.@philsimon peers into the future of MDM.
A huge proportion of big data is unstructured text (such as client interactions, product reviews, call center logs, emails, blogs and tweets). Organizations starting to invest in advanced analytics often overlook the value text analytics could add to the process. But when data scientists or analysts get to work exploring […]
.@philsimon on what's next for MDM applications.
The post The next wave of MDM: Integrating structured and unstructured data appeared first on The Data Roundtable.
Of course everyone has heard all the hype on big data and how it can help business’ become more successful. But have you thought about the different types of big data? How the different types of data can support different initiatives within your business?
Structured versus unstructured data in retail is a key topic to first understand in order to create a successful plan. Structured data is data that sits in a database, a file, or a spreadsheet. It is generally organized and formatted. In retail, this data can be point-of-sale data, inventory, product hierarchies, ect. Unstructured data does not have a specific format. It can be customer reviews, tweets, pictures, and even hashtags.
So now that you know what structured versus unstructured data in retail is, let’s talk about how to use it. Customer reviews are a great way to understand why a certain product is or isn’t working. Word clouds are a tool to visualize large amounts of customer reviews. Finding key words that are continuously being used can give insight in to product defects. For example, if ‘fits small’ is frequently used then you can be proactive by adding this to the product description or above the size selection. This will reduce customer returns and money lost on shipping fees.
Unstructured data can also be analyzed for sentiment analysis. This gives insight in to whether the customer’s response is positive, negative, or neutral. A great example of this is being able to analyze your customer’s twitter responses. Let’s say you post a tweet with products you are thinking about buying for your spring line and your brands hashtag. This enables retailers to understand your customers’ response before you even buy the product. This technique can also be used in-season and give insight to merchants on areas of opportunity or risk so that open to buy can be managed. Break down the silos between merchandising and marketing and enhance collaboration.
It doesn’t take a data scientist to use unstructured data analytical techniques either. If you’re looking to use unstructured data in your business process, check out more information on SAS Visual Analytics. Also, take a look at the 2015 Forrester Wave report where SAS was named a leader in Big Data Predictive Analytics Solutions.
Analyzing text is like a treasure hunt. It is hard to tell what you will end up with before you start digging and the things you find out can be quite unique, invaluable and in many cases full of surprises. It requires a good blend of instruments like business knowledge, […]
You have to be "in it to win it" as they say. This is becoming the case for many organisations that need to start using data to make better, evidence-based business decisions. Today, using analytics is not so much a data lottery as a data necessity. Some businesses may not […]
~ This article is co-authored by Biljana Belamaric Wilsey and Teresa Jade, both of whom are linguists in SAS' Text Analytics R&D. When I learned to program in Python, I was reminded that you have to tell the computer everything explicitly; it does not understand the human world of nuance […]
The post Text analytics through linguists’ eyes: When is a period not a full stop? appeared first on The Text Frontier.
You might have lots of data on lots of customers, but imagine if you could suddenly add in a huge dollop of new, highly informative data that you weren’t able to access before. You could then use analytics to extract some really important insights about these customers, allowing you to […]
The post Use Hadoop to visualize what wasn’t visible before appeared first on SAS Voices.
We’ve all been there. You’ve knuckled down, cleaned out the garage, the attic, and that cupboard under the stairs, thrown away a ton of stuff, only to need it again the very next week. Until recently, that’s exactly what many businesses did with their data. The data explosion has radically […]
The post Don’t throw away that data – it might be worth something! appeared first on SAS Voices.
Just this morning, the course leader at our newly created SAS Space & Astronomy School told me that they picked up a broadcast signal from outer space. By analysing all the data they have been collecting, they were able to quickly spot a spike in the trend pattern, which helped […]