Time is in short supply for most small business owners. Despite working 80-plus-hour weeks, there’s always more to do. Finding the best use of your time and money is a constant challenge since the opportunity cost of choosing the wrong option is high.
Technology has advanced to the point where you can now increase your time’s ROI effortlessly. One particular trend is the rise of augmented analytics. Once the dominion of large enterprises, augmented analytics are now fully accessible for small businesses.
Here are three ways in which augmented analytics will benefit your small business and help you get more out of your time and money.
Augmented Analytics Boost Revenues
Technology research firm Gartner identified the rise of augmented analytics as a key trend to look out for in 2020. One of the reasons for this is their ability to unlock buyer behavior patterns and reduce sales cycles.
Augmented analytics can take you inside your prospects’ minds and provide your marketing and sales teams with valuable information. Many businesses launch marketing campaigns based on “feel.” They think customers will appreciate a discount and plan sales strategies accordingly.
However, augmented analytics can help you figure out whether there’s any rationality behind this decision. If your customers prefer a new product feature, instead of a pricing discount, you can work accordingly and give them what they want.
Should you sell more of your stock online or through physical stores for greater profits? Your analytics platform can take customer behavior into account and give you a clear answer. You won’t have to rely on “feel” to predict buying patterns anymore.
Augmented Analytics Reduce Costs
The average salary of an entry-level data scientist in the United States is $95,000. It’s fair to say that companies needed significant resources to build their data analysis capabilities. You might think this means installing an analytics platform is too expensive.
However, augmented analytics require far less investment thanks to their ML-driven algorithms. One of the advantages of augmented platforms is that they eliminate so-called “busywork” from the analytics process. So no more tasks that are clerical in nature and don’t drive profits.
Data scientists have to spend a lot of time cleaning and prepping data before being pushed for analysis. Most of the time, cleaning involves ensuring consistent data values. For example, state names might be entered as “OH” or “Ohio,” and values have to be changed to a consistent one.
Augmented data preparation removes this time-consuming task and allows you to push data for analysis faster. It also helps you access insights easily compared to a traditional BI platform thanks to Natural Language querying, or NLQ. Instead of writing long lines of code, you can ask questions in plain English.
For example, your platform will understand input such as “What is the average sales price of <<item name>> during July in all stores within Zip Code 43201?”
Insights are thus more readily accessible to business users, and linking analysis queries to business goals is straightforward. You won’t have to translate business questions into technical requirements. Your ML-powered platform will do it for you.
You can also democratize analytics throughout your organization thanks to NLQ. Your employees can ask questions in plain English, much like they search for information on Google, and receive insights. The more people you have running analytics, the greater is the probability that you’ll receive deep insights.
Augmented Analytics Reveal Courses of Action
While NLQ allows you to ask questions in plain English, Natural Language Generation or NLG allows you to receive answers and insights in easily understood language. Previously, BI platforms would display insights via dashboards that displayed database information in the results to queries.
For example, the average sales trend in a graph would be listed under its database name and not its English name. This made it tough for business and non-technical users to decipher what actions to take. There was also the danger that conclusions would get lost in translation when technical teams would inform business users of conclusions.
NLG eliminates this danger since conclusions are presented in easily understood language. Not only that, action paths are easy to determine since ML algorithms present data interpretation instead of leaving it up to the user to derive results. The algorithm will cluster data automatically and present conclusions to users instead of displaying a graph with all variables present on it.
Of course, if you wish to dig under the hood and play around in a visual dashboard, you can do this as well. However, NLG makes it easy for business users to get quick answers to their questions.
Augmented Is the Future
As ML training techniques improve by leaps and bounds, small businesses can access the power of analytics more than ever before. Thanks to its ability to lower costs and improve efficiency, augmented analytics is a no-brainer for businesses that want to build a sustainable future.