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5 Reasons Your Data Lake Isn’t Giving Good BI

As blending technology with business becomes more mainstream, companies must evolve the way they advertise. Using data from personal information found online is the best way to discover what consumers want and need, both now and in the future. The Internet of Things, or IoT, is only one of the ways companies are collecting data.

Data lakes are full of raw information directly from the source. The lakes have no overarching rules, other than being existing hubs for loose scraps of data. Companies are choosing to use data lakes to collect information, but the approach doesn’t always work out. If your data lake isn’t having the desired benefits for your business, here are some possible reasons why.

1. No Organization

The data lakes are composed of floods of data pouring into one spot where everyone can take their fill. On the surface, a lake sounds like a good idea for companies wanting to sift through data they already mined data. In practice, disorganized data lakes are detrimental to their purpose.

Analysts who want to use the data lake have no starting points and no structure to begin. Finding one piece of workable data may not lead to a similar piece, or any piece at all. The amount of data in the lake is practically endless and always growing, making someone’s job to swim through for good information incredibly tedious and difficult. While it is possible for bots to do the sifting for the company, even bots will take a lengthy amount of time.

Data lakes, just like their real-world counterparts, are stagnant. Even if a piece of data serves the right purposes, the data could be way too outdated to be relevant anymore. With the modern political and economic landscape changing daily, a piece of data from two months ago could be completely worthless.

2. No Standards or Control

How does the data lake get so disorganized? Because there are no rules. Data could come in anytime in combination with other pieces of entirely unrelated information. Part of a data lake’s appeal and purpose is to be a source of unfiltered, raw data, so inflicting rules on a mass scale may be impossible.

However, organizations can govern their data lakes to enact rules for better management. Data needs to be up to date, trustworthy and secure for use.

With a private lake and the implementation of regulations, companies can implement some loose organization filter. In other words, a data lake can become a workable mine if a business starts from the ground up.

3. Enlist Third-Party Help

If a business can’t handle the data lake on its own, asking for help is OK. Programs exist to make the onerous task of shifting around raw data more manageable. Security services can check if the information is valid, like email addresses, phone numbers and IP addresses. Automation for security can protect a business and the consumers they mine the data from. There’s no shame in calling in an expert, either. Experts from all over the world are available to help businesses figure out how to use a data lake for their goals. Depending on their price range, the location of their business and what exactly they need help with, an expert in this field should be available.

4. A Lake Is Not a Warehouse

Data warehouses have been around for a long time and are far more organized than data lakes. While the two approaches essentially have the same goals, some companies think using the same programs to search for data will work. Sadly, this is a misconception.

A company that searched the data lake using the same process they use to search their data warehouse will miss a lot of important data. Getting the most out of a data lake requires reworking the entire mindset of searching and which questions to ask.

5. Someone Else Owns the Data Already

Analysts will have a hard time viewing data that belongs to someone else. Often, shifting compliance laws change what data is and isn’t available. Other times, data in the lake may be in place for someone else’s later use, which makes it unavailable to others. When compliance is a concern, businesses should figure out their regulatory needs before using the data lake at all.

Businesses using a lake will also need to be flexible in their rules to adjust to others’ continually changing rules and make the data usable.

Resolving the Problems

The best fix to gather better business intelligence, or BI, is to make your own corporate, digital data lake.

A lake with all the ground rules your company needs from the data is the best way to keep out irrelevant or outdated data. Getting expert help when setting up your data lake isn’t a bad idea, either.

The critical thing to remember is the data will be abundant, but disorganized. If the concept of a data lake isn’t working for your business’ needs, switching to a warehouse might be best.

About the Author

Contributed by: Kayla Matthews, a technology writer and blogger covering big data topics for websites like Productivity Bytes, CloudTweaks, SandHill and VMblog.

 

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