Essentially, through numerous vehicles consumers (consciously or unconsciously) are providing near-continuous streams of data about themselves, and thanks to the “network effect”, the total data generated is expanding at rapid logarithmic rates. Every day, consumers are creating quintillion bytes of data. A vast majority of real-time data created today is unstructured data. This flood of data resulting from a growing number of customer contact channels requires business leaders to learn how to realize the promise of data and leverage and analyze it to optimize results.

Smart Marketers Incorporate Insights from Big Data to Make Better Customer Decisions

Smart marketers are incorporating this data into their customer models in order to predict what customers will want, and then adapting their marketing strategies to give customers the right products when, where, and at the price they want. Study after study shows that the majority of marketers struggle with mining this data and analyzing data in order to derive valuable insights and actionable intelligence.  A number of companies continue to struggle with using Big Data.  Only 49% of large businesses are currently looking towards big data implementation. This is compared to 21% of small businesses and 19% to 26% of mid-sized businesses. Businesses with 50 to 249 employees were far more likely to implement a Big Data plan than businesses with 250 to 999 employees.  Gaining insights from Big Data is no longer an optional luxury. It’s vital to long-term success. Studies by Dell revealed that organizations utilizing data were able to grow 50% faster than their peers.

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Answer Important Questions

Big Data isn’t new; it’s just gone mainstream. Big Data incorporates multiple data sets, including but not limited to customer data, competitive data, online data, and 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 add to the Big Data sources. Most companies use Big Data to monitor their own brand and that of their competitors.

Big Data is a valuable tool for Marketing when it comes to strategy, product, and pricing decisions. Many marketing questions require being able to perform robust analytics on this data. 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. These types of questions often require large sets of data, or what is being referred to as Big Data.

Data and Content Creation and Consumption

Big Data entails analyzing all the data surrounding content creation and consumption. Which is injecting high-velocity requirements into Data capture, Analytics, and reporting. It’s easy to see the challenges associated with Big Data. There are many difficulties associated with amassing, analyzing, and using these large and disparate data sets. As a result, many companies aren’t able to maximize their use of Big Data. In their report, Templeton and Partners still find that “the constantly increasing speed and volume of data being generated and collected, the limited diversity of that data, as well as the need for more accurate and reliable data will be the main challenges for organisations dealing with data.”

5 Navigational Steps to Realize the Promise of Data

The effort associated with managing Big Data is more than worth it. The promise of Big Data is more precise information and insights, improved fidelity of information, and the ability to respond more accurately and quickly to dynamic situations.

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

1. Clarify the question. Before you start undertaking any data collection, be sure you have a clear understanding of the question you are trying to answer. Using Big Data starts with knowing what you want to analyze. By knowing what you want to focus on, you will be able to better determine what data you need. Some common questions asked are “Which customers are the most loyal” and “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. Clarify 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. 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 aid with this step. You will want to construct your Data Cubes so they contain elements and dimensions relevant to your questions.

4. Identify Data sources that need to be linked. It’s important to consolidate and link Data if you want to run analysis against it. Once you identify the question and how you want to use that Data you will have insight into what data you need. More than likely 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 disparate locations, centers, and channels. These tools include massively parallel processing databases, Data Mining Grids, Distributed File Systems, Distributed Databases, Dat Clouds/Lakes, and Scalable Storage Systems.

5. Organize your Data. Create a Data inventory so you have a good understanding of all your Data points.

6. Create a mock version of your Data output. This is a key step to help you determine the Data sets. It will also help you think about how you will convert the results into a business story. A good analyst can use insights to tell a story that will illuminate trends and issues, forecast potential outcomes, and identify opportunities for improvement or course adjustments.

Need some help with implementing these steps to improve how you use data to support Marketing? This is what we do best, so let’s chat.

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