Friday, 3 May 2019
Gartner Augmented Analytics
The business world has declared Data as the new Oil, and nearly everyone agrees that data analytics is good for business. If data analysis is done properly, which isn’t easy, then data can drastically increase revenue, streamline processes and identify anomalies.
The problem with data analysis, at least when you are trying to do it properly, is it’s difficult. Data on its own is useless, Data + Business Understanding = Value!
Business Problem Identified – but Why?
For example, the data has revealed that the company profits have decreased 10% in the last quarter. What it hasn’t done is tell me why they have decreased?
What are the key factors that have contributed to the profits decreasing? To understand you’d need to go deeper; How many sales? Was the value of the sales lower but volume of sales higher? How much expenditure did the company have in the last quarter? It’s possible that you can’t obtain all this information from one system, you may need data from a variety of sources.
Once you have that data, what it may not reveal is business context, such as a new strategic vision, new product launch, a product removed from sale, new website, or a key member of staff leaving?
When you combine data with business context, we can start to find out what connects what, which area directly impacts another. As we start to reveal this insight, it starts to become actionable insights, which can help the company, in this example, identify why profits have decreased, and therefore what action needs to be taken to stop the decline in profits.
To be able to do all this it sounds difficult, complex and honestly expensive. You may need to hire individuals to perform these steps for your business, often known as Data Scientists or Data Engineers.
However, there is a new wave emerging in 2019, Augmented Analytics. The ability to utilise machine learning and natural language processing to automate data preparation and enable insight to be delivered to business users. The outcome is to present clear results and provide access to sophisticated tools so business users can make day-to-day decisions with confidence. Users can go beyond opinion and bias to get real insight and act on data quickly and accurately.
Recently I’ve been preparing a presentation on ADW and OAC for the Ireland OUG. For the presentation I used a synthetic dataset to present what could be achieved through ADW and OAC. Within minutes of loading the data into OAC, I could see overall description of the fraud field as well as revealing key attributes which have influencing factors on the fraud column. This insight would prove invaluable as I went on to build supervised machine learning models to predict fraud on future transactions.