Based in Ottawa, canada, Tuesday Standard is a blog about consumer insights, digital measurement, online engagement and marketing.

Less Big Data Analytics – More Smart Data Analytics

Less Big Data Analytics – More Smart Data Analytics

When I first started this blog, the idea of a measurement strategy was floated as a great way to get started. This provided an opportunity for the organization to understand what was needed to be measured – which data was available – and what insights were required to effectively run the organization for today and to plan for tomorrow. This covers you if you are an e-commerce giant, a B-to-B growth company, or a non-profit or a government organization.

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But one of the things I have witnessed, is the amount of effort put into collecting data that isn’t used, or when the organization is measuring the wrong things. Sometimes, it’s the right things if only there were the capacity to analyze the data – or worse – measuring the right things and analyzing them incorrectly. It is important to scale your data collection with your ability to effectively drive business or organizational decisions from it. Focusing on quality and not quantity is an important rule of thumb in many aspects of the work we do – and data collection is one of them.

An organization’s ability to thrive or compete will increasingly be predicated by its ability to leverage the data it collects, apply analytics and glean insights from this data, and implement strategy, change, or innovative products for the future. So more data isn’t the answer – smart data is.

What is smart data or smart data analytics? Wired Magazine penned a great definition of Smart data back in 04/2013 where they said: “Smart data” means information that actually makes sense. It is the difference between seeing a long list of numbers referring to weekly sales vs. identifying the peaks and troughs in sales volume over time. Algorithms turn meaningless numbers into actionable insights. Smart data is data from which signals and patterns have been extracted by intelligent algorithms. Collecting large amounts of statistics and numbers bring little benefit if there is no layer of added intelligence.”

Five reasons to limit your data collection until you are ready include:

  1. Privacy and risk considerations
  2. Internal capacity to analyze or manage the data
  3. Cost of storage
  4. Friction in your relationship as you collect the data – particularly with new customers
  5. Overcomplicates the process of business improvement where data clouds analysis

Absolutely - data can help you make decisions. But you still need to understand which levers and KPIs affect your business. If you don’t understand these, then more data is just added noise and won’t get you closer to the answers you need.

Going back to your measurement strategy is a great way to check in with your organization and all the stakeholders to make sure you are collecting the right data – and only the data that is needed.  As your organization becomes more comfortable with collecting data and analyzing the analytics to help make decisions, it should get better at managing measurement in the planning stages and not as an afterthought. For example, there is nothing worse than implementing a new feature in a web product or website or app and then finding out the metrics you need to understand it’s use, are not available.

If you have any questions about this or other articles in The Tuesday Standard, please let me know. Thank you for reading,

Introducing Work-Product

Introducing Work-Product

Measuring E-Commerce - Top Tools and KPI's to Consider

Measuring E-Commerce - Top Tools and KPI's to Consider