and processes need to be reviewed side by side before making a large-scale commitment to any organizational change, including Cloud.

 

Q: How does a Cloud strategy align to the Data Strategy?

A: The two are related and can support business decisions for one another.  Despite having very secure and solid methods for providing access to data within the Cloud, there is always additional risk as confidential or sensitive information is in any location – Cloud, 3rd Party Data Center and/or Internal Data Center (private Cloud included).   It just adds complexity if data has to travel to multiple locations and/or processes, including crossing through different vendors or products.  All of this should be considered together – where data is accessed, manipulated and ultimately stored all factor into how Cloud is approached.

 

Q: What are the values of using an External Cloud Vendor?

A: Besides that you can offload (i.e. outsource) tech functions to a vendor at a potentially reduced price point, the vendor not only will provide hosting capabilities, but other services that enable faster development that go well beyond traditional hosting/storage vendors.  Each vendor offers different services and different ways to approach and align to your company’s needs.  These additional capabilities should also be reviewed for extension of other development or technology goals.   Finally, as many other companies are taking advantage of vendors in this area, the growth in this space is tremendous, with all the lessons learned being passed along to future customers.

 

 

Amy Nichols – Head of Technology Integrity Group, Chief Technology Organization at Wells Fargo Technology, Wells Fargo & Company

Amy Nichols is head of the Head of Technology Integrity Group, a key area within Wells Fargo Technology.  Wells Fargo has one of the world’s largest and most innovative information technology groups with more than 20,000 talented team members who help keep Wells Fargo at the forefront of America’s diversified financial services companies.

Amy is responsible for providing strategic thought leadership to critical major initiatives.  Currently she is driving oversight for key areas of control including Cloud, Segregation of Duties and Secure Development. This also includes improvements to implement cybersecurity requirements into the full application portfolio.

She previously served as Command Center leader for the Technology Cybersecurity Program, which had oversight for areas of improvements across many disclipements including Asset Management, Network Perimeter Controls, Identity and Access Mgmtm and Cybersecurity Risk Practices.  

Amy also headed the legacy Information Services (IS) Strategic Initiatives Office and served in leadership roles spanning Wells Fargo’s lines of business, specifically related to Cash Management (wires and ACH), Treasury Services (Corporate services, Reporting, Lockbox), Imagining Services, and Shared Services (Asset Management, Program Management, and Systems Development Lifecycle Tools). 

Outside of work, Amy supports the local Charlotte tech community in various ways.  In 2019, she formed CyberHype Charlotte with four other community leaders, which supports career advancement by using social media and other marketing collaboration for all Charlotte tech meetups and technical sessions across the Charlotte metro area.

Amy graduated from Queens University with a bachelor’s degree in Computer Science.  Amy also supports Queens in support of the future talent pool through Alumni activities and scholarships.

 

Apex talks to Phanii Pydimarri, Senior Director of AI & Advanced Analytics at Stanley Black & Decker. Phanii is Global Data Analytics Leader with over 15 years of experience in end-to-end Data Management. Today Phanii discusses key elements in the evolving role of the CDO and strategies for fueling cross-functional business growth.

 

Q: What is the difference between a Chief Data Officer and a Chief Analytics Officer? Are they one in the same?

A:

• To be frank, the definitions on these titles are still evolving. Organizations are figuring out the difference, their area of focus and apparently calling their Data Leader with one of these titles or even better combining them and calling the roles as Chief Data & Analytics Officers
• The major difference I see between them is the Chief Analytics Officers must be more Customer/Business focused. Their main goal should be to understand and identify opportunities to build Analytics products and solutions using the Data managed and enabled by the Chief Data Officer. The Chief Analytics Officer to me is much of a Product Owner, operating like one, designing new data analytics related products that could be both internal and external facing. The eventual goal should can be to monetize the analytics products and provide a revenue source for the organization
• Chief Data Officers on the other end must be able to focus on identifying potential new data sources to tap into, build the corresponding modern data platform and provide high quality, highly governed and compliant data to the analytics teams across the organization. Chief Data Officers can also look for ways to monetize the data by working with both internal and external partners
• Both these roles are evolving and at the present they can be operated by one individual but cannot be called as the same as they both have different areas of focus

 

Q: How have you seen the role of CDO change? How do you partner with the CIO? Have you encountered any challenges facing the CDO function?

