Big data continues to fascinate business leaders across all industries. Yet few agree on a standard definition, much less a best approach to capitalize on the new opportunities and how to leverage new technologies. To better understand the differing perspectives, ESG undertook a series of in-depth interviews with the self-proclaimed leaders of big data initiatives at ten different companies. The goal was to establish a baseline of the most common strategies, as well as identify the philosophical and environmental differences that lead to more distinctive efforts.
The companies themselves will be kept anonymous, but they span the following vertical segments:
- Consumer products
- Financial Services (including retail banking, credit cards, and insurance)
- Media and publishing
While it would be overly confident to offer an authoritative and prescriptive methodology from ten discussions, many enlightening ideas can be found to complement the more quantitative research surveys on enterprise trends around big data, analytics, business intelligence, and related data platforms. What has emerged is as much about the various philosophies as the specific projects at each company.
This is the first of a series of postings to come on particular aspects of big data perspectives. Today we look briefly at the business goals.
People often say there are only three fundamental business goals:1. Increase revenues, 2. Decrease costs, and 3. Reduce risks. While at some level this may hold true, when asking organizations about their business objectives from big data initiatives, there are more than three default answers. Even within a single project there may be multiple simultaneous goals defined. IT and business leaders see big data as having multiple applications across the enterprise, if not perhaps as a miracle “cure-all.” They are also mindful about making too bold promises, even as these advantages are cited as justifications for significant investment of time and money. The general feeling is that traditional management of expectations around “under promise and over deliver” is still necessary, and benefits will be achieved in smaller increments over time. Few articulate an instantaneous, big bang transformation as desirable if even possible. Still the broader list of business goals include:
- Strategy planning for accelerating revenues and market share.
- Innovation around new and improved products and services.
- Understanding customers, segmentation, and engagement.
- Business agility around market shifts, macro-economy, and competition.
- Understanding all aspects of business operations, activities, and administration.
- Increasing efficiency of sales, marketing, manufacturing, logistics, and back office.
- Risk management, particularly security, service quality, loss, and fraud.
Some of the best practices that resulted aim at using big data in these ways:
- To more successfully "fail fast" in testing ideas.
- To identify non-obvious correlations between actions and results.
- To make decisions with more confidence and less guessing.
- To provide access and communicate more information to more staff.
- To model customer interactions and predict response.
- To model product mix and optimize pricing for profitability.
- To focus on matching analytics to business unit processes.
- To provide "right time" data, just when it's needed.
- To optimize resource utilization, both equipment and human.
Anyone will note that these are long and varied lists of goals and use cases, and in some ways that's the joy of big data. It is what you need it to be. Each business can define its own strategy. As this series continues, we'll look at other topics, like technology goals, evaluative criteria, inherent barriers, and preferences. Stay tuned, lots more to come.