Preparing to Compete in the Digital Age

The Digital Age is upon us, and it brings with it challenges and opportunities for businesses. Using advanced analytics, businesses are able to glean new insights from their data. The collection, assessment, interpretation, and use of data are enabling companies to create new business models and make existing ones more efficient. Most organizations now believe that enhancing their digital and analytical capabilities is critical to their continued success and survival.

Yet a forthcoming study by IMA^® (Institute of Management Accountants) found that implementation of leading-edge analytics remains very much a work-in-progress for most organizations, with few having completely executed their goals in analytics techniques and technologies. Reasons for this include the wide variety of technologies that are being adopted, the varying stages of maturity of those technologies, and the benefits that can be realized by each technology, among others.

BECOMING DATA-DRIVEN

There are four essential elements in establishing a data-driven organization. These include data-savvy people, quality data, appropriate tools, and processes and incentives that support analytical decision making. IMA’s study finds that organizations attempting to adopt leading-edge analytics often face challenges in each of these dimensions. The result is an inability to effectively support managerial decision making through the use of analytical technologies.

Much of the focus on implementation of advanced analytics has been on the tangible elements of a successful data-driven organization (people, data, and tools). Less attention has been paid to the fourth factor–organizational intent. Yet this last factor might be the most important of the four. An organization committed to the goal of being data-driven will work to develop the people, data, and tools needed to accomplish that objective.

Becoming a data-driven organization requires creating structures, processes, and incentives to support analytical decision making. It requires the organization to resolve to be data-driven and define what it hopes to accomplish through the use of Big Data and analytics. The top leadership of the organization needs to describe how analytics will shape the business’s performance.

IDENTIFYING KEY FACTORS

While many organizations are striving to implement a data-driven culture, success isn’t assured. Achieving this goal requires that certain elements be present. Our study identifies six key factors for successfully establishing a data-driven ­organizational culture.

1. Having the right tone at the top. Setting the right tone at the top is critical for most organizational initiatives, and this includes developing a data-driven culture. In most organizations, executives are championing the use of leading-edge analytics, although in some companies the initiative is being led from the bottom up, with various departments being first to embrace it.


2. Having strategies for the effective use of technology. The ability to use leading-edge analytic techniques effectively is important for a variety of reasons. Companies whose decision making is reactive to the competition are less likely to have developed strategies for the effective use of techniques and technologies. Being reactive instead of proactive implies that these organizations lack the ability to predict trends or to turn customer data into useful insights that can be used to enhance the organization’s business.


3. A commitment to collecting and using data from both internal and external sources to support ­analytics efforts. To harness the potential of leading-edge analytics, organizations need to utilize a wide variety of data sources. This is especially true when it comes to strategy development and execution. In this regard, about half of organizations use data from both internal and external sources. Of concern is that the other half of organizations are only using internal data, only using data to validate strategy post-execution, or (in a few cases) not using data at all! Using a wide variety of data sources yields better insights. Organizations that truly want to derive value from their data must be comfortable with complexity and remain flexible enough to respond to what the data tells them.


4. Using both monetary and nonmonetary rewards to promote analytical decision making. Slightly more than half of organizations use incentives to promote analytical decision making. These can be monetary, nonmonetary, or both. Yet nearly half of organizations aren’t doing so. This may be a mistake: The use of incentives is key to conveying the importance of developing enhanced analytics capabilities throughout an organization. Those that do believe in the importance of developing such capabilities are more likely to create the appropriate culture by providing incentives to their employees.


5. Willingness to adequately provide resources to the analytics efforts. Organizations often are facing resource challenges concerning the development of enhanced analytics capabilities. By far, the most frequently cited challenge is the ability to find staff with the necessary skill set. The next most common resource challenge is budget. A third challenge, related to the previous two, is a lack of staffing resources and competing priorities. Clearly, the four essential elements needed for companies to develop advanced analytical capabilities—data-savvy people, quality data, state-of-the-art tools, and organizational intent—are interrelated.


6. Alignment of analytics efforts throughout the organization. Responsibility for analytics can reside in various parts of an organization. It has been argued that CFOs should “own” analytics as they are regarded as impartial “guardians of the truth.” Most companies seem to agree, with finance being an owner (although often not the sole owner) of analytics. Other popular options include analytics being owned by IT, a dedicated analytics group, or ­operations, or having each department independently maintaining its own analytics capabilities. Of course, these options aren’t mutually exclusive, with a variety of possible combinations, the most popular being analytics jointly owned by finance and IT.

The benefits of implementing a data-driven culture are clear–organizations possessing such cultures more effectively perform key business processes such as strategy formulation and performance evaluation. In implementing such a culture, establishing processes and incentives that support analytical decision making (i.e., organizational intent) is critical.

Our forthcoming study identifies the organizational factors that are keys to establishing a data-driven culture. When deciding to venture along the path of implementing leading-edge analytics, evaluate the extent to which the six factors discussed above are present in your organization. By ensuring that they are, you can improve the chances of successful implementation and achieving the competitive benefits that come with being data-driven.