Automation and robotics in retail is rapidly changing the retail landscape – so much so that there are clearly winners and losers. I’m not talking about the war between brick and mortar stores and digital marketplaces, but rather I’m talking about the retail digital revolution where the winners are delivering greater than 4.5% comparable store/ channel sales growth compared to their brothers that have not embraced automation and robotics.
For this blog post, I’m going to take a step back and not go into data visualization best practices. Rather, I’m going to explore what you can do with your data before arriving at a final visualization – what I like to call “re-expressing” your data. Accordingly, we are going to look at the topic of transforming your data.
Can you keep a secret? What will it take for me to trust you to keep and protect a secret that I share with you? If you are a friend or family member, I may not need more than you saying “Yes”, but if I don’t know you, I will likely want additional guarantees or proof that I can trust you. This is particularly true if you are an organization handling personal information about me.
Intel Optane DC persistent memory (Optane DCPMM) has higher bandwidth and lower latency than SSD and HDD storage drives. These characteristics of Optane DCPMM provide a significant performance boost to big data storage platforms that can utilize it for caching. One of such platforms is Apache Kudu that can utilize DCPMM for its internal block cache.
Consultancy firms and system integrators are starting to productize analytics. They’re creating turnkey solutions for customers and adding value to them by offering managed services. If you’re thinking of creating an analytics solution for your customers, there are three things you need to think about when choosing a BI vendor to partner with.
One of my favorite Talend customer success stories is the International Consortium of Investigative Journalists (ICIJ). I love this story not only because they transformed investigation journalism with data, won the Pulitzer prize for the Panama papers, and helped the public to recover billions of dollars lost to illegal tax evasion.
The retail landscape is in the midst of a dramatic, data-driven renaissance. New tools help to build new connections — between consumers and retailers, and across supply chains. Data analytics and machine learning further these connections to better understand and predict customer behavior and improve demand forecasting. In this emerging era of smart retail, organizations have access to a range of powerful new capabilities and tools.
Most of the day to day work for knowledge workers is spent helping the business make better decisions, like choosing whether it’s worth expending the effort (or actual money) to achieve the desired business goal. The example I often use when talking about ML is churn prediction (and I’m starting to think I’m overusing it now). It costs money to retain a customer who is thinking of moving, but this is less than the cost of getting new customers.
In February 2017, I was out on a routine training ride when I made a turn on loose gravel. Next thing I knew, I was laying on the pavement. Just like that, I broke my hip and collar bone, and I knew I woudn’t be able to race in the Ironman Lake Placid that year. So, I adjusted from racing to recovery. And, I’m happy to report, I returned to racing in 2019, completing the Ironman Lake Placid that year.
In today’s business world, marketing segmentation is a must have for every organisation. It helps you process and aim different targets in a market into multiple customer or prospect segments to enhance your marketing actions. Through this discipline, you can hold a crucial competitive advantage over your competitors because you can adapt your offer and your communication according to the identified groups of personas you want to address.
