What is Data Analysis?

Data analysis is the process of manipulating your data for evaluation purposes with the end goal of improved business operations and sharper decision-making.

Data analysis involves obtaining raw data, organizing and cleaning it, storing it in an accessible place, and finally, applying diverse statistical techniques to extract insights and produce visualizations. 

Every company generates data, both from internal and external sources. This data originates in various formats, may come from any department (sales, payroll, marketing, operations, production, etc.), and can be generated by processes, such as transactions with partners, potential leads, and customers.

All this information serves as inputs to the analysis process, which is customized according to each organization’s requirements. 

Traditionally, all this data was stored on-premises, in servers and databases. However, cloud computing has become the default method of data storage for most companies today, as it is more agile, flexible, and cost-effective.

Different cloud-based applications are used for a variety of functions: Popular tools include Marketo for marketing automation, Salesforce for sales contacts, and AWS for large-scale data storage or data lakes such as Hadoop, Amazon S3, and Microsoft Azure.

Your BI platform must be able to communicate effectively with all these sources and storage solutions. 

For proper analysis, the raw data must be accessed in its storage database, cleaned, transformed, and transported into an analytics tool like Sisense. This step involves data pipelines and data integrations between storage tools, as well as a process called ETL (extract, transform, and load).

Engineers extract the data from its sources, transform it into a uniform format, and load it into the new repository.

The combination of a powerful storage solution and an efficient BI and analytics platform allows analysts to transform huge amounts of data from cloud warehouses to interactive dashboards, infuse insights into products, and more. 

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Why do you need data analysis?

Data analysis turns your thousands of data points into meaningful insights that can highlight areas for improvement, reveal the best directions for your organization, and even point to clues on future trends in your industry if you know the right questions to ask.

Successful companies know that they need to equip every worker with the skills, data, and tools they need for better job performance and smarter decision-making. Sisense CEO Amir Orad says on Forbes.com that data analysis is reshaping business operations and enabling new revenue streams all across the global economy.

“Soon, data itself will become a primary product for nearly every business, and data analytics (some use the loose term ‘AI’) will form the core of every company’s business model.

Nearly every product on the market will be forced down one of two paths, becoming either ‘smart’ (i.e., analytics- and data-driven) or obsolete. This may sound extreme, but I truly believe it and have seen it happen firsthand.”

One of the biggest barriers to success in the realm of data analysis is that typically only data experts (data engineers, analysts, scientists, and developers) know how to use data to answer questions and drive business decisions.

As access to data analytics gradually expands to all employees, it’s more crucial than ever that everyone learns how to take advantage of their company’s greatest asset — its data. 

Every company is a data company. And with Sisense, users of all kinds can connect to any data source, predict insights with advanced analytics, create analytic applications, and embed everywhere.

Types of data analysis

There are several main types of data analysis. Some answer questions about the past (for example, “Why did this happen?”), and some look to the future (for example, “What will happen if we do this or that?”).

Each utilizes different techniques and provides a unique solution. Each type has a place in business operations. They can even build on each other to provide increasingly complex and relevant answers and insights. 

Descriptive analytics, the initial step in most companies’ BI processes, analyzes historical data about what has already happened. It’s a fundamental starting point and provides needed context, but has limited applicability.

It’s typically used for tracking past performance and key performance indicators (KPIs). Most KPI dashboards and revenue reports are informed by descriptive analytics. 

Diagnostic analytics dives deeper into data to uncover more valuable insights, looking at the reasons behind certain results. Establishing a data investigation to identify the questions you will be looking for answers to is key to setting up the analysis process.

Your answer may come from a single data source, or you may need to look at multiple datasets to isolate patterns and find a correlation. 

Predictive data analytics allows you to anticipate future events by using your organization’s current and past data. You can try to forecast the details about a forthcoming event or a particular situation.

A common use of predictive analytics is to provide options for the user on clothes they might like to buy or TV shows that might interest them, based on their past choices. 

In prescriptive analytics, a data model is set up to gather information from a variety of both descriptive and predictive sources. This method of combining existing conditions and considering the repercussions emphasizes actionable insights over monitoring data.

It can answer the question, “Which way should I go?” by measuring different consequences of a decision, based on multiple future scenarios.

Data analysis methods and techniques

Now that we have a handle on what data analysis is, we can look at the main techniques that each type of data analysis requires to achieve the best results. 

The usual first step of data analysis, descriptive analytics, employs simpler mathematics and statistical tools (such as arithmetic, averages, and percent changes).

It also includes the introductory stages of data aggregation, or gathering your data and presenting it in a summarized format, and data mining, an automated process that lets you extract the information you want from a massive set of raw data. All these are part of the initial processes of most data analytics software. 

Diagnostic analytics is usually performed using techniques such as data discovery, drill-down, and correlations. In the discovery process, analysts identify which data sources will help them interpret their results.

Drilling down (easily done using Sisense’s BI platform) involves focusing on a certain facet of the data or into a widget to get an in-depth view of a selected value. And finding consistent correlations in your data can help you pinpoint the parameters of your search.

There are a number of additional techniques that you might employ in diagnostic analytics, such as regression analysis, probability theory, filtering, and time-series analysis. 

Predictive analytics also uses data mining to pick out patterns. Other methods it employs are statistical algorithms to build models and machine learning to update models as new data arrives.

Finally, prescriptive analytics gathers the data from a variety of descriptive and predictive sources, and uses statistical methods from computer science and mathematics to answer questions about the repercussions of each decision.

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