Over the past few years, the availability of consumer data has increased and retailers now have a vast reserve of data from which to gather insights. Fast, reliable data is essential for delivering critical information to business users at the right time. Retailers are now looking for technology solutions that provide automated analytics in order to make decisions relevant to their customer base.
Why is automated analytics important?
Moving from a mass-market supply chain strategy to one that is tailored to the individual needs of the consumer increases the number of decisions that need to be made by manufacturers and retailers and the granularity of those choices. Due to the volume of data available, and the speed at which decisions need to be made, automating analytics to gain actionable insights is becoming increasingly important.
A prerequisite to meaningful automated analytics is having a deep understanding of the issues or circumstances that are being analyzed and realizing the key performance indicators (KPIs). It is critical that insights produced are relevant to a particular decision. As for those interpreting the insights, individuals must have an understanding of what the insights mean and the relevant actions that should be taken based on the consequence of those insights.
What do manufacturers and retailers need to look for in a data automation solution?
Everyone has access to the same statistical models so it is difficult to differentiate solutions based on algorithms alone. Instead, manufacturers and retailers need to focus on the solution’s ability to seamlessly connect to other systems used within the supply chain and harmonize the data collected. Without data harmonization, the insights produced by automated analytics could lead to detrimental actions, and loss of profit.
Data, or analytics, does not have an inherent value. The value is created by the actions that are prescribed or indicated by the insights gathered from this data, and the real world ability for the organization, people and systems to digest and put into action those analytical outputs.
What about people?
One of the biggest questions in the technical world is whether or not automated analytics will replace data scientists altogether. However, the human factor will remain important.
The technology recognizes if data coming into the systems is in the wrong format or missing—and that’s where humans come in to play. Now instead of manually entering and cleaning large quantities of data, data scientists can manage data on an exception basis where they receive an alert only wrong or incomplete data comes into the system. This saves time and money, while elevating the role of analysts to one of action instead of one buried by data.
Where to start?
Start with the end in mind. Evaluate the end goal and target audience and go backward. Questions that should be answered first include:
- What are the actions needed to make that impact?
- Who needs to take what action and when?
- What are the parameters which say take this action versus another action?
- What statistical methodology and algorithms are needed to arrive at the right actions or alerts and what data is needed to feed those analytics (which one creates insights, which creates action and which impacts the business)?
In order to gather actionable insights quickly, you need automated analytics. Limited access to data slows down processes, negatively affecting businesses and their consumers. Implementing automated analytics allows businesses access to real time data, providing a competitive advantage in the marketplace.