In data analytics, you need to evaluate whether your data has value before you start analyzing it.

With the explosion of data that your department and organization has access to, whether it’s generated from daily operation or social media, it’s really intimidating for you as managers to decide whether you should and how you can make sense of them. As a business analyst that has help many fortune 500 companies solved various types of data analytics based business problem and a student that is pursuing my master of science degree in integrated marketing communication with concentrations in marketing analytics and digital marketing at Northwestern University, here are the metric that I develop for managers like you to evaluate whether your data can add value.

Relationship at Scale, Data Velocity & Design for Analytics
In the article “Every Business is a Digital Business”, the Accenture Technology Vision 2013 identifies seven technology trends that are shaping business in the near future. Three of the trends Accenture identified are related to the accessibility and quantity of customer data, which are essential for analytics managers like you to understand and utilize. First, “Relationship at scale”. Rather than “faceless digital transactions or a cookie file”, customers are real people with real differences. This brings to you as a marketer the new responsibility to utilize available date, redesign your relationship with your customers and improve their experiences. The second technology trend progresses with "Data Velocity". It is “the pace at which data can be gathered, sorted and analyzed in order to produce insights that managers can act on quickly.” Needless to say, matching the speed of decisions to that of actions comes to be the key for marketers to win their market. Third, “Design for Analytics”. Accenture argues that despite several relevant functions from the currently available software, the next generation of software will be “designed for analytics” in the first place, giving you the option to outsource real-time analytics while performing customer segmentation analysis internally.

source: http://www.accenture.com/SiteCollectionDocuments/technology/Accenture-whats-your-data-worth.pdf

Evaluate your data's worthiness 
With possibilities derived from huge data quantity, two important questions should be raised. Does your company have valuable data? What is the right business model to extract value from worthy data? In the article “What’s your data worth?” , Accenture Interactive Research and Finding provides us useful metrics to understand data. First of all, the data your organization has should have the following characters to be consider valuable: reflecting consumer behavior, customer identification, transaction frequency and being accessible. After ascertaining the value of your data, the next step is to monetize the data through the process of data value chain. In this process, you need to refine the raw data into processed data, to interpret the processed data into insights, to profile the insightful segments  into presentations and to use the presentations to adjust actual transaction. 

After reviewing these two articles, there are three action items you should consider immediately to lead your department or company to move forward.

1.       Evaluate your data’s worthiness

Evaluate the available data of your organization to see if they can reflect consumer behavior, customer identification and transaction frequency.

2.       Understand your data’s velocity

Estimate the time, capability and budget needed for making sense of available data through the five stages, which are raw data, processed data, insights, presentation and transact.

3.       Post hoc or real-time analytics

Decide how you want to analyze the data you have in terms of whether you want to conduct post-hoc analytics such as segmentation and targeting or you want your data to be analyzed in real-time such as web monitoring. I would recommend marketers to do segmentation internally and outsource real-time analytics to design for analytics software providers.

It’s true that the word big data is over used while little people really understand why and how to actually utilize it. But given the fact that every business is a digital business and that the availability of data on your business operation, it’s important for you as analytics and marketing manager to start right away to evaluate the value of the accessible data, estimate the energy needed to make sense of it and to decide whether you should analyze it internally in order to be customer-centric and gain customer growth. If you want a detailed check list of how to evaluate the data worthiness and how to transform raw data to actual business transact, please feel free to reach out.

Joey Bian is a business strategist and data analyst that is passionate about transforming data into actionable business strategies. He's got 2 years' relevant experience across industries and has solved various type of analytics based business problems. He is now pursuing his master of science's degree in integrated marketing communication with concentrations in marketing analytics and digital marketing at Northwestern University. And he wants to continue his career in business/marketing analytics afterwards. 

Contact him on  LinkedIn  or follow him on Twitter @JoeyWantsJoy to continue the conversation.

Digital Analyst: New mindset to excel in the New Data Economy

In the new data economy, digital marketing and analytics is not a should-do thing but a must-do thing. As a graduate student in Northwestern University Medill's M.S. Integrated Marketing Communications program, with a focus on marketing analytics and digital marketing, I have discovered 2 essential articles for Digital Analysts who want to help companies in the new data economy to leverage digital analytics towards better performance.

One of the fascinating things in doing digital marketing is that it is always changing and evolving. According to an article by a blogger Joe McKendrick on Informatica Blog, Analytics 3.0 – The Emerging Data Economy, he summarizes, from a report by International Institute for Analytics, the evolution of data analytics from Analytics 1.0 which was based on relatively small and structured data sources internally to current Analytics 2.0 which are leveraging big data stores and employing prescriptive analytics to target customers. Then the arises Analytics 3.0, which is “Large organizations across industries are joining the data economy and they are not keeping traditional analytics and big data separate, but are combining them to form a new synthesis.” This new state of management “Analytics 3.0” coined by the International Institute for Analytics are the concepts and practices of competing on analytics are no longer confined to data management and IT departments or quants rather analytics are embedded into all key organizational processes. That means models in Analytics 3.0 are often being embedded into operational and decision processes, with dramatically increasing their speed and impact. So as a digital analyst, it is really important to understand this trend and seize the value of Analytics 3.0 to embed real-time analytics into business decisions.

Source: http://blogs.informatica.com/perspectives/2014/03/05/the-emerging-data-economy/

Another important step in doing digital analytics is to have a clear framework to guide through the all process. Digital analytics can be confusing as data is overflowing, so as a digital analyst, you should have the clear objectives and goals to do the analysis and move them into business suggestion and decisions. In an article written by Avinash Kaushi (Author of Digital Marketing, Evangelist – Google and Co-founder - Market Motive), Digital Marketing and Measurement Model it introduces a digital marketing & Measurement Model to guide you as digital analyst to integrate digital analytics into digital marketing. According to Avinash Kaushi, you should always start with identify business objectives whether it is to create awareness, generate leads or highlight events. Then you identify the specific goals for your business objectives and identify the Key Performance Indicators associated with these goals. Finally, you should identify the Targets and valuable Segments for analytics. In a nutshell, the article maintains that it should always be associated with the objectives to have the better measurement.

After reviewing these two articles, as a digital analyst, there are three action items you should immediately consider for your company.

1.   Go real-time with Analytics: Integrate Analytics into real time business decisions.

2.  Always relate to the business objectives: When doing digital marketing, start with business objectives, stick with business objectives and end with them to measure.

3.  Just identify your Key Performance Indicators: The KPIs you measure when doing digital analytics should be specific to your digital marketing

Today digital analytics is the new sexiness in business world. However, as a digital analyst you should really following the framework in this data economy-Analytics 3.0, which means you should embedded real time analytics into your business decisions, always be aware of the business objectives, and identify your KPIs to better measure the ROI of digital marketing.

Feng Liu is a master candidate of Integrated Marketing Communications in Medill, Northwestern University. Previously, he had experience as a market analyst and marketing researcher in China. And he has always been interested in marketing analytics and digital marketing and is dedicated to purse a career on these fields. If you have questions or comments,  please contact me through Twitter @blakeliu0711 or Email: blake.liu@u.northwestern.edu