Explore Key Data Methods to Empower Informed Decisions

Madhu Puranik
Published 08/27/2023
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Applications of analytics for decision makingIn today’s world of ever-evolving trends, relying on the “go-with-your-gut” approach is a colossal mistake.

Intuition-led decisions can be biased and thus can restrain decision-makers from making the right choices.

On the other hand, data-driven insights offer real-time information that promotes informed decision-making.

So, as a high-level decision-maker, you must prioritize a data-driven approach.

However, it’s easier said than done.

In my experience, leveraging data analytics is pivotal to achieving this goal. It can empower your teams, including IT, data research, security operations, and more, to gain valuable business insights.

For instance, a data analytics tool can allow different teams to collaborate, share, and analyze relevant data to make strategic decisions. This enhances customer experiences and drives overall business growth.

In this post, I will share the top four types of data analytics that can foster accurate decision-making and bring positive business outcomes for your firm.


What is Data Analytics?

It is an advanced method that helps analyze large volumes of raw or unstructured data to draw meaningful insights for accurate decision-making.

It encompasses an array of activities like data cleansing, modeling, visualization, and forecasting.

As a decision-maker, you can leverage data analytics to unearth industry trends, customer patterns, correlations, and more.

This can help you make accurate decisions, thus helping your organization thrive.


4 Key Types of Data Analytics for Businesses

Here are the four key types of data analytics you can leverage for your business.


#1: Descriptive Data Analytics

This data analytics method allows decision-makers to analyze data on past events to understand their current business performance.

So, implementing this data analytics can provide you with insights into your past business strategies.

However, my suggestion to fellow decision-makers is to avoid relying on this analytics method.

While descriptive analytics provides an understanding of your past performance, it is only the beginning of the data-driven approach.

To truly harness the power of data, you must embrace more advanced methods, which I have shared in the upcoming sections.

Before we dive into advanced methods, let me share a quick example.

This will help you understand my opinion.

Say, a chief information officer (CIO) wants to evaluate the past performance of their IT team.

Here are a few crucial aspects that they can find with descriptive analytics:

  • What was the downtime and uptime of our firm’s servers during a specific period?
  • What was the IT team’s response time to resolve critical IT-related incidents?
  • What was the average time taken to deploy software updates or system upgrades?

The answers to these questions can help the CIO get an overview of their IT infrastructure.

For in-depth analysis, one must leverage advanced data analytics methods.


#2: Diagnostic Data Analytics

This data analytics method leverages historical or past business data to identify and address the gaps, thus easing the hassles of authorities.

Unlike descriptive analytics, diagnostic data analytics focuses on “why” rather than “what.”

Simply put, you can figure out why you attained specific business outcomes rather than what happened in the past.

Take, for instance, the success story of Alibaba Group, the world’s largest B2B eCommerce platform.

The company collects millions of data points from its users across the globe. The information includes the customer’s location, demographic data, preferences, etc.

The top management of this company uses data to identify customer attributes and behavior.

This allows them to figure out the reasons for their past success and redefine the eCommerce shopping experience for online merchants.


#3: Predictive Data Analytics

This data analytics method allows key stakeholders to leverage data and forecast future business outcomes.

So, you can identify – “What will happen?” and forecast your success rate.

See how it helps leaders in various teams address use cases, such as –

  • IT Security: Identifying vulnerabilities, predicting emerging cyber threats, and implementing security measures to safeguard data and security systems.
  • Cloud Computing Team: Predicting demand, optimizing resource allocation, and boosting scalability in cloud infrastructure.
  • Computer Vision Team: Forecasting image processing and object detection outcomes.
  • Data Science Team: Forecasting trends and extracting valuable insights from massive datasets.

I strongly advocate leveraging prescriptive analytics.

This can help you stay ahead of emerging trends, make informed choices, mitigate potential risks, and capitalize on opportunities.

Consider the example of Philips Innovation Campus (PIC), the software R&D arm of the esteemed Philips Group.

The B2B company uses AI-powered predictive data analytics. The system collects user data from various sources to help key stakeholders make informed decisions and build the best healthcare solutions.

Sage is yet another example of a firm leveraging predictive analytics.

This cloud accounting and financial management solution provider uses this method to identify business opportunities and forecast trends.

This approach has helped them stay abreast of the competitive landscape.


#4: Prescriptive Data Analytics

This data analytics can help top management and C-suite to analyze upcoming trends and take actionable steps to reach key business goals.

By leveraging this method, you can understand “What can you achieve and how?”

Most companies, including SurveyMonkey, Amazon Web Services (AWS), Oracle, IBM, and Tableau, leverage this advanced analytics method to create business strategies.

Being a SaaS entrepreneur, I have witnessed the transformative power of prescriptive analytics, just like these firms.

Here’s how my IT team lead leveraged prescriptive data analytics to improve the functionalities of our SaaS-based revenue management platform.

They analyzed customer usage patterns to identify areas for improvement within our platform.

Here are a few prescriptive analytics recommendations we implemented.

  • Customizable dashboard with customer insights
  • Automated task management
  • Alerts to identify anomalies

This helped our team enhance the client experience, thus driving higher conversion rates and customer satisfaction.


Summing Up

Data is the most crucial asset for companies, but it’s pivotal to use it correctly.

Its real value lies in how you harness it.

The four data analytics methods I shared in this post can help you achieve this goal. Implementing them can help you unearth meaningful insights and empower you to make informed decisions, thus taking your business to the next level.

Data analytics has immensely contributed to my team’s enhanced performance, and it can help you too!

So, embrace data analytics and maximize your business profitability.


About Author

Madhu Puranik is the Founder and CEO of Revlitix, a revenue analytics platform that leverages AI to automate analytics for revenue teams. He loves sharing his thoughts about startups and all the matters relating to SaaS tools.


Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.