How Do You Leverage Information In Your Data Lake Efficiently?

Innovation How Do You Leverage Information In Your Data Lake Efficiently? Daniel Fallmann Forbes Councils Member Forbes Technology Council COUNCIL POST Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based) Jul 8, 2022, 07:30am EDT | Share to Facebook Share to Twitter Share to Linkedin Daniel Fallmann is Founder and CEO of Mindbreeze , a leader in enterprise search, applied artificial intelligence and knowledge management.

getty The data lake stands out among a variety of different data management systems. Before recognizing how you can leverage information in your data lake efficiently, it’s essential to know exactly what a data lake is and how it acts within an organization’s information insight goals. In short, a data lake stores unstructured and structured data that can be utilized for future use.

Structured data refers to already organized material—for example, data found in Excel files or structured query language (SQL) databases. This could include dates, names, addresses, phone numbers and credit card information, all formatted in specific rows and columns. Unstructured data typically consists of large text documents, website copy, videos, audio files or social media content.

Unstructured data is unorganized, and entities, like numbers and facts, need to be extracted from the files. A data lake ingests the contents from all of the above structured and unstructured data sources. This permits flexible analysis and the understanding of hefty amounts of information and data—a very beneficial practice for numerous functional areas across the business landscape.

MORE FOR YOU Google Issues Warning For 2 Billion Chrome Users Forget The MacBook Pro, Apple Has Bigger Plans Google Discounts Pixel 6, Nest & Pixel Buds In Limited-Time Sale Event A common mix-up with data lakes is that they are the same as data warehouses. However, this is not the case at all. There are some very vital differences to unpack here.

How Does A Data Lake Differ From A Data Warehouse? Data warehouses only hold structured data that has been fully processed for a particular purpose. Data lakes are more fluid, consisting of raw data from unstructured and structured sources. Data in a data lake is stored, and its exact use is determined at the time of need.

No pre-defining allows organizations to use it for several different purposes throughout their operations. A data lake adds more flexibility, less cost and fewer complications when changes must be made. Business needs change frequently, making data lakes the best storage option for future information insight.

Raw data is constantly generated, and data lakes give it a location to flow directly into. Data Lakes: Key Features Explained With raw data constantly flowing into a data lake, more storage is necessary than in a data warehouse. Storage can be seen as an issue by some IT leaders.

However, there is too much value in other areas that makes data lakes a promising approach for companies around the globe. Data created in real time, with a lot of it in large enterprises, can be consolidated into a data lake. Every piece of information gathered from an organization’s data sources stays in its original format.

This ingestion process gives companies effortless options for transforming, scaling and ultimately defining data structures. Additionally, all data coming from numerous applications and internal and external databases are secure, ensuring the protection of your confidential company data protection. Data lakes are available to different departments and roles within each department.

As long as proper access rights are granted by management, any user needing the knowledge within a data lake can view those details without additional tools or software—no need for third parties. Data lakes provide ease of access to revolutionary insights within your organization. Artificial intelligence (AI) and machine learning (ML) technology have extraction capabilities that help predict outcomes and see patterns based on data analytics within your company’s data lake.

So, What’s The Point? Uses Of A Data Lake The storage of more data in a fraction of the time allows businesses to put a data lake to work across their entire corporation. One example of this involves customer experience (CX) and having meaningful interactions with current and potential clients. Relevant data exists in social media posts, marketing campaigns, sales documents and support tickets.

Consolidating information from applications that hold this data allows organizations to understand customer intent and behavior on an entirely different level. The insights retrieved can be used for future CX efforts and help set you apart from the competition, and this all begins with storing your data intelligently. Another example is R&D innovation.

Data lakes can serve management with information that allows them to properly analyze study results that lead to product ramifications and the most effective solutions for their clients. In addition, accessible data storage significantly reduces operational costs over time and increases the quality of an organization’s approach to data and knowledge management. Whatever industry you’re in, you can count on data lakes for data storage that is easy to scale, no matter the amount of data your company may hold.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? Follow me on LinkedIn . Check out my website .

Daniel Fallmann Editorial Standards Print Reprints & Permissions.