Today, most technology aficionados think of data engineering as the capabilities associated with traditional data preparation and data integration including data cleansing, data normalization and standardization, data quality, data enrichment, metadata management and data governance.  But that definition of data engineering is insufficient to derive and drive new sources of society, business and operational value. The Field of Data Engineering brings together data management (data cleansing, quality, integration, enrichment, governance) and data science (machine learning, deep learning, data lakes, cloud) functions and includes standards, systems design and architectures.

There are two critical economic-based principles that will underpin the field of Data Engineering:

Principle #1:  Curated data never depletes, never wears out and can be used an unlimited number of use cases at a near zero marginal cost.

Principle #2:  Data assets appreciate, not depreciate, in value the more that they are used; that is, the more these data assets are used, the more accurate, more reliable, more efficient and safer they become.

There have been significant exponential technology advancements in the past few years ; data engineering is the most topical of them. Burgeoning data velocity , data trajectory , data insertion , data mediation & wrangling , data lakes & cloud security & infrastructure have revolutionized the data engineering stream. Data engineering has reinvented itself from being passive data aggregation tools from BI/DW arena to critical to business function. As unprecedented advancements are slated to occur in the next few years, there is a need for additional focus on data engineering. The foundations of AI acceleration is underpinned by robust data engineering capabilities.

YourStory & AIQRATE curated and unveiled a seminal report on “Data Engineering 4.0: Evolution , Emergence & Possibilities in the next decade.” A first in the area , the report covers a broad spectrum on key drivers of growth for Data Engineering 4.0 and highlights the incremental impact of data engineering in the time to come due to emergence of 5G , Quantum Computing & Cloud Infrastructure. The report also covers a comprehensive section on applications across industry segments of smart cities , autonomous vehicles , smart factories and the ensuing adoption of data engineering capabilities in these segments. Further , it dwells on the significance of incubating data engineering capabilities for deep tech startups for gaining competitive edge and enumerates salient examples of data driven companies in India that are leveraging data engineering prowess . The report also touches upon the data legislation and privacy aspects by proposing certain regulations and suggesting revised ones to ensure end to end protection of individual rights , security & safety of the ecosystem. Data Engineering 4.0 will be an overall trojan horse in the exponential technology landscape and much of the adoption acceleration that AI needs to drive ; will be dependent on the advancements in data engineering area.

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In the time of uncertainty and disruption….Soon, organizations will increasingly be competing on the AI prowess and their supremacy. AI promises to play a critical role ; artificial intelligence can detect patterns in complex data sets at extreme speed and scale, enabling dynamic learning. This will allow organizations to constantly adapt to changing realities and surface new opportunities, which will be increasingly important in an uncertain and fast-changing environment.

But for companies to compete on AI, it is not enough to merely adopt AI, which alone can accelerate learning only in individual activities. As with previous transformative technologies, unlocking the full potential of AI and future of workforce will require fundamental organizational innovation , transformation and disruption. Leaders will need to re-invent the enterprise as an AI driven organization :

• Velocity & Scale : The growing opportunity and need to perform at high velocity bringing scale driven by AI is well known—algorithmic trading, dynamic pricing, real-time customized product recommendations are already a reality in many businesses. But it is perhaps under-appreciated that slow moving forces are also becoming important. For example, trade institutions, political structures and social attitudes are slowly changing in ways that could have a profound impact on business. Gone are the days when business leaders could focus only on business and treat these broader variables as constants or stable trends. But such shifts unfold over many years or even decades. In order to thrive sustainably, businesses must learn at high velocity .
• Rebalancing Humans and Machines equation : Machines have been crucial components of businesses for centuries—but in the AI age, they will likely expand rapidly into what has traditionally been considered white-collar work. Instead of merely executing human-directed and designed processes, machines will be able to learn and adapt, and will therefore have a greatly expanded role in future organizations. Humans will still be indispensable, but their duties will be quite different when complemented or substituted by intelligent machines.
• Integrating External ecosystems with corporate strategies : Businesses are increasingly acting in multi company ecosystems that incorporate a wide variety of players. Indeed, seven of the world’s largest companies, and many of the most profitable ones, are now platform businesses. Ecosystems greatly expand learning potential: they provide access to exponentially more data, they enable rapid experimentation, and they connect with larger networks of suppliers of customers. Harnessing this potential requires redrawing the boundaries of the enterprise and effectively influencing economic activity beyond the orchestrating company.
• Evolving the Organization : The need for dynamic learning does not apply just to customer-facing functions—it also extends to the inner workings of the enterprise. To take advantage of new information and to compete in dynamic, uncertain environments, the organizational context itself needs to be evolve in the face of changing external conditions.

