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Why You Can’t Skip the Data Preparation Process in Data Discovery
In the modern world, data is king. It tells us everything we need to know about, well, just about everything. Data finds its usage in all spheres, from governmental processes to business, multinational enterprises, and more. But how does one go about analyzing data? Despite all the technology available, it’s not as simple as inserting some digits into a computer and getting all the answers you need. There are a few stages in between. Below we’ll talk you through data discovery and data preparation as part of it.
What is data discovery?
Before we get into the ins and outs of the data preparation steps, let’s backtrack a little and look at what the overall process of data discovery is. The data discovery process is a vital step in the business problem-solving framework or any data-requiring solution, and data preparation is a step within data discovery.
Data discovery is often connected with business intelligence, meaning it is used to help companies make smarter, data-based decisions. But what happens in the data discovery steps? At this stage of data processing, data discovery tools compile multiple data sources together, creating a singular unified base of data.
From there, the company undertaking the data discovery process develops its initial data model. It then utilizes it to test various hypotheses or discover valuable insights. One data discovery example would be a company using the power of data discovery in big data to uncover vital company insights that would help the project progress and meet market needs. For instance, a new feature to be added to an app, or designing an entirely new piece of software.
How is data discovery generally done?
As we said previously, data discovery is a process undertaken in the broader problem-solving framework. Here’s how it fits:
- Business issue understanding — at this stage, the issue at hand is defined. This allows the data scientists to refine which questions they need to answer from the data they will use. If done right, the data will support the project. If not, then it might be time to start from scratch.
- Data understanding — here, all the required is defined and brought together from various databases to be collected and used for further processing.
- Data preparation — at this stage, data becomes refined and prepared for further analysis.
- Analysis and modeling — using the prepared data, the first round of analysis is undertaken, and models built for data analysis.
- Validation — the trained model is tested using a defined data set to check if the model is valid.
- Visualization and presentation — here, the final results of the analysis are available and ready for data scientists to present them. Visualization tools help make data more understandable and readable to the human eye.
Can any data be used for data discovery, or do I need big data?
Data processing can be used at almost any stage of the company’s growth process, and you don’t need big data to get started—although it does help. In theory, to begin analyzing data, all you need are a few hundred rows of data, which can be collected via customer surveys, company dashboards, Google analytics, and more. What’s important here is the quality of the data, and that is where the data preparation comes into play.
What is data preparation?
Data preparation is one of the most time-consuming phases of a data-based project. According to studies, it covers 70%-90% of all project time. With automation, however, and we’ll talk a little bit more on that later, this can be reduced to around 50%. Automating then leaves more time to polish data models and focus on getting the best analysis from the data.
So, what is data preparation anyway? Data preparation is a process of enriching and cleansing data, making it more useful to give quality analytics. One way to look at it is by thinking of a diamond in the rough. When it comes out of the mine, it’s rough, dirty, but once polished. It becomes a beautiful stone that can be used to make a valuable piece of jewelry. But in this data preparation example, the diamond in the rough is data, and the polishing is the data preparation process. The result is valuable insights.
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Why do you need data preparation?
Data processing and preparation may seem time-consuming, and indeed it is. However, that doesn’t mean it isn’t worthwhile. Quite the opposite. Instead, many companies find that employing the right data processing tools gives them the insights they require. Some of the benefits they boast are:
- Improved decision-making capabilities
- Easier data access on the whole
- Increased analytical efficiency and flexibility
- Time saved for making decisions
- Comprehensive view of relevant data
Although it’s important to note that data science is an evolving profession, and as technology advances, so do the results. As we continue to refine the available data using the latest methods, more information will become available.
What are the steps involved in data preparation?
When it comes to data science, the tools involved are only as powerful as the quality of the data, and that’s what makes data preparation so essential. So what’s involved in data preparation? To understand that, let’s take a look at the data process steps:
1. Collecting the data. While this is closely associated with data understanding, it’s also the first step in data preparation—getting the data you need to do the work.
2. Assessing the data. Each dataset within the data should be discovered. This means knowing its purpose and context before you go any further.
3. Cleansing and validating the data. Now the hard work begins. In this time-consuming process, data is cleaned, and gaps are uncovered. Using manual and automated tools, such as machine learning (ML), data scientists can remove outliers, fill in data gaps, check if data conforms to a pattern, or review if data-protection issues have occurred.
4. Transforming and enriching data. At this stage, data may be formatted or further defined to ensure a better analytical outcome. Enriching may also occur, which means adding data or connecting the dots to unveil hidden insights for analysis.
5. Storing data for future usage. Once the data has been prepared, it must be stored the right way. Taking into account data protection requirements, such as GDPR, and the future usage of this data, it’s essential to store it correctly.
What are the challenges of data preparation?
No technology or process is without its challenges. Here are some of the issues companies find when engaging in data preparation and processing.
Companies remain unsure how to use the data
Data is great, and having lots of it can empower your business with market conquering insights, but only if you know how to use it in the right way. Many businesses struggle to define the exact data they need, what it shows, and how to effectively implement it in business decisions. Getting the right people on board at the beginning can help your business make better use of the data it has to support your business goals and get that competitive edge.
Biased data can slip through the cracks
Although AI technology has come on in leaps and bounds, it is built by humans, and therefore the algorithms it uses may be subject to bias. For example, exclusion bias means holding back info from the data set, leaving it incomplete. This means the data assessment will be flawed. Or consider selection bias. For example, data collected may differ from the target group, making data meaningless. Addressing these challenges means undertaking comprehensive data preparation to ensure data is suitable for use.
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How to do data the right way?
- Collaborate. Data science isn’t a one-team job. By collaborating between departments, for example, IT and business, companies find themselves getting results that are closer to actual business needs, not data for data’s sake.
- Ensure good data governance. Data management is key, and this isn’t just a question of data security. Effectively managing your data means putting into action processes for data storage, data use and clearly defining the responsibilities of the teams that use the data. A little bit of organization goes a long way to getting the best results.
- Get the right tools at your fingertips. The world of data has grown in recent years, far beyond Excel spreadsheets. Instead, it often requires advanced software or tools to help complete increasingly complex tasks. But that shouldn’t mean fear of technology. Getting the right tools on board early boosts the chances of good data and analytic outcomes.
How to get the most from your data for your business?
With data science specialists some of the most elusive IT specialists on the market and vacancies set to grow by an estimated 15%, it may seem that getting the right person on your team is next to impossible. But there is a solution. Data discovery services can be covered by a skilled outsourcing provider. At the same time, you search for your data superstar, or perhaps you’d like to continue to outsource and take advantage of access to a team of knowledgeable data professionals.