Big Data and Data Driven Transformation

Big data and analytics have climbed to the top of the corporate agenda. Together, they promise to transform the way companies do business, delivering the kind of performance gains last seen in the early days of ERP, when organizations redesigned their business processes. As data-driven strategies take hold, they will become an increasingly important point of competitive differentiation.

There are 3 vital elements to any Data driven Transformation

1. Companies must be able to identify, combine, and manage multiple sources of data.
2. They need the capability to build advanced-analytics models for predicting and optimizing outcomes.
3. And most critical, management must possess the muscle to transform the organization so that the data and models actually yield better decisions.

The under pinning of this strategy are two important factors:  Clear strategy for how to use data and analytics to compete and the deployment of the right technology architecture and capabilities. But just as important, a clear vision of the desired business impact must shape the integrated approach to data sourcing, model building, and organizational transformation.

This distinction will help you avoid the common trap of starting by asking what the data can do for you. Leaders should invest sufficient time and energy in aligning managers across the organization in support of the mission.

A complete data-driven transformation is nearly impossible unless the organization is prepared to deconstruct old-school methodologies and pave the way for establishing a data culture. Like business transformation, data-driven transformation must provide an end-to-end solution. Data-driven transformation calls for a Big Data and Analytics Center of Excellence to be deep-rooted within the organization. Organizations need to involve cross-functional teams like BI, Marketing, IT, and external consultants to establish data governance and set up a data and decision-science culture within the organization. This is especially important for companies that have IT and marketing services departments heavily outsourced because stringent service-level agreements with vendors often kill innovation and don’t provide the space to take risks and “fail fast.”

The first essential step in the process is choosing the right data for the objective.  The universe of data and modeling has changed vastly over the past few years. The volume of information is growing rapidly, while opportunities to expand insights by combining data are accelerating. Bigger and better data give companies both more panoramic and more granular views of their business environment. The ability to see what was previously invisible improves operations, customer experiences, and strategy.

Often, companies already have the data they need to tackle business problems, but managers simply don’t know how they can use this information to make key decisions. Operations executives, for instance, might not grasp the potential value of the daily or hourly factory and customer-service data they possess. Companies can encourage a more comprehensive look at data by being specific about the business problems and opportunities they need to address.

Managers also need to get creative about the potential of external and new sources of data. Social media generates terabytes of nontraditional, unstructured data in the form of conversations, photos, and video. Add to that the streams of data flowing in from sensors, monitored processes, and external sources ranging from local demographics to weather forecasts. One way to prompt broader thinking about potential data is to ask, “What decisions could we make if we had all the information we need?”

Data are essential, but performance improvements and competitive advantage arise from analytics models that allow managers to predict and optimize outcomes. More important, the most effective approach to building a model usually starts, not with the data, but with identifying a business opportunity and determining how the model can improve performance. We have found that such hypothesis-led modeling generates faster outcomes and roots models in practical data relationships that are more broadly understood by managers.

The lead concern senior executives express to us is that their managers don’t understand or trust big data–based models and, consequently, don’t use them.

Such problems often arise because of a mismatch between an organization’s existing culture and capabilities and emerging tactics to exploit analytics successfully. The new approaches either don’t align with how companies actually arrive at decisions or fail to provide a clear blueprint for realizing business goals. Tools seem to be designed for experts in modeling rather than for people on the front lines, and few managers find the models engaging enough to champion their use—a key failing if companies want the new methods to permeate the organization. Bottom line: using big data requires thoughtful organizational transformation.

Many initial implementations of big data and analytics fail because they aren’t in sync with a company’s day-to-day processes and decision-making norms. Model designers need to understand the types of business judgments that managers make to align their actions with broader company goals. Conversations with frontline managers will ensure that analytics and tools complement existing decision processes, so companies can manage a range of trade-offs effectively.

Managers need transparent methods for using the new models and algorithms on a daily basis. By necessity, terabytes of data and sophisticated modeling are required to sharpen marketing, risk management, and operations. The key is to separate the statistics experts and software developers from the managers who use the data-driven insights. The goal: to give frontline managers intuitive tools and interfaces that helps them with their jobs.

Even with simple and usable models, most organizations will need to upgrade their analytical skills and literacy. To make analytics part of the fabric of daily operations, managers must view it as central to solving problems and identifying opportunities. Efforts will vary, depending on a company’s goals and desired time line. Adjusting cultures and mind-sets typically requires a multifaceted approach that includes training, role modeling by leaders, and incentives and metrics to reinforce behavior. Adult learners, for instance, often benefit from a “field and forum” approach, in which they participate in real-world, analytics-based workplace decisions that allow them to learn by doing.