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The Business Case for Big Data

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Big Data Risks and ROI

Big Data Risks & Challenges

Like all disruptive technologies, Big Data isn’t without its risks.

A recurring problem I see is companies that put the technology ahead of the processes, people and specific outcomes. They essentially work forward from technology, instead of backwards from business outcomes. While many understand the value of information harnessed from a myriad of internal and external sources, fewer understand how to make that information accessible and actionable at the exact point where it can be used by knowledge workers across the organization. These challenges to leveraging Big Data for SMART business objectives are no different than other information analysis methods and must start with an integrated people, process and technology plan which includes the processes to identify and capture the data, the tools to manage (access, sync, merge, store, tag, annotate, etc.) the data, and the right-time distribution of that data to the person or interaction where it can be applied for specific purposes and consistent results.

Another pervasive challenge with big data is data relevancy. With more data comes more noise. Business analysts will need to classify data into a spectrum running from noise to signals in large part based on the use case and weighted results of the data.

Other challenges such as data privacy, information security, information distribution, data presentation and even data overload (aka analysis paralysis) are not unique to Big Data, and the risks and resolutions can be learned from the lessons and best practices of other business analytics solutions.

Big Data ROI

Big Data technology investment can be relatively low due to underlying open source tools. The bigger cost is the labor needed for planning, cultural alignment, process definitions and deployment. And when labor is the largest cost involved in a technology deployment, that cost can be reduced or multiplied based on the specific resources allocated. Clearly, untrained resources apply a trial and error approach while leveraging expert resources streamlines the effort and generally gets it right the first time.

Also, I’ve found then when working with clients that initially struggled in Big Data implementations, they often lacked the creative thinkers which then slowed or stalled their project. The likelihood of project success will increase dramatically if you allocate one or more creative thinkers or innovators, who can collaborate with executives and line of business managers to flush out decisions that will benefit from increased data, and quickly hypothesize the types and sources of information that will aid those decisions. Huge sets of external data are available from Government and NGO bodies, social media and commercial services, and those people versed in these and other data sources will of course accelerate the process.

While deployment can be challenging, the really encouraging news about Big Data projects is that they generally deliver big paybacks. A recent Nucleus Research report titled The Big Returns from Big Data found that big data projects which connected internal data sets with social media earned, on average, 241 percent ROI. For example, one ROI analysis demonstrated how a vacation resort company drastically cut labor costs by syncing its scheduling process with data available from the National Weather Service. In the research survey, IF the big data implementation was successful, it likely earned a significant ROI well in excess of 100 percent.

Seize the Data

Big Data has not yet crossed the chasm to mainstream adoption, but is clearly delivering success for early adopters and is now at an inflexion point. For most businesses, Big Data methods are as unique as their corporate cultures and business processes, and the technologies are more bespoke than packaged. This leaves Big Data success to those business champions that can act as change agents, rally staff around high payback projects and innovate new processes merged with enabling technology. For these reasons, Big Data will provide big payback for adopters, but it’s not for everybody just yet. Business innovation and technology laggards will only acquire Big Data solutions when they are more packaged, easily deployable and no longer offer competitive advantage.

The ability for businesses to glean valuable nuggets of information from near limitless sources and apply that knowledge at just the right time to achieve tactical goals will clearly elevate those businesses over their competitors who operate without such knowledge. This is the power of Big Data and it will separate competitors in terms of business success.

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Comments (20) — Comments for this page are closed —

Guest Samuel McGovern
  I like this article because it really shows the business opportunity, it’s extremely pragmatic and you wrote the whole thing without using the words “terabyte” or “petabyte”. Great article.

Guest tierra4ever
  Social media is adding to the big data deluge in a big way. I’m sure there’s an opportunity but like social media I’m not seeing solid and specific business opportunities, improved business processes and ROI from even more compounding and unorganized data.
  Guest lenny
    Big data success occurs when you can evolve big data into small insights. Big data really begins with exploration and a goal to identify patterns, trends or uncover information that leads to those ‘aha’ moments.
  Chuck Chuck Schaeffer
    I think big data and social media are symbiotic in several ways and offer a powerful combination for businesses to really understand their customers’ wants and needs. For the businesses that can effectively mine the data volumes and extract the signals from the noise, this improved understanding can impact how companies create new products and services that are more enthusiastically consumed by customers, how companies market to customers with more relevant and personalized messaging, and how companies apply more and better information to support customers in the many post-purchase service scenarios. Big data is a complex undertaking, but it’s hard to argue these types of benefits wouldn’t deliver impressive ROI.

Guest Ahmed Hasama
  We’re in the throws of big data. The biggest challenge is refining our mapping and presentation to improve the signal to noise ratio. With each iteration it gets better. Clearly the biggest benefit is making more fact based decisions, and having much more confidence in those decisions. This has been especially true in making first time decisions about new products and expanding into new geographies.

Guest Jhiggins
  I feel a bit intimidated to ask this question to this crowd, but can you define big data? I get the characteristics and much of the context, but I'm still unclear how to describe this technology.
  Chuck Chuck Schaeffer

Big data is defined by the three characteristics of volume (the growth and run rate of data), variety (the types of data) and velocity (the speed at which data cycles). Because of its constructs it cannot be leveraged using traditional BI processes and tools, as these tools cannot handle the volumes or variety of data which now include raw, structured, semi-structured and unstructured data that often cannot be put on your own servers.

