Every day, customers and consumers are creating quintillions of bytes of data due to the growing number of customer contact channels. Some sources suggest that 90% of the world’s customer data has been created and stored since 2010. Unfortunately, the vast majority of this data is unstructured.

It is not surprising, then, that study after study shows that the majority of marketers struggle with mining and analyzing this data in order to derive valuable insights and actionable intelligence. A report by EMC found that only 38% of business intelligence analysts and data scientists strongly agree that their company uses data to learn more about customers. As marketers, we need to learn how to leverage and optimize this flood of data and incorporate it into customer models we can use to predict what customers want. Read on for proven practices for taming Big Data and turning it into big insights.

Big Data Defined

Many marketing questions require being able to perform robust data analytics. For example, understanding what mix of channels is driving sales for a particular product or in a particular customer set, or what sequence of channels is most effective, requires large sets of data, which is what is being referred to as Big Data.

Big Data isn’t new; it’s just gone mainstream. A recent survey of US data aggregation leaders found that:

• 49% defined Big Data as an aggregate of all external and internal web-based data.
• 16% defined it as the mass amounts of internal information stored and managed by an enterprise
• 7% defined it as web-based data and content that businesses used for their own operations
• 21% of respondents were unsure how to best define Big Data

IDC defines big data as: “a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis.”

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Big Data Both Requires and Enables, a Holistic Approach

Big Data incorporates multiple data sets—customer, competitive, online, offline data, and so forth—enabling a more holistic approach to business intelligence. Big data can include transactional data, warehoused data, metadata, and other data residing in extremely massive files. Mobile devices and social media solutions such as Facebook, Foursquare, and Twitter are the newest data sources. Most companies use Big Data to monitor their own brand and that of their competitors. The use of “Big Data” has become increasingly important, especially for data-conscious marketers. Big Data is a valuable tool for marketing when it comes to strategy, product, and pricing decisions.

Big Data offers big insights, and it also poses big challenges. A recent study by Connotate found the top challenge with Big Data was the time and manpower required to collect and analyze it. In addition, 44% found the sheer amount of data too overwhelming for businesses to properly leverage. As a result, many companies aren’t maximizing their use of Big Data.

But the output from investing the needed resources is more than worth it because Big Data provides more precise information and insights, improved fidelity of information, and the ability to respond more accurately and quickly to dynamic situations.

Employ 7 Steps to Tame Your Data

So while Big Data might seem a bit daunting, these steps will help you navigate using Big Data:

1. Clarify the question. Before you start any new data collection project, have a clear understanding of the question(s) you are trying to answer. Using Big Data starts with knowing what you want to analyze. By knowing what market you want to focus on, you will be better able to determine what data you need. Some common questions asked are “which customers are the most loyal” and/or “which customers are most likely to buy X”? Big Data is about looking beyond transactional information, such as click-through data or website activity.
2. Specify how you want to use the data. Will you be using the data for your dashboard, to define a customer target set for a specific offer, or to make program element decisions (creative, channel, frequency, etc.)?
3. Think beyond the initial question. Invariably, the answer to one question leads to more questions. If you’re not sure, hold a brainstorming session to explore all the ways the data could be used and potential questions the answers might prompt. Structure your data in a dynamic way to allow for quick manipulation or sharing. Aggregate data structures and data cubes facilitate this step.
4. Construct your data cubes. The data cubes you construct should contain elements and dimensions relevant to your questions.
5. Identify data sources that need to be linked. Once you identify the question(s) and how you want to use that data, you will have insight into what data you need. To run analysis against data, you will need to consolidate and link it. More than likel,y you will need to collect the data from disparate data sources in order to create a clear, concise, and actionable format. It may be necessary to invest in some new tools so you can pull and analyze data from different locations, centers, and channels. These tools include massively parallel processing databases, data mining grids, distributed file systems, distributed databases, and scalable storage systems.
6. Organize your data. Create a data inventory so you have a sufficient understanding of your market and data points.
7. Create a mock version of your data output. This is a key step to helping you determine the data sets. It will also help you consider how you will convert the results into a business story.

Best-in-class marketers use data to tell a story that will illuminate trends and issues, forecast potential outcomes, and identify opportunities for improvement or course adjustments. They use the data to gain big insights into customer wants and needs, market and competitive trends. Tame Big Data and tap into big insights that will enable you to take advantage of market opportunities, deliver an exceptional customer experience, and give your customers the right products when, where, and at the price they want. Learn more about how to tame big data by watching the recording of the webinar “Transform Data Into Insights to Create a Winning Strategy for Competitive Advantage.”

FAQ:

• 49%: an aggregate of all external and internal web-based data
• 16%: mass amounts of internal enterprise information
• 7%: web-based data/content used for business operations
• 21%: unsure how to define it
  This variation reflects how quickly Big Data has gone mainstream and how broadly it spans sources and use cases.

1. Clarify the question: Define what you are trying to answer (e.g., loyalty, propensity to buy).
2. Specify how you will use the data: Dashboard, targeting, program decisions (creative/channel/frequency), etc.
3. Think beyond the initial question: Anticipate follow-on questions; brainstorm uses; structure data for flexibility (e.g., aggregates, cubes).
4. Construct your data cubes: Build dimensions and elements aligned to the questions.
5. Identify data sources to link: Consolidate disparate sources; consider tools needed to pull/analyze across locations and channels.
6. Organize your data: Create a data inventory to understand available data points and coverage.
7. Create a mock version of the output: Prototype the intended output to validate required data sets and shape the business story.

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