Big data holds a lot of promise with vast quantities of different types of data made available to mine for deep insights. From creating a central data repository to data discovery and analytics, there is no shortage of possibilities with big data. While these possibilities excite data enthusiasts, we continue to see a hesitation to fully embrace big data by business users.
Challenges Associated with Embracing a Big Data Environment
We have found that this hesitation is partially due to four obstacles:
Let’s dive into each of the obstacles to understand the issues and address the elephant in the room – how does one turn the problem into an opportunity? We need to keep our focus on ways to make incremental improvement which results in a greater percentage of business users embracing their big data environments to realize value in the multi millions of investment in big data.
Data Quality: One of the most prevalent issues with big data is its quality. Early in the evolution of data lakes, big data environments were thought of as dumping grounds for different types of data without any need for quality. The idea was to siphon as much data with the intent to use it in the future. But those days are far gone as businesses are now trying to mine big data for insights and spending 50 – 80% of a user’s time scrubbing data is counterproductive. We all know now that “garbage in, garbage out” also applies to big data. To get the most out of big data, you need to ascertain ways in which the quality of the data can be improved without spending absorb anent amounts of time fixing it. As big data environments continue to receive different types of data from both internal and external 3rd party sources, more frequently and at higher volumes, quality becomes even more important. Traditional data quality tools and other tools built using dated technology are not well equipped to provide data quality in big data environments. It’s equivalent to trying to put a square peg in a round hole which is why you can’t solve today’s problems with yesterday’s technology. What is required is a next generation data quality tool that is built specifically for big data environments. A tool that automates big data quality process by offering turnkey self-service options with simple drag and drop functionality opens up the opportunity to enable more resources, that aren’t coders, to clean up the data in order to improve the use and adoption of big data.
One Stop Shop: The need for multiple tools is another major issue that hampers the adoption of big data. A suite of tools, on different platforms, with different standards and learning curves are required for ingesting, preparing, analyzing and operationalizing insights from both traditional databases and big data sources. This has been the status quo in terms of how traditionally, data is accessed and analyzed. One solution that some pursue is to use complementary tools provided by a single vendor. With this option, integration across those multiple tools is often fraught with unnecessary limitations and doesn’t solve the underlying problem. A better option is a platform tool that runs natively in a big data environment and capitalizes on the power and performance of Spark to shorten the time frame from data ingestion, preparation, analysis, visualization to operationalization in a big data environment. The major benefit of a unified tool is that it provides a simple way to go back and seamlessly make any changes at any point in the process without requiring cumbersome integration steps.
Business Use Accessibility: Often times we hear that the data in big data environments is simply not usable by business users because the data sources are not easily accessible by non-technical users. In addition, the data tends to be in unreadable formats and requires significant data preparation. Tools available for these functions are not designed for the average business user because they require advanced technical skills. With the rise of the ‘citizen data scientist,” we know that business users are seeking a more active role in the data-to-insights process. The right tool that empowers business users and makes it easy for them to own the process will substantially increase the adoption of big data.
No SQL Here: Closely linked to the point above is that advance skills are required to successfully access, prepare and analyze data in big data environments. An efficient solution is to equip current employees with a tool that is intuitive and easy to use without the need for any R or SQL experience. This might seem like a needle in a haystack, but technology exists that does not require programming experience.
Solving Big Data Environment Adoption Problems
While these obstacles may seem insurmountable, this is certainly not the case. Business users can be empowered with a next generation tool that easily aggregates data from various sources, pinpoints data of interest, performs aggregations and transformations, evaluates and reviews data quality, and combines and correlates data from different sources using a visual data prep process. Every single step in the process can be accomplished without using SQL queries to create repeatable, automated analytics that eliminate errors and accelerates time to insight.
To learn more about a solution that can easily be used by your business users, check out the data sheet below.
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