Data lakes are central to data management and business analytics, providing organizations with the insights needed to track patterns, make smarter decisions, and increase value to customers. Dan Onions, head of master data management solutions, Quantexa, discusses four ways to tell if your data lakes are in trouble.
Data lakes have become central to data management and business analytics. With the right implementation, they can allow organizations the insight they need to track patterns, increase value to customers, and help businesses make smarter decisions. These benefits point to why the market is predicted to expand rapidly over the coming years. A 2020 reportOpens a new window by Grand View Research predicts the data lake market, already valued at more than $7.6 billion, is expected to grow at a compound annual growth rate of 20.6% over the next six years.
With all the business benefits and growth in sight, it may come as a surprise to leaders when they realize their data lakes are in trouble. These issues often develop slowly over time, but once you spot them, they are tough to ignore. Here are four signs your data lakes may be in trouble and what you can do to improve.
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1. Your Data Is Disorganized
There’s enormous value in raw data if you can actually sort through and process it. Many companies find their data lakes become a catch-all for all varieties of data from multiple sources.
With mountains of raw data, it is easy for data lakes to become disorganized, quickly turning into a mess that becomes harder to derive value from. What once was a powerful tool has now become a data swamp, resulting in missed insights and opportunities. If you don’t see your data lake adding value and being used as an opportunity for growth, you may be in need of a data lake revamp.
2. You Can’t Aggregate the Data
Disorganization can make certain aspects of data lakes unusable over time as users create bespoke views based on raw data. Data lakes often store a variety of data types, including customer information, addresses and transaction records, to name a few. Each of these data sets may be stored in a different structure, resulting in poor quality data and the inability to aggregate the data.
A lack of data aggregation results in lengthy manual processes as team members have to stitch together data to use with dashboards, analytics and reporting. Human error can take a toll here, as employees spend time making their way through data quality issues rather than focusing on the analysis and insight. If you can’t aggregate data within your data lakes, you will lose time and energy to sorting, reformatting and resolving issues on your own.
3. Your Employees Get Lost In the Data
As employees pour more time and energy into data lakes, they can easily get lost in the weeds and tied to tasks they shouldn’t be undertaking at all. For example, data lakes naturally become the realm of data scientists and engineers, highly valuable team members who often are already pressed for time.Â
To gain meaningful insights from raw data, scientists and engineers need to have a single view of their data records. A data lake is poised to be the perfect spot for this view, but more often than not, employees must spend time converting data from one structure to another to carry out a task. Because of this, they spend time wrangling, cleaning, and reformatting data to bring it all together into one digestible view.Â
Data scientists spend time hand-coding or using extract-transform-load tools, which will successfully get them to a format to combine all data — for that one particular task. The next time they need the same data but in a different format, they must start the process over again to make the data usable for this new task. Instead, their skills are better used to carry out complex analysis to deliver insights to help your company thrive.
4. You Can’t Deliver Operational Data
Data lakes are not operational or designed to shift data to outside applications. It is another way data lakes don’t deliver without employee intervention, as individuals must spend time moving data to operational data technology.Â
The data that has been moved may result in complex operations where mistakes are common, and companies begin to rely on non-operational technology. It is probably a far cry from the streamlined, efficient method you could be using.
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How You Can Improve Your Data Lakes
The first step to improving a data lake is to identify the connections between your data records and combine those that are the same. This is achieved using entity resolution, a powerful process that provides a single view of data.Â
Next, companies need to understand the relationships and connections between each record. Using network information, organizations can create information profiles from across multiple sources allowing data scientists to use as powerful features in analytics and for operational use in applications. Entity resolution and network generation simplify and streamline operations to help organizations understand and drive better decisions from data.Â
You should also focus on creating a data catalog to understand the value of different types of data, how they flow through the organization and how they link together. Organizations need to focus on creating strong joined-up data assets for data scientists without undertaking exhaustive projects to transform data, which can be expensive and time-consuming. Find a balance instead by building on existing data structures, creating a better format without reinventing the wheel.
Most organizations haven’t perfected their approach to data lakes. But with the right technologies like entity resolution and network generation, leaders can make it easier to access and analyze raw data, finally harnessing the full power of data lakes for their companies.Â
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