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Avoiding Big Data Train Wrecks While Maximizing Business Value

By October 28, 2013Article

Major technology companies are positioning themselves to exploit the Big Data tsunami, and most are already rolling out a product or service that involves Big Data. More than just another piece of technology, Big Data has become a major topic among big and small businesses across all industries, and increasingly it is driving significant and positive changes in how day-to-day business is getting done. Gartner has further accelerated interest in this area by predicting Big Data will drive $232 billion in IT spending by 2016. It’s a party that just can’t be missed.

But lurking behind this wave of momentum are many potential catastrophes. As with many emerging technology initiatives, sometimes the hype gets ahead of the reality. And unfortunately, like many parties, some things are taken to excess and a bit of caution may be appropriate before you start “‘drinking the Kool-Aid.”

This article and Part 2 shed light on several key attributes of Big Data and make key observations with broad implications for the success of Big Data. I’ll present examples of how a few cutting-edge companies are innovating and providing significant business value or failing and missing enormous opportunities. I’ll also suggest some fundamental questions about Big Data that every management team should strive to answer before embarking on a Big Data journey.

1. Big Data is not a one-size-fits-all solution

Judging from everything you read, it almost sounds like Big Data is a single solution or piece of technology. Unfortunately it’s not that simple. Generally considered to be the collection and analysis of all kinds of digital data gathered from consumers, enterprises, websites, social media, mobile devices and machine data, Big Data is very broad. It can be used in many ways and in many places, for an almost limitless range of purposes. A number of key attributes have far-ranging implications for Big Data adoption.

Key attributes of Big Data

  1. It can encompass both structured and unstructured data.
  2. It comes from disparate sources both inside and outside of organizations.
  3. It comes in many formats with many different levels of quality.
  4. It may be used by many different functional areas: marketing, sales, support, finance, supply chain, manufacturing, customer service, operations, IT, etc. Each has its own distinct needs, information and business processes.
  5. Sometimes the data may be used for analysis offline and in other situations it may be used for real-time and/or automated decision making.
  6. Most importantly, it has little value if a set of users or automated systems can’t analyze it, make decisions or execute actions that will positively impact business performance.

Some of the above attributes have been present in previous technology waves such as databases, analytics, ERP systems, data warehouses, business intelligence, corporate performance management, etc. But rarely have all of these issues been present at once. This is what adds to the complexity and risk in the convergence of Big Data into a “perfect storm.”

In short, Big Data is not a one-size-fits-all solution. Rather, it is a concept that can be applied in many different functional areas for a number of distinct decisions with completely different value for each functional team. How marketing wants to use Big Data to analyze improving customer experience will be very different from how manufacturing assesses operational efficiency in the interest of optimizing asset utilization. In the same way, what sales wants to evaluate around territory management will be very different from the way a call center tracks customer sentiment through social media.

Each one of these initiatives may use Big Data, but for completely different reasons, with different types of data from different sources with radically different decisions. These attributes have a direct and profound impact on the successful adoption of any Big Data initiatives.

Like any good project, you should start with the end in mind and work backwards. Instead of focusing on the technology, you need to start by looking at the end users or customer and the business value they can derive from Big Data. Here’s an example of an innovative new offering from an emerging company that embodies this approach.

Profit Velocity Solutions uses Big Data and the profit velocity metric to help manufacturers increase EBITDA by 3+% of revenues

Profit Velocity Solutions focuses on helping asset-intensive manufacturing companies with large numbers of SKUs to increase their profitability. It leverages the Big Data that exists not only in accounting and ERP systems but also enhances it with data from Manufacturing Execution Systems (MES). By combining data on costs, revenues and margins (down to the SKU level) with factory-floor data on the velocity that products flow through plant and equipment, they are able to provide decision makers with an entirely new metric called Profit Velocity. It takes manufacturing profitability analysis to a whole new level.

The combination of Big Data and the Profit Velocity metric is unique. It enables manufacturers to measure and manage the cash contribution they are receiving from each customer, order, product and asset they manage in every day decision making. They don’t need to wait for a special end-of-month or quarterly analysis, which is too late for operational decisions.

Profit Velocity frequently reveals that products and customers that were thought to have high margins are actually slow at producing profits since they are low-velocity products. In many cases marketing products that are slightly lower in margin but higher in velocity actually increases cash contribution.

Unlike general business intelligence offerings or broad performance management tools, Profit Velocity Solutions has focused on aggregating very specific types of data that have traditionally existed in separate data silos. By merging these disparate data elements, creating the new Profit Velocity metric as well as providing advanced visualization and “what if” capabilities, PVS delivers an innovative approach for manufacturers to increase their profitability.

