Unstructured data can be your biggest challenge - or your greatest opportunity. Managing raw datasets can feel as challenging as playing golf in a hurricane. In high-stakes insurance, raw exposure data or Statements of Values (SOVs) are critical for risk assessment, pricing, and underwriting. Dealing with unstructured data, however, is a plight that extends beyond insurance. This article will explore different types of data, the importance of standardization and augmentation, and how AI, ML, NLP, and blockchain-driven solutions can help you master the game of data management, regardless of the complexity of the course.
Not all Data is Created Equal
Raw datasets can be as diverse as golf courses, with some appearing as straightforward par 3s while others are more daunting par 5s. We can classify them into three main formats: structured (the par 3s), semi-structured (par 4s), and unstructured (the par 5s) datasets, or worse. Understanding the nuances of each format is essential to master the game of data management. It is also important to establish what course your data has followed and whether it has been altered in any way. Sending your raw data to a black box email cleansing service without a human in the loop is the equivalent of getting a single shot at a par 5 with a plow. To make matters worse, the lack of transparency is the equivalent of being blindfolded while taking your one and only shot. If you are not using the right tools, you'll be standing on a smaller pile of profits when the game is over.
Standardization and Augmentation: The Ace up Your Sleeve
Data standardization ensures that raw data can be accurately stored, analyzed, and reported on. Structured data offers benefits like improved searchability, seamless reporting, and data integrity that professionals can rely on. By standardizing data, you will gain valuable insights and make informed decisions like a true pro.
However, it is crucial to recognize that not all datasets are created equal, and treating them all the same can lead to pitfalls. Unstructured data, for example, requires more attention, time, and resources than structured or semi-structured data. Adopting a one-size-fits-all approach to data management can be inefficient and fail to address the unique challenges posed by unstructured data.
But, structuring data is not enough. Often, data is incomplete or inaccurate, and the need to enrich a dataset becomes essential. You cannot win with an Eagle in a golf game without knowing the layout of the course. Therefore, it is crucial to have complete data before analyzing it. Just because a dataset looks daunting, it doesn't mean you should avoid it and forfeit a significant profit margin.
The Perils and Pitfalls of Processing Raw Data
Manually processing semi-structured and unstructured data can feel as unpredictable as a sand trap, with hazards such as missing information, inconsistent formatting, and human errors lurking at every turn. Manual data processing can be as time-consuming as waiting for the perfect swing, impeding overall efficiency. Offshore services charge a premium for manual processing, ensuring accuracy (to an extent) but sacrificing efficiency in the process. Others offer automated processing at a fraction of the cost but struggle with semi-structured and unstructured data, resulting in inaccurate output.
As these types of data significantly impact the risk one is taking, even if they occur less frequently or in smaller numbers, it is essential for the solution to distinguish and identify how best to treat each category of data accordingly. This means avoiding a one-size-fits-all approach and addressing unstructured data with more care and consideration.
Golden Rules for Structuring Unstructured Data
Structuring unstructured data can be complex, as it involves organizing and transforming raw information into a more accessible and useful format. Companies can do far more with a lot less by defining their goals, enriching data from multiple sources, extracting meaningful features using AI, ML, NLP, and blockchain to crowdsource information, and minimizing (not terminating) human input over time.
There is a dangerous misconception in insurance that cheaper technologies will save the day for processing raw data, which makes accuracy a victim. Inaccurately processing SOVs for less does not just reduce expense ratios. It also has a major detriment to insurers' loss ratios. Hard and soft market cycles exist in insurance because of such inaccuracies. If every insurer accurately priced their policies, no one would go out of business, finger-pointing or blaming catastrophe models in the aftermath of disastrous losses. After all, insurance was designed as a resilience mechanism for these very situations.
Embracing AI and Insurtech
Advancements in Data Science are revolutionizing the insurance industry, transforming risk assessment and decision-making. Sophisticated AI solutions offer unparalleled speed, accuracy, and value by accurately processing structured, semi-structured, and unstructured datasets in seconds.
On simpler par 3 datasets, advanced tools will help you achieve a hole-in-one with just a single click. You might need a few more clicks for more complex par 5s or worse datasets, but these solutions still ensure you will score birdies, putting you way ahead of the competition.
However, just having AI for the sake of automation is not enough. It is crucial to develop and implement solutions that have the agility and capability to address the unique challenges of unstructured data, as opposed to adopting a one-size-fits-all approach that may work for structured or semi-structured data but falls short when dealing with more complex information.
The Future is Bright: AI and Insurtech
The future looks bright for insurance professionals embracing the right AI, ML, NLP, and blockchain technologies specifically designed and optimized to conquer data management challenges. By harnessing the power of these cutting-edge advancements, insurers will overcome data management challenges and unlock new opportunities for growth and success.
One such solution that has cracked the code in terms of structuring unstructured data is SOV Wizard by Wizard Analytics. This powerful tool combines AI, ML, and blockchain technologies to tackle the unique challenges of unstructured data, enabling insurance professionals to handle all forms of raw data with confidence and precision.
The insurance industry is witnessing a paradigm shift as it embraces the potential of sophisticated Data Science. With the right tools like SOV Wizard, insurance professionals will now be empowered to tackle the complexities of unstructured data with confidence and precision. As we venture into a new era of insurance, leveraging cutting-edge solutions is no longer an option but a necessity for those who aspire to lead the industry.
With AI fast becoming the buzzword for every new data solution out there, it's almost understandable for every automated tool to be viewed as a miracle cure-all to reduce one's handicap. However, it would be prudent to adopt a more practical lens while assessing tools for automation and selecting the right ones with some care. It is crucial to acknowledge that simply having AI for the sake of automation is not sufficient. A successful data management strategy must address the diverse needs of structured, semi-structured, and unstructured data with care and consideration. Although unstructured data may be less common, errors in standardization and augmentation here significantly and negatively alter the risk landscape for insurers and must not be overlooked.
By developing and implementing data management solutions like SOV Wizard that cater to each data category's unique needs, insurance professionals can avoid a 'one size fits all' approach and turn data management from a daunting obstacle into a competitive advantage. In the end, those who master the art of data management will navigate the course with ease, sinking hole-in-ones amidst the storm and securing their place as frontrunners in the race for market dominance.