Embed advanced analytics into every aspect of your digital business

Advanced analytics, a boundless supply of data and new machine-augmented capabilities are creating entirely new opportunities for organizations to compete in an age of transformation.

Gone are old-style IT reports and dashboards, replaced with agile business processes and artificial intelligence (AI)-driven systems that can deliver business insights in context, with speed, to everyone in the organization. Leveraging intelligent analytics accelerates the fusion of minds and machines and, as work becomes more automated, the information worker becomes a creative worker — one who will drive the new economy forward.

Surviving and prospering in this age of analytics-enabled disruption is the most pressing issue organizations will address in the coming years. Those that put analytics at the core of their strategy and operations will benefit from an accelerated pace of change.

Analytics-enabled business models

Enterprises need to think of their business in terms of information flows. For example, APIs unlock information and deliver it with low friction to enrich and enhance business processes. APIs can further be interlinked into information flows to improve their relevancy. Often these flows are disconnected in enterprise business processes that have manual steps (i.e., people providing information) that could be automated. Further, analytics can be applied on the information flows to enhance outcomes of business processes or activate alerts when additional attention is required.

From Economist Films, watch how analytics is changing professional basketball.

So, the way to drive the next wave of productivity and growth is to think about business processes, interactions and transactions as information flows. The goal is to embed advanced analytics in each process, interaction and transaction, effectively enabling frictionless integration of information and analytic models directly into process flows. Advanced analytics takes advantage of a wealth of unstructured data and sensor data to complete the feedback loop between business processes and information, creating an analytics capability that is so central to the operation of the enterprise, it actually becomes the business model.

Augmenting business process flows with relevant information and outputs of analytic models not only makes business processes more flexible, but also enhances decision-making power at each process step by adding visualization of the process flow status and decision-making recommendations produced by the analytic models. Here are several examples:

• As an early adopter of advanced analytics, the securities industry is now defined by automated, algorithm-centric trading and AI-based advisors that outperform professional money managers.
  
  
• Insurance carriers, which famously clung to decades-old legacy hardware and software, are wholeheartedly embracing analytics-driven systems to target profitable market segments, speed up claims processing, reduce fraud and increase the number and degree of fully automated processes.

• Analytics derived from data generated by a wide variety of sensors in all aspects of manufacturing operations are helping to optimize manufacturing processes, enabling organizations to manufacture more efficiently, with reduced asset maintenance costs and reduced working capital.

• In healthcare, advanced analytics are poised to bring benefits to providers, payers and patients by improving fraud detection, population health, asset tracking and the overall patient experience.

Advanced analytics take advantage of a wealth of unstructured data and sensor data to provide predictive and prescriptive analytics and business models, as well as rules to drive optimal behaviors across complex enterprise ecosystems. Astute organizations are harnessing advanced analytics to profitably upsell and cross-sell, optimize operations, and run their own information systems and processes more efficiently.

Be the disruptor, not the disrupted

The race is on, and successful organizations are already using data and advanced analytics as competitive assets. Those that successfully harvest vast troves of data can improve productivity, make faster and more accurate decisions, reduce costs, increase competitive advantage, discover new business models and innovations, and better engage customers, employees and partners.

They will adapt quickly and predict trends by continuously discovering value from data and turning it into insights to drive value. These organizations will be recognized as disruptors in the digital revolution, capable of driving exponential organizational value and continuous improvement. They will be branded as high Analytics IQ organizations.

Organizations that do not have a high Analytics IQ will not be able to scale and industrialize their analytics and AI efforts; instead, they will remain stuck in the realm of ad hoc analytics characterized by many repeated proof-of-concept exercises and pilot projects, with no visible business outcomes.

By comparison, high Analytics IQ organizations will have strategy, culture and continuous-improvement processes in place to enable them to identify and develop new digital business models to better serve their customers and remain competitive. They will have moved from ad hoc to operationalized analytics.

Raising your organization’s Analytics IQ

The journey to a higher Analytics IQ comprises three steps: accelerate the business transformation agenda, build a data-centric foundation, and operationalize analytics and AI across the organization.

1. Accelerate the business transformation agenda

Rapid innovation and productivity breakthroughs require an accelerated business transformation strategy that melds people, business processes, advanced analytics, and new human/machine interaction technologies.

Today, it is the supervised machine learning segment of AI that is generating the most economic value. But as business transformation accelerates, the abundance of data that AI can consume will drive the speed of AI adoption even faster, including its unsupervised learning segment.

