Think about your competition for a moment. What have they got that you don’t? That’s the age-old question, isn’t it? Perhaps the better question to start with is this: what assets does your organization currently have that could be better managed?

In a ground-breaking study of analytics competitors, Thomas Davenport, President’s Distinguished Professor of Information Technology and Management at Babson College, and his colleagues at the Working Knowledge Research Center studied 32 organizations that “made a commitment to quantitative, fact-based analysis.” In other words, these organizations have taken obvious and practical steps toward Big Data management. These organizations are data virtuosos. They’re simply excellent at handling their data. Davenport assessed:

“At such organizations, virtuosity with data is often part of the brand. Progressive makes advertising hay from its detailed parsing of individual insurance rates. Amazon customers can watch the company learning about them as its service grows more targeted with frequent purchases.”

Is the status of data virtuoso attainable for your organization? Absolutely.

The activities that made those organizations powerful analytics competitors today are the same activities that can make your organization an analytics competitor, too. Davenport explains three key marks found in these analytics giants:

  1. Top management had announced that analytics was a strategic priority.
  2. The organization had more than one initiative involving complex data and statistical analysis.
  3. The organization managed analytical activity at the enterprise (as opposed to the departmental) level.

In this three-part series, we will look at breaking down this seemingly insurmountable task into practical steps that will help your organization begin to move closer to managing Big Data in better ways.

Step 1 | Create a Clear Plan

This step starts with business objectives and leads to business outcomes. This seems intuitive and obvious, but many organizations have yet to link their business objectives to their business outcomes as they relate to harnessing Big Data. In the same way that strategic plans can create a common purpose and a common language throughout the organization, the same phenomenon can occur with a Big Data plan.

Be Sure to Include the 3 “Great Plan” Components

In “Big Data: What’s Your Plan?” the McKinsey Global Institute writes that any great plan will include three key elements: data, analytic models, and tools. These three elements are defined as follows:

1.  Data

How will you assemble the data that you already have? How will you integrate it across teams, departments, or functions? You need to think through those ill-devised silos that exist within your organization. How can you break down walls, integrate essential functions across teams, and more? Where is collaboration necessary? Select just two areas to get started in.

Best Practice: Harrah’s

For example, Harrah’s, named an analytics leader in an early study (2006) by Davenport out of the Working Knowledge Research Center, chose to focus on marketing and customer service. As a result, they focused much of their “analytical activity at increasing customer loyalty, customer service, and related areas like pricing and promotions.” Gary Loveman, Harrah’s chief operating officer (who would eventually become its CEO), began working on the “data” component of the plan by bringing in “a group of statistical experts who could design and implement quantitatively based marketing campaigns and loyalty programs.” That’s certainly one way to start: hire outside experts and bring them inside.

If, in reading this, you are already feeling ill-prepared and overwhelmed, let’s pause for a moment. In “Competing on Analytics,” Davenport wisely counsels: “You will have to understand the theory behind various quantitative methods so you can recognize their limitations.” However, “if you lack background in statistical methods, consult experts who understand your business and know how analytics can be applied to it.” There’s nothing wrong with asking for outside help.

2. Analytic Models

Can you identify key areas or functions in which analytics will create value for your organization? In those cases, what key staff will need to use these solutions? As you scale up, or increase the use of analytics, how will you monitor consistency, accuracy, and more? How will you manage and assess quality? The McKinsey Global Institute report explains it this way:

“A plan must identify where models will create additional business value, who will need to use them, and how to avoid inconsistencies and unnecessary proliferation as models are scaled up across the enterprise.”

Best Practice: Walmart

In Davenport’s study, Walmart is listed as a supply chain exemplar, along with Amazon and Dell. These organizations use statistics and modeling for functions such as simulating and optimizing supply chain flows and reducing inventory and stock-outs. “The most proficient analytics practitioners don’t just measure their own navels – they also help customers and vendors measure theirs. Wal-Mart, for example, insists that suppliers use its Retail Link system to monitor product movement by store, to plan promotions and layouts within stores, and to reduce stock-outs.”

3. Tools

For this part of your planning, it’s imperative that you work through the question: build or buy? You must sit down and weigh the pros and cons of building your own systems in-house or purchasing solutions from outside sources. What are the trade-offs?

No matter what you decide (to build or buy), the key here is intuitive, user-friendly tools. The tools that you choose must be accessible and understandable for the everyday end user. Yes, your teams and staff will be trained in analytics (at the most basic level), but most of your key staff won’t operate at the expert level. Keep that in mind. Select or build solutions that enable your staff to work smarter, not harder. Again, the McKinsey Global Institute explains:

“What’s needed are intuitive tools that integrate data
into day-to-day processes and translate modeling outputs into tangible business actions: for instance, a clear interface for scheduling employees, fine-grained cross-selling suggestions for call-center agents, or a way for marketing managers to make real-time decisions on discounts.”

For example, data management platforms (DMPs) or centralized intelligence engines are often a good starting point for organizations looking to optimize their audience or customer-facing processes such as marketing, loyalty programs, customer service, and more. According to Forrester Research, these essential tools create “a unified view of the customer, regardless of channel.”

It All Starts with a Plan

I know I promised seven steps and I’ve only delivered one. Those next six steps will follow in the coming weeks. For now, focusing on planning your Big Data management plan is a great start in moving your organization forward.