Advanced analytics: Nine insights from the C-suite PART I
Conversations with hundreds of business leaders reveal nine ways that they are—and are not—adapting to the analytics revolution.
Data and advanced analytics have arrived. The volume of available data is growing exponentially, with more added every day from billions of phones, sensors, payment systems, and cameras. Machine learning is becoming ubiquitous, but organizations are struggling to turn data into value.
The stakes are high. Those who advance furthest, fastest will have a significant competitive advantage; those who fall behind risk becoming irrelevant. Analytics cannot be the sole province of the chief information officer (CIO), as is sometimes the case. The CIO may not understand the business as a whole well enough to spot opportunities and threats, or be influential enough to ensure that the company addresses them appropriately. While the expertise the CIO brings is of course essential, business-unit leaders and CEOs must be in charge of analytics to accelerate the pace of change and to ensure intelligent investment. This is beginning to happen: McKinsey has found that more than 50 percent of CEOs consider themselves the primary leader of the analytics agenda, and that figure has been growing steadily.
With this in mind, we spoke to more than 300 top executives of major companies. Here we offer nine insights based on these conversations, and suggest actions for business leaders to take.
Analytics can create new opportunities and disrupt entire industries. But few leaders can say how
“Where do we want to be in five years as a result of advanced analytics? What are the implications to our business model, culture, portfolio mix, and value proposition?” CEOs all over the world are asking these questions—for good reason. Analytics has the potential to upend the prevailing business models in many industries, and CEOs are struggling to understand how. The need is urgent.
Beyond reorienting the existing business models, analytics leaders are also learning how to create and capitalize on new opportunities. Organizations are moving from hoarding data to sharing it. Some are pooling data as part of industry consortia, increasing their comprehensiveness and therefore their value. Product-based organizations are adding data and analytics to their offerings as value-added services. Some have gone further, charging for the analytics-enabled service rather than directly selling the product. For example, some jet-engine manufacturers now sell flight hours instead of the engines; this is only possible because sensors provide the data that help them understand usage and required maintenance.
There are two areas to explore. First, to understand how analytics can disrupt existing business models, set aside the time to focus on the long term. What can be learned from other industries that are farther along? What customer needs can be better met through new business models?
Second, to capture new opportunities, start with the data, analyzing what they are worth, how distinct they are, who would find them valuable, and how they can be combined with other sources to increase their value. Then, think through the business model. A simple way to get started is to conduct a market scan of the data and analytics players, as well as a competitor scan to understand what others may be doing. Identify where and how to play within this ecosystem.
Surprisingly few companies know where and how analytics can create value
Analytics create value when big data and advanced algorithms are applied to business problems to yield a solution that is measurably better than before. By identifying, sizing, prioritizing, and phasing all applicable use cases, businesses can create an analytics strategy that generates value. For example, a CEO of a global consumer-packaged-goods company told us that the application of advanced analytics and machine learning to business functions such as revenue-growth management and supply-chain optimization uncovered as much as $4 billion in benefits.
Few executives, however, have such a detailed view of value across their business units and functions. More typical is this kind of comment: “Sometimes I feel we are doing analytics for the sake of doing analytics. We need to have more clarity on what business value we are trying to create,” one senior executive said. Most have experimented with a handful of use cases, but lack a comprehensive view. Even fewer have considered how analytics can create new sources of revenue. Lacking an enterprise-wide view of opportunity, business leaders struggle to make a considered business case for analytics. They may also struggle to communicate why analytics matter—and that is essential to get the organization committed to change.
Start a rigorous process with the executive team to decide where the most promising sources of value exist. To start, identify which functions or parts of the value chain have the most potential. For consumer-goods companies, for example, it could be product development or inventory optimization; for insurance companies, it may be risk models. Then come up with possible use cases—as many as 100 for a large company—and how new data and techniques could be applied to them. Using outside benchmarks can be useful to get a sense of how valuable a given use case might be. Finally, decide the order of priority, considering economic impact, fit with the business, feasibility, and speed.
Data science is the easy part. Getting the right data, and getting the data ready for analyses, is much more difficult
As data science enters the mainstream, commercial analytics platforms and code-sharing platforms are providing algorithm libraries and analytics tools. For most organizations, this simplifies the practical application of data science. But that still leaves the matter of what to do with it. In our conversations, we heard a familiar refrain: “The majority of our time is spent getting the data,” said a senior executive at an advanced-industries company. “Once we have that in a good place, the modeling is quick.”
