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.