Augmented analytics might sound too technical of a term but … Ok! We can’t lie, it is technical but it’s also fascinatingly interesting and important for business owners and entrepreneurs that want to be ahead of the competition. What is augmented analytics and why is it considered the next big tech trend? Let’s investigate and simplify.
Analytics have been on the forefront of business objectives and priorities for a few years now. Business decisions are based on evidence and data mined from the interaction of customers with the product/business. Measure and analyze are words that lead board meetings in all industries and business sectors. What do you do once you have the data?
It’s Not About Gathering Data, It’s What You Do With It
Gathering data is just the first, and quite frankly easiest part of the process. Collecting data has now become a standard requirement for all businesses. The difference maker is not getting hold of the data but what you do with it. How much information can you extract, what can you learn and how actionable is the data for your business? Data analysis and data mining is where successful companies set themselves apart from the competition.
Data scientists are not business analysts. The data set needs to make business sense and this is a completely different task.
Here’s an Example
Let’s contextualize this with an example. Imagine you run an analytics report on the monthly sales of your online shop. Sales have gone up by 5%. This is good news for your company and you can clearly show that number to your investors, boost your own confidence and re-invest the money into the company. If you just do that, you’re just scratching the surface of what you can learn about your company and your product. You’re just reading analytics on face value.
The real value of that 5% will come upon investigating the reasons behind the rise. What inferences can you make? What insights can you draw? How can you use the learning going forward in order to repeat the success? Possible questions that could be asked in order to extract such information are:
- What time of day did the spike in sales occur?
- Was it the beginning or the end of the month, correlating it to when people get paid.
- Where is the traffic coming from? Social media? Organic? Paid promotion?
- Were the sales related to a specific product?
- Were the sales related to men or women?
As you can see, the level of detail you can go to is deep and the more you dig, the more you’ll learn. The information you can gather from the data analysis gives you immediate ammunition to use against your competition. The insight you gain is actionable and can be used in the future to replicate success or alternatively avoid mistakes.
The current process for doing that requires manual work from data scientists. The data scientist talent pool is something to be mindful of. Given that this field is relatively new, the supply of talent is a big factor for companies trying to get ahead in the data analytics game. The McKinsey Global Institute had this to say on the topic:
“This trend is likely to continue in the near term. While we estimate that the number of graduates from data science programs could increase by a robust 7 percent per year, our high-case scenario projects even greater (12 percent) annual growth in demand, which would lead to a shortfall of some 250,000 data scientists.”
The shortage of talent means one thing and one thing only: automation. Just like with most cases in life, when you’re short of resources, you need to become creative and find a more efficient way to do things.
Enter augmented analytics.
Augmented analytics automates the extraction of insights from large data sets using machine learning and artificial intelligence algorithms. An augmented analytics engine can scan through a company’s data and automatically come to reasonable assumptions that can help the business grow.
“In the extreme data economy, there will be winners and losers: those who collect data, and those who know how to use it,” said Raskin. “It’s not enough to stockpile data and analyze it on demand anymore. Now, businesses need to capture data, continuously assess it and instantly take action. If you can combine and analyze billions of live and historical data points continuously and automatically, you shape your decisions instantly.”
Linking back to our previous article “Debunking Tech Myths: Technology is Taking People’s Jobs”, getting a machine to do some of the data analytics work does not necessarily mean that jobs will be lost. What it does translate to is job roles that evolve. In the era of augmented analytics, people who deal with Business Intelligence will no longer have to have a technical, mathematics and statistics background but more of a business acumen that will allow them to make informed business decisions.
Whilst the first iteration of augmented analytics uses historical data sets to draw conclusions and identify patterns and trends, the real value comes in the second generation of this technology. Imagine an algorithm that can accurately predict customer behaviour and prescribe the suitable business course of action. That would be something to behold, wouldn’t it?
Watch this space as we will be back with updates on this fascinating piece of tech that has vowed to transform the Business Intelligence and data analysis space.