DATA ANALYTICS - TYPES OF DATA ANALYSIS - Series -2
Data analysis involves
different methods that allow organizations to draw conclusions from data,
making decisions on a well-informed basis.
Each
type of data analysis has different purposes, enabling organizations to find their
way through the intricacies of data and gain useful insights to inform their
strategies and operations.
Through the use of the right form of
analysis for certain challenges, organizations can better enable themselves to
discover actionable insights, predict trends and maximize results.
The subsequent sections
discuss the different types of data analysis, their specific purposes and uses.
Through knowledge of these methods, businesses can develop stronger analytical
skills and innovation in all manner of fields.
1) DESCRIPTIVE ANALYSIS:
Descriptive
analysis is the primary form of data analysis, used to summarize events in the
past and address the question of "what happened."
Descriptive analysis collects and
structures data so it can be easily understood. Descriptive analysis usually
applies statistical quantities such as mean, median and mode to define data
sets and look for patterns.
Applications:
Ø Assessing
sales trends to determine peak performance times.
Ø Customer
demographics to customize marketing campaigns.
Ø Examination
of employee performance indicators in organizations.
Key Techniques:
• Data visualization: Bar plots, pie
charts and line graphs.
• Central tendency measures: Mean, median
and mode.
• Distribution analysis: Frequency
distributions and histograms.
Advantages:
§ Quick
to implement and understand.
§ Offers
a snapshot to stakeholders.
§ Highlights
historical trends for reference.
Limitations:
§ Focuses
only on past data and does not explain reasons behind trends.
§ Tomorrow's
values may not lead to actionable future decisions.
Descriptive analysis is the foundation for
additional analytical methods, a precursor to more advanced data exploration.
2) DIAGNOSTIC ANALYSIS:
Building
upon descriptive analysis, diagnostic analysis seeks to uncover the reasons
behind certain events by answering the "why" of observed patterns. It
involves more in-depth investigations, often analysing related data sources and
historical data to explain anomalies or trends.
Applications:
Ø Pinpointing
the root causes of a sudden decline in product sales.
Ø Determining
causes of high staff turnover rates.
Ø Analysing
healthcare outcomes to enhance patient care quality.
Key Techniques:
• Root cause analysis
• Drill-down techniques
• Comparative analysis with benchmarks
Benefits:
§ Helps
to identify problem areas.
§ Enables
targeted action plans.
§ Simplifies
comprehension of events in history.
Limitations:
§ Takes
longer and is more skill-intensive than descriptive analysis.
§ Can
be resource-intensive depending on the complexity of the data.
By providing greater insight, diagnostic
analysis enables organizations to treat problems at their roots, enhancing
decision-making and operational efficiency.
3) PREDICTIVE ANALYSIS:
Predictive
analysis uses historical information and statistical models to predict the
future. Through finding patterns and trends, it enables organizations to
predict outcomes and act ahead.
Methods in predictive analytics tend to
use machine learning algorithms and time series analysis, thus it is a tool for
proactive decision-making.
Applications:
Ø Forecasting
demand for products during seasonal sales.
Ø Customer
churn prediction to use retention measures.
Ø Anticipating
equipment failures to schedule preventive maintenance.
Key Techniques:
• Regression analysis
• Decision trees
• Machine learning algorithms
Benefits:
§ Enables
proactive planning.
§ Reduces
risks by predicting possible challenges.
§ Enhances
operational efficiency by anticipating resource needs.
Limitations:
§ Dependent
on quality and availability of historical data.
§ Predictions
are probabilistic and not guaranteed.
Predictive analysis empowers businesses to
stay ahead of trends, making informed decisions that align with future
possibilities.
4) PRESCRIPTIVE ANALYSIS:
As
a development of the previous forms of analysis, prescriptive analysis not only
predicts future situations but also advises on what to do in response to these
predictions.
It
marries insights from descriptive, diagnostic and predictive analytics to make
actionable recommendations.
Applications:
Ø Streamlining
supply chain logistics for cost-cutting and greater efficiency.
Ø Creating
dynamic price models for online stores.
Ø Real-time
traffic management systems in intelligent cities.
Key Techniques:
• Optimization models
• Scenario simulations
• Advanced decision algorithms
Advantages:
§ Delivers
actionable strategies.
§ Enhances
operational efficiency.
§ Supports
long-term strategic planning.
Limitations:
§ Highly
complex and resource-intensive.
§ Needs
to merge several data sources.
Prescriptive analysis promotes innovation
by converting analytical findings into implementable strategies, facilitating
dynamic problem-solving and decision-making.
5) EXPLORATORY ANALYSIS:
Exploratory
analysis is also used to examine data sets without hypotheses, enabling the
analyst to identify patterns, correlations and anomalies.
This kind of analysis frequently ends up
with new hypotheses and questions to answer, forming a basis for more
formalized investigation.
Applications:
Ø Identifying
new customer groups in marketing data.
Ø Finding
unanticipated relationships between variables in research.
