If the job of the data analyst was to build reports and dashboards, there would be no need for a discussion on reducing time to decision. But, since the job of the analyst is to extract valuable insight from data, let’s have that discussion. There are 100’s business intelligence tools on the market that all claim to simplify dashboard building and speed the time it takes to put something in front of the business. Most of those do things the same way.
Yellowfin BI puts assisted insights in the hands of the data analyst, allowing one single analyst to do the work of many.
A Day in the Life of a Data Analyst
Think about the typical day in the life of a data analyst. They spend time searching for the right data, preparing data, building reports, designing dashboards, and responding to the data and analytic needs of the business. They are constantly working to better profile their data, looking for new insight and finding better ways to share insight with the business. Every week, requests pour in from the business, new data is added to the database, and new business users come on board in the business intelligence system. There is never enough time in the day and their work is never done.
How Machine Learning Algorithms Change the Everyday
It sounds like an oversimplification, but it’s not: Yellowfin has changed the life of the data analyst with the click of a button. Some tools already use machine learning algorithms, but they tend to provide a dump of all possible insight and make the analyst sort through volumes of possibilities. What Yellowfin has done, that no one else has done, is provide a context for their machine learning algorithms. The analyst chooses one or two items on a preliminary report and then, based on that context, can choose to explain the Why behind the data point, or compare a couple of data points.
Once that is done, Yellowfin machine learning algorithms run in the background looking for patterns in the data, finding the right models to run against the data, and returning the best possible insight for the user. The analyst ends up with insight curated on the fly, within the specific context that the question is asked. The results include three components: insights, visualizations, and narrative.
Where do you begin when you get new set of data or augment an existing set? When you examine the preliminary data and dashboard, most discoveries are visual – what you can see with the human eye. Yellowfin machine learning algorithms run in the background to identify options far beyond what is visible. They discover what is meaningful and answer the question, “Why is this meaningful?” For example, with sales data, you may want to choose to compare regions or time periods, especially if one is higher than usual and another is lower than usual. Yellowfin will bring up a set of potential causes for the unusual results in the data with no effort on the part of the data analyst.
Think, also, about all the time you spend trying to figure out which visualization to use for new dashboard components. When Yellowfin brings back a set of new insights, it also uses a visualization recommendation engine to bring up in the best possible visualization for that set of data. For example, if there was a region where sales were up for the period under investigation, it might also bring up data on a map of that region, so you can see where the issues were at a more granular level.
3. The “Why” Narrative
How many times a day do you get asked “why” the data came out the way it did? “Why” must be the most common question asked by business users. With Yellowfin Assisted Insights, the data analyst automatically receives a “why” narrative along with the new insight. With a few simple clicks, the analyst can publish the new insight and visualization straight to a dashboard and make it available to business users. Publishing the most important “why” answers before the questions are asked, eliminates most of the business inquiries altogether.
Publishing insight for the business used to take days, now it takes minutes. You can slice this data any way you would like. Analysts can now do more with less, reduce decision time, and provide more value to the business.
Find out more about how machine learning and artificial intelligence are affecting the landscape of the data analytics industry in this webinar.
This webinar reveals brand new research from Computing Magazine, from February 2018. It highlights the barriers that organizations are facing when it comes to mastering data-driven decision making and looks at how the next generation of BI and analytics tools can use machine learning to automate insight. Discover what the potential of this breakthrough might be.