Data is crucial to Modern Marketing, but what do all those buzzwords mean? This guide explains all.
Data is the key to all successful Modern Marketing techniques, helping to identify new opportunities using all of the available information to hand. But like any new development, marketers have had to introduce new terminology to explain the specifics of each.
Here we explain three of these new buzzphrases – big data, intelligent data and mixed data.
As the name implies, big data involves collecting vast amounts of information for analysis. In fact, big data involves capturing as much information as possible from any and all sources and placing it into one giant repository ready for real-time analysis and action.
For big data to work, your business needs:
- Enormous data storage capacity.
- Complex data management systems capable of collating and querying the information.
- Skilled data management professionals who can translate data into actionable insights.
Aside from a distinct lack of suitably skilled engineers, most businesses cannot afford the outlay required for a proper big data implementation. More importantly still, the jury is still out as to whether big data generates the size of returns many pundits claim.
Big data has the distinct advantage of being usable by every business unit in your company for improving performance, creating new products, working more efficiently or offering better customer service based on the insights available.
“Big data is a top business priority and drives enormous opportunity for business improvement.”
Your business almost certainly maintains a number of different data stores for each business unit. You probably already have:
- A mailing list for marketing.
- A CRM system for sales and service.
- An accounts system for invoicing and payments.
Each tool is perfectly suited to the task, but the data exists in “silos” – service data is not available to the marketing team, for instance. Because of this, many potential opportunities or insights into customer preferences and history are missed by the CMO and their team.
Mixed data techniques allow you to tie these systems together so that analysis can be performed across them. Mixed data allows you to gain deeper insights into your customers, without the same cost implications as big data.
“Besides analyzing their own data to learn how they have performed in the past, businesses need to be able to look forward and change before the market does. It’s this data that allows businesses to provide personalization and customization tailored to the specific needs and wants of individual consumers.”
Lauren Walker, analytics leader at IBM UK & Ireland
If big data is out of the question and budgetary constraints prevent investment in a mixed data solution, the CMO needs to consider intelligent data techniques. This means taking the existing data available in your sales and marketing systems, and analyzing and extrapolating the data you already have to try and identify new opportunities.
The danger with this is of “drowning in information, but starving for knowledge”. Successful intelligent data analysis relies on both technology and creativity. You need effective computer-aided modeling tools as well as background knowledge and critical reflection skills.
Intelligent data is more of an ethos than a technology. The process may include:
- Using non-standard techniques to query the database supporting your marketing system.
- Involving other business units to supply experience and knowledge to interpret data and trends.
- Having the existing system amended or extended to capture new data for future analysis.
The investment required to take advantage of intelligent data is minimal because it is based around the marketing system you already have in place.
“Little data can yield big results for many departments of small businesses, for everyone from the sales department to the Executive Director.”
Dave Becerra, Vice-President of strategy and business development at Roambi
Ultimately, the correct data solution is specific to your organization based on:
- Budget – if it is large, big data may be for you.
- Technical ability – if you lack data analysis skills in-house, mixed or intelligent data may be a better fit.
- Projected ROI – carefully consider which of the strategies best aligns with your ROI requirements.
- How the system aligns with your business strategy – your data choice must support business strategy or you will never properly realize the ROI.