We need only look at how quickly conversational AI (CAI) has become part of our everyday lives as we query Alexa, Siri or Cortana. But in the enterprise the interactions can be extremely complex, such as “Hey , summarize the minutes and action items from the recording of the last board meeting.” We are limited by only our imagination and — significantly — access to high-quality, well-organized data.

The accelerated AI adoption will in turn drive better understanding of how to customize AI for the relevant business context and drive business transformation to new levels. It will provide instant measures of business performance down to the smallest task, leading to more predictive business outcomes, as well as enhance productivity and 24x7 business operations through automation of business processes and algorithmic work.

As AI permeates every facet of the organization, organizations will need industrialized AI with strong governance and data quality. They will need to manage analytics models as assets to avoid algorithmic bias, retrain analytics models in a timely manner and ensure proper use of private data.

As we become better at blending advanced analytics technologies with how we think and work, there will be massive implications for how we run our companies and live our lives. It will be up to all of us to make sure that advanced analytics are used for ethical purposes.

Organizations should define their long-term objectives, clearly understand where and how new business value will be created, and design their digital journey maps. Once a business outcome and measurable business value is identified, organizations should proceed with developing analytics and AI/ML models and implement them in business operations. To achieve this, investment in a data lake containing cleansed enterprise and external data is required to build the analytics models and solutions. This requires a data-centric foundation.

2. Build a data-centric foundation

The next step is to build a data-centric foundation that can scale with growing organizational needs, enable innovation, increase predictability, improve forecasting accuracy, detect new behavior patterns, and deliver information insights in the context of processes and applications. Companies such as Apple, Amazon, Google, Netflix, Uber and Airbnb have shown how to build such data-centric foundations and disrupt traditional markets.

To build a data-centric foundation, adopt a hybrid data management (HDM) approach and reference architecture, and implement the industrialized analytics and AI platforms based on it:

• Hybrid data management. HDM is the foundation of a modern approach to advanced analytics and involves optimizing traditional business intelligence (BI) and data warehousing, blending in big data analytics, creating analytics and data solutions across the spectrum of edge/cloud/on-premises resources, and embedding prescriptive analytics models into operations and business processes. HDM provides a comprehensive data strategy for instituting industrial-scale analytics integrated into organizational processes and systems that leverage any type of data wherever it resides and enables organizations to become analytics-driven and agile.

• Hybrid data management reference architecture. The HDM-RA is the foundation for building technical design blueprints for analytics and AI solutions. HDM-RA is an end-to-end architecture, a selection of recommended technologies and implementation blueprints for each functional domain, and use case-based design patterns to deliver HDM solutions in cloud, on-premises and edge deployment models with security and information governance that meets compliance and regulatory requirements. HDM-RA addresses distributed aspects of data, such as which data should reside where, so it fully supports the data and analytics needs of a modern, predictive organization.

• Industrialized analytics and AI platform. An industrialized analytics and AI platform is an integrated infrastructure, software and services solution based on HDM-RA — often with a managed data lake at the center. It manages data and analytics model life cycles and enables business insights in context of the work being performed, to augment the decision making of creative workers who need timely information to do their jobs. It analyzes all organization-relevant data from any source, in any format, and from any location — with extreme speed, security and scale. As the data generated by devices explodes, the internet of things (IoT) will not deliver the benefits promised without the ability to analyze and handle data at scale. An industrialized analytics and AI platform also gives organizations the flexibility to move seamlessly between cloud and on-premises deployments to meet organizations’ dynamic data and analytics requirements. As technological complexity increases, new technological components can be rapidly integrated into the platform, thanks to HDM-RA.

3. Operationalize analytics and AI across the organization

Operationalized analytics are the interoperation of multiple disciplines to support the seamless flow of data, from initial analytic discovery to embedding predictive and prescriptive analytics into organizational operations, applications and machines. It is a key element for delivering value from IoT. The impact of the embedded analytics is then measured, monitored and further analyzed to circle back to new analytics discoveries in a continuous improvement loop, much like a fully matured industrial process.

An example of operationalized analytics is the industrialized AI utility depicted below. It enables automatic access and collection of data, ingesting and cleaning of the data, agile experimentation through automated execution of algorithms, and generation of insights.

Figure 1. Four functions of an industrialized AI utility

Operationalized analytics build on HDM, HDM-RA and the industrialized analytics and AI platform to enable organizations to implement industrial-strength analytics as a foundation of their business transformation. Operationalized analytics encompass the following:

• Data discovery includes the data discovery environment, methods, technologies and processes to support rapid self-service data sharing, analytics experimentation, model building, and generation of information insights.

