The purpose of diagnostic analytics is to give a business more actionable information than descriptive analytics alone. It enables you to find out why something is working (or not working), allowing you to correct any wrong assumptions you may have had. The benefits of diagnostic analytics include:
Greater insights: It allows for deeper insights into your data when used in conjunction with other types of data analytics.
Forming and testing hypotheses: Having evidence of what has previously happened helps businesses to form and test new hypotheses more easily.
Identifying anomalies: Diagnostic analytics helps you determine whether data outliers were one-off anomalies or useful, significant findings.
Avoiding future mistakes: It helps you identify when and where something didn’t perform well, enabling you to improve efficiency, reduce waste, and avoid costly mistakes.
Ease of understanding: Diagnostic findings are generally simple to understand, and once they have been turned into data visualizations, they can be easily shared with stakeholders.
Limitations of diagnostic analytics
One limitation of diagnostic analytics is that it is easy to mistake correlation for causation. For example, there is a correlation between ice cream sales and bee stings, but that doesn’t mean that one caused the other. They are in fact both dependent on a third factor (warm temperatures). So any correlations in your data must always be fully investigated before assuming a causal link.
Diagnostic analytics can’t predict the future, or make suggestions about what should be done — it can only explain why something happened, and any further information can only be gained either from a knowledgeable person making educated guesses or from predictive or prescriptive analytics. Nor does it answer the question “What should we do?” — this is answered by the field of prescriptive analytics.
Diagnostic analytics doesn’t give definitive answers. It can’t tell you that A definitely caused B, only that a certain percentage of people who encountered event A did (or did not) encounter event B. The accuracy of outcomes can be improved, however, with better-quality data, larger data sets, and the involvement of domain experts in interpreting the data.
How to use diagnostic analytics in your business
The first step in diagnostic analytics is deciding on the questions you want answers to. These may include questions like:
"What causes customers to cancel their subscriptions to our online product?"
"Why has web traffic decreased by so much this month?"
"Why are so many of our employees quitting their jobs this year?"
"Why do sales always increase in November?"
You should ensure that you have access to a reasonably large data set containing good-quality data that’s relevant to your question. This will help you to draw useful inferences and avoid making decisions based on outliers or the opinions of a vocal minority. Some examples of the kinds of data sets that are large enough to be set.
3. INTRODUCTION
PRESENTATION
TITLE
3
Diagnostic analytics is a form of
advanced analytics that
examines data or content to
answer the question, “Why did
it happen?” It is characterized
by techniques such as drill-
down, data discovery, data
mining and correlations.
4. BENEFITS
Data plays an ever-increasing role in every company. Using
diagnostic tools will allow you to get the most out of it by
translating your complex data into visualizations and insights that
everyone can take advantage of. Sisense creates tools that you
can use to uncover answers to your data questions and easily
share insights around the company.
Diagnostic analytics helps you get value out of your data by
asking the right questions and making deep dives for the
answers. And this requires a BI and analytics platform that’s
versatile, agile, and customizable. Then you can get answers that
are specific to your business and your particular challenges and
opportunities.
5. EXAMPLES
PRESENTATION
TITLE
To understand the “why” behind what happened, here
are some steps you can use to perform diagnostic
analytics on your internal data, and it may be
necessary to include outside information as well. First,
set up your data investigation – what questions you will
be answering. This might be an investigation into the
reasoning behind a problem, like a decreased click-
through rate, or a positive change, like a dramatic rise
in sales during a particular period or season.
6. KEY TAKEAWAY
PRESENTATION
TITLE
6
.Diagnostic analytics is a type of analytics that
helps companies answer the question, “Why did
this happen?”
.Diagnostic analytics enables companies to
make more-informed decisions about how to
remediate problems and drive continued
success.
.Diagnostic analytics can use a variety of
techniques, including data drilling, data mining
and correlation analysis.
7. STEPS FOR DIAGNOSTIC
ANALYTICS
DIAGNOSTIC
ANALYTICS
E
7
Descriptive analytics is usually the first step in analyzing data. By examining data often
stored in a data warehouse, then summarizing and highlighting trends in historical trends
on source systems beyond the data warehouse with multi-source analysis, you can try to
answer the question, “What happened?” Financial and other business reports are
examples of descriptive analytics.
Diagnostic analytics then examines the causes of those trends, helping companies
understand why they occurred. For example, if the latest sales report shows a better-than-
average increase in sales, the company can drill down into internal sales data to see
whether specific customers or new products were responsible for the increase. Diagnostic
analytics can also look at external data about weather patterns or competitors’ activities.
8. DIAGNOSTIC
ANALYTICS
8
Predictive analytics looks at how
trends might unfold in the future and
their potential impact.
Prescriptive analytics suggests actions
to respond to those future trends and
improve business outcomes.
9. IN SUMMARY
DIAGNOSTIC
ANALYTICS
9
Diagnostic analytics is one of the ways we
uncover insights from our data and make it work
for us. There are infinite ways to ask questions
of data, so concentrate on which questions are
the most critical for your organization. The goal
of any analytics program should be more
relevant information, which will lead to more
valuable decisions and a more complete
understanding of your business landscape.
