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We Need Information, Not Data

1–2 minutes

It’s time to start talking about data! While data can be a starting point on our journey, it’s not the final destination, and should never become a barrier to solving our business challenges.

The wall of data platitudes. Let the data speak for itself. Data will tell us everything we need to know. Data is the new oil. Data is power, If we track it we can improve it. Data never lies. It's all about the numbers.

Content and Utility

Let’s start at the very beginning and look at the different types of content based on their origin and intended use.

Content is anything created and shareable. Machines create attributes. Analysts use data. Customers need information. What is the difference between these types? What is useful and what is useless. How to transform between the different types?

Unclassified digital representations are the foundation of content. Without context, a human cannot tell what they represent or what their purpose is.

Attributes are any digitized representation. Must allow for storage, access, and conversion to different formats. utility is low as they have no immediate use or relevance to humans.

Classifying attributes produces data that can be understood by humans. Every classification will introduce biases and assumptions. Completely objective data does not exist, as all classification carries some level of undefined preconception.

Data is classified attributes. Making attributes meaningful and usable through clear classification. Utility is medium and all classifications carry bias and assumptions.

Data becomes information once it is fully interpreted and ready for the end user. If any further processing is required, it is still considered data. Keep in mind that desired information may not be obtainable from any data, and similarly, available data may not yield any desired information.

Information is interpreted data. preparing data for immediate use by the final user. utility is high and this definition is very strict as no further processing is needed.

Analytics and Advanata

A precise definition of analytics shows it is more involved than just delivering data. Advanata’s unique approach uses information exclusively, enhancing both the efficiency and effectiveness of analytics.

Analytics is the application of tools and techniques to analyze inputs and transform them into outputs that solve customer problems. The collaboration of the customer, analyst and machine give the best results. Customer only needs output. Advanata exclusively uses information at each stage.

Through its problem decomposition process, Advanata uses information as input, uniquely enabling customers to actively participate in parameter estimation.

Advanata uses information as input. The business problem framework can use alternative sources that do not rely on data. This reduces data needs and allows use of prior analytics. Whereas conventional analytics rely on data which limits the use of alternative resources.

When outputs are solely information, no further processing is required which makes the analytics process complete and self-contained.

Advanata outputs information. The data effectiveness methodology makes outputs immediately usable. This increases utility and eliminates execution risk. Conventional analytics output data that require additional interpretation.

Importantly, information is used internally to clarify how inputs transform into outputs. This key advantage empowers customers to lead the analytics process.

Advanata uses information within the transformation. The inductive approach structures the process in a manner that is transparent and easy to critique for all parties. this allows for customer review and buy-in. Conventional analytics are opaque to all but analysts and entirely unreadable in the case of machine learning transforms.

Go Beyond Data

Data is only part of the solution and not the end product. By reconsidering why we need data, we realize that what we truly need is information. By using information exclusively at every stage, Advanata overcomes traditional analytics barriers and empowers customers to effectively solve their pressing business challenges.

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