A guide to understanding the different types of data available to marketers
Your customers are engaging with your business across an increasing number of touchpoints – websites, social media, in-store, mobile and tablets. But regardless of how they engage, they expect a customized, personalized, and consistent experience.
This expectation continues to be a challenge for businesses, which have to manipulate enormous amounts of data to try to understand how to effectively engage each individual.
In our age of big data, brands must be able to fully exploit all sources of data and content for insight. But with so much data out there, how do you tell the difference between the different types?
Big data solutions offer a way to avoid storage limitations or reduce storage costs for massive amounts of data.
Relational databases cannot solely deliver a real-time contextual solution. It will hinder a marketer’s ability to source actionable insights, as well as the ability to react in real-time.
Big data is a valuable tool when you need to handle data that is arriving quickly and that you can process later. You store the data in its original format and then process it when required using a query that extracts the required result set and stores it in a relational database.
Put simply, a relational database operates like someone finding a book in a library – by choosing their category first and then sourcing the chosen text alphabetically.
A big data solution will immediately find the result – the same way Google does when you type a query into their search bar.
As an open source framework for distributed storage and processing of large sets of data on commodity hardware, solutions built on Hadoop, for example, enable businesses to quickly gain insights from massive amounts of structured and unstructured data.
Brands need to be able to extract information not only from structured data (usually a fixed field record or file), but also from unstructured data (anything that doesn’t reside in a traditional row column database).
Unstructured data includes both text and multimedia content. It is estimated that 80% of organizational data is unstructured and this figure is growing at twice the rate of structured data. It has traditionally been very difficult to analyse unstructured data.
Some tools however do this effectively – extracting meaning from the large volumes of information found in both these forms. ERP (Enterprise Resource Planning) is more traditionally known as ‘accounting software’.
It reflects a more core solution capability that can manage supply chain, operations, reporting and HR. Again, some tools can also find meaning and capitalize on the opportunities found within precious ERP data.
ETL (extract, transform and load) refers to a process in database usage and data warehousing. These are the three functions needed to get data from one big data environment and put into another data environment.
The process of data transformation is made far more complex because of the staggering growth in the amount of unstructured data.
Given the growth and importance of unstructured data to decision making, ETL solutions are now offering standardized approaches to transforming unstructured data so that it can be more easily integrated with operational structured data.
ETL now can support solutions to provide big data extraction by insights and other data management platforms
Using social media, brands have an unparalleled opportunity to hear what their customers and potential customers think and feel about them, gathering insight and intelligence.
Current approaches to natural language processing (NLP) combine both linguistic or grammatical approaches as well as machine learning techniques.
The holy grail of NLP has been to convert unstructured data (text and multimedia) into structured data. This leads to insights solutions such as social segmentation and therefore more targeted marketing campaigns.
NLP should be used to generate insights, offering capabilities such as personalized email, recommendation and mobile apps.
Insights come from many and varied data sources, including:
In today’s connected world, data needs to be collected and analysed in real-time, and any data needs to be instantly actionable, preferably in a predictive way. Without these capabilities, marketing messages are less compelling and response rates fall.
Conversely, those brands that embrace real-time contextualization through powerful and flexible big data see huge uplifts in campaign responses.
Marketers are now recognising the imperative of these multichannel, contextualized communications with their prospects and customers. The more personalized the experience, the happier the customer.
The happy customer isn’t just a customer who wishes to purchase more, it’s a customer that is retained, upsold and – perhaps most importantly – the customer who becomes an advocate for your brand.