Theoretically, data preparation is the process of exploring, combining, cleaning, and transforming raw data into curated datasets for data integration, data science, and data analytics. Practically, that’s easier said than done.
Data Do’s: Putting Your Data to Work
Businesses everywhere have learned that data is the key to success. The global Big Data Analytics market is expected to reach $105.08 billion by 2027, and companies are hungry to gather reliable, high-quality data that reveals a complete and updated picture.
But companies must overcome particular challenges on their way to data-driven success. Here’s what you need to know about data preparation challenges and how great technology can solve them.
Data Don'ts: Failing to Reach Data Goals
Data-related activities are many and involve tracking and accessing data, blending and cleansing it, transforming raw data into high-quality, governed data lakes and warehouses, and more. These tasks are necessary to make the data available, cohesive, and usable. Without this elaborate process, AI-based use cases are simply impossible to execute.
A long line of studies reveals an alarming truth and teaches us that many companies struggle to turn data into business insights. They invest tremendous resources in transferring and loading huge amounts of data into data lakes and warehouses, but are disappointed with the outcome.
According to Gartner, 85% of big data projects fail. Further research estimates that data scientists spend most of their time collecting, cleaning, and organizing data, which they consider the least enjoyable part of their work. The result is a massive waste of resources that damages company success and employee happiness. Enterprises know they need a solution, but are often left asking themselves, “What is data preparation?” and wondering how it can be achieved.
The Challenges of Data Preparation
Enterprises often find it difficult to launch data projects because specific obstacles are standing in their way. The answer to the question, “What is data preparation, really?” is never complete without discussing today’s data challenges.
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The exponential growth of enterprise data: The data inflation we’re experiencing doesn’t allow companies to put this resource to good use. In fact, 55% of the data collected by companies remains unused or unknown, earning it the nickname “dark data”. Too much data makes it hard to sort and manage, in order to form high-quality insights the organization can use. Any data preparation solution we choose must be able to handle the massive data qualities born every second across the organization.
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Fragmented data: Data is gathered from multiple legacy databases, data lakes and data warehouses, which creates a severe fragmentation problem for companies that struggle to connect the dots, and support clear use cases. In the healthcare industry, for example, data fragmentation is considered “the biggest barrier to determining the total cost of care.”
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Self-service tools with no automation: As if a considerable amount of data broken into countless storage areas wasn’t problematic enough, enterprises also face the problem of ad hoc solutions that lack automation. The self-service data preparation tools that are available on today’s market continue to force data scientists to invest too much effort in organizing and cleaning the data. It’s becoming evidently clear why the highest-paid data pros spend so much of their time trying to sort through the data, instead of harnessing their coveted skills towards building analytical models that generate business insights.
Today’s data preparation tools automate traditional ad hoc, manual processes.
What is Data Preparation Technology and Why Do You Need It?
Today’s organizations need a one-stop-shop for collecting, transforming, and ingesting data to data lakes and data warehouses, with solid automation capabilities. They need a single solution that can optimize, automate, and operationalize the data preparation process.
A unique new approach to operationalizing data preparation answers the above challenges and needs. It enable companies to prepare their data, and pipeline it, using predetermined digital entities that serve specific business purposes.
Building a data pipeline in a Data Product Platform, offers a comprehensive set of capabilities covering every stage of the data pipeline process, including data integration, transformation, cleansing, enrichment, masking, tokenization, and more. The result is a clear procedure that is connected and current. Patented Micro-Database™ technology enables data engineers to build ready-to-use automation flows that data scientists can quickly invoke, to start harnessing data right away.
