Through Data, Software, and Community Platforms
Re-envisioned IT, careful planning, and first-rate talent management will be key determinants of which companies excel.
Information technology and the vast array of data it is capable of generating are driving a revolutionary change in the way organizations interact with their clients and customers. Transformations within the IT business itself are powering this shift. Only a little more than a decade ago, the IT departments of most businesses were run from office building basements by just a handful of operatives. Since the rise of tech giants such as Amazon, Facebook and Netflix, many companies are beginning to recognize that potential IT functions go far beyond simply handling office infrastructure, and are now integral to growth as intelligent, data-centric business models evolve.
IT-led business partnerships are helping service product lines and identifying corporate efficiencies and customer trends, as well as creating new revenue streams. In order for this business environment to flourish and grow, companies must adapt to this new operating model, shaping agile, IT-focused teams to help build enterprise platforms that use data to automate processes and workflows and generate insights to make decisions.
As business technology rapidly evolves, corporate and public enterprises are pushing for IT modernization at what is, for many, a bewildering pace. These organizations are being driven to adopt new business models—compelled by demand from customers—that embrace this technological revolution. Based around intuitive technologies such as AI and machine learning, and utilizing the vast analytical capabilities of cloud computing, enterprise data platforms can be built to help generate and analyze any type of data. While compelling for both consumers and businesses, such technology must be wisely implemented.
Organizations looking to move their IT modernization projects forward should begin this crucial transition by rethinking the services they are already providing. As the tech needs of organizations become more complex in response to the growing demands of consumers and clients, the next generation operating models are rapidly replacing traditional, static IT systems that lack the operational and analytical intelligence to generate insights based on data generated by user community actions on their software infrastructure.
These erstwhile startups are now the giants of global industry and leading the charge into an IT-driven business operating model that will, in time, become the worldwide standard. They’ve gotten where they are in large part by understanding that the most vibrant, organic expansion stems from leveraging the intersection of consumer communities and the data these populations generate to create innovative, experiential public-facing strategies.
Effective data aggregation starts with a robust analytics platform. All departments of the business can be connected through appropriate IT networks, building an infrastructure that can access all touchpoints, from internal users to end customers and clients. AI software is becoming an important facet of such data analytics—not only improving the efficacy of research, but also assembling a comprehensive overview of business operations and consumer behavior.
Amazon and Netflix are among the many companies using such data techniques, ensuring that every click on their websites helps them build up a better understanding of their users, and feeding information to all departments—thereby interacting flawlessly throughout their businesses while seamlessly integrating vital, up-to-the-minute consumer information. Organizations must determine how to automate as many data-gathering operations as possible—the greater the human input in this process, the higher the chance of introducing errors. High-quality enterprise software should be used throughout the business, and the integrity of the data itself must be monitored regularly to avoid worthless analyses.
AI technology that runs algorithms to analyze the data is then able to produce valuable insights from customer and client behavior. As user consumption increases, the quality of the data gathered improves these insights exponentially, enabling the AI software to make better decisions and, in so doing, exponentially increasing the performance of the business. A virtuous loop is thus created—not only boosting business output, but also improving customer experience.
The traditional operating model, often using outmoded legacy software, is incapable of performing on the scale needed for such a data strategy. IT modernization projects that fail to recognize the benefits of a wholesale shift in their organization’s business model increase the risk that the business will fall behind its competitors and lose the trust of its customers or clients.
Of course, the resources required to build the architecture for such a unique operating model must not be underestimated. The project needs dedicated investment: aside from the costs of the network and software infrastructure, data engineers and scientists must be employed to deploy the technology and continue to improve the software and algorithms.
But this new operating model—which is being adopted across the industrial spectrum—is a necessary prerequisite to taking an organization’s IT modernization to the next level. Financial services companies are utilizing this data strategy to broaden their scope into auxiliary markets, monetizing the data they gather on their customers, while many of the big technology companies are moving into financial services in order to offer their customers a seamless, one-stop experience. The public sector is also benefiting as outdated IT networks are being replaced to incorporate this new data strategy, allowing organizations to overcome the scalability problems experienced in delivering services to a growing population on ever-diminishing federal budgets.
Such a transformational shift in an organization’s operating model need not be daunting. Many businesses considering large modernization projects partner with trusted advisors—independent consultants with many years of experience in helping guide public and commercial entities through the often labyrinthine task of organizational change.
Such relationships are important in several respects, especially insofar as an advisor can provide the guidance needed to select the appropriate tools for the project and help determine how to implement them in the most cost-efficient manner. Furthermore, the advisory partner delivers independent thought leadership that helps overcome inevitable biases, transcending and dissolving departmental and knowledge silos that build up over the years when internal stakeholders feel they need to protect their territory.
Whether industry and the public sector like it or not, business operating models are changing. Corporations and public services alike must improve their capacity to accept and address the changes that are already in play—and those to come. They will certainly need agile teams to respond to related challenges. Moreover, their IT modernization approach must acknowledge that a new operating model focusing on data strategy will almost inevitably be the key to future growth.
Transitional projects should not be undertaken without sound advice and thought leadership. Principles of change management may need to be employed to get all internal stakeholders on board, while the dismantling of the silo mentality will also need the deepest scrutiny. Investment will be high, but the total cost of ownership of the infrastructure—platform, networks, AI software, and talent—will be more than recovered as the quality of data improves.
Recognizing that the overall experience is what keeps people coming back, organizations must now treat their customers as a “community” rather than consumers. It is this community that will drive tech-led modernization projects. Thus, for new-age IT to be successful, organizations must complete the virtuous loop to exponential growth by developing the relevant communities and using the data to improve the experience at each point of customer or user transactions.