In supply chain and operations, raw materials are substances that are used in the  manufacturing of goods. They are the commodities to be transformed into another state that will either be used or sold. For algorithms or predictive models, data is the raw material that every insight begins with.

A piece of data, or collection of it, can help drive a predictive analytics process and uncover insights. Data are the building blocks and inputs, and without data it is nearly impossible to find answers and make decisions. That said data is not the destination. Data is not a decision. And, while data may take on many forms and be used for many things, data by itself is not insight.

Data Is Information In Its Raw Form

Information is a collection of data points that we can use to understand something about the thing being measured.

Insight is gained by analyzing data and information to understand what is going on with a particular thing or situation. The insight can then be used to make better business decisions.

Data on its own is meaningless. It is just a raw material that needs to be transformed, analyzed, and turned into understanding.

Data on its own is meaningless. It is just a raw material that needs to be transformed, analyzed, turned into understanding and shared by people with the skills, training and commitment to do so. At the same time, predictive modeling or any business insight without data is equally as meaningless. No matter how skilled you are, or how good your model is, it is like trying to produce a finished product without the proper parts.

There’s no arguing the power of data in today’s business landscape. Businesses are analyzing a seemingly endless array of data sources in order to glean insights into just about every activity – both inside their businesses and out. Right now, it seems that enterprises cannot get their hands on enough data for analysis purposes. They are looking at multiple sources and forms of data to collect and use to learn more about customers and markets, and predict how they will behave.

What Are The Different Types Of Data?

We can think about data in terms of how it is organized, as well as the source. Data may be either structured or unstructured and the source can be either internal or external.

Forecasting data types

Knowing what types of data you have, and where they come from, is crucial in the age of Big Data and analytics.

Internal Sources: Internal sources of data are those which are procured and consolidated from different branches within your organization. Examples include: purchase orders, internal transactions, marketing information, loyalty card information, information collected by websites or transactional systems owned by the company, and any other internal source that collects information about your customers.

Before you begin to look for external sources, it’s critical to ensure that all of a business’s internal data sources are mined, analyzed and leveraged for the good of the company. While external data can offer a range of benefits (that we’ll get into later), internal data sources are typically easier and quicker to collect, and can be more relevant for the company’s own purposes and insights.

External Sources: External sources of data are those which are procured, collected, or originate outside of the organization. Examples include external POS or inventory data from a retail partner, paid third party information, demographic and government or other external site data, web crawlers, macroeconomic data, and any other external source that collects information about your customers. Collection of external data may be difficult because the data has much greater variety and the sources are much more numerous.

Structured Data: Is both highly-organized and easy to digest and generally refers to data that has a defined length and format. It is sometimes thought of as more traditional data which may include names, numbers, and information that is easily formatted in columns or rows.  Structured data is largely managed with legacy analytics solutions given its already-organized nature. It may be collected, processed, manipulated and analyzed using traditional relational databases. Before the era of big data and new, emerging data sources, structured data was what organizations used to make business decisions.

Unstructured Data: Does not have an easily definable structure and is unorganized and raw, and typically isn’t a good fit for a mainstream relational database. It is basically the opposite of structured and includes all other data generated through a variety of human activities. Common examples are comments on web pages, word processing documents, videos, photos, audio files, presentations, and many other kinds of files that do not fit into the columns and rows of an excel spreadsheet.

These new data sources are made up largely of streaming data coming from social media platforms, mobile applications, location services, and Internet of Things technologies. Since the diversity among unstructured data sources is so prevalent, businesses have much more trouble managing it than they do with traditional structured data. As a result, companies are being challenged in a way they weren’t before and are having to get creative in order to pull relevant data for analytics.

Don’t Get Left Behind When It Comes To Data

You may believe that only super large companies with massive funding or technology are implementing data analytics and pushing the limits of the types of data that are collected.  While 90% or more of data today is internal structured data, it is important to understand that 90% plus of the data ‘out there’ (external data) is unstructured.

It is important to understand that 90% plus of external data is unstructured.

With this increase in data and the need to be competitive, along with the expansion of data storage capabilities and data analytics tools, the playing field has leveled. While data is not insights, new forms and types of data have given rise to demand for newer insights and this focus on data has embedded itself into the culture of more and more businesses.


Eric will reveal  how to update your S&OP process to incorporate predictive analytics to adapt to the changing retail landscape at IBF’s Business Planning, Forecasting & S&OP Conferences in Orlando (Oct 20-23) and Amsterdam (Nov 20-22). Join Eric and a host of forecasting, planning and analytics leaders for unparalleled learning and networking.