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From cost reduction to product development, #bigdata can provide solutions for any business

How Big Data will Change Business

dilbert cost cutting

Tom Davenport | Babson College 

Let’s say that you are intrigued by the possibility of big data and want to begin capitalizing on its potential for your business and industry. What do you do first? Buy a big Hadoop cluster for your data center? Hire a bunch of data scientists? Copy all the internet data ever created and store it in your data center?

Hold on! First, you need to do some thinking about where big data fits into your business. There will be plenty of time to pursue the other tactical steps I’ve mentioned, important as they are. But the most important step is to decide on a particular strategy for big data.

You need to assemble your senior management team and start talking about what big data can do for your company and which of its many possibilities you want to pursue. That process should start with some serious thinking about the objectives you want big data to fulfill.

What’s Your Big Data Objective?

Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings. Like traditional analytics, it can also support internal business decisions. Which of these benefits are you seeking?

The technologies and concepts behind big data allow organizations to achieve a variety of objectives, but you need to focus a bit—at least at first. Deciding what your organization wants from big data is a critical decision that has implications for not only the outcome and financial benefits from big data, but also the process—who leads the initiative, where it fits within your organization, and how you man¬age the project.

Cost Reduction from Big Data Technologies

If you’re focusing primarily on cost reduction, then the decision to adopt big data tools is relatively straightforward. It should be made primarily by the IT organization on largely technical and economic criteria. Just make sure that they take a broad perspective on the cost issues, pursuing a total cost of ownership approach.

You may also want to involve some of your users and sponsors in debating the data management advantages and disadvantages of this kind of storage, but that’s about it. No detailed discussions about the future of your industry are necessary.

Time Reduction from Big Data

The second common objective of big data tools is reduction of the time necessary to carry out particular processes. Macy’s merchandise pricing optimization application provides a classic example of reducing the cycle time for complex and large-scale analytical calculations from hours or even days to minutes or seconds. The department store chain has been able to reduce the time to optimize pricing of its 73 million items for sale from over 27 hours to just over one hour.

Developing New Offerings

To my mind, the most ambitious thing an organization can do with big data is to employ it in developing new product and service offerings based on data. One of the best at this is LinkedIn, which has used big data and data scientists to develop a broad array of product offerings and features. These offerings have brought millions of new customers to LinkedIn, and have helped retain them as well.

Another strong contender for the best at developing products and services based on big data is Google. This company, of course, uses big data to refine its core search and ad-serving algorithms. Google is constantly developing new products and services that have big data algorithms for search or ad placement at the core, including Gmail, Google+, Google Apps, and others.

Google even describes the self-driving car as a big data application. Some of these product developments pay off, and some are discontinued, but there is no more prolific creator of such offerings than Google.

If an organization is serious about product and service generation with big data, it will need to create a platform for doing so—a set of tools, technologies, and people who are good at big data manipulation and the creation of new offerings based on it. There should probably also be some process for testing these new products on a small scale before releasing them to customers.

Obviously, anyone desiring to create big data–based products and services needs to be working closely with the product development team, and perhaps marketing as well. These projects should probably be sponsored by a business leader rather than a technician or data scientist.

Taking a product/service innovation focus with big data also has implications for the financial evaluation of your efforts. Product development is generally viewed as an investment rather than a savings opportunity. With this focus, you may not save a lot of money or time, but you may well add some big numbers to your company’s top line.

Supporting Internal Business Decisions

The primary purpose behind traditional small-data analytics was to support internal business decisions. What offers should you present to a customer? Which customers are most likely to stop being customers soon? How much inventory should we hold in the warehouse? How should we price our products?

Business decisions using big data can also involve other traditional areas for analytics, such as supply chains, risk management, or pricing. The factor that makes these big, rather than small, data problems is the use of large volumes of external data to improve the analysis. In supply chain decisions, for example, companies are increasingly using external data to measure and monitor supply chain risks.