A:

• The role of the CDO has been evolving towards maturity for some time now. Organizations are understanding the role better and identifying critical success factors of the role and eventually bringing it into the corporate leadership mix
• A major welcome change that I would like to call out here is more and more organizations are creating the CDO role and giving the organizational-wide responsibility of managing Data assets to it which shows the gain in prominence the role has gotten over the last few years
• Partnerships across the C-level is critical for CDOs to be successful. A CDO must be viewed as the common thread between the Technical and Business functions within an organization.  CDO has the crucial responsibility of collaborating through CIOs and CTOs along with other business side C-level executives.
• Having a great relationship with CIO is important as CDOs may still rely on traditional IT to provide infrastructure and technology support which is crucial for the success of the Data & Analytics initiatives. About 35% of CDOs continue to report into the CIOs which assumes the partnership
• Lack of cross-functional collaboration and inter-functional siloes are major challenges for a CDO

 

Q: What is the current state of Big Data and AI investment and do you sense the pace of Big Data and AI investment changing?

A:

• There has been a significant increase in corporate spend on Big Data and AI. Organizations are realizing the fact that not recognizing data as the most valuable asset is affecting their competitive advantage and are losing out on potential business opportunities
• I can say that there is significant improvement in realizing value of and from big data by organizations
• On the other hand, investments in AI are just getting started. There needs to be a lot of value-driven measurement that needs to be done by the organizations to really understand if AI can add value to their business model. There are organizations jumping onto the AI bandwagon without doing the due diligence of understanding the ROI and have lost significant investment for nothing
• I expect organizations to improve their investment into AI, Big Data and RPA in the coming days, but would recommend operating with caution by understanding the true value proposition from the investment

 

Q: What advice would you give an early stage CDO joining an enterprise organization?

A:

• Spend a lot of time understanding the current state
• Identify where the organization falls on the maturity scale
• Plan to move up the maturity scale with small achievable goals
• You do not always need AI, do your due diligence to understand ROI
• You do not always need modern data infrastructure to meet the organizational needs
• Communicate, Communicate, Communicate

 

Q: How do you balance the need to ensure that non-revenue generating data-driven transformation efforts receive the commitment and funding that are required to sustain these efforts?

A:

• Finding the right balance is the most important task of the CDO. It is highly critical to break down the between Business (Product, Marketing, Sales etc.) and Operational (Manufacturing, Supply Chain, Customer Support etc.) functions. An organization needs equal investments in both areas to see success from being data driven. Organizations tend to ignore operational focus areas as they are not direct revenue generators, but the CDO must take the responsibility of educating the executives, Senior and mid-level management the importance of this. 
• A best example I can think of is organizations investing millions of dollars in creating newer products and offerings to their customers but doing minimum to the complaints they receive on their call center lines, social media, or other forums. Organizations can lose out on the sales of their innovative products with bad customer support
• As a CDO it is important to focus on Data Literacy and educated leadership on the importance of investing in both direct and indirect value generators for the organization

 

Q: What are your top data priorities: business growth, data security/privacy, legal/regulatory concerns, expense reduction…?

A:

• Business growth will be major data priorities, but for that to happen you expect data that can be trusted, highly secure and gaining the confidence of the customers and making them feel their data is safe and secure
• Improving operational efficiencies can eventually be an indirect factor for business growth

 

Q: Have you developed a business driven data strategy; is there support for it and is your Organization becoming more data-driven? What steps are you taking to ensure all areas of the business are data driven?

A:

• Yes, I have been developing business drive data strategies across multiple organizations over the years. As a CDO I have the responsibility of gaining the support from the right stakeholders within the organization
• A common challenge I encountered while gaining support is helping the business leadership understand the value proposition. Everybody comes in with a “What’s in it for me?” mindset and I as the data leader have the responsibility of explaining the benefits of investing (and not investing!) and supporting the data strategy
• Steps I have taken to ensure I focus on all areas of business are the following
  •  Understand the most impactful area from which you can get catalysts to your initiatives
  • Identify low hanging fruits with quick easy wins
  • Prioritize your areas of focus. Remember none of us can do everything at the same time
  • Show the business stakeholder what they get from this. This is highly critical
  • Communicate, Collaborate, Coordinate and Educate

 

 

Phanindra Pydimarri – Senior Director of AI & Advanced Analytics at Stanley Black & Decker

Phanii is a Global Data Analytics Leader with over 15 years of experience in end-to-end Data Management with key focus areas in Data Strategy, Data Analytics, Data Science and transforming organizations into data-driven culture.

Phanii started his career as a BI Consultant, traveling across the US and working for various clients in different industries. He is a strong believer of economies of scale, is outcome driven and is passionate about solving key business challenges by using Data as a key corporate asset. He has strong experience using Data Science to improve key operational challenges and have experience standing up Data Governance programs across various public and private sector organizations. Over the years, Phanii has transformed cultures and showcased data as a value-added resource that can be leveraged to deliver measurable improvements at Bose Corporation, Sabre Corporation, Dallas Area Rapid Transit and many other global organizations.

Phanii is currently working as the Senior Director of AI & Advanced Analytics at Stanley Black & Decker.