By now, most organizations know that the ability to understand what data means in a business context drives differentiation, competitive advantage and a fatter bottom line. And we’re generating and capturing more data than ever before, in structured and unstructured forms.

All this data can overwhelm companies already managing thousands of campaigns per year or retailers dealing with hundreds of thousands of product SKUs. Then there’s the growth of emerging marketing channels like mobile and social, adding increasing layers of complexity for marketers setting out to monetize their customer information and transform data into a competitive advantage.

There are many tools to help us understand this data and put it into a context where it becomes actionable information. And though the terms ‘business intelligence,’ ‘data visualization,’ and ‘analytics’ are often lumped together or used interchangeably, in reality they’re three very different, though related, disciplines.

Three disciplines

Business intelligence (BI) is the older of the disciplines. In fact, the term was first coined in 1865 by Richard Millar Devens, describing the ability to collect information and react to it accordingly. BI as we understand it was defined by Garner Group analyst Howard Dresner in 1989 as a term for technologies that provide fact-based decision support.

BI allows an enterprise to drill down into past performance—by region, by store, by product, by sales rep, by any discretely collected data point.

Data visualization (DV) applies a visual and graphical interface to the data. This allows a user to see things in the data that might be impossible to discover by poring over BI reports, especially as data volumes grow. The ability to visualize, explore and pivot on data is the key difference between DV and BI.

Analytics gives a user the ability to anticipate and predict relationships in the data; for example, how a 20% decrease in promotional budget in a certain region would affect revenue or who we should target a discount to in order to raise customer satisfaction. Broadly speaking, there are three types of analytics. Descriptive analytics are the simplest and are used to provide straightforward information or answer the question ‘what has happened?’ Predictive analytics up the game and use mathematical techniques to model and forecast the data to understand the future or answer the question ‘what could happen?’ Prescriptive analytics take it a step further and use statistical models and machine learning techniques to optimization the possible outcomes and answer the question ‘what should we do?’

The short version: BI is simple reports; DV is visual exploration; analytics is optimizing the outcome. In a retail context, for example, BI can tell an enterprise how many units of a particular stock-keeping unit (SKU) were sold in July; DV can help determine that sales are affected by customer vicinity and promotional activity; analytics on the other hand predict that a specific promotion delivered to customers in a particular postal code will drive up sales while other promotions will not. Analytics provides the why behind the what.

If you use Google Trends as a barometer, interest in BI is waning; the use of the term has been in a slow but steady decline. By the same token, DV and analytics are on the uptick yet all three have relevance and applicability for organizations today. Many organizations are using all three disciplines for different departments and different purposes within the enterprise. For example, logistics may be using simple BI tools to determine the frequency and number of deliveries to each store, while marketing is using an advanced analytics platform to predict the sales impact based on promotions used to boost sales. And that’s ok, but it’s important to create a closed loop decisions system so that the forecast funnels back into the BI solution to ensure logistics and manufacturing are prepared to deliver.

How marketers are capitalizing on advanced analytics

The rise of data has undoubtedly improved the science of marketing but in a world of seemingly infinite data possibilities, one thing is true: not all data is created equal or capable of yielding equal returns back to an organization. While technology is at a unique stage where it can consume more data than ever before, and at a rapid pace, when faced with decisions about which data to connect and bridges to build between systems, management will always need to prioritize.

The lens through which to view those decisions always needs to be from the customer first. A data strategy which traces the interactions that a customer has with your brand—both sales and service, direct and indirect are essential to understand because they shape the brand experience and resulting loyalty and share of wallet. The outer layers of information (and there are many) which don’t directly touch a customer are always secondary in a customer-centric business model, so prioritize them accordingly. Be excellent at customer-level data availability, freshness, quality and governance and your customer insights will be exceptional too. That data will form the foundation of solid customer interactions.

Marketers who are nimble at converting data into insight through analytics will stand to gain a great competitive advantage but those that take an integrated analytics approach and put analytics at the core of their business stand to benefit most.

This article originally appeared in the May 2016 issue of Direct Marketing.

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Steve Holder

As the national practice lead for analytics at SAS Canada, Steve Holder is responsible for driving the software go to market plan for SAS and providing customers with thought leadership around analytics, BI and Big Data. Steve is focused on defining creative opportunities to apply SAS technology to drive tangible benefits for clients. @holdersm

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