Data Lake vs. Data Swamp: Achieving Informational Clarity

Data Lake vs. Data Swamp

The dawn of the technological age is upon us. Nearly all organizations use data storage to facilitate daily operations. Cloud storage and servers have long since replaced file cabinets and manilla folders. While this newfound sense of innovation has changed the way information is stored, it has also created vast amounts of data clutter.

A recent study found up to 73% of stored data goes unused for analytical purposes. What's the use of storing all this data if it doesn't ever see the light of day?

 

Data is only useful if it's properly stored, organized, and analyzed. Otherwise, it's merely a bunch of arbitrary numbers, names, and other extraneous information floating around the digital universe.

 

Many organizations have begun to realize that real-time business insights are only achieved with clear, concise, distinguishable data. Ensuring your company's storage is a Data Lake instead of a Data Swamp is crucial to executing strategies using business intelligence. 

 

Data Lake, Data Swamp...What's the Difference?

A Data Lake can ingest vast amounts of data while assessing, categorizing, and managing each record. On the other hand, Data Swamps generally have little to no organizational system rendering it mostly unusable. Data swamps can cause time inefficiencies and waste valuable resources that are better spent elsewhere.

 

Data Lakes provide clear cut information that has been appropriately managed, stored, and homogeneously categorized for easy retrieval. Data Swamps, however, are simply a muddied mess with no systematization in place. Transforming a company's Data Swamp into a Data Lake can help clear the way for improved resource management and time allocation.

 

Why it Matters

Highly skilled professionals such as Data Engineers and Data Scientists make a living analyzing complex data sets and synthesizing information into easily digestible content for company decision-makers. If they waste too much time categorizing and relabeling data, they can become bogged down by menial tasks. Data Lakes equip companies to retrieve and use data effectively.

 

How Data Swamps Occur

Machines can have difficulties recognizing commonalities within data. As a result, people have to manually ensure data sets lineup in terms of identity and similar characteristics. There are several ways data can become muddled, thereby causing unreliable results.

 

Identity Problems 

The human brain can read acronyms and assess contextual clues to give more information about specific items. Machines, on the other hand, are unable to do so. The abbreviation MS could refer to a disease (Multiple Sclerosis), a company (Microsoft), a state (Mississippi), or a degree (Master of Science).

 

The data must be extracted and appropriately categorized to ensure it is processed efficiently and coherently.  

 

Location Matters

There's an old saying that goes: location, location, location. While in a different context, the same is true of data processing. Several different counties, cities, and streets share similar names. Failure to correctly identify specifics may result in data that is difficult to analyze for data engineers.

 

Let's say your company is performing a research study that is attempting to account for customers by state. A place such as Orange County may refer to as many as eight different counties in eight separate states. 

 

The same is true of city names. There are nearly 50 known cities in America with the name "Springfield." Confusion can occur if steps aren't taken to ensure data sets are appropriately categorized. A Data Engineer will find it difficult to analyze information sets if they do not have homogeneous data to review.

 

Homogeneous Data

For text data to be useful, it must use the same formatting, patterns, and style, and for numeric data to be helpful, it must have a common base, scale, and precision. Otherwise, analytics teams won't be able to provide reliable data findings. 

 

Dates are one example of how data can become misconstrued. The way a date is conveyed can depend upon personal preference or several other factors. Day/month/year, year/month/day, or month/day/year are all valid ways of representing dates. Two or four digits can represent years. As you can imagine, this can make it difficult to review data.

 

Time can also be shown as a standard twelve-hour period or using a militarized twenty-four-hour format. Your team will have a difficult time obtaining useful results, and data corruption may start to creep in if not lined up in terms of identity. 

 

Another critical issue with dates is to ensure that they are all using, or at least referencing, the same time zone. One common problem that we've encountered is articles published with only a date rather than a date and time and time zone, and this can cause difficulties in reconciling data because of issues with up to plus 13 or minus 12-hour time zone differences, putting things in the wrong day.

 

Drain Your Data Swamp with Bitvore

Data sets must be strictly categorized and analyzed for all factors so that analytics teams can create succinct, clearly documented results. That's where Bitvore comes in.

 

Bitvore gives specific results from data instead of just providing arbitrary information. Using AI and machine learning, Bitvore creates normalized business-centric data that can easily be used by data scientists to analyze and build predictive models. 

 

As a corporate research platform that ingests multiple unstructured data sources using advanced AI-techniques, Bitvore provides material events, trended sentiment, and growth and risk scoring to help drive decision making.

 

For additional information on Bitvore AI and how it can help provide data clarity, check out our latest case study.

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