Part 3: Data-Driven Organizations Will Lead The Digital Revolution
Part 3 of 3: Key Enablers for Succesful Transformation
Financial institutions must transform themselves into data-driven organizations to be in a strong position to take advantage of the opportunities presented by big data. As described in Part 2 of this series, the democratization of data experimentation is a key attribute of leading data-driven organizations that are already realizing the value of their data. Indeed, agile and iterative data experimentation is a key differentiator for successful organizations in the digital age. More digital business means more data to leverage.
Business transformation has been a key concern of the financial industry for decades. Organizations that possess the business discipline to properly govern such initiatives have the greatest chance of success. And, to be sure, data-driven transformation is no different in requiring the usual hallmarks of effective change management:
- Strong leadership that provides an integrated strategic vision and clear goals for the organization.
- Strategic business outcomes that are prioritized, incremental, and well understood at all levels.
- A transformation initiative that is enterprise-wide and cross-functional.
- Active executive sponsorship.
- A cross-functional change-management program that provides guidance and conflict resolution at executive levels.
But let’s take a closer look at four factors that are essential to digital transformation: well-aligned cross-functional initiatives; continuous learning experimentation; the importance of change management and executive sponsorship; and the intelligent cloud platform.
Cross-functional initiatives aligned to business strategy
With initiatives that span across multiple lines of business and with vast amounts of data to explore and rationalize, a business beacon is needed to guide their scope and priority. The traditional question, How can I monetize my big data assets? is problematic and extremely difficult to execute on as it leads to open-ended exploration that usually results in scope-creep and loose definitions of success. We should ask a different question, rather: What are the top business challenges the organization needs to solve? Or, in other words: What are the questions we need to answer to address top business challenges?
Big data initiatives should be tightly aligned to corporate business strategy. Once a problem area has been identified as a top priority with specific business impact for the organization, cross-functional teams of business analysts, data scientists, and information architects can begin to explore the multiple data sources and appropriate data models to solve the problem. Scoping experiments to yield specific business outcomes such as improved customer experience or real-time fraud detection, and linking to key strategic problems will position initiatives for success.
To help financial institutions create just this kind of alignment, Microsoft Services offers Digital Advisory Services, which drive top-down business outcome discovery with our customers, resulting in a program of change with clear outcomes tied to top business priorities.
Iterative and continuous learning experimentation
No data sources will ever be perfect; data models are in constant flux to account for environmental change and, besides, “you don’t know what you don’t know.” Thus, iterative experimentation coupled with early validation against defined goals is required. Agile iteration provides for continuous learning, delivers incremental improvements to data quality, identifies additional needs for data, and ultimately accelerates time to value. With early validation of business outcomes, the resulting value can be recognized or re-worked as needed. There is no need to operationalize a model that has not proven its business value, saving time and money.
With each iteration, data usability and information management practices improve incrementally, and data assets become integrated and shared among multiple lines of business, igniting insight and delivering unexpected results that surface new questions that influence business strategy. This is how a continuous learning works—and it is the kind of virtuous cycle that Microsoft Services can help financial institutions put in place. We can help financial institutions mature their advanced analytics competencies, from a three-week jumpstart to custom, longer-term engagements in enterprise data modernization.
Cross-functional transformation supported by leadership and change management
All cross-functional initiatives for large organizations bring complex stakeholder relationships and competing priorities from different business units. This is particularly true for the financial industry. Cross-functional collaboration represents a deep transformation in organizational culture. Establishing a cross-functional, big data initiative requires strong leadership to provide the strategic vision and a top-down approach with on-going executive sponsorship.
These initiatives necessitate careful, structured change management to ensure desired business outcomes are achieved. Business transformation is hard; it is also a journey. A structured approach to change management, planning, and implementation is needed for adoption and to achieve the desired return on investment. A change management cross-functional program with active executive sponsorship provides guidance and helps resolve conflicts at executive levels in a timely fashion.
This is where Microsoft Services can make a difference for your organization, because we understand that structured change management is required to land cross-functional, digital transformation programs. We can provide expertise and consultative services that support your transformation journey, no matter the fundamental challenges. And challenges are not always related to technology; people and process issues can often be as challenging as the adoption of new technology solutions.
Intelligent cloud enables big data and advanced analytics experiments
There has never been a more exciting time in the world of data science and advanced analytics, with the convergence of big data, cloud technology, and systems of intelligence. This convergence in the digital domain makes business transformation possible. Delivering value from increasingly complex, unstructured data, in combination with traditional siloed systems, would not be possible without the help of advanced analytics. The rise of machine learning and advanced analytics, combined with the power of the cloud and its unlimited capacity for data storage/computation, marks a unique point in history.
The cloud provides the vehicle to economically and reliably integrate, capture, and persist the vast data resources from siloed LOB systems, transactional systems, and external sources. Data experiments and models can be validated against business expectations iteratively and before they are operationalized, thus reducing costs and accelerating time to value.
The intelligent cloud is the enabler for data-driven organizations in establishing a cross-functional information management practice that integrates, shares, learns, experiments, automates, and innovates with agility to increase their speed to value.
With the help of the systems of intelligence, financial institutions will shift from looking at siloed historical data to understand what happened in individual lines of business (forensic analytics) to harnessing advanced analytics of integrated data sources that predict what will happen—and out of that will emerge recommended actions for business performance improvements. The diagram below is illustrative.
Intelligent action in four questions
Among the benefits of the Microsoft intelligent cloud are fast and flexible deployments with a simple monthly subscription, which means that you no longer need to deal with the huge up-front investments for dedicated infrastructure like in the past. It allows for scale-up or scale-down based on business needs. Cortana Intelligence Suite is a key aspect, providing capabilities such as machine learning, big data storage and processing, and perceptual intelligence—i.e., vision, face, and speech recognition.
We have democratized the team data science process and have bundled years of experimentation in preconfigured solution models that have been validated by our data scientists for the most common problems. The result is a fast deployment of experiments ready to be customized and fine-tuned for specific problems in financial services and other industries.
Our specific advanced analytics solutions for Banking and Capital Markets help our customers fast-track time to value of their data assets so they can more quickly resolve critical problems such as fraud detection, proactively assess and manage risk, determine the next best action, and obtain customer insight.
The time to transform into a data-driven organization is now. Find out how one leading bank in Canada is is doing it. How can we help you do the same?