External sources of supplier data can furnish information on suppliers’ technical capabilities, financial health, quality management, delivery reliability, weather and political risk, market reputation, and commercial practices. The most advanced firms are monitoring not only their own suppliers but their suppliers’ suppliers.

Discovery versus Production

There are two primary activities relative to big data analysis, based roughly on the stage of development involved. One is discovery, or learning what’s in your data and how it might be used to benefit the organization. The other is production.


Data discovery has long been a feature of conventional analytics, but the management challenges and business opportunities big data affords make this a particularly important activity. Discovery requires different skills, organizations, tools, financial orientation, and cultural attributes than production-oriented work with big data.


The production stage for big data applications is just that—putting the application into production processes at scale. It might mean merging new data and scoring approaches into a pricing algorithm, or moving a new product feature from beta release to a full-featured offering. It requires scale, reliability, security, and all those pesky attributes that customers, partners, and regulators care about.

Of course, not all discovery ideas should go into production. Not all ideas fit an organization’s culture or processes or have a clear payoff. If you’re sending even half of your discovery projects into production, you’re probably being a bit too liberal.

What Big Data Area to Address

Where should you look for opportunities to use big data successfully within your company? Most organizations should pursue a two-pronged approach in making this decision. One is to understand the opportunities available from a data perspective. Are you sitting on a goldmine of data that could shape or transform your strategy? If you’re an insurance firm, perhaps you have a lot of claims data that hasn’t been analyzed.

The other prong involves pursuing the big data applications that your business needs, rather than what is available. This is a time-honored strategic approach that involves going through your business needs, rather than what is available. This is a time-honored strategic approach that involves going through your business strategy and noting goals, objectives, and initiatives that might be advanced through big data.

How Rapidly to Move Out

The final dimension of your big data initiative I’ll discuss is how rapidly and aggressively you should be moving. Should you jump in headfirst or put a cautious toe in the water? Should you have multiple projects or just one? Should you hire a group of data scientists or maybe rent one or two for a while?

The answers to these questions depend on your industry, the activities of your competitors with big data, and how technically innovative your organization wants to be. I’ll describe three overall levels of speed and aggressiveness in big data adoption.

What’s the Right Speed of Big Data Adoption?

You should move conservatively if:

  • Your competitors aren’t doing much with big data
  • Technology hasn’t driven industry transformation in the past
  • You don’t have much data on customers or other important business entities
  • Your company typically isn’t a first mover in industry innovation

You should be moderately aggressive with big data if:

  • Your industry is already active with big data or analytics
  • You want to stay ahead of competitors
  • Your company is typically facile with technology and data
  • You have at least some people who can do big data work

You should be very aggressive if:

  • Someone in your industry is already being very aggressive
  • You have been an analytical competitor in the past
  • You have used technology to transform your industry in the past
  • You have assembled all the necessary capabilities

I’ve tried to give you some tools for thinking about your organization’s strategy for big data. You’ve now got at least four different objectives to choose among, two different stages of big data to consider, a couple of approaches to identifying applications, and three different levels of big data aggressiveness. In short, you’re not lacking in options and in decisions your organization needs to make. goldbrown2

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Do people still need convincing?

Our 6+ month sales cycle is testimony to the effort that it takes to convince people to do something. 

Data - or rather the insights that are revealed on proper analysis - exposes a lot of the inner workings of an organization. There is fear from function owners, there is fear from the IT department who are gradually losing control as data moves to the cloud and to departmental analytic projects. 

On the surface, yes of course we can cut costs or find huge new opportunities - you'd look foolish if you didn't support initiatives like that. But to get budget - because a data benefit opportunity is hard to really quantify and because it competes for much needed projects with more tangible benefits. Once the budget is available, liberating the data from its silos is the next hurdle. 

That's just in the larger enterprises. The smaller ones have barely got their Business Intelligence capabilities up to scratch. As corny as it is, that teenage sex meme does ring true. 

Wow. Thank goodness it is good for them or it would be an even longer sales cycle!

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