Big Data

Avoiding Big Data Train Wrecks While Maximizing Business Value (Part 2)

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In Part 1 of this two-part article about Big Data, I pointed out that there is a huge amount of attention being given to the technology, infrastructure, platforms, components and capabilities of Big Data. However there is too little attention being paid to “what you can do with it,” “who benefits from it” and “how it will be harnessed to produce “real” business value.” In Part 2 I’ll focus on two more key points with specific examples of companies that exemplify them and then conclude with a set of questions you should answer before funding any Big Data initiatives.

Big Data is not a panacea — just because you have it, doesn’t mean you can use it

Although many individuals and teams may think if you can “just get a Big Data project going” they will be able to solve many of the company’s problems, that is just not so. Going after a broad, general Big Data project may generate a lot of interest and work effort but may not necessarily provide real value for any one group or functional area in the organization.

Even if you have the proverbial “perfect information,” would your organization act on it? Consider the following factors:

  • Are managers receptive to change the way they do business?
  • Will teams be empowered to change fundamental decisions about forecasting, sales, customer service and profitability; or will organizational inertia deter any significant change to implement new decisions based on the analysis of new information gleaned from Big Data analysis?
  • If new systems are required or change management is needed, will the organization’s culture facilitate or hinder that change?

In more than 100 companies that I’ve advised, too often I’ve seen functional teams that achieve new insights from better data but can’t act on it. Or they drag their heels on taking action on the insights because it would require too much change in the organization. They can’t take advantage of it due to inertia or organization/cultural antibodies that prevent the change needed to exploit these new insights. Comcast is a good example of this dilemma.

Comcast, despite masses of data, provides some of the most inept customer service

Many people have suffered through various support, customer service and billing interactions with Comcast and similar large cable or telecommunications providers. What is amazing is how much data they have and how little action they take based on it. Their organizational divisions, internal communications, management structure, IT systems, business model and calcified view of their business prevent them from providing better service, reducing their churn rates and increasing revenues.

Although spending millions of dollars on Big Data projects or other types of technology and systems might help marginally, those efforts would be the proverbial “rearranging deck chairs on the Titanic” action compared to solving their real problems. Hence Big Data will not be a panacea for Comcast or similar companies until they deal with more fundamental issues they need to solve first.

Let’s dig a little deeper and just look at one issue. First of all, it is clear from their actions that they view their support as a cost center and try to reduce, shorten and minimize every customer interaction, rather than focusing on maximizing long-term customer satisfaction. While this may be counterintuitive, this is the unfortunate situation at Comcast and many other large companies.

It is a perfect example of a company having masses of data, but the management culture, business model and organizational structure prevents them from using the data to improve the business. Hence my point that having Big Data is no panacea — it’s how you use it and the willingness to use it to change the business.

It is a real shame to consider the extremely high level of service, brand loyalty and increases in revenues they could enjoy if they were to get their house in order. Consider what kind of data Comcast has and what they could do with the data in the areas of customer service, support, billing, marketing and other customer-facing areas. Here is some of the data Comcast has scattered around throughout all their systems. They know:

  • Where you live
  • If someone physically visited your house/apartment for installation at some point
  • Your billing information
  • Credit rating
  • Payment history
  • Viewing/channel preferences
  • When you watch TV
  • What types of movies you may have downloaded or streamed on-demand (paid or free)
  • How many times you renewed your contract
  • How many times you called for service and for what type of problems
  • Many other data points on you and your behaviors

Many companies would “kill,” so to speak, to have this much data on their customers — and even more importantly their customers’ buying behaviors and interests. They could use it to better target customers’ needs, offer them new services, increase customer satisfaction, reduce churn and increase revenues.

Unfortunately Comcast is mired in organizational, structural and management issues as well as basic system issues that prevent them from improving their business.

Recipients of Comcast’s inept level of service can only hope that Comcast will wake up and address these issues or that their directors and shareholders will start demanding customer-centric changes to maximize company value and shareholder wealth. Having access to lots of Big Data is not their problem; using it is.

Big Data initiatives require support from high-level execs to break down silos

If you’re going to maximize the potential gains from Big Data, your initiatives need to be aligned with strategic company goals and supported by upper-level managers. They need to ensure any initiatives are closely tied to the core strategies of the company and that they are properly resourced.

