"Big Data Part 2" builds off my earlier blogs called “Before Big Data” and “Big Data Part 1.”
In this blog we will explore the different types of data and explain the differences at a high level. I thought of breaking this blog into three blogs due to length, but felt the subject matter was better served in one article.
So what's the difference in these various data types?
The first cylinder represents structured data. This includes data from ERP sytems, mainframes and data warehouses. Although structured, these data types are structured differently.
In my earlier blog, "Big Data Part 1," I separated these structured data types into two separate circles. That's because they are structured differently.
ERP data and other transactional systems are structured in a way that allows for easy data entry.
Data warehouse and business intelligence solutions are structured in a way that allows for easy retrieval of information. This is why I had them in separate circles on the previous blog. That said, both transactional and analytical systems are structured.
As described in my blog on "Analytical versus Transactional Business Intelligence," ERP and other transactional data sources are designed to RUN your business. Data warehouse and business intelligence solutions are designed to help MANAGE your business. These are data sources typically stored in a traditional database and therefore has structure to them.
The second cylinder contains unstructured data. This is data mainly found out there on the web. This includes social media data that includes things like “Tweets” and “Comments." But unstructured data also includes your activity, including your searches.
The internet captures a lot of different activity. Today, your social authority or clout can be tracked by determining how many followers you have and how many people follow you and how many times things you post are reposted, etc. Different applications apply different algorythms, but social authority is tracked in a variety of ways.
Authority can be tracked based on the number of people you have the capacity to influence. Someone with 100 followers does not have the same clout as someone with 3000 followers for example. However, someone with 3000 followers who is never on-line commenting, compared to someone who has 500 followers and regularly posts or tweets what they hear, could have a higher ranking authority level.
Big Data received a lot of attention in the press this summer. There were a lot of concerning stories. In June, "The Wall Street Journal" published an article that the NSA, America’s National Security Agency, was obtaining a complete record of all Verizon customers and their calling history, including all local and long distance calls within the US.
This made the news because it made a lot of people upset. The idea that the government is listening in on our calls means a potential invasion of privacy. Government claims it tracks and uses this information to help identify terrorists. We hope that’s true. But the fact that they have the capability and are monitoring this information can be unsettling.
Big data has also come up in recent stories associated with the monitoring of certain journalists calls and activities. In addition it is related to the IRS scandal which required search capabilities that would targeting certain non-profit, applications. Regardless of political affiliation, most people found this disturbing because targeting groups for political gain is wrong.
Monitoring these activities requires the government to leverage big data. But right or wrong, for good, for bad or for profit the capability to capture and leveraging big data does exist.
Most companies leverage big data to target market and to manage their brand and company reputations. Either way, technology exists today that allows us to track and monitor and profile just about whatever and whomever we want.
The last cylinder represents multi-structured data or hybrid data. A lot of data sources can fall into this space.
For the purposes of a consumer goods manufacturer, I used common outside data sources in the cylinder to represent hybrid data. Lets use point of sale data for example. Point of sale (POS) data comes in from multiple retailers with varying data elements at different times of the month. Even one retailer could have multiple ways of providing POS data.
Target is a good example of the ways in which POS data can arrive. If you are vendor for Target, you might get POS data in an EDI 852 file. You might also get POS data from Info Retriever or IRI. In addition, you might purchase data from A.C. Nielsen or Symphony IRI. All these sources contain different data elements. But they also all contain point of sale (POS) data.
Let's start with the POS data coming in from an EDI file. That EDI file is structured. However, although it’s supposed to be standardized, it is not. Different retailers provide different data. Rules aren't followed. Files can be missing days or data elements. EDI from one retailer will be different from another retailer. Also, EDI from Target today, might be different than the EDI coming from Target was last year. There could also be missing or duplicate data. In addition, retailers often "recast" data, etc. We classify this as "hybrid" data because of the inconsistent, lose, structure of the data and all the work around it required to make it work well with other data.
In addition to missing or invalid or duplicated data. Data has different hierarchy's, end dates, etc. Outside data needs to align with your internal hierarchy’s and calendars. It also needs to be aligned with outside data sources like weather trends, currency conversion, A.C. Nielsen, Symphony IRI, NPD and other data sources.
These are just a few examples of data issues that arise from outside data sources. In other words, there is some structure to it, but the structure needs to be altered to be managed, integrated into other sources and ultimately provide more value.
Watch for my next week blogs where I explain in more detail the way big data is further defined and described by the industry.