Ø Exploring
large data sets to detect anomalies or outliers.
Key Techniques:
• Clustering
• Heatmaps
• Multivariate analysis
Advantages:
§ Facilitates
innovation and discovery.
§ Highlights
new areas for research.
§ Encourages
data-driven creativity.
Limitations:
§ Can
provide ambiguous or inconclusive findings.
§ Lacks
clear objectives compared to other analysis types.
Exploratory analysis stimulates innovation
and inquiring minds, setting the stage for pioneering understandings and new
applications.
6) INFERENTIAL ANALYSIS:
Inferential
analysis utilizes a random sample of data to draw conclusions about a larger
population. It uses statistical methods to make conclusions and test
hypotheses, enabling organizations to know more about wider trends from small
data sets.
Applications:
Ø Market
research surveys to determine customer satisfaction.
Ø Using
representative polling to forecast election results.
Ø Assessing
the performance of new products through pilot studies.
Key Techniques:
• Hypothesis testing
• Confidence intervals
• Sampling techniques
Advantages:
§ Cost-effective
for large-scale inferences.
§ Offers
information on population-level trends.
§ Facilitates
evidence-based decision-making.
Limitations:
§ Results
depend on the representativeness of the sample.
§ Chances
of sampling biases or errors.
Inferential analysis fills the gap between
information and actionable conclusions, allowing strategic decisions to be made
based on limited but representative data.
COMBINING DESCRIPTIVE AND DIAGNOSTIC
ANALYTICS FOR ENHANCED INSIGHTS:
Among
the major advantages of diagnostic and descriptive analytics is that they
complement each other in helping companies view more than simply a historical
description of things that have already occurred.
Whereas descriptive analysis tells them what
has transpired, diagnostic analysis gets beneath the surface and seeks to
discover the reasons why events have occurred.
Through the synergism between these two
techniques, companies can realize not only trends but also the causative
factors underlying performance changes.
For
example, take a retail firm examining the performance of their seasonal sales.
Descriptive analysis may show that sales were at their highest at a particular
point in time, but diagnostic analysis will be able to pinpoint underlying
drivers like successful promotional campaigns, shifts in customer behaviour, or
external economic trends that led to the spike.
By combining these, companies can learn
from history and leverage those lessons on future strategies.
HARNESSING PREDICTIVE ANALYSIS FOR
PREEMPTIVE DECISION-MAKING:
Based
on the conclusions drawn from descriptive and diagnostic analysis, predictive
analysis offers firms the capacity to foresee future directions and make
pre-emptive choices.
Using past facts and advanced modelling
methods like regression analysis and machine learning algorithms, firms can
predict numerous scenarios and make preparations for likely developments.
For
instance, a business that monitors customer tendencies can forecast what
products will be in demand during the coming months, allowing them to stock and
allocate resources in advance.
In
the same way, predictive analytics can assist companies in spotting possible
risks, such as changes in demand or the probability of supply chain
interruptions, allowing them to prepare and avoid possible problems ahead of
time.
IMPROVING OPERATIONAL EFFECTIVENESS
WITH PRESCRIPTIVE ANALYTICS:
Prescriptive
analytics takes the insights derived from descriptive, diagnostic and
predictive analytics to the next level by recommending specific actions based
on those insights.
It not only anticipates what might happen
but also guides businesses on how to respond effectively. This makes
prescriptive analysis an invaluable tool for optimizing operational efficiency
and decision-making.
A
traditional application is in supply chain management, where prescriptive
analytics can assist businesses in deciding the most cost-effective routes for
delivery, forecasting demand variations and recommending cost-saving
initiatives.
With the integration of optimization
models and decision algorithms, organizations can make decisions automatically
that would otherwise be made manually, saving costs and enhancing overall
efficiency.
EMPOWERING INNOVATION THROUGH
EXPLORATORY ANALYSIS:
Exploratory
analysis is an important factor in driving innovation within a company. Through
the examination of data without hypotheses or expectations, companies are able
to discover concealed patterns and reveal new opportunities that might not have
been possible with conventional methods of analysis.
This non-deterministic approach sparks
curiosity and imagination, resulting in new insights and breakthrough concepts.
For
instance, an online shopping platform could employ exploratory analysis to
uncover emerging customer segments or observe unacknowledged product category
correlations that have not been noticed before.
These insights could inform product
development, marketing initiatives and customer outreach programs. Exploratory
analysis also ensures organizations remain competitive by continually looking
for new areas to improve and grow.
LEVERAGING INFERENTIAL ANALYSIS FOR
COMPREHENSIVE DECISION-MAKING:
Although
inferential analysis provides a more thorough insight into population-level
tendencies from smaller populations, it is especially useful when a decision
needs to be made when it is impossible to collect information for the whole
population.
This analysis allows companies to make
informed decisions from representative data even where only a proportion is
available.
For
example, a firm carrying out a market survey can utilize inferential analysis
to determine customer tastes in various areas so that they can adjust their
marketing strategy appropriately.
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