Data Preparation: Master the First Step to Analytics

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The world is on a path of rapid digitalization, which means our machines, people, and departments are generating more data than ever. But how do we make sense of so much data to uncover meaningful trends and utilize it effectively to optimize our businesses? This is where Analytics comes in.

Did you know that the average business today uses over 300 applications to run the enterprise? This explosive rate of application adoption requires enterprises to equip themselves with insights extracted from quality data to keep themselves ahead of the competition. For which, data preparation proves to be an effective means to convert raw data into usable inputs for enabling analytics. But gathering, cleaning, transforming, and preparing the data for analytics is no easy task.

One of the major concerns with data preparation traditionally is that it is a very time-consuming process. However, savvy, new-age data preparation tools and solutions help overcome this concern and more by carving out a niche for themselves, in alignment with the market demands.

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

What is data preparation?

Data preparation is a pre-processing step where data from multiple sources are gathered, cleaned, and consolidated to help yield high-quality data, making it ready to be used for business analysis. It typically involves:

  • Discovering data
  • Reformatting data
  • Combining data sets into logical groups
  • Storing data
  • Transforming data

Why is data preparation important?

As a first step in the analytics value chain, data preparation sets the foundation for the data used for analytics. And as they say, if the foundation fumbles, so will the entire structure built on it. Since data preparation takes in data in raw format from various sources and churns out superior quality data, it keeps the risk of inaccurate analytics at a minimum. By structuring, formatting, cleaning, and enhancing data, it helps organizations make better informed business-critical decisions, resulting in higher revenue and satisfied customers. It also:

  • Provides data in a more accessible manner to users
  • Gives users more control over the information that is relevant to them.
  • Ensures data accuracy and quality
  • Enhances the processing speed, accelerating the speed to analytics
  • Streamlines processes and communication across departments
  • Creates a more data-driven culture overall

Common challenges with data preparation

It is no news that the amount of data in the world is increasing exponentially, and so is its complexity. 80% of the entire analysis process is consumed by cleaning and preparing the data, as stated by Forbes, making it a lengthy process that requires a lot of investment in terms of time, cost, and resources. This, therefore, ties down an organization’s resources with preparing data, which distracts them from other critical challenges at hand, even before the value of the data yielded is leveraged.

The increase in the volume and complexity of data in recent years has only made data preparation tenser and often requires assistance from technical experts. With the process being too technical, it requires resources with specific knowledge, which means additional costs to the company. Moreover, data analysts typically steer clear of the data preparation process because they lack the required visibility and accessibility into the raw data, which has the power to translate their analytics and, subsequently, their requirements.

Also, manual handling of the process is a big reason for efficiency taking a hit. Many companies and professionals still rely on and prefer to use manual means to clean data, resulting in delayed data initiatives, besides discouraging new useful insights to surface. Additionally, there is also a concern of incorrectly prepared data, which can have a considerable impact on the organization. That is why it is crucial that we understand the nuances of different data types to ensure it is conformant, up-to-date, and consistent.

Expectations vs. reality

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

The current trends in data management point towards:

  • AI-enabled processes with the power to influence an entire data value chain and increase performance enhancement by automating redundant, repetitive, and complex tasks.
  • Semantic data catalog, which helps to easily prepare and curate data to track, access, and translate data effectively through visual representation by making sense of data from various sources.
  • Data fabric, which presents a single setup for data preparation, management, and integration, removing the need for additional tooling.

On the flip side, although organizations are adopting efficient data-driven product development to reverse the above-discussed challenges and accelerate the process, concerns persist. Even with data preparation, modern self-preparation tools and innovative solutions enable users to extract insights quickly but still leaves scope for inaccuracies. Several steps involved in data preparation, dysfunctional collaborations/integration with other software, and managing high volumes of data continue to prevent organizations from reaching their full potential leveraging analytics.

So how do we ensure businesses unlock the power of data analytics to the best of their ability? Read below to know!

How to simplify data preparation

Simplifying the data preparation process can unravel tremendous potential for the rest of the analytics lifecycle. As a result, you do not have to spend an enormous amount of time and money, or deal with fundamental complexities of the process.

Here are a few aspects to consider before carrying out data preparation in your organization, to make the process smooth and easy:

Gathering the right data

Quality data gathered is usually neglected in the data preparation process. Getting straight to cleaning and transforming data without putting in the effort to gather the right or high-quality data can create extra work and yield inaccurate insights in the end. Sourcing good data can put you on the right path to realizing effective analytics.

Selecting the right tools

With numerous options available in the market, the process of choosing the right tool for data preparation can be taxing. The right data preparation tool can help you eliminate manual processes and make better use of your resources. Tools that help you process large volumes of data and make them easily accessible without a developer’s support and give you more time to focus on core business operations are the need of the day.

Monitoring data quality

It is not feasible to clean every small bit of data properly, especially when you are dealing with large volumes. This is where actively checking the quality and fixing any errors present is beneficial. Assessing each record carefully by creating data validation rules and data sets with the right attributes can help maintain quality and prove to be time-efficient, eliminating the need to create them each time.

Delivering value

An effective data strategy is a great way to ensure an optimized analytics lifecycle overall. Handling data preparation the right way will enable quality decision-making. Streamlining your data preparation process empowers all business users to prepare data for various functions/operations, making the entire process fast and delivering value.

What’s 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 accelerating analytics in your organization. Good data preparation ensures that inaccuracies and errors that generally occur during the data processing stage are limited, leading to efficient analysis and more accurate data.

Data Modeling Studio, an end-to-end enabled analytics solution by To-Increase, addresses the critical challenges of data preparation and makes the entire process simpler, besides enabling efficient data extraction as well.

Want to know more about our Data Modeling Studio? Download the factsheet to further understand its role in helping you simplify the data preparation process.

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