Data Preparation: Master the First Step to Analytics

The world is on the path of rapid digitization, which means that our devices, people, and departments are producing more data than ever before. But how do we understand so much data to reveal meaningful trends and use it effectively to improve our business? This is where Analytics comes in.

Did you know that the average business today uses More than 300 apps to run the project? This massive rate of app adoption requires companies to provide themselves with insights extracted from quality data to stay ahead of the competition. Therefore, data preparation proves to be an effective way to convert raw data into usable inputs to enable analytics. But collecting, purifying, transforming, and preparing data for analytics is not an easy task.

One of the main concerns with preparing data traditionally is that it is a very time consuming process. However, smart and modern data preparation tools and solutions help to overcome this concern and more by creating a niche for themselves, in line with market demands.

In this blog, we will delve into why data preparation is critical to achieving business intelligence, what are the common challenges to data preparation, and how we can prepare ourselves to improve our organization’s speed with analytics.

What is data preparation?

Data preparation is a preprocessing step where data from multiple sources is collected, cleaned, and consolidated to help obtain high-quality data, making it ready for use in business analysis. It usually includes:

  • data discovery
  • Data reformat
  • Combine data sets into logical sets
  • Data storage
  • data conversion

Why is data preparation important?

As a first step in the analytics value chain, data preparation lays the foundation for the data used in analytics. And, as they say, if the foundation stumbles, then the whole structure will be built on it. Since data preparation takes data in a raw format from various sources and outputs high-quality data, it keeps the risks of inaccurate analyzes to a minimum. By structuring, orchestrating, cleaning and optimizing data, it helps organizations make better-informed critical business decisions, resulting in increased revenue and satisfied customers. as well as:

  • Provides data in a way that is easily accessible to users
  • It gives users more control over the information relevant to them.
  • Ensures data accuracy and quality
  • Boosts processing speed, speeds up the speed of analytics
  • Streamlines operations and communications across departments
  • creates more Data based culture in general

Common challenges with data preparation

It is not news that the amount of data in the world is steadily increasing, and so is its complexity. 80% of the entire analysis process is consumed by data cleaning and preparation, as mentioned in Forbes, making it a lengthy process that requires a lot of investment in terms of time, cost and resources. This, in turn, connects enterprise resources to data preparation, distracting them from other critical challenges at hand, even before the value of the resulting data can be harnessed.

The increase in the volume and complexity of data in recent years has increased the stress of preparing data and often requires the assistance of technical experts. Since the process is very technical, it requires resources with specific knowledge, which means additional costs for the company. Moreover, data analysts usually shy away from the data preparation process because they lack the required visibility and access to the raw data, which has the ability to translate their analyzes, and thus their requirements.

Also, manual processing of the process is a big reason for efficiency. Many companies and professionals still rely on and prefer manual means to clean data, which delays data initiatives, as well as discourages the emergence of new useful insights. In addition, there is also the concern of incorrectly prepared data, which can have a significant impact on the organization. This is why it is so important to understand the nuances of different data types to ensure that they are compatible, up-to-date, and consistent.

Expectations vs Reality

Companies are constantly evolving and becoming more data-centric to stay ahead of the competition. However, if the numbers are to be believed, only 25% are satisfied with their current state in terms of data management, according to CompTIA.

Current trends in data management refer to:

  • AI-powered processes with the ability to impact the entire data value chain and further improve performance by automating redundant, repetitive, and complex tasks.
  • Semantic data catalog, which helps prepare and organize data easily to effectively track, access, and translate data through visual representation by understanding data from different sources.
  • Data Fabric, which offers a single setting for data preparation, management, and integration, eliminating the need for additional tools.

On the flip side, although organizations are embracing effective data-driven product development to reverse the above challenges and speed up the process, concerns remain. Even with data preparation, modern self-preparation tools and innovative solutions enable users to extract insights quickly but still leave room for inaccuracies. Many steps involved in preparing data, dysfunctional collaboration/integration with other software, and managing large amounts of data continue to prevent organizations from accessing analytics to their full potential.

So how do we ensure companies unlock the power of data analytics to the best of their ability? Read below to find out!

How to simplify data preparation

Simplifying the data preparation process can unlock huge potential for the rest of the analytics lifecycle. As a result, you don’t have to spend a huge amount of time and money, or deal with the basic intricacies of the process.

Here are some aspects to consider before doing data preparation in your organization, to make the process smooth and easy:

Gather the right data

Collected quality data is usually neglected in the data preparation process. Direct access to data cleaning and transformation without effort to collect correct or high-quality data can create additional work and ultimately provide inaccurate insights. Having good data can put you on the right path to achieving effective analytics.

Choosing the right tools

With so many options available in the market, the process of choosing the right tool for preparing data can be cumbersome. The right data preparation tool can help you eliminate manual processes and make better use of your resources. Tools that help you process large amounts of data, make it easy to access it without developer support, and give you more time to focus on core business processes is a need today.

Data quality control

It’s not worth cleaning every little bit of data properly, especially when you’re dealing with large amounts. This is where quality checking and fixing any bugs comes in handy. Carefully evaluating each record by creating validation rules and data sets with the right attributes can help maintain quality and prove that they are time-efficient, eliminating the need to create them each time.

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An effective data strategy is a great way to ensure the overall lifecycle of improved analytics. Handling the preparation of data in the right way will enable quality decisions to be made. Streamlining the data preparation process enables all business users to prepare data for different functions/processes, making the entire process fast and deliver value.

What is the next step to consider?

Combining the aspects discussed with the right business analytics solution can make all the difference when it comes to simplifying data preparation and speeding up analytics in your organization. Good data preparation ensures that inaccuracies and errors that generally occur during the data processing phase are limited, resulting in efficient analysis and more accurate data.

data modeling studio, an end-to-end analytics solution enabled by To-Add, addresses critical challenges of data preparation and makes the whole process simpler, while also enabling efficient data extraction.

Want to learn more about our data modeling studio? Loading statement of facts To further understand its role in helping you simplify the data preparation process.

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