Big Data, Analytics, and Really Big Data Analytics

First a few definitions:

Big Data is the process of taking data that is very large in variety, velocity or volume (or any combination) and converting it to a form that is usable for a given purpose.

Analytics is the process of attempting to discover and communicate meaningful patterns in data.

I’m going to suggest that “Really Big Data Analytics” is the application of Big Data and Analytics to address enterprise (or inter-enterprise) questions. Or, stated another way, endeavoring to answer “Really Big Questions”.

That means “Getting results that are usable, integrating them in the business processes and using these results to improve the organization.”

The pace of business is fast and getting faster. The length of the day, however, remains constant.

What does that mean? It means that more decisions are required per hour than in the past. Our ability to process data is impressive. Our ability to create, integrate, and analyze data sources to answer critical questions is slower than it must be for us to stay competitive.

It is lagging not because of technology, but because we are not defining the complete view of the data we need to answer the right questions. Note that this is plural (questions), because every time we answer one question, many others pop-up. This is the happy nature of analytics and the quest for knowledge.

Get Me Everything About Everything is not an information strategy

But what information do we need? We can’t just say, “Get me everything about everything ”. We need to define the questions in data and analytic terms.

Here’s an example. A company produces a new mobile device. It needs to:
• Make sales projections
• Integrate and adjust its own market research with results harvested from social media
• Fold that consolidated information into the company’s production plan
• Compare the production plan with the current parts inventory
• Assess the current reality of that against its’ parts providers’ inventory and production plans… (Etc., etc., etc.)

The result is not a single analytic, but many actors all looking at the same analytic result together with other associated analytics results. This leads to a set of complex data or information integration tasks that are needed to support the overall intra- and inter-enterprise processes.

This is a simplification of the problem but it makes the point that even good analytic results are only a part of the solution.

What a lot of companies do is answer the first part of the problem, such as, “what is social media saying” and simply allow the other parts of the business to consume that information. The other parts of the business are left to figure out for themselves how (or even if) they will use this information. Generally speaking, the further from the source the smaller the probability that the analytic will have any impact.

Asking the Right Question

Part of the solution is defining the information or data space that is needed to answer the enterprise/inter-enterprise level question phrased something like:

“How do I get the right number of devices produced?”

Which is a very different question from, “How many phones should I produce?”

The good news is that by taking a step back and deciding on the right questions to ask and drilling down on that to the decision-data level, we can quickly define the target. Too many analytics and data projects are started as vertical efforts and remain that way. By beginning with the vertical and driving it to a conclusion without doing some initial lateral thinking and blueprinting, we risk developing a great solution for only a small part of the problem. And the part of the problem being solved may not be the most important part.

There are structured processes, like Analytics Framework™, that spend a few weeks at the front end to define the enterprise and intra-enterprise questions and the data needed to answer them.

Taking the time to create the target allows us to understand the value, feasibility and dependency of a range of projects. It allows us to:

• Balance value and feasibility in selecting projects
• Leverage results across business processes.
• Avoid the, “Single version of the truth” problem

By spending a small amount of time at the front-end we can solve the big problems quicker and at a lower cost. And therefore begin answering Really Big Questions through Really Big Data Analytics.

For more information on the Avalon’s Analytics Service visit

Wayne Applebaum About Wayne Applebaum

VP of analytics and Data Science for Avalon Consulting, LLC.

I have over 30 years of experience in data analytics and enterprise consulting. It is my belief that, Great Analytics can only be enabled by Great Data.

I hold a doctorate in statistics and have spent 30 years working for companies like SAP, Oracle, Business Objects, and EDS to guided Fortune 500 executives in aligning analytics with business needs.

Many companies are only using a fraction of the data they need. When information is lacking, analytics projects fail. Events over the past 10 or so years have provided us with a unique opportunity to help companies leverage both structured and unstructured data to create a foundation that drives innovation and measurable business results.

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