Also, alignment with and among the mid-level managers and their functional teams is critical. Key players involved in these Big Data projects need to see the benefit, understand why it is necessary and buy into its value in order to overcome any resistance to change — especially if it involves breaking down functional silos. Ensuring alignment with strategic goals and facilitating change may involve change management, process reorientations, new objectives and revised incentive systems. While Comcast may be an example of how hard it is for some companies to change, others that do see the advantage and go through change management can reap enormous benefits.

I described in Part 1 of this article how Profit Velocity Solutions created innovative offerings for manufacturing. Real Status is another example of what can be done when silos are broken down so Big Data can be used to provide new business insights and create much greater business value.

Real Status uses Big Data to break down silos of network information and increase productivity

Real Status is a software company that has applied unique 3D and gaming technologies to Big Data to solve some of the thorniest cloud and network management issues. It is currently heavily involved in Software Defined Networking (SDN) and hybrid cloud initiatives. It provides modeling and visualization capabilities that allow network managers and IT specialists to see their entire infrastructure and how well it is operating in a single 3D model.

Most importantly, it also enables them to manage their networks and IT infrastructure to provide higher levels of service and productivity. Its main product, Hyperglance, enables individuals to see and interact with their entire infrastructure. In many cases these maps represent hundreds of data points for each of many thousands of devices. With user-defined filters applied to all of this Big Data, all kinds of new insights can be gleaned from the network to speed root cause analysis, improve system performance, increase network uptime and prioritize business critical issues in the network — not just deal with narrow technical problems. The key is that Hyperglance not only visualizes and provides details on thousands of key hardware and software components, but it also can show critical business relationships among systems and their dependencies.

For example, it’s important to see a communication link that has slowed down for some reason. Being able to see the three critical cash flow or equity trading business applications out of 100 others that are dependent on that link allows a manager to focus on those specific issues that are really business critical for the company’s operations, rather than just fixing every application in random order or focusing on the ones that are easiest to fix.

The key here is that Hyperglance goes beyond looking at problems in silos of data, applications, servers, routers, storage devices, clouds, etc. and aggregates the information into a scalable, interactive, real-time view to see what business issues are affected. But it is key that CIOs, CTOs and IT managers break down individual technology silos to bring greater value to the business to ensure the organization benefits from higher uptime, saves money in its network planning and provides higher SLAs to its business units.

Six questions to consider before you plan a Big Data initiative

From my studying of dozens of companies that have suffered through various technology adoption processes, there are a few things that are quite clear. If there are two or three key strategies the CEO or management team wants the organization to achieve in the coming year, then Big Data projects should be hitched to those wagons. Whether the goals are to reach new customers in new markets, streamline operations and supply chain, reduce churn through improved customer support, or some other business strategy, Big Data can help. But each one of these goals has very different data sets, coming from various sources, owned by different functional teams that will make independent decisions.  (See Part 1 of this article for a list of key attributes of Big Data.)

Here are a few questions mid-level managers should ask before embarking on any Big Data initiatives. These supplement some of the higher-level strategic questions listed in Part 1 of this article.  These questions will help execs evaluate, set priorities and ensure the greatest business value is attained.

  1. What types of decisions regarding these strategic objectives will be made using the information provided by a Big Data initiative?
  2. Which functional areas/teams/systems will use this data to make those decisions? (Consider developing personas or use cases to best characterize and define them.)
  3. How are the functional areas/teams/systems making related decisions today and how will they change with the new insights from Big Data? (Consider developing use cases/scenarios to best define usage and validate with potential decision makers.)
  4. What kinds of new information are needed to make these decisions? Detailed questions: (a) What data is being used today? (b) Where is it located and how is it managed now? (c) If there are problems with the data now, will it be cleaned up and improved? (d) How will these disparate sources of data be integrated, if at all? (e) With what other sources of data will the new information need to be integrated? (f) In what form do users need this data? (g) How do they access this info today and how will they get to it in the future? (h) How will this transition occur and will change management initiatives be needed to reorient the organization?
  5. What resources are required from IT, line-of-business or functional teams to make the Big Data project successful?
  6. What benefits or improved results can be achieved if the Big Data project is successful?

Only after answering these types of basic questions are you ready to start to outline what or how a system might be built and used, what resources will be required, etc.

Recapping Parts 1 and 2 of this article, here are a few key points to remember:

  • Big Data is not a “one size fits all” solution
  • Don’t let the Big Data “tail” wag the dog
  • Big Data is not a panacea
  • Big Data initiatives require support from high-level execs

Hopefully these guidelines and the examples provided will help increase your chances for success in your Big Data journey while avoiding the many potential pitfalls. I look forward to your comments and experiences you may wish to share.

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 kocher@greyheron.com.

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