Retail’s Latest Ventures are Rooted in Analytics
Although manufacturers and health care providers were among the first entities to really embrace big data, others are now starting to leverage business intelligence tools to ensure they maintain a competitive edge in increasingly saturated marketplaces. Virtually every sector has been impacted by major digital trends ranging from online commerce to the Internet of Things (IoT) and enterprise mobility. As retailers need to connect, track and collect data from their assets and customer shopping patterns, business intelligence tools have become must-have rather than nice-to-have resources.
Now that firms have become more proficient in modern analytics management -- including the often troublesome front-end requirements for data preparation -- there is so much more to be gained from the technology. A new report from Gartner argued that retail ought to be the next area of analytics adoption growth.
Analytics for the future
Gartner focused on the importance of having access to real-time and predictive analytics in retail given several major complexities that have arisen of late, including the IoT, mass digitization of competition and higher data volumes. This need extends to virtually every type of retailer, including both wholly digital and brick-and-mortar operations, with a particular emphasis on the need to streamline decision making through the use of these technologies. Gartner’s conclusion: retailers are in need of stronger analytics.
"Big data discovery tools put information in the hands of business analysts and business users to enable better decision making. Since these analysts and users are less skilled than their counterparts in traditional BI roles, they will only handle less complicated problems, which will take them less time to fail or succeed," Robert Hetu, a research director at Gartner, affirmed. "They are also closer to the business complexities, making it easier to establish interactive analytic processes that will speed up results. This will allow big data sources to be explored more often, feeding valuable information into the business and yielding faster results."
Gartner analysts pointed out that the types of products and services many retailers sell now fall into the IoT category, which means that they are more functional and "smarter" than in the past. This will inherently increase customer expectations, and retailers that do not leverage analytics to boost client experiences will almost certainly lose ground by way of loyalty and brand stature.
Starting small
Retail has not yet truly embraced analytics on a large-scale, and many of the entities that have deployed the technology are still in the earlier stages of use.
A good starting point for retailers is market basket analysis, a modelling technique for intelligently stocking store shelves with products people are likely to buy. Market basket analysis is heavily dependent on third-party data, such as consumer demographic or geographic data, which is often made available in multi-structured formats like PDF files.
Retailers are also more heavily report-dependent than other industries since they often lack the talent and capabilities to query their backend systems.
And retailers are increasingly needing to combine things like troublesome log files from POS systems, or loyalty card and credit history data to get a more complete view of customers. In all these examples, it’s critical to be able to blend data from internal and external sources in both structured and multi-structured sources to get actionable insights to improves sales and operations.
By first getting data preparation and initial archival-related strategies right, retailers can better position themselves for long-term success with their analytics investments.
-Jon Pilkington, Chief Product Officer
Datawatch Corp.
Now that firms have become more proficient in modern analytics management -- including the often troublesome front-end requirements for data preparation -- there is so much more to be gained from the technology. A new report from Gartner argued that retail ought to be the next area of analytics adoption growth.
Analytics for the future
Gartner focused on the importance of having access to real-time and predictive analytics in retail given several major complexities that have arisen of late, including the IoT, mass digitization of competition and higher data volumes. This need extends to virtually every type of retailer, including both wholly digital and brick-and-mortar operations, with a particular emphasis on the need to streamline decision making through the use of these technologies. Gartner’s conclusion: retailers are in need of stronger analytics.
"Big data discovery tools put information in the hands of business analysts and business users to enable better decision making. Since these analysts and users are less skilled than their counterparts in traditional BI roles, they will only handle less complicated problems, which will take them less time to fail or succeed," Robert Hetu, a research director at Gartner, affirmed. "They are also closer to the business complexities, making it easier to establish interactive analytic processes that will speed up results. This will allow big data sources to be explored more often, feeding valuable information into the business and yielding faster results."
Gartner analysts pointed out that the types of products and services many retailers sell now fall into the IoT category, which means that they are more functional and "smarter" than in the past. This will inherently increase customer expectations, and retailers that do not leverage analytics to boost client experiences will almost certainly lose ground by way of loyalty and brand stature.
Starting small
Retail has not yet truly embraced analytics on a large-scale, and many of the entities that have deployed the technology are still in the earlier stages of use.
A good starting point for retailers is market basket analysis, a modelling technique for intelligently stocking store shelves with products people are likely to buy. Market basket analysis is heavily dependent on third-party data, such as consumer demographic or geographic data, which is often made available in multi-structured formats like PDF files.
Retailers are also more heavily report-dependent than other industries since they often lack the talent and capabilities to query their backend systems.
And retailers are increasingly needing to combine things like troublesome log files from POS systems, or loyalty card and credit history data to get a more complete view of customers. In all these examples, it’s critical to be able to blend data from internal and external sources in both structured and multi-structured sources to get actionable insights to improves sales and operations.
By first getting data preparation and initial archival-related strategies right, retailers can better position themselves for long-term success with their analytics investments.
-Jon Pilkington, Chief Product Officer
Datawatch Corp.