When Data Isn’t Enough: How Change Management Can Predict the Success or Failure of Your Big Data Implementation

Volumes of data surround us. The internet, advertising, social media, connected cars and smart homes have driven an exponential increase in information, primarily unstructured data. But, this data is useless unless we can also comprehend, analyze and make operational, tactical and strategic (yet democratized) decisions based on the information. This realization has pushed organizations to rethink business strategies and outcomes as related to big data and digital transformation. It’s also prompted an investment in technologies that enable data mining, as well as prescriptive, descriptive and predictive analytical solutions.

While we all hear about business intelligence, data aggregation, data consumption, data virtualization, data utilization and data security, most organizations don’t truly understand how to harness their potential to create the desired business results.

Most transformational strategies also focus on having the “right data” be it Cloud First, Digital by Default, Internet of Things, Analytics & Insights or Blockchain. To enable this, organizations are increasingly moving away from traditional business intelligence systems to big data platforms. Unlike mature business intelligence platforms, big data is constantly evolving and fluid, requiring organizations to build new competencies, promote data literacy, data governance and usage of meta-data-driven ecosystems.

Understanding and embracing these new platforms are key to business transformation. But is having the tools and technology to enable big data enough to create lasting change within the business?

Is Your Big Data Investment Enough?

As it turns out, investing in the technology behind big data is just the beginning. To ensure success, companies must do much more than focus on the tools of the trade.

Big data transformations deal with sophisticated and interconnected data. This can impact a wide spectrum of business operations and have implications on people, culture, organization, processes, reporting and skillset capabilities. The glue that connects and holds these all together is, of course, the people.

Organizations can improve the success of digital transformation by applying an organizational change management (OCM) approach using concepts such as gamification, data driven outcomes, social media sentiment analysis, predictive decision-making through real-time insights and visual boards. Moving beyond traditional change management and enabling people to understand, leverage and deliver on the promise of big data is the ‘more’ that is needed.

The Why, What and How - On the Road to Big Data Adoption

As we’ve said, having the right tools, technology and skillsets (such as data scientists and analysts) in place is no longer enough to harness the power of big data. But why is this?

In big data programs, people create and consume content using varied tools and sources. The idea of data security and privacy is still evolving, requiring organizations to understand and accept the fact that the skillsets to handle big data platforms will continue to change - necessitating an open mind to learn, leverage and explore the new functionalities and make them relevant to business as needs change.

What this means is that managing change requires individuals to adopt a big data mindset and continuous learning behavior as an extension of their work. It is only when big data is used actively to explore new avenues and solve real problems, that the investment in big data can be justified.

How this is accomplished requires a structured impact-led approach to gather, address and mitigate the impact of change. The approach has five “tenets of change” that apply to all big data transformations.

  1. Create a compelling case for change
  2. Operationalize how change is understood
  3. Embrace the culture of insight-driven action
  4. Create change ownership
  5. Prepare your workforce for the future

Create a compelling case for change

Creating a clear case for OCM is a necessary starting point. This compels the organizations to move away from retaining unstructured or dark data, and creates a compelling story that defines what big data analytics mean to the organization for external markets, competitors, customers and internal policies, processes, employees. This foundation helps communicate critical use cases to test for success and outlines the exact change impacts for each use case. It also helps organizations understand the impacts of big data changes at the beginning of the process.

To create a clear case for change, an organization must have a thorough understanding of what each change means and where those changes occur across the spectrum of business and IT operations.

Operationalize how change is understood

The next step toward big data readiness is to define the changes in operational terms that employees can relate to; meaning, explain how upcoming changes might impact structure, processes, skills and performance goals.

Employees will be exposed to new roles, capabilities, competencies and ways of working, so how companies ready them for this new world is critical. A traditional change management approach would provide basic messages. But this will not be enough. Your OCM approach will need to be more context-driven, focused on educating employees with relevant role-based information and equipping them to become data evangelists for the organization. This personalized approach to making change real and meaningful will drive readiness.

Embrace a culture of insight-driven actions

Big data is creating a cultural shift, primarily in how decision-making is done. With big data, decision-making leverages hard data-driven analytics much more so than relying on experience and gut feeling. It also requires a culture of collaboration. Only then will organizations discover the insights and relevant business context that support solid decisions and action.

Agile and DevOps are quickly becoming best practices to manage big data transformations. This change also adds a layer of complexity for employees, where the traditional walls between organizational teams are displaced to form collaborative teams. Organizations need their leaders to embrace change willingly, communicate with employees about the changes happening, and be open to listen and learn from employees.

In this new world, building temporary cross-functional teams like task forces will not be enough to solve complex business problems or build innovative solutions. Organizations must be willing to encourage informal groups where individuals are encouraged to seeking and uncover hidden opportunities or problems they can address. For leadership, it is equally important to acknowledge the contributions made by such groups, in order to sustain them over the long term.

Create change ownership

Without question, the best way to manage the complexity of transformation is to create ownership among the stakeholders who will ultimately own – and must deliver on – the promise of new technologies and capabilities.

The idea of finding traditional “change agents” or establishing a “change agent network” is old school. Instead, a new innovative model recognizes the fact that the operational leaders are the most trusted source of information and credibility within an organization, and thus should deploy both the new technology and supporting OCM solutions.

It’s equally important to create a story for any transformation, one which leaders can embrace. One way to do this is through the effective utilization of targeted business impact workshops. Through these workshops, we can enable early adopters among leadership to work collaboratively with their consulting partners to sequentially deploy OCM solutions to direct reports and beyond.

This expanding concentric circle model for OCM can touch all the stakeholders an organization will need to deliver on the promise of big data. It’s the difference between embedded adoption and ownership, and reduces the all too common response of ‘why are they doing this to us?

Prepare your workforce for the future

Blending education with gamified elements focuses employee learning and development efforts, and enables employees to pick up new competencies and skills beyond the technical aspects of digital or cloud-based big data solutions. With blended learning, organizations can also cater to the evolving needs of new roles that employees play in the big data world, such as chief data officer, data visualizer, data architect, data warehouse manager, data strategist and more.

A Continuous Model for Big Data Transformation

Big data transformations increasingly require a new way to think about how change impacts people, culture, organizations, processes and more. We can’t look at OCM like a small part of the process – it must be part of the entire digital transformation journey.

For example, leaders should maintain an ongoing blended learning and development program that engages employees (including organization leadership) in process and system simulations to build understanding and familiarity over time. In the big data world, pre-go-live training may become the capstone course rather than the first experience an employee has understanding, let alone operating, a new solution. Ongoing support helps employees embrace agile culture and creates practitioners who learn in small increments continuously, to build knowledge and expertise.

A continual approach also takes into consideration the learning preferences of a multi-generational workforce and is effective where there is workforce turnover – planned or unplanned. In either approach (traditional or ongoing learning), the impact of change is the core input to a well-planned curriculum, fact-based content and messaging, and accurate material development and testing.

As data needs evolve for organizations, the five tenets of change should become the foundation of a program that leads to success in big data transformations.


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