As a marketer more and more of your day is likely to be taken up by data and data-related tasks. It’s okay. That’s normal. Every business is a digital business these days. Google said so.
Just because data is all around us, it doesn’t mean we understand it all. As we know, the world of marketing is never happy unless it can continually bombard you with new and ambiguous buzzwords to make you feel inadequate.
Well, cast aside those insecurities as we break down some of the most commonly trending big data terminology for you.
Let’s start at the top. Big data is essentially what marketers used to refer to as simply ‘data’. Only now there is massive amounts of it, exponentially increasing every second of the day. Being so vast and unstructured and collected from so many sources means it cannot be processed and analysed using traditional database and software techniques.
Big data is relatively old news (it doesn’t even get capital letters any more) and most of it has a limited shelf life. If it isn’t put to use quickly (in many cases immediately) then it ceases to be useful. This is where fast data steps in – analytic applications that can react to and unlock the value in big data in near- or real-time.
It may be that we’ve read too many fantasy novels, but dark data isn’t as menacing as it sounds. Simply put, dark data is the information that organisations collect, process and store during normal business activities but do not use. For example, raw survey data, log files, financial statements, call centre logs and social media feeds. Can it be put to use? Sometimes, if you have the right technology in place and know what you want to do with it. Otherwise, it just needs to be kept ‘dark’ for compliance reasons.
With big data, trying to find information that is useful and actionable can be like finding grains of salt in a huge bag of sand. Smart data is what analytics and visualization technology can extract, filtering out the useless sand to leave you with contextually useful, actionable insight for intelligence decision making. Essentially, smart data is the ‘good stuff’.
Got a lot of data but don’t know where to put it? A data warehouse, also known as an online analytical processing (OLAP) database, is a central repository that consolidates data from all your disparate sources. A bit like a Single Customer View, right? Er, no. We explain why here.
The main purpose of a data lake it to solve the problems relating to organizational silos as somewhere to keep ALL your data. As we know, disparate data is bad. Consolidation is good. Still, while a warehouse contains structured data, a data lake is more like a dumping ground for enterprise-wide information, in its raw format. Theoretically, this makes data more accessible – but it’s still likely to take a lot of digging (or, rather, diving) to find it, process it and use it.
Call them what you like: wranglers, wizards, miners, engineers – they are all likely to do much the same thing, and are considered all hot property. A data scientist is the person you turn to when you need someone to make sense of big data. They have the skills to extract your raw data, manipulate it, and turn it into useful insights. Sure, marketing technology is starting to close the gap when it comes to letting marketers get these insights out themselves but big brands with big budgets want them to do this labour intensive work.
Fear not, dirty data doesn’t require an unsavoury internet search (although who knows what you might find after Google page 7). In terms of customer data, when it is clean that means it is accurate, up-to-date, de-duplicated and compliant. Dirty data, on the other hand, can be misleading, incorrect and unfit for marketing use unless you are asking for trouble. You can learn a lot more about dirty data here.
Want to know how to get started with big data? Then read Getting All Your Data and Sticking It in One Place.
Big data is all about gaining actionable insights from consolidating all your structured and unstructured data into one place that can be accessed for profitable gains.
Download this white paper if you are:
- A CRM or Marketing professional looking to exploit big data potential gains.
- A CRM or Marketing professional looking to understand the key areas to review when considering the beginning of a big data project
- A CRM or Marketing professional who wants to understand the meaning of structured and unstructured data