All Posts

Going Beyond Data-Driven: Are You Ready to Become an Insights-Driven Organization?

An insights-driven organization makes decisions about its customers based on data, primarily internal data. This concept goes beyond customer-centric organizations in which “The customer is always right,” to understand the activities and sentiments of their customers. These organizations use information to send the right content at the right time to the right customer.

Read: How To Use Data Driven Decision Making To Improve Ecommerce Outcomes

The data may come from anywhere in the organization, and may include everything from social media content to product data. The information is organized and classified and then mined to produce reports based on user-specific criteria. Sounds easy, no? Well, getting the data is easy, but organizing and classifying it takes a bit of effort. Finding the right tools to extract the data, reliably store it, and create reports and visualizations also takes some effort.

Let’s start with the data.

Trust your data

A solid governance process is required in order to ensure data quality. Governance processes address how to assess, manage, and maintain data quality when changes to the taxonomy or metadata fields are made or inconsistent classifying has occurred. Here are some basic steps to develop data that your company can trust:

  1. Get the right people in the room to review and approve changes to the corporate taxonomy, metadata changes, and content classification inconsistencies. Identifying the right people may require an assessment of your content to understand who owns it, who classifies it, who publishes it, and who consumes it. Identify roles. Representatives on the governance team should come from many parts of the organization. The team may be quite large, depending on the size of the organization, but not every member must attend all the meetings. Identify which team members must be present for the different cases that will be addressed by the team.
  2. Socialize the activity. Data governance is not a full-time job; it is usually added to an employee’s other tasks. Team members must understand the time commitment and why their role is important. The activities must be approved by their manager, which may require explaining to the manager the necessity of the team, focusing on how data quality directly impacts insights-driven decision-making.
  3. Understand your data. Especially if you are getting data from third-parties, it’s important to know how those data values are defined, and how (and when) they were captured or calculated. The data values need to have a consistent meaning in order to be used correctly.  Data dictionaries can help ensure consistent meaning, and they can also be useful in interpreting missing values.
  4. Identify the metrics around data quality. In order to demonstrate the effects of the governance process to your business, you need to identify and track metrics that validate data quality. .

Learn more about how we help companies set up governance teams here: Data Governance and Digital Transformation - Strategies That Work

Organize your data

Some data will arrive in the form of unstructured data, such as the words used in social media messaging, or numbers tabulated (and buried) inside PDFs. Before you can extract structured data from this unstructured content, the content documents themselves need to be classified. This classification provides useful clues for understanding the extractable data. For example, knowing whether your document is a quarterly financial statement or a product design specification is important if you want to tell sales performance totals apart from equipment manufacturing costs.

Metadata can be system-generated, but the true value of the metadata comes from non-system generated metadata. Every organization has tags that are unique, providing insight into what its content is, who it is for, and what it is about. To keep metadata consistent, taxonomies are often assigned to regulate the values used in metadata fields and to limit the options for entry. Governing field values where possible is important regardless of whether the metadata is being entered automatically by a tool or manually by the publisher or an editor.

Read more at How to Improve Search Results with Auto-Classification

Access your data

Many platforms are available for storing data and creating visualizations, but they often don’t work well together. Quick access to the data from all sources should be provided. The reporting and visualization results should tie in seamlessly with the data analysis. If this is not the case for your organization, then it may be time to evaluate your needs, identify your requirements, and find alternative platforms that will work for your organization.

You may need to update your analytics platform. Today, insights-driven platforms create a big data infrastructure upon which analytics and data management tools sit. This configuration allows for better integration with the data, faster response times, and more flexibility with.

Selecting the right vendor for your requirements does not have to be a daunting experience. EIS has extensive experience writing up use cases and requirements, which can help you create the RFIs for the insights-driven platforms.

To learn how we use information architecture as the foundation for digital transformation read our whitepaper: "Knowledge is Power: Context-Driven Digital Transformation



Recent Posts

Use Customer and Behavior Data To Create Personalized Experiences

The more quickly customers can find the product they are seeking, the more likely they are to complete a transaction and to return to the site in the future. Personalizing offers and making well- targeted recommendations can bring customers and products together faster, and are effective ways to engage customers by creating a more positive customer experience. In order to do this, companies need to capture and use as much relevant information as possible. The more that is known about the customer, the more effectively the recommendation system works. Customers generate many signals through their online behavior, and those signals can also be used to understand their interests, purchasing patterns, and needs. Reading their digital body language accurately and creating a valid customer model is essential to anticipating and fulfilling those needs.

How to Instrument KPIs Throughout the Customer Journey

You're probably using metrics to determine if your marketing programs are effective. But, have you selected the right metric at each stage of the customer journey?  Which ones connect to your strategic goals? In this session Seth Earley and Allison Brown talk about how each stage of the journey can be instrumented to use feedback from course corrections to further improve the process. You'll learn: Types of operational and user experience metrics and KPI’s How to select and collect the right metric for each stage of the customer journey How KPIs can be used for data-driven decisions How to manage conflicting goals and metrics

First Party Data - Managing and Monetizing the "Data Exhaust" From Your MarTech Stack

Understanding, anticipating and responding to the wants, needs and behaviors of your customer is the competitive battlefield of 2022. However, with new limitations and regulations regarding second and third-party data and tracking cookies, marketers, digital leaders and ecommerce executives have to consider their own methods of collecting and acting upon the data they gather about customers. In this webinar Seth Earley will talk with industry experts about how you need to model, collect, normalize, organize, manage, analyze, and act on customer information. The time to do so is now and we’ll discuss practical ways to move the needle on customer data, customer analytics and orchestration of the customer experience.