The result is that PE firms investing in manufacturing companies and consultants that work with them have found the Profit Velocity metric enables them to increase cash contribution worth more than three percent of total revenues. This has huge impact on profitability and company valuations.

2. Don’t let the Big Data “tail” wag the dog

As with many emerging technology initiatives, sometimes the hype gets ahead of the reality. The irony of many new consumer and business technologies is that their success or failure is usually more correlated with human, social and adoption factors than with the attributes of the technology itself.

There is a lot of talk about the technology, infrastructure, components, platforms, power, new features and capabilities of Big Data. All of that is great. But the question needs to be asked: “So what?” 

What can you do with it? Who will benefit? How can it be harnessed to address real business issues? If these questions aren’t addressed up front, CEOs and boards will soon ask themselves, “Is this just another technology solution in search of a problem?”

Before authorizing significant spending on Big Data initiatives, business and IT executives should ask some fundamental business questions about what it brings to the table. It may be applicable across many functional areas; however, limited resources to collect, store, cleanse, analyze, integrate, interpret, distribute and otherwise manage all this data, and support business teams, will force prioritizations.

So which Big Data initiatives are most important? Here are a few sample questions that management teams should ask themselves.

6 key management questions to consider before embarking on a Big Data initiative

  1. In which key strategic business initiatives can we apply Big Data to gain new insights and improve our business?
  2. How should we prioritize and align these initiatives to be consistent with our business objectives and requirements?
  3. How will Big Data deliver a critical strategic advantage for our company?
  4. Can it help increase revenues, accelerate profits and boost shareholder wealth?
  5. Is it possible to use Big Data to reduce costs, decrease waste and improve bottom line profitability?
  6. What can it do to help improve customer experience, attract new customers, speed the sales cycle, reduce churn or create more loyalty?

Only after core business value questions like these and other functional-area-specific questions are answered should you start scoping out how to prioritize Big Data initiatives. Otherwise you risk going on a technology hunt with no objective measures of how to set direction and prioritize activities.

Here is a very simple and specific example of using Big Data to add real business value without regard to the Big Data technology being deployed. The pharmacy chain Walgreens, in addition to its mobile phone app that provides all kinds of services, has a seemingly very, very simple prescription refill service. I say “simple” because it may appear that way to the user on the receiving end, even though Walgreens is integrating multiple data sets in a highly regulated, compliance-oriented industry.

Walgreens harnesses Big Data to improve customer experience and increase sales

By tracking the last time a person refilled a prescription and knowing the prescribed rate of use, Walgreens knows exactly when a prescription needs to be refilled. They can check to make sure the prescription from the doctor is still valid through their databases. Based on having a customer’s communication preferences in a profile, they are able to easily reach a person to let them know when a specific prescription is due for a refill.

Rather than just notify a customer by email to call or visit a local pharmacy and place an order, they have taken it a step further. They allow a user to simply hit “reply” to the email — with no message and without typing anything. Just hit “reply.” It’s almost like Amazon’s one-click purchase. Walgreens tracks who the email was sent to, verifies the reply, knows that it was for a specific refill, places that order and then sends the customer another email notifying them their refill is ready for pick up.

All of this is made possible with Big Data that is automated, fast and requires virtually no effort on the part of the customer. This provides great customer experience, helps increase patient compliance with drug prescriptions, shortens the time to refill, reduces costs and helps drive customer loyalty through repeat purchasing behavior. 

From a business-value point of view, it shortens the sales cycle, increases sales and improves customer loyalty. It also provides a competitive advantage when other pharmacies don’t offer similar capabilities and makes it less likely someone will switch, even if there are similar services from competitors. In short, this application of Big Data has real business value regardless of how the technology is arrayed behind it to produce these results.

Note I have not described any of the databases, systems, data flows, messaging, middleware, etc. Those are attributes that can be worked out after you identify this type of use case and the value it creates for the customer and the organization. But make no mistake about it; this is Big Data at work: multiple data types, multiple sources, in different formats, for use in accomplishing specific goals in the customer service and sales areas. This is an example of the customer experience “dog” wagging the Big Data “tail” and not the opposite.

Part 2 of this article highlights more key observations with implications for the success of Big Data and provide an example of a very large company that is exploiting Big Data to some extent in their marketing efforts but failing miserably and missing enormous opportunities in their customer support, call centers and customer experience areas.

Chris Kocher is a founder and managing director of Grey Heron, a high-tech, business strategy consulting and advisory firm. In addition to managing pioneering products at HP and a business unit at Symantec, he has advised over 100 emerging growth companies. He specializes in increasing revenues and growing company valuations through innovative product, business and marketing strategies as well as interim exec roles. He can be reached at




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