It’s both a perception and fact that data lakes are the wave of the future, yet adoption and utilization continues to be lackluster. The source of this trouble is well articulated in a February 2017 global study conducted by Dimensional Research which states that 87% of the 300 data management professionals surveyed believe that bad data pollutes their big data while 74% state bad data is currently in their big data repository.
Many have found the issue resides in the need to literally hoard data for the proverbial “rainy day.” Most business departments want to store just about everything for that one time it might be valuable. This philosophy of “more is better than less data” is diluting the value of the data lake and turning it into a false sense of value. When someone states the size of their big data lake as some kind of measure of achievement you know there is a problem. An extra terabyte of data doesn’t make it more valuable if that data is riffled with bad data.
Organizations need to be smart about the data they collect. Even if you feel you have the data ingestion under control, it’s critical to put big data quality controls with metrics in place in order to address two issues:
The challenge is that big data quality is different than the traditional data warehouse quality in three ways:
Better to Cast a Rod than a Net
A data lake is a storage repository that has a huge appetite for data. The data lake not only can store data for long periods of time, but can accommodate data growth. Data lakes are also dependent on new data being fed into them to keep the big data environment fresh and the data relevant. Another benefit to data lakes is their ability to ingest data in varying formats. Data lakes take all data in its original form and store it all—raw, unstructured, semi-structured and structured. While this sounds like the ideal solution for all organizations looking to store large amounts of data, data lakes are only effective if the data entering the lake is relevant, as irrelevant data can bog down a data lake.
For an enterprise to draw value from their data lake, companies need to make sure they have goals and KPIs in place to help identify key metrics and a defined sense of direction they want the data to take. With a specific vision in place, the data scientists or analysts won’t have to collect extraneous data, but will be able to cast their expertise on pinpointing specific data. The data allows for the organization to have greater flexibility with time and decisions. Sounds great, right? But what if organizations can’t trust their data? When it comes to using big data, we marvel at the possibilities of making groundbreaking predictions and gaining game changing insights that transform the fortunes of companies and leapfrog the competition. But beware; the caveat with big data is that it loses its potency if we lack confidence that the data is reliable.
Enriching the Flow of your Data Lake
On top of retrieving the precise data from the data lake, the quality of the data is critical. Enterprises can’t afford to make a decision based on inaccurate data. High-quality data saves an organization time and money, and can set an organization back months because of misleading information. The importance of data quality is even more profound when poor quality data undermines analytical initiatives or becomes a sticking point when management dismisses the analytical insights based on the assumption that the data used is of sub-par quality.
Making your data lake work for you relies on more than just redirecting data flow to a data lake. Without a strong understanding of the data quality, traceability and proper data governance, using a data lake can be a risky endeavor. Not to mention an ineffective use of hundreds of thousands of dollars. However, with the right big data platform that bridges the gap between business and IT, data lakes will remain crystal clear and the data will be trustworthy.
Analytics-Enabled Data Quality
With data lakes, turning raw data into insights at breakneck speed requires an integrated platform to ingest, prepare, analyze, and act on data to then communicate insights derived. So, when organizations are searching for a big data platform, they should look for one that combines the power of analytics with data quality checks to execute high-performance validations natively in Hadoop. Also, it should seamlessly integrate the data-to-insights process and empower every user to operationalize the insights generated from analyzing big data. The platform should also apply machine learning and predictive analytics to improve data quality results in the future.
Data lakes are essential to capitalize on big data opportunities, but most enterprises have little understanding of how to get the real value out of it. Having a clear vision on the business problems you’re trying to solve allows your big data platform to feed you relevant insights from your data lake tailored to the frontline of an enterprise’s daily operations. Besides, the right platform helps you take the right actions based on accurate information, significantly improving productivity and outcomes.
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