Big data is unbound data, compared with traditional data for analytics which is cleansed, staged, normalized, appended to a defined data schema and then placed into a data warehouse where it becomes accessible within fixed parameters. The benefit is that by preserving the data fidelity, you keep data management costs lower and facilitate new data exploration with vast amounts of data for discovery and new business insights. What I personally find exciting about Big Data is that it enables forward thinking business leaders to find answers to questions they didn’t know to ask, thereby discovering new insights and new business opportunities that can grow businesses exponentially, not just incrementally.

The dynamic nature of Big Data requires new methods and tools to source, sync, search and deliver information in right time to the person or interaction where it can be applied contextually with maximum effectiveness and/or predicted results. To capitalize on Big Data, companies must use tools which can manage all types of data, including structured and unstructured, relational and non-relational, text, images, audio, video, sensor data, transactional and more.

Guest breeze411
  Any big data use cases for marketing?
  Chuck Chuck Schaeffer

Marketers have the opportunity to harvest large volumes of conversational data from prospects and customers in order to improve customer intelligence by appending what is generally stale and static demographic and transactional data with real-time and dynamic behavioral data. From a tech perspective, the social listening, text mining and CRM analytics tools for this purpose are quite sophisticated but the sentiment analysis tools are still a work in progress. Improved customer intelligence dramatically enhances customer segmentation with attributes and dimensions that more accurately predict intent and demand, and enable advanced segmentation techniques such as micro-targeting and campaign triggers. More importantly, this improved segmentation permits marketers to deliver better messaging and offers that are more personalized, relevant and timely; all characteristics that significantly improve conversions, campaign performance and marketing spend.

  Guest Zach Kirk

Our team is in the beginnings of what you are describing. We’re using a combination of commercial and open source software tools to extract conversational data and link behaviors with customer attributes in a way that is improving responses and actually starting to predict responses with a basic level of accuracy. Over time I think our prediction model will totally change our marketing performance, and the company’s revenues.

  Chuck Chuck Schaeffer

That’s a great example, thanks for sharing. When you act on what you learn from customers you are improving the customer relationship and your business objectives, which as we all know are achievements easier said than done. When you can predict customer behaviors to your offers and products, you’re in a position to accelerate top line revenues in a big way. JetBlue discovered that every one point change in its NPS (Net Promote Score) realized between $5M and $8M in additional revenues. This example is just the tip of the iceberg.

Guest Bart Albright
  The innovation of big data is the use of more varied data to discover new and unexpected relationships. The challenge is developing the data sourcing skills needed to identify and gather the best data for these new decisioning models. An entire data sourcing industry is now underway.
  Chuck Edy Hanao

I think the upside of big data and more information for decision makers is clear but the two challenges that stand in the way are data sourcing and simple, effective visualization of the most relevant results. Making sense of the data in a simple and visual way so that decision makers can quickly interpret and act on the data is where I'm seeing many of the big data software apps make the most progress.

Guest Rodney Jamison
  Can you expand upon the Hadoop product and why there as so many Hadoop flavors?
  Chuck Chuck Schaeffer

Hadoop is a top level Apache (version 2) open source project developed in Java and originally created with technologies from Google and Yahoo to facilitate data intensive applications distributed over large clusters of commodity hardware. Hadoop has essentially become the nucleus in providing big data core services, including a cloud-based architecture, a massively parallel processing (MPP) engine, in-database analytics, mixed workload management and a storage layer. Hadoop’s adoption success has spawned a number of additional commercial products from start-ups and existing IT companies which offer enterprise grade distributions of Hadoop or otherwise extend Hadoop with what are normally proprietary offerings for additional services or capabilities such as added functionality, data integration, performance enhancements (scalability), high availability, information security, platform manageability and advanced analytics. Some of the more recognized IT vendors which build products on top of Hadoop include Amazon Web Services (with its Amazon Elastic MapReduce), Cloudera, DataStax, EMC Greenplum, Hortonworks, IBM (with its BigInsights), MapR, Outerthought, Pentaho, and Zettaset. Traditional RDBMS vendors such as Oracle and Microsoft are also evolving their data management technologies to support Hadoop.

Guest Lenny Kirk
  Great article Chuck. You’ve given me enough information to get off the sidelines as you say and give this some serious consideration.
  Guest Kelvin Sauders
    I think the Billy Beane/Moneyball story really drove home the power of big data in a way that got many people to think and act.
  Chuck Chuck Schaeffer

Thanks Lenny. My purpose for this post was to share some good experiences as an impetus for others to consider Big Data in their own contexts. I take satisfaction that you’re moving forward with such consideration.

Guest Kevin Corniellon
  Big data is a big deal. When you recognize the world creates 2.5 quintillion bytes of data every day it really makes you wonder how you can tap into that data for your own goals. IBM says 90% of the data that exists today has been created in the past two years, suggesting that this trend will only continue, and continue to be a business opportunity for those that can figure out how to capitalize.

Guest Anonymous
  Business leaders can easily find themselves drowning in data and starved for information. What's most commonly missing is a tool that can help turn this data into smarter decisions. Maybe big data are the missing link, I'm not so sure.



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The Business Case for Big Data



Those business leaders that successfully extend their existing information repositories and tools with Big Data will dramatically increase their information availability and consequently their insights and decision making. It’s really a simple fact, those business leaders that construct the methods to gain additional insights from vast amounts of new data will use that information to make better and faster decisions than their competitors who choose not to follow suit.


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