Introduction

The world is changing, and we are in a never-ending chase after more efficiency and competitive power. This is the only way to remain up-to-date and maintain sustainable growth. Our most powerful tool to achieve these goals is data, empowered with technology. The field of data analytics allows us to (i) distill information from raw data and (ii) knowledge from this information. Uncovering hidden insights and creating knowledge can empower us to make better decisions to grow our business and discover untapped opportunities.

To be competitive, it is crucial to remain up-to-date with the latest technological advancements.

In the 21st century, we have two important weapons to remain competitive in any market: (i) data and (ii) technology.

The power of data stems from the fact that it allows us to read our surroundings in a more reliable fashion where uncertainty and decision errors are minimized. This allows our decision-making processes to be much clearer with high confidence. This -of course- translates to (i) higher growth, (ii) increased revenue, (iii) higher profitability, (iv) more efficiency, (v) lower internal and external problems, and (vi) better risk management capacity.

Although data can offer a lot of benefits, to uncover the true potential of the data, we need to process and analyze this data with technology. Advancements in data processing, data visualization, and web technologies allow entrepreneurs and communities to develop powerful tools to (i) process, (ii) analyze, and (iii) visualize our data, information, and knowledge.

At the intersection of statistics and business, there sits data analytics.

But what is data analytics?

Whether you’re a sales manager trying to understand how to maximize your team’s sales performance or a business leader looking to understand your company’s overall performance, data analytics is what you need!

“Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain.”

Managers often need to analyze their data to identify substantial opportunities or, oppositely, recognize red flags in their operations. Advanced knowledge of data analytics tools and competency in statistical and data science knowledge can flourish a company’s operations as long as the organization has the domain expertise. Therefore, data combined with available tools can help us make a difference.

But are all data and the applicable analytical approaches created equally? Well, not really. The value of different analytical approaches should be evaluated in a case-by-case fashion, but we can categorize them into four different types:

1 — Descriptive Analytics

2 — Diagnostic Analytics

3 — Predictive Analytics

4 — Prescriptive Analytics

In the next sections, we will explore these 4 Types of Analytics:

Descriptive Analytics

Descriptive Analytics answers the question: “What happened?”

Descriptive Analytics provides single descriptive results. For example, sales numbers, churn rates, growth percentages, and employee headcount are found using descriptive analytics.

It focuses on asking a question and making a qualitative decision to analyze what it can be defined as in an analytical approach. Descriptive Analytics is one of the most commonly used analytics approaches; added with data visualization, it is a good fit for communicating descriptive methods since the charts, graphs, and maps could help you see data trends clearly and comprehensively.

Diagnostic Analytics

Diagnostic Analytics answers the question: “Why did this happen?”

Using diagnostics analytics, you can find answers to questions like (i) why do company sales drop in a given period? or (ii) did we gain subscribers in winter and lose a bit during summer?

Combining or reflecting variables takes the analysis a step further, with coexisting trends or movements in motion, uncovering the correlation between the variables and determining if there is a causal relation between them. Thus, It focuses on comparing and combining variables that cause that particular behavior/result using diagnostic analytics to help understand.

Predictive Analytics

Predictive Analytics answers the question: “What might happen in the future?”

By integrating diagnostic and descriptive analytics outputs with statistical methodologies, predictive analytics can help us predict the future of descriptive metrics such as sales numbers, employee headcount, or growth rates.

Predictive analytics focuses on the predictions or possibilities of the future. It refers to analyzing historical data and predicting future outcomes with different confidence levels. With the combination of diagnostic analytics, the data can help give an informed prediction and help strategize your decisions.

Prescriptive Analytics

Prescriptive Analytics answers the question: “What should we do next?”

Prescriptive analytics is built on top of all the other analytics types; therefore, it is the most advanced and complex version of data analytics. With prescriptive analytics, we not only predict future outcomes but also develop strategies to deal with uncertainty and manage it in the most efficient way so that the business can survive under the worst circumstances and thrive tremendously under average circumstances.

Prescriptive analytics considers all possible factors and suggests actionable recommendations so that the decision-makers can create business strategies.

How to Adopt Data Analytics Quickly

These four types of analytics give businesses a great chance to investigate past performances, analyze their reasons, explore future options, and suggest the best long and short-term actions. On the other hand, taking advantage of what data analytics offers can be cumbersome work without appropriate tools. Luckily, starting from the first automated data processing operations in the mid-19th century, we have come a long way.

However, we were still limited with data analytics capabilities until the widespread adoption of computers. In fact, we may mark the introduction of Microsoft Excel as a turning point for data processing and analytics operations. However, the real rise of data analytics tools started in the early 2000s with a massive influx of tools that can handle data in numerous ways.

Today we have so many tools for all sorts of data operations, such as processing, cleaning, storing, visualizing, and analyzing.

We use Google Looker Studio, Tableau, and Power BI at VIZIO AI for our no-code dashboard projects. We use React and Flask for code-based dashboards. We take advantage of Plotly, Chart.js, D3.js, Apex Charts, and many other data visualization technologies. We take advantage of pandas, NumPy, SQL, and other appropriate technologies for data processing. We use TensorFlow, PyTorch, and scikit-learn for machine learning and predictive analytics work. The list goes on and on.

It is even more important to combine these technologies and develop sustainable and value-generating data analytics products to benefit from data analytics continuously.

Discovering data-oriented opportunities is easier than ever with a wide variety of software and technologies as long as you have access to the right combination of the required talent.

Final Notes

In this article, we covered four types of data analytics used and how they can help your organization uniquely. If your organization has a considerable size, hiring data and BI analyst is a very good investment for the company’s future growth. However, for small organizations, specialized data agencies can still do miracles.

At Vizio.AI, we provide Fractional BI/Data Analytics Team services and our main aim is empower businesses who don’t have data analysis teams and make their data more value added item for their businesses.

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