Square up Your Data to Get Your Marketing Analytics Game On.
By Laura Patterson, President
Tuesday, 20th December 2011
Businesses tell us they believe in the concept of using analytics to drive decisions, yet, a Bloomberg study conducted earlier this year among 930 businesses across the globe in various industries, found that only one in four organizations believes its use of business analytics has been ‘very effective' in helping to make decisions.

Even though more than half of the companies in the survey said that they rely heavily on data and metrics when making decisions, many admitted that intuition and business experience still tip the scale when it comes to decision-making.

In 2005, Tom Davenport, Don Cohen, and Al Jacobson, shared the results of their work in the area of using analytics to create a competitive advantage. This work resulted in the well-known book, Competing on Analytics: The New Science of Winning, (Davenport and Harris, 2007) which discusses how high-performing enterprises are using sophisticated  quantitative and statistical analysis and predictive modeling now as the basis for their competitive strategies.

Despite these advances, marketing professionals remain challenged in regards to analytics. We often encounter three questions from marketers on the topic. These questions essentially boil down to: What is meant by analytics, how can marketing use analytics and what do we need to be successful with analytics?


The first question, what do we mean by analytics, can be answered by turning to what has become a common definition. Analytics are algorithms, advanced and/or mathematical techniques on large volumes of data. Business analytics require data analysis to guide decisionmaking and address business issues and strategies. Businesses use the insights derived from analynics to optimize internal processes and reduce the amount of time required to solve problems and make decisions.

While analytics may not fully replace experience and knowledge, they certainly provide insights and color that should be taken into consideration. The answer to the first question (what are analytics?) leads us to the answer to the second question (what are they for?): marketers use analytics to translate data into actionable insights to help drive marketing and customer strategies and optimize marketing efforts.

Success Factors

It's the third question that takes us to the heart of the matter: what do we need to be successful with analytics? There are three key ingredients: skills, culture and of course data

Skills: There's no shortage of data. It's the ability to turn the data into actionable insight that requires skill. Our research and other studies find that many organizations lack analytical talent. In May, a McKinsey study forecasted that in 2018, theUnited States could face a shortage of 140,000 to 190,000 people with deep analytical skills. Without the right people, it won't matter how much you invest in analytical tools. There's no time like now to invest in developing and growing this talent. 

Culture: Organizations that emphasize fact-based decisions, measurability, and process reflect a culture that is more predisposed to analytics and the associated investments needed to build and leverage this competency. These organizations view data as a strategic asset and the ‘backbone' of effective decision making. They take a holistic approach to data. 

Data: Now we can turn our attention to data, which is fundamental to performing analytics. Data management is often the biggest challenge for most companies when it comes to the adoption and usage of analytics. Many organizations still struggle with data accuracy, integrity, and consistency. The sheer amount of data and varied types only compound the issues of consistency and accuracy. A recent SAS-sponsored study of 586 senior executives found that while data collection in their organizations has increased, most of the useful and valuable data goes untapped. And for some marketers, there is even the issue of data access.

Before you can effectively use analytics, you must be able to manage your data. This can be extraordinarily difficult if your data is siloed. To be useful, data must be accessible and integrated. This is a challenge because the data needed to perform analytics is often housed in disparate systems. To get your analytics game on, you need to be squared up when it comes to your data. Once you understand your data you'll be able to connect and use it.

There are two steps any company needs to take to move their data management and analytics efforts forward. First, inventory your disparate data and second, address data gaps. These two steps entail creating a data source inventory and dictionary.

Data Source Inventory
Feeling deluged by data? Too much data can actually slow you down. This is why it's important to have a clear idea of what data you need to perform you analytics. A good place to start for managing your data is to create a data source inventory. At a minimum you want your data source inventory to list the following:
  • All your data sources,
  • What data is in each source,
  • Where the data comes from,
  • Who owns and uses it,
  • The primary purpose of the data,
  • How frequently it is used and updated,
  • When it was last cleaned and by whom,
  • How the data is stored and displayed, and
  • What metrics (if any) the data is tied to.
We recommend that you categorize your data by subject area so the output can be structured in way that the next person won't have to repeat your work. Don't be surprised if you find there are inconsistencies in your data. This is common and one of the valuable outputs from the inventory.

Once you inventory your data, you will be able to determine what kinds of gaps exist and what data from which source you will need to perform the analytics. This step will help with preprocessing and as you embark on your analytics the insights might identify potential data filtering options, which you will want to reflect in the inventory.

Once you complete the data source inventory you can use it as the basis for your data dictionary. The dictionary defines and describes your data, includes titles, captions, and how the data is displayed. It also includes a description of the attributes for each of the columns and tables associated with the data. While data management is fundamental to analytics, without a CMO who advocates analytics and fact based decision making, who goes ‘to the mat' to secure the necessary resources (talent, tools, etc.), integrates the science side of marketing into their organization, and holds their marketing organization accountable, the organization will not be able to realize the full potential of using analytics to gain a competitive advantage.

Lastly, most marketing organizations have developed some level of data management and rudimentary analytical skills. They are using basic statistics to support their analytical efforts. As marketers come up the learning curve, some organizations will plateau and others will outpace the rest, taking their journey the next level, using predictive analytics and modeling. Don't get left in the dust, get your analytics game on! .

Laura Patterson is president and co-founder of VisionEdge Marketing, Inc., a recognized leader in enabling organizations to leverage data and analytics to facilitate marketing accountability. Laura's newest book, Marketing Metrics in Action: Creating a Performance-Driven Marketing Organization (Racom: www.racombooks.com ), is a useful primer for improving marketing measurement and performance.


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