- Big Data and Data Science Requires Digital Analytics
- Defining Digital Analytics
Defining Digital Analytics
But what is digital analytics? Digital analytics is the current phrase for describing a set of business and technical activities that define, create, collect, verify, or transform digital data into reporting, research, analysis, optimizations, predictions, automations, and insights that create business value.
The activity of digital analysis, at the highest and best application, helps companies increase revenue or reduce cost. The activities performed in digital analytics require coordinating processes, people, and technology internally within a company and externally from partners and vendors to produce analysis that answers business questions, makes recommendations based on mathematically and statistically rigorous methods, and informs successful business activities across many functions from sales to marketing to management.
Digital analytics can help a business in many ways. The two goals for the highest and best usage of analytics are to create value by 1) generating profitable revenue, and 2) reducing cost. The McKinsey Global Institute (MGI) claims that a 60 percent increase in retailers’ operating margins are possible with big data, whereas just location-based big data has the potential to create a $600 billion market annually. The opportunity to generate commerce in an ethical and productive way is possible with digital data, but how does a person, a business, and a global enterprise get there? The answers are in this book with comments on the activities critical and necessary to analyze data, from the technical and process work (requirements/questions, data collection, definition, extraction, transformation, verification, and tool configuration) to the analytical methods to apply to data in order to analyze, report, and dashboard it. By bringing together data from different systems to create cohesive and relevant analysis, you can understand how digital data and analytics can be used to answer business questions and provide a foundation for fact-based decisions.
This book explains how to build and manage digital analytics teams to tell “data stories” based on answering “business questions” asked to the analytics team by stakeholders. The analytical insights in these answers can provide recommendations and data-oriented guidance to management that helps make their company money. Digital analysts, the people on the digital analytics team, are able to navigate effectively the upstream technical and downstream social and organization processes inherent in executing a data-driven communication function via processes that unify teams across technology and the business. If that last sentence is hard to deconstruct or if it makes perfect sense, read on because this book covers the following topics:
- The fundamental building blocks to understanding and creating processes for digital analytics, called the Analytics Value Chain. The Analytics Value Chain is a new concept I created for describing the process and work necessary for tactical and strategic success with digital analytics. The Analytics Value Chain starts with understanding business requirements and questions, to defining and collecting data, to verifying, reporting, and communicating analytics to the next steps of optimizing, predicting, and automating from digital data using data sciences. The goal of the value chain is, of course, the creation of economic value from digital analytics.
- The P’s of digital analytics: people, pre-engagement, planning, platform, process, production, pronouncement, prediction, and profit
- Business considerations when justifying investment in the analytics team, and how to propose an investment consideration for funding the creation or enhancement of a digital analytics team and its operations
- Creating tactical and strategic goals for the analytics team and the responsibilities of the team
- Buying or building analytics tools and what it takes to succeed with tool deployment and maintenance, including discussions about social media and mobile analytics tools
- The importance of storytelling with analytics and using Exploratory Data Analytics (EDA) to understand digital analytics data
- Applied analytics techniques, as a go-to reference for the types and shapes of data, including a business-focused review of basic statistics such as the mean, median, standard deviation, and variance and other more advanced statistical concepts
- A review of data visualization techniques, such as plotting data, histograms, and other charts and visualizations
- Analysis of digital data for a businessperson: data correlation, and linear and logistic regression
- Good ideas and best practices when experimenting with data, sampling data, and building data models
- How digital analytics fits into other analytics, research fields, and qualitative disciplines such as competitive intelligence, market research, and Voice of Customer (VoC) data
- Data governance and the role of defining, collecting, testing, verifying, and managing changes to data, analysis, and reporting and how the Data Governance team plays a critical role
- How to set up a digital optimization program; a review of optimization using digital data with A/B (champion/challenger) and multivariate testing, while reviewing the statistical and mathematical models behind optimization and optimization engines, such as Taguchi and Choice modeling
- An overview of common and popular KPIs used by consultants, brands, and practitioners—and a review of useful ways to get started creating and extending your KPIs
- The importance of reporting and analysis and the difference between them, including RASTA dashboarding (Relevant, Accurately actionable answering, Simply structured and specific, Timely, Annotated, and commented) and LIVES reporting (Linked, Interactive, Visually-driven, Echeloned, and Strategic)
- The use of digital data for the many types of targeting—from geographic to cookie to behavioral and more
- A discussion of omnichannel data and the convergence and integration of data from multiple channels for understanding the customer, media, audiences, and for creating addressable advertising solutions using digital data
- The future of analytics from interacting with data in customer experiences to using sense and respond technologies for customer interacting and alerting to perceptual analytics
- The Analytical Economy and the importance of consumer and customer privacy and ethics within all facets of digital analytics now and into the future