TIL Emerging Technology Series: Big Data

Hello from Anna!

​Hi! It’s me again! I’m back with another post in our emerging tech series at TIL! I’m well into my internship at TIL South and am learning a lot!

Next in emerging technologies, we investigate big data. What is big data exactly? Well, to put it simply, big data is a combination of different types of data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling, and other advanced analytics applications.

According to SAS, the concept of big data gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three V’s: Volume, Velocity, and Variety. Big data is important, as you can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions 3) new product development and optimized offerings and 4) smart decision making. Companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns, and take other actions that, ultimately, can increase revenue and profits. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as determining root causes of issues and defects in near-real time, recalculating entire risk portfolio in minutes, and detecting fraudulent behavior before it affects your organization.

The Present Landscape

​The present landscape of big data is vast and has paved the way for innovation to further its abilities. There are various examples of current systems in big data. DataOps, for instance, is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization. DataOps is a process-oriented methodology, used by analytic and data teams, and used to improve the quality and reduce the cycle time of data analytics. With properly governed data, businesses can derive better insights that lead to new market opportunities while managing complex regulation compliance, data privacy and artificial intelligence (AI) model accuracy.

Additionally, Data Lakes are an important aspect of the present landscape of big data. A data lake is a storage space for all forms of data in an organization, whether raw or processed, structured or unstructured. Data lakes can store data in any format or file, allowing businesses to hold unprocessed data indefinitely. Data lakes are next-generation data management solutions that can help business users and data scientists meet big data challenges and drive new levels of real-time analytics. Both systems are important in serving as a basis for the future of big data!

The Near Future of Big Data

​In the near future, the landscape of big data is shifting to meet further data collection needs that expand upon the current technologies available. As outlined in the Emerging Technologies Roadmap: Big Data, a few of the significant technologies growing in the next 2–5 years include Data 3D Fusion Mesh, Spectral Data Clustering, and Dynamic Data Masking (DDM).

Data 3D Fusion Mesh is the process of getting data from multiple sources to build more sophisticated models and understand more about a project. This often means getting combined on a single subject and combining it for central analysis. Spectral Data Clustering is an exploratory data analysis technique that reduces complex multidimensional datasets into clusters of similar data in fewer dimensions. The goal of spectral data clustering is to cluster the full spectrum of unorganized data points into several groups based upon their similarity. Lastly, Dynamic Data Masking (DDM) is a strategy for controlling or limiting unauthorized access to data, where data streams from a database or production environment are altered or “masked” as they are requested.

The Future of Big Data

​The future of big data is everchanging, and current technologies serve as a basis for the upcoming prospects in this environment. One of the leading big data technologies changing the narrative of big data is Auto Kernel Spectral Clustering Analysis. KSCC, or Kernel Spectral Curvature Clustering) is an extension of the Spectral Curvature Clustering (SCC) algorithm. This system uses kernels at two levels to allow a manifold modeling to be converted to hybrid linear modeling in an embedded space. Within this process, K-means clustering refers to the division of objects from spectral clustering into k clusters such that some metric relative to the centroids of the clusters is minimized. Applications of this data analysis is used for statistics, computer science, biology, social sciences, and psychology. According to Bain’s “Ten Technology Trends Moving into 2021,” “Healthcare’s big data market is expected to reach nearly $70 billion in 2025, almost six times its 2016 value of $11.5 billion” (Bain 2020). __________________________

My Final Thoughts

​Overall, big data is forever changing to adapt to the needs for increased data storage, and the management of said data. The internet is a vast space, and much like security, crucial aspects of the success of technology today would be lost without proper data accumulation and support for data collection. Big data is a significant part of how we gather information and utilize it for the tool it serves as. It’s not limited to one form, and the marketplace for systems that support large capacities of data are more vital than ever. As technology advances, the programs that produce big data capabilities are introduced to the market to continually meet the necessity for expanded information gathering and organization.

As a young businesswoman, current college student, and TIL South intern, I appreciate the progress that corporations have taken thus far to take advantage of the multidimensional uses of big data, and I am excited to see what the future may hold in such an immersive field. There is a continual need for advanced big data properties, as its relevance in everyday industries grow, and the capabilities for modeling and advanced applications of data collection grow in importance. The uses of big data are endless, and the future is a sure sign of this fact. Big data is substantial in formatting and organizing the information current technologies gather and is paving the way for what the future of big data will look like.

I’ll be back soon with another emerging tech post. In the mean time, visit our website and YouTube channel to learn more about what TIL does and how we can help your business or organization!

-Anna

References

“What Is a Data Lake?: Definition & Meaning.” Webopedia, 24 May 2021, www.webopedia.com/definitions/data-lake/

“Big Data: What It Is and Why It Matters.” SAS, www.sas.com/en_us/insights/big-data/what-is-big-data.html.

Botelho, Bridget, and Stephen J. Bigelow. “What Is Big Data and Why Is It Important?” SearchDataManagement, TechTarget, 27 May 2021, searchdatamanagement.techtarget.com/definition/big-data.

“Data Lake Solutions.” IBM, www.ibm.com/analytics/data-lake?p1=Search&p4=43700050331863130&p5=p&gclid=40d9244cbc2d16f8c94b449ebbf6bda2&gclsrc=3p.ds.

“DataOps.” IBM, www.ibm.com/analytics/dataops?p1=Search&p4=43700059835429976&p5=p&gclid=4f44a91424c91df1cc7ec9c6219062e6&gclsrc=3p.ds&cm_sp=Scheduler-_-CopyChng2-_-C.

Gartner_Inc. “Definition of DataOps — Gartner Information Technology Glossary.” Gartner, www.gartner.com/en/information-technology/glossary/dataops.

Techopedia. “What Is Data Fusion? — Definition from Techopedia.” Techopedia.com, Techopedia, 16 Nov. 2017, www.techopedia.com/definition/32735/data-fusion#:~:text=Data%20fusion%20is%20the%20process%20of%20getting%20data,single%20subject%20and%20combining%20it%20for%20central%20analysis.

Vohong, Truc Mai Dupont. “Ten Technology Trends Moving into 2021.” Bain, 9 June 2021, www.bain.com/insights/ten-technology-trends-moving-into-2021/.