• Analytics production and management focuses on the processes required to support rigorous treatment and ongoing management of analytics models and analytics intellectual property as competitive assets.

• Decision management provides a clear understanding of, and access to, the information needed to augment decision making at the right time, in the right place and in the right format.

• Application integration incorporates analytics models into enterprise applications, including customer relationship management (CRM), enterprise resource planning (ERP), marketing automation, financial systems and more.

• Information delivery of relevant and timely analytics information to the right users, at the right time and in the right format is enabled by self-service analytics and data preparation. This improves the ease and speed with which organizations can visualize and uncover insights for better decision making.

• Analytics governance is the set of multidisciplinary structures, policies, procedures, processes and controls for managing information and analytics models at an enterprise level to support an organization’s regulatory, legal, risk, environmental and operational requirements.

• Analytics culture is key, as crossing the chasm from ad hoc analytics projects to analytics models integrated into front-line operations requires a cultural shift. Merely having a strong team of data scientists and a great technology platform will not make an impact unless the overall organization also understands the benefits of analytics and embraces the change management required to implement analytically driven decisions.

• DataOps enables organizations to evolve their approach and improve their ability to work with data at scale and to respond to business events as they happen. The emerging practice of DataOps brings together specialists in data science, data engineering software development, and operations to align development of data-intensive applications with business objectives and to shorten development cycles. Simply put, DataOps is a new people, process, and tools paradigm that promotes repeatability, productivity, agility, and self-service while achieving continuous analytics model and solutions deployments. DataOps improves the speed and accuracy of the analytics pipeline from desired business outcome, analytics model goals, data access, quality control, automation and integration, to model development, deployment and ongoing model improvement. DataOps further raises Analytics IQ by enabling faster delivery of analytics solutions with predictable business outcomes.

Test your Analytics IQ here to determine your organization's maturity level and analytics needs, and see if you’re ready for the analytics-driven future.

Raising an organization’s Analytics IQ enables it to thrive on change. It allows the organization to use data and analytics as competitive assets, adapt quickly and predict trends by continuously discovering value from data and turning it into insight. Ultimately, it helps the organization become a disruptor in the digital revolution.

The time to act is now. Those that choose to wait are far more likely to find themselves with a very different fate.

How DXC and its partners can help

DXC Technology is a trusted partner helping organizations to realize their highest Analytics IQ.

DXC Analytics offers a comprehensive portfolio of proven analytics services to rapidly provide insights and accelerate the business transformation journey. We offer a full suite of services — from advisory services to technology and industry solutions — automated through our DXC Bionix^TM digital generation services delivery model, which is underpinned by Platform DXC, our digital-generation delivery platform. Platform DXC allows DXC to quickly build and deliver partner-engineered, at-scale, repeatable offerings and solutions that help drive business transformation.

DXC’s unmatched Partner Network plays an integral role, enabling organizations to build and leverage the advanced analytics solutions that drive desired outcomes.

DXC Analytics consultants advise, support and manage our customers’ business transformations based on their technical and financial goals, current readiness (Analytics IQ), IT culture, operational practices and compliance requirements. Let DXC help you unlock the insights needed to successfully implement and operationalize analytics.

Now is the time to act. Don’t be disrupted — be the disruptor. Let us help you innovate and transform to differentiate with speed and quality.

Learn more at dxc.technology/analytics

About the authors

Dragan Rakovich, DXC Technology’s chief technology officer for analytics and Distinguished Technologist, leads the company’s analytics technology and innovation strategy. Dragan brings strategic advice and thought leadership to customers in actionable analytics, machine learning (ML) and AI, business intelligence, internet of things (IoT) and analytics platform domains to create advanced analytics solutions. Prior to this role, he served as Hewlett Packard Enterprise Services chief technology officer for analytics and data management. Dragan has more than 20 years of experience in analytics, ML/AI, business intelligence, management consulting, solution delivery, enterprise architectures and software engineering.

Srini Kompella is head of Analytics Americas and acting vice president and general manager of Analytics at DXC Technology. He is responsible for leading the company’s analytics offerings that create value for customers through advanced solutions and services that combine industry, technology and partner expertise. Srini creates a culture that promotes an evolving analytics journey focused on industrialized artificial intelligence and machine learning, analytics platforms, actionable analytics and business intelligence, and internet of things (IoT).

Contributors

Chris Daniel, mapping consultant, Leading Edge Forum

Jerry Overton, data scientist and industrialized AI lead, DXC Technology, and DXC Fellow