The topic of big-data has been taking some heat for quite a while now as agencies, consultants and their clients try to determine the best way to assign value to the various digital channels they utilise.
But, what is big data? And why is it important to business?
Simply put, big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Challenges can include capture, curation, storage, search, sharing, transfer, analysis, and visualisation.
The trend towards larger data sets is due to the additional information derivable from analysis of a single large set of related data, when compared to separate smaller sets with the same total amount of data, allowing correlations to be found from everything to spotting of business trends and determining the quality of research, to preventing diseases, linking legal citations, combating crime, and determining real-time roadway traffic conditions.
The key to the success of big data is the ability to extract useful and relevant information for the collected data, there’s more about this here in a useful blog from Danny Brown titled: ‘Without context, data is meaningless’.
Companies aren’t short on data. With the average large business storing more than 200 terabytes, they have more than enough data to tell them who is buying their product, as well as how, when and where the buying happens.
Big-data absolutely matters for analytics and related disciplines such as market research and competitive intelligence because it offers distinct benefits that can otherwise be hard, or even impossible, to come by.
This SiliconANGLE blog illustrates how this can be applied to business using a case study from the United States Agency for International Development.
Many in the industry refer to big-data in terms of size, variety, and velocity. Essentially, they must ensure that their web database development and ecommerce database design is top notch from the offset to allow for the continuous monitoring and evaluation of data.
Size pertains to data getting too big to be stored or analysed by standard software.
More data often means we can do more with analytics, especially advanced analytics. Approaches such as time-series-based marketing mix modelling, for example, have taken off in a big way simply because the required data is now more easily available.
Variety refers to the many new types of data we have now readily available —forum discussions, social media data such as blogs and postings on Facebook and Twitter; clickstream data; Google search data; retail scanner data; data from mobile devices. And, on top of all these, companies have their own data. This includes surveys, transactional data, financial data, customer complaint data, and so on.
Validation is a key success factor if a firm wants to benefit from analytical insights. Businesses are investing more into web database development and ecommerce database design to ensure the data can be traced and collected effectively.
Replication refers to the creation of copies of data in case one gets erased.
(6) In-market Predication
In-market prediction is now enabled by big data. In this past, marketers have had to make ‘guess work’ predictions about market trends based on market research from the past. Now big data can deliver real-time information and show correlations and trajectories of market trends.
Velocity refers to data being available much faster and sometimes in real-time. Standard market research easily takes more than a month before its results are fully available; internal data can be available in a week; clickstream data could probably be obtained an hour after it’s captured — provided the initial setup and coding has been done — and social media comments can be watched instantly in real-time.
In some cases they can inform decisions immediately. For example, global bank ING posts a question of the day, each day, on its Netherlands’s Website. It gets around 60,000 daily responses for use in its analytics.
The key to the success of big data is ensuring appropriate database web development is in place from the offset and then the ability to extract useful and relevant information for the collected data. So once we have decided how best to collect the data, how can we be best measure the effectiveness of a campaign.
You’d think that measuring the effectiveness would be simple – surely it’s a matter of just taking the amount spent on each media channel and dividing it by the number of units sold?
However, companies are spreading their marketing spend across various channels. This includes leveraging strategies like search engine marketing, search retargeting, site retargeting, branding/awareness campaigns on premium sites, contextual targeting, etc.
Given that people connect with businesses in many ways through various campaigns before they make a purchase, figuring out which was the one that drove them to a purchase is much more complicated, and a suitable attribution model must be in place.
In next week’s blog I will be exploring exactly which attribution models can be used to ensure the data is analysed effectively and gives meaningful and useful results for the benefit of your business.
Get in touch if you want to know more about big data and its relevance to your business.
We will be exploring attribution and the various models in depth in next week’s blog – so watch out for that if you’re keen to learn more.
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