 Today’s organizations, which were designed for more stable business environments, are not well-suited to perform these functions. Reinventing the organization for the next decade will require embracing four imperatives:

1. Integrate AI into the core operating model for survival
2. Migrate human cognition to mature work spheres
3. Re balance the relationship between machines and humans.
4. New age leadership & management approaches

1.Integrate AI into the core operating model for survival : As powerful as today’s level of AI is , it will yield only incremental gains if it simply enhance individual steps of existing processes. The effective rate of an organization’s learning is gated by its ability to act on new insights. And classical organizations act slowly, owing to their reliance on human decision making and hierarchy. Organizations will need not only to automate but also to “embed AI in to the operating model” of significant parts of their businesses.

In order to truly accelerate the speed of learning to algorithmic timescales, organizations will need not only to automate but also to “embed AI ” into significant parts of their businesses. In traditional automation, machines execute a pre-designed process repeatedly and consistently. In AI led transformation, machines use continuous feedback to act, learn, and adapt on their own—without the bottleneck of human intervention.

AI driven systems are designed by combining multiple algorithms into integrated learning loops. Data from digital platforms automatically flows into AI algorithms, which mine the information in real time to facilitate new insights and decisions. These are wired directly into action systems, which continuously optimize outcomes under changing conditions. These actions produce yet more data that can be fed back through the cycle, closing the loop and allowing the organization to learn at the speed of algorithms.

In contrast, traditional organizational approaches—for example, unchanging rules or hierarchical decision processes—can impede companies’ ability to harness the rapid learning potential unlocked by AI ; Actions that companies can take to harness AI include :

• Gather real-time data on all aspects of the business by leveraging algorithms
• Deploy AI at scale, integrated with data and decision-making systems.
• Take human hierarchy “out of the loop” of routine, data-based decision making.

2. Migrate Human Cognition to Mature Work Spheres :The widespread adoption of AI naturally raises the question of what role human workers will play in the organization of the future. Today, there is already widespread concern about the speed at which AI will disrupt the future of work. To shape this future—and to maximize organizational learning capabilities—businesses need to focus human cognition on its unique strengths. Humans should increasingly focus their efforts on these higher-level activities. For example, while correlative analysis is generally sufficient for learning about repeated actions on fast timescales, it is less useful for learning about slow-moving forces, such as political, social, and economic trends. These shifts are unique and depend on the historical context and trajectory, which means there is no repeated data set in which to find patterns. Human abilities, such as understanding causal relationships and generalizing from limited data, are necessary to decode these forces and adapt the organization accordingly.

Counterfactual thinking is also critical, as businesses need increasingly to compete on Imagination. Existing business models are being exhausted faster, and long-term growth is declining, which means companies must continually generate new ideas to grow sustainable. But businesses today, which are often implicitly designed for efficiency and the maximization of short run financial outcomes, are not conducive to imagination. Organizations will need to better facilitate individual and collective imagination.

In addition to imagination and making sense of non-repeated events, there will be many other activities where humans are advantaged, including organizational design, algorithmic governance, ethics, and purpose, to name a few. In these domains of human activity, organizations will need to become more effective at dynamic collaboration to get the most out of their teams. This requires emphasizing self-organization and experimentation by creating an organizational context in which responsive decision making and learning can thrive, rather than by relying on direct instructions.

3. Rebalance the Relationship Between Humans and Machines : The first two imperatives call for a hybrid organization, one that combines the comparative advantages of machines and humans: machines’ ability to rapidly identify complex patterns in big data and humans’ ability to decode complex causal relationships and imagine new possibilities. Together, these will enable the organization to learn on an expanded range of timescales—faster and slower.

But in hybrid organizations, humans and machines will increasingly have to collaborate in new and more effective ways. This includes tasks that require thinking on multiple levels or timescales simultaneously, as well as tasks that demand social interaction, another dimension in which humans are currently far more effective. Organizations will thus need to reimagine the relationship between humans and machines to bring the best out of both and maximize synergies.

 Today’s AI models tend to be “black boxes” that are not designed to be interoperable and may therefore impede trust. For these new types of human-machine relationships to succeed, organizations need to develop effective human-machine interfaces that allow for seamless collaboration. Organizations will need to overcome these hurdles by developing and implementing interfaces that provide transparency into how AI makes recommendations, allowing humans to understand and validate machines’ actions. Similarly, humans and algorithms are rarely matched for bandwidth and complexity. Choosing the right level of abstraction and compression for communication between humans and computers is critical: too much compression will suppress subtlety and prevent the tinkering through which human innovation proceeds, while too little will overwhelm human overseers.

 4. New Age Leadership & Management Approaches :Collectively, the above imperatives point to a very different way of designing and operating organizations with AI —which in turn will significantly change the role of leadership. In particular, leaders will need to focus on several new challenges.Developing governance principles for AI and autonomous machines. : As machines play a greater part in learning and action, the role of leadership in setting guardrails and priorities will take on greater importance. In the last decade, tech companies could sidestep these topics, as the promise and potential of new technologies gave them a license to move fast. But as social scrutiny of technology increases, questions about governance, trust, and ethics are coming to the forefront. And as AI is adopted more widely, all businesses will have to deal with these difficult questions.

 The organizations that will survive and become pioneer will look much different from today’s: they will use different AI driven capabilities; they will operate at different speeds and scales of influence; they will contain different structures and responsibilities; and they will embody different leadership models to enable all of the above. AI will become a force multiplier and will define the DNA of tomorrow’s organization.At the end of the day, its a matter of survival….

No conversation around technology today is complete without a reference to artificial intelligence (AI). While the term AI has been around for decades, in recent times, its application across industries, from mining to healthcare, education and finance, among others, is changing the way we live, both at work and at home. 

The increasing ubiquity of AI can be attributed to greater processing power and the declining cost of achieving these tasks at increased speeds. The adoption of AI has been so rapid that technology leaders across industries can no longer ignore it for long-term and sustained financial growth.

At the third edition of YourStory’s Future of Work event in Bengaluru on Saturday, AI was the focus of a panel discussion, which drew attention to ‘How AI is disrupting business models and value chains and unleashing transformative and innovative growth opportunities’. 

The session was moderated by Sameer Dhanrajani, CEO and Co-founder, AIQRATE, and the panelists were Rajan Sethuraman, CEO, LatentView Analytics; Soumendra Mohanty, EVP Analytics and COO, Tredence; Prithvijit Roy, CEO and Co-founder, BRIDGEi2i; and Iqbal Kaur, Co-Founder, Zylotech. The discussion yielded many insights on the top trends around the technology.

1. Experiments at scale are the need of the hour

It is clear that we are at a stage where we need to experiment at scale.

Global Capability Centers(GCC’s) are at an inflection point as the pace at which AI is changing every aspect is exponential and at high velocity. The rapid transformation and innovation of GCC’s today is driven largely by ability for them to position AI strategic imperative for their parent organizations. AI is seen to the Trojan horse to catapult GCC’s to the next level on innovation & transformation. In recent times; GCC story is in a changing era of value and transformative arbitrage. Most of the GCCs are aiming towards deploying suite of AI led strategies to position themselves up as the model template of AI center of Excellence . It is widely predicted that AI will disrupt and transform capability centers in the coming decades. How are Global Capability Centers in India looking at positioning themselves as model template for developing AI center of competence? How have the strategies of GCCs transformed with reference to parent organization? whilst delivering tangible business outcomes , innovation & transformation for parent organizations?

Strategic imperatives for GCC’s to consider to move incrementally in the value chain & become premier AI center of excellence

AI transformation

Artificial Intelligence has become the main focus areas for GCCs in India. The increasing digital penetration in GCCs across  business verticals has made it imperative to focus on AI. Hence, GCCs are upping their innovation agenda by building bespoke AI CoEs. Accelerated AI adoption has transcended industry verticals, with organizations exploring different use cases and application areas. GCCs in India are strategically leveraging one of the following approaches to drive the AI penetration ahead –

• Federated Approach: Different teams within GCCs drive AI initiatives
• Centralized Approach: Focus is to build a central team with top talent and niche skills that would cater to the parent organization requirements
• Partner ecosystem : Paves a new channel for GCCs by partnering with research institutes , start-ups , accelerators
• Hybrid Approach: A mix of any two or more above mentioned approaches, and can be leveraged according to GCC’s needs and constraints.

Ecosystem creation : Startups /research institutes/Accelerators

One of the crucial ways that GCCs can boost their innovation agenda is by collaborating with start-ups, research institutes , accelerators. Hence, GCCs are employing a variety of strategies to build the ecosystem. These collaborations are a combination of build, buy, and partner models:

• Platform Evangelization: GCCs offer access to their AI platforms to start-ups
• License or Vendor Agreement: GCCs and start-ups enter into a license agreement to create solutions
• Co-innovate: Start-ups and GCCs collaborate to co-create new solutions & capabilities
• Acqui-hire: GCCs acquire start-ups for the talent & capability
• Research centers : GCCs collaborate with academic institutes for joint IP creation , open research , customized programs
• Joint Accelerator program : GCCs & Accelerators build joint program for customized startups cohort

To drive these ecosystem creation models, GCCs can leverage different approaches. Further, successful collaboration programs have a high degree of customization, with clearly defined objectives and talent allocation to drive tangible and impact driven business outcomes.

AI Center of Competence/ Capability

GCCs are increasingly shifting to competency , capability creation models to reduce time-to-market. In this model, the AI Center of Competence teams are aligned to capability lines of businesses where AI center of competence are responsible for creating AI capabilities , roadmaps and new value offerings, in collaboration with parent organization’s business teams. This alignment and specific roles have clear visibility of the business user requirement. Further, capability creation combined with parent organization’s alignment helps in tangible value outcomes. In several cases, AI teams are building new range of innovation around AI based capabilities and solutions to showcase ensuing GCC as model template for innovation & transformation . GCCs need to conceptualize a bespoke strategy for building and sustaining AI Center of Competence and keep it up on the value chain with mature and measured transformation & innovation led matrices.

Talent Mapping Strategy

With the evolution of analytics ,data sciences to AI , the lines between different skills are blurring. GCCs are witnessing a convergence of skills required across verticals. The strategic shift of GCCs towards AI center of capability model has led to the creation of AI , data engineering & design roles. To build skills in AI & data engineering, GCCs are adopting a hybrid approach. The skill development roadmap for AI is a combination of build and buy strategies. The decision to acquire talent from the ecosystem or internally build capabilities is a function of three parameters –Maturity of GCC ’s existing AI capabilities in the desired or adjacent areas ,Tactical nature of skill requirement & Availability and accessibility of talent in the ecosystem. There’s always a heavy Inclination towards building skills in-house within GCCs and a majority of GCCs have stressed upon that the bulk of the future deployment in AI areas will be through in-house skill-building and reskilling initiatives. However, talent mapping strategy for building AI capability is a measured approach else can result in being a Achilles heel for GCC and HR leaders.  

GCCs in India are uniquely positioned to drive the next wave of growth with building high impact AI center of competence , there are slew of innovative & transformative models that they are working upon to up the ante and trigger new customer experience , products & services and unleash business transformation for the parent organizations. This will not only set the existing GCCs on the path to cutting-edge innovation but also pave the way for other global organizations contemplating global center setup in India.AI is becoming front runner to drive innovation & transformation for GCCs.

Data Engineering has come out as a prominent area within the AI arena; building robust data pipes has assumed significant importance to fuel machine learning algorithms. At Future of Work 2020 event, YOURSTORY & AIQRATE jointly unveiled a bespoke report on “Data Engineering 4.0: Evolution, Emergence & Possibilities in the Next Decade”.

The report identifies 5G, Data Pipeline+, AI Managed Data Lakes, Edge Cloud and IoT as some of the main drivers for the emergence of Data Engineering 4.0 to realize the much anticipated cross industry collaboration enabled business models involving Smart Cities, Autonomous Vehicles, and Smart Factories.