Since the inception of TurtleBay Advisory Services, our consultants have worked with organizations and supported their efforts to swim through an ocean of information in our data-driven world. Making sense of all the organizational unstructured data stored by institutions and circulated among business partners and counterparties poses the greatest challenge. Filtering through all that mess, searching for useful information and even data that exposes companies to unintended risk can consume resources and impede management’s decision-making ability.While the technology has evolved, Artificial Intelligence (A.I.) appears to now provide a real answer for data intensive businesses. A.I. has emerged as a powerful tool that can analyze and interpret all that messy, unstructured data, enabling it to uncover precious insights that previously demanded manual management and often required expensive professional resources.
How would you define structured data?
Well, structured data is frequently classified as quantitative data by organizations because its highly organized form allows machine learning and artificial intelligence systems to quickly absorb and analyze it. So, as an example, anyone working with a relational (SQL) database can easily input, find, and manipulate structured data. Organizations have effectively replaced paper-based systems with structured data and are now exploring unstructured data for prospective growth.
How would you define unstructured data?
Unstructured data, on the other hand, falls into the category of qualitative data. Conventional tools cannot process and analyze it. Interestingly, more than 80% of the data stored/managed by institutions is unstructured in nature and continued rapid growth. Businesses are now giving more importance to unstructured data and recognizing the potential behind it.
Data management practitioners agree that there are credible “use cases” for A.I. especially when applying it to projects with objectives of increasing organizational productivity, achieving higher levels of customer satisfaction, and, most importantly, mitigating risk. However, achieving these benefits is difficult to realize without management/business support in adopting a comprehensive data strategy, having the availability of A.I. qualified data experts and, of course, the needed technology tools and infrastructure. Once again, organizations can employ A.I. to categorize large complex “big data” sets as structured and unstructured within a business relevant timeframe, thereby giving the data strategy focus.. Let’s delve deeper into what structured and unstructured data exactly is.
A.I. – an indispensable tool for understanding and managing unstructured data.
Business entrepreneurs are eager to explore the prospects and hidden insights of unstructured data with the help of A.I. and M.L. (Machine Learning), which can propel business growth.
A.I. uses techniques such as Natural Language Processing (NLP), Pattern Recognition and Sentiment Analysis, Named Entity Recognition (NER), and Text Classification to understand the intricacies of complex unstructured data.
Also, combined with the power of deep learning, topic modeling, clustering, and text mining, it analyzes the nuances hidden in unstructured data. By applying AI-driven analysis to unstructured data, organizations gain a competitive edge in the market. Let’s take a look at some of the advantages A.I. offers.
The benefits of A.I. in extracting insights from unstructured data.
- It promptly handles vast amounts of unstructured data, which would take humans plenty of time.
- Not only that, but A.I. excels at accuracy, effortlessly identifying intricate patterns and relationships within data.
- With real-time analysis, it delivers insightful recommendations, empowering businesses to make quick and informed decisions.
- It’s also cost-effective, saving small and medium-sized businesses money on manual analysis. A.I. is a game changer, revolutionizing how we use data!
However, businesses must overcome significant obstacles before they can realize the potential of unstructured data. Moreover, despite spending millions of dollars, they have discovered a key fact: A.I. initiatives fail unless data platforms are A.I.-ready. Segregated inaccessible, and poorly curated data cannot drive A.I. to its full potential. In fact, to reap the benefits, businesses must proactively develop an ontology—a framework that organizes and characterizes business data architecture.
While unstructured data presents vast possibilities in various fields, it also brings its fair share of challenges. Let’s explore a few obstacles that can arise and how to address them:
Some of the major concerns and challenges in analyzing unstructured data.
- Actively ensuring governance compliance.
- Automating data workflow to streamline process. Unstructured data is often untidy and unreliable, resulting in incorrect analysis.
- Bias: A.I. algorithms can be biased towards certain groups or perspective.
- Developing innovative tools to better interpret human-generated data. A.I. analysis of unstructured data can be hard to interpret.
- Making it more accessible.
In this journey of unraveling the potential of unstructured data, A.I. is the guiding light driving strategic growth in this digital era. So, let us embark on this data-driven journey to unlock effective prospects, keeping in mind that companies cannot leapfrog best practices but must invest time and money in automated and structured data analytics. This will enable companies to effectively capitalize on the potential of A.I. technology in transforming their businesses, thus, paving the way for success and innovation in today’s ever-evolving business landscape.
References:https://www.needl.ai/,https://hbr.org/2017/06/if-your-company-isnt-good-at-analytics-its-not-ready-for-ai, https://www.ibm.com/cloud/blog/structured-vs-unstructured-data, https://www.researchgate.net/publication/369195609_Challenges_to_Opportunity_Getting_Value_Out_of_Unstructured_Data_Management