Each data set is unique, and it takes time to prepare it for analyses. One major issue is that it can be difficult to agree on a “single source of truth,” because different departments often use different ways to measure the same metric. For example, the sales function may measure the volume of goods sold by transaction, while operations may measure by inventory movement. Most companies have not yet incorporated real-time data into day-to-day business processes. Many also struggle to identify what data are needed to improve competitive advantage, and therefore what they need to create. Other common challenges are implementing a unique identifier to link different data sets (such as transaction data and customer profiles) and filling in gaps to increase quality and usability.
The sea of data is vast and growing exponentially. To avoid drowning, executives must connect the data strategy to the analytics strategy. When exploring new data sources, it helps to have specific use cases in mind and to reflect on how data are acquired—whether through commercial vendors or via open sources. Know what data the business owns; this can become an asset to monetize. To continuously improve data quality, put in place governance and processes, and ensure that the rightful owners have direct access. Mandate good data and metadata practices and build automatic data-reconciliation processes that constantly verify that new data meet quality standards. To drive new insight, interconnect different data sets, potentially in a centralized repository (or “data lake”). Resist the temptation of complexity. Rather than building a data lake for all legacy data—a project that can take years—fill the lake gradually. Start with data required for priority use cases, and gradually add to it. Get started with what you have, and don’t let perfection be the enemy of the good.
Data ownership and access needs to be democratized
The most common excuse that businesses roll out for refusing to adopt counterintuitive analytics insights is that the underlying data are not valid. This claim is much more difficult to make if accountability for data quality rests with the business, and if business leaders have ready access. Successful analytics organizations give as many people as possible access to the data, while making sure there is a single source of truth, so that employees can play with them and come up with new ideas, or discard old ones that are past their prime. “The way we are thinking about eliminating the finger-pointing between business and IT on data,” said the CIO of a large pharmaceutical company, “is by making data available to everyone.” By doing so, a data-driven decision-making mind-set gets infused throughout the organization.
Design effective data governance, specifying who is responsible for data definition, creation, verification, curation, and validation—the business, IT, or the analytics center. Embrace the dual principles of business ownership and broad access. Hold the business accountable for data, even if the IT department houses and supports them. Create data-discovery platforms, such as web-based self-serve portals that allow frontline staff to easily extract data. Host data-discovery sessions to build data literacy.
Embedding analytics is as much about change management as it is about data science
Old ways of working are deeply ingrained, especially if there is an underlying distrust of analytics. Another question, then, that executives are asking is how to influence frontline staff to use the insights delivered by analytic tools to change how to make decisions. The CEO of GE, Jeff Immelt, told McKinsey: “I thought if we hired a couple thousand technology people, if we upgraded our software, things like that, that was it. I was wrong. Product managers have to be different; salespeople have to be different; on-site support has to be different.”
There are some success stories. One common and essential factor is that leadership has to commit to analytics, visibly. One executive told us how the head of a business unit used analytical tools to crunch the numbers regarding stock levels. He then presented the results to the weekly leadership meeting and required each channel manager to take action.
It is also essential to integrate insights into the daily work flow. Another executive spoke about how the sales staff resisted using leads generated by the analytics model, preferring to rely on their instincts. His team was able to engineer the work flow so that the recommendation engine was “invisible”: the sales team was simply presented with leads, and then acted on them—successfully.
People buy into change when they understand it and feel they are part of it. The design of analytics solutions therefore needs to be user led and have business-process participation from the start. Have a “translator”—someone who not only understands the data science but also how it can be applied to the business—lead use-case development from start to finish. Match the talent to the task. The business identifies the opportunity, the data scientists develop the algorithm, the user-experience designers shape the user interface, the software developers run production, the process engineers reengineer work flows, and the change agents do the implementation. Develop a playbook for each use case, making sure critical adoption elements such as training and communication are not neglected. Beyond individual use cases, design a broader change program that builds analytics literacy and shifts the organization toward a data-driven culture. Organizational change management is generally well understood; it is a matter of applying these principles to analytics.
CONTINUES IN PART II