Democratising data for better decisions


22 June 2016
Bernard Segarra of AT Internet

With an increasing number of customers coming online, more data is being generated each day, presenting growing opportunities for marketers to understand the customer psyche. In Southeast Asia alone, 150 million consumers have researched products or engaged with sellers online, and 100 million have made a digital purchase, according to a study by Bain & Company and Google in 2016.

With the abundance of data, new types of decision makers are starting to emerge and digital analytics has become absolutely fundamental to every business, process and person in the company. As a result, digital analysts often find themselves being stretched.

The challenge here is to give these new data consumers enough independence and autonomy so as to free up the analysts to focus on their core activities such as conducting advanced real-time performance analyses, investigating, testing, forecasting, making recommendations and optimising performance.

Here are a few simple steps to break down web analytics and democratise it across the business for users of all levels to use.

1) Use custom analyses

If each department becomes a data consumer, then each user will need to own the digital analytics tool and truly make it his or her own. With custom analyses, analysts can build reports designed for each business user that are adapted to the business sector, job role, level of analytics expertise, and business goals. The analyst’s job is to design the framework for these analytics reports by speaking to end users about their current needs and potential future requirements.

A product manager for an ecommerce site, for example, could pull out data which would be relevant to his or her goals, which is to gain insights on a product’s performance. Relevant data can include details on acquisition, conversion, retention, sales and revenue.

2) Put data analytics in the hands of the everyday user

Data should be in a clear and readable format adapted to its audience so that it can help support rather than overwhelm business users in operational roles. By ensuring data is easily accessible, business users are able to extract immediate value from the data and apply the insights that they obtain to their day-to-day activities.

For example, for retailers who run their operations on both a brick-and-mortar and an e-commerce store, the sharing of shoppers’ data across channels enables marketers to have a complete view of a customer’s habits. They will be able to pull up a shopper’s profile when they enter the physical store, and then give suggestions for clothing based on their online shopping patterns. When real-time performance of products is shared across the board, marketers also know what kind of products to push and can tailor their promotion materials accordingly.

3) Share data for “right-time” decisions

Sharing data is key to helping business users make decisions without delay. In order for analytics not to become a bottleneck, the business will need to be able to use the data to support rapid decision making. The data not only has to be “real-time” but “right-time” as well, delivering the right insights at the right time that it is needed.

The widespread use of mobile devices means customers are coming online at all moments, from all places. They can easily purchase products anytime and from all kinds of devices. Especially in the retail sector, businesses need to be able to respond quickly and adapt their strategies based on information of current happenings. For instance, if an e-commerce business only receives information about a seasonal sales campaign after it has ended, the insights gathered will not be as valuable. If business users can receive real-time data on their offers, being able to identify problems instantly, such as glitches in the web page, can help prevent any loss in potential customers during the sales campaign.  

There is  trend towards “self-service analytics” as more tools emerge to democratise data analytics and help business users access the information they need. This does not mean that digital analysts will disappear. Instead, their work will focus more on high value-added activities like preparing analysis models, training users, implementing data governance strategies and ensuring the quality of processes to develop a winning strategy for the company.

* Bernard Segarra is the editorial manager